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Sample records for wireless electroencephalogram eeg

  1. Household wireless electroencephalogram hat

    Science.gov (United States)

    Szu, Harold; Hsu, Charles; Moon, Gyu; Yamakawa, Takeshi; Tran, Binh

    2012-06-01

    We applied Compressive Sensing to design an affordable, convenient Brain Machine Interface (BMI) measuring the high spatial density, and real-time process of Electroencephalogram (EEG) brainwaves by a Smartphone. It is useful for therapeutic and mental health monitoring, learning disability biofeedback, handicap interfaces, and war gaming. Its spec is adequate for a biomedical laboratory, without the cables hanging over the head and tethered to a fixed computer terminal. Our improved the intrinsic signal to noise ratio (SNR) by using the non-uniform placement of the measuring electrodes to create the proximity of measurement to the source effect. We computing a spatiotemporal average the larger magnitude of EEG data centers in 0.3 second taking on tethered laboratory data, using fuzzy logic, and computing the inside brainwave sources, by Independent Component Analysis (ICA). Consequently, we can overlay them together by non-uniform electrode distribution enhancing the signal noise ratio and therefore the degree of sparseness by threshold. We overcame the conflicting requirements between a high spatial electrode density and precise temporal resolution (beyond Event Related Potential (ERP) P300 brainwave at 0.3 sec), and Smartphone wireless bottleneck of spatiotemporal throughput rate. Our main contribution in this paper is the quality and the speed of iterative compressed image recovery algorithm based on a Block Sparse Code (Baranuick et al, IEEE/IT 2008). As a result, we achieved real-time wireless dynamic measurement of EEG brainwaves, matching well with traditionally tethered high density EEG.

  2. Smartphone Household Wireless Electroencephalogram Hat

    Directory of Open Access Journals (Sweden)

    Harold Szu

    2013-01-01

    Full Text Available Rudimentary brain machine interface has existed for the gaming industry. Here, we propose a wireless, real-time, and smartphone-based electroencephalogram (EEG system for homecare applications. The system uses high-density dry electrodes and compressive sensing strategies to overcome conflicting requirements between spatial electrode density, temporal resolution, and spatiotemporal throughput rate. Spatial sparseness is addressed by close proximity between active electrodes and desired source locations and using an adaptive selection of N active among 10N passive electrodes to form m-organized random linear combinations of readouts, m≪N≪10N. Temporal sparseness is addressed via parallel frame differences in hardware. During the design phase, we took tethered laboratory EEG dataset and applied fuzzy logic to compute (a spatiotemporal average of larger magnitude EEG data centers in 0.3 second intervals and (b inside brainwave sources by Independent Component Analysis blind deconvolution without knowing the impulse response function. Our main contributions are the fidelity of quality wireless EEG data compared to original tethered data and the speed of compressive image recovery. We have compared our recovery of ill-posed inverse data against results using Block Sparse Code. Future work includes development of strategies to filter unwanted artifact from high-density EEGs (i.e., facial muscle-related events and wireless environmental electromagnetic interferences.

  3. 21 CFR 882.1855 - Electroencephalogram (EEG) telemetry system.

    Science.gov (United States)

    2010-04-01

    ... signals by means of radio or telephone transmission systems. (b) Classification. Class II (performance... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Electroencephalogram (EEG) telemetry system. 882... Electroencephalogram (EEG) telemetry system. (a) Identification. An electroencephalogram (EEG) telemetry system...

  4. Analyzing Electroencephalogram Signal Using EEG Lab

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    Mukesh BHARDWAJ

    2009-01-01

    Full Text Available The EEG is composed of electrical potentials arising from several sources. Each source (including separate neural clusters, blink artifact or pulse artifact forms a unique topography onto the scalp – ‘scalp map‘. Scalp map may be 2-D or 3-D.These maps are mixed according to the principle of linear superposition. Independent component analysis (ICA attempts to reverse the superposition by separating the EEG into mutually independent scalp maps, or components. MATLAB toolbox and graphic user interface, EEGLAB is used for processing EEG data of any number of channels. Wavelet toolbox has been used for 2-D signal analysis.

  5. Nonlinear dimensionality reduction of electroencephalogram (EEG) for Brain Computer interfaces.

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    Teli, Mohammad Nayeem; Anderson, Charles

    2009-01-01

    Patterns in electroencephalogram (EEG) signals are analyzed for a Brain Computer Interface (BCI). An important aspect of this analysis is the work on transformations of high dimensional EEG data to low dimensional spaces in which we can classify the data according to mental tasks being performed. In this research we investigate how a Neural Network (NN) in an auto-encoder with bottleneck configuration can find such a transformation. We implemented two approximate second-order methods to optimize the weights of these networks, because the more common first-order methods are very slow to converge for networks like these with more than three layers of computational units. The resulting non-linear projections of time embedded EEG signals show interesting separations that are related to tasks. The bottleneck networks do indeed discover nonlinear transformations to low-dimensional spaces that capture much of the information present in EEG signals. However, the resulting low-dimensional representations do not improve classification rates beyond what is possible using Quadratic Discriminant Analysis (QDA) on the original time-lagged EEG.

  6. Modular, bluetooth enabled, wireless electroencephalograph (EEG) platform.

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    Lovelace, Joseph A; Witt, Tyler S; Beyette, Fred R

    2013-01-01

    A design for a modular, compact, and accurate wireless electroencephalograph (EEG) system is proposed. EEG is the only non-invasive measure for neuronal function of the brain. Using a number of digital signal processing (DSP) techniques, this neuronal function can be acquired and processed into meaningful representations of brain activity. The system described here utilizes Bluetooth to wirelessly transmit the digitized brain signal for an end application use. In this way, the system is portable, and modular in terms of the device to which it can interface. Brain Computer Interface (BCI) has become a popular extension of EEG systems in modern research. This design serves as a platform for applications using BCI capability.

  7. Electroencephalogram (EEG) duration needed to detect abnormalities in angelman syndrome: is 1 hour of overnight recording sufficient?

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    Robinson, Althea A; Goldman, Suzanne; Barnes, Gregory; Goodpaster, Luke; Malow, Beth A

    2015-01-01

    Approximately, 90% of patients with Angelman syndrome present with epileptic seizures. Obtaining an electroencephalogram (EEG) with sleep improves the chances of detecting ictal, interictal, and benign abnormal rhythms in Angelman syndrome. However, electroencephalograms, even when obtained during sleep, can be challenging in this population because of tactile sensitivities as well as anxiety related to a novel environment. We tested the hypothesis that 1 hour of sleep on an electroencephalogram would provide as much information as an entire night of electroencephalogram recording, yet more than a routine electroencephalogram conducted during the day. Overnight polysomnograms were collected in 14 children with Angelman syndrome seen at Vanderbilt University. All patients who obtained sleep within the first hour of the overnight electroencephalogram had interictal discharges recorded. Our results show that when sleep is obtained, a 1-hour electroencephalogram yields just as much information as recording an entire night. © The Author(s) 2014.

  8. Quantitative assessment of dynamic electroencephalogram (EEG) organization as a tool for subtyping depressive syndromes.

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    Ulrich, G; Fürstenberg, U

    1999-07-01

    Up until now, no subclassification of affective psychoses has been validated biologically. This follows unavoidably from a research practice of defining diagnostic subtypes in consensus conferences and only thereafter allowing their validation. There is evidence that electroencephalograms (EEG) may be a useful tool in psychiatry, provided that the relevant information is extracted. Our EEG quantification procedure aims at an assessment of both the amount and range of variation of spontaneous changes of topographical alpha-power distribution, developing within a certain period of recording under resting conditions. Our measures were designed to characterize the dynamic organization of the EEG. This is quite obviously an eyeball evaluation but it has nevertheless been neglected in research. The study design was done retrospectively. Included were inpatients with a primary depressive disorder. Main exclusion criteria were an age older than 62 years and psychotropic drugs other than antidepressants. The psychopathology and other clinical data were routinely assessed within three days after admission by the AMDP documentation. An EEG was also routinely performed at admission. We made use of robust, generally known non-parametric statistics. Those patients who exhibited a dynamically rigid EEG are especially prone to recurrences, have a relative late onset of their illness, and show an acute symptomatology characterized by organic-like features. The findings lend support to our contention that the quantitative assessment of the dynamics of the EEG-Gestalt allows the delimitation of a clinically important subtype that is characterized both cross-sectionally and in long-term respects.

  9. Using Electroencephalogram (EEG to Understand The Effect of Price Perception on Consumer Preference

    Directory of Open Access Journals (Sweden)

    Fitri Aprilianty

    2016-06-01

    Full Text Available The research examines the influence of price as product cues on consumer’s perception and evaluation by using the application of electroencephalogram (EEG. This method can give objective information about consumer reactions towards product cues that will drive consumer’s choice. The main research objective was to observe and evaluate consumer’s brain activity in different brain regions while they were being exposed by several price levels (low, medium, high of underwear as stimuli and focused mainly on liking/disliking the stimuli. The participants consist of 10 female and 10 male consumers within 18-24 years old, have normal vision, right handed, and considered as potential purchasers of underwear. The participant’s brain activity was collected using Emotiv EPOC neuroheadset (EEG with international 10/20 system and was obtained in Beta frequency bands (13–30 Hz. The result indicated that there was a clear and significant change (p<0.05 in the EEG brain spectral activities of right and left hemisphere in the frontal (F3 & F4, temporal (T7 & T8, and parietal (P7 & P8 regions when participants indicated their attentiveness towards each price level stimulus. The results show, the male and female participant’s tactile sensations in parietal lobe does not give more favorable attention towards particular price stimulus, but the difference price perceptions in parietal lobe can lead to rational preference and give most favored response towards high price stimulus. Analyzing of price perception may help to understand the differences in price-related emotions and preference, which can gain insights into an alternative pricing strategy that can lead to influence consumers buying decision.

  10. Synchronizing MIDI and wireless EEG measurements during natural piano performance.

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    Zamm, Anna; Palmer, Caroline; Bauer, Anna-Katharina R; Bleichner, Martin G; Demos, Alexander P; Debener, Stefan

    2017-07-08

    Although music performance has been widely studied in the behavioural sciences, less work has addressed the underlying neural mechanisms, perhaps due to technical difficulties in acquiring high-quality neural data during tasks requiring natural motion. The advent of wireless electroencephalography (EEG) presents a solution to this problem by allowing for neural measurement with minimal motion artefacts. In the current study, we provide the first validation of a mobile wireless EEG system for capturing the neural dynamics associated with piano performance. First, we propose a novel method for synchronously recording music performance and wireless mobile EEG. Second, we provide results of several timing tests that characterize the timing accuracy of our system. Finally, we report EEG time domain and frequency domain results from N=40 pianists demonstrating that wireless EEG data capture the unique temporal signatures of musicians' performances with fine-grained precision and accuracy. Taken together, we demonstrate that mobile wireless EEG can be used to measure the neural dynamics of piano performance with minimal motion constraints. This opens many new possibilities for investigating the brain mechanisms underlying music performance. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. The Encephalophone: A Novel Musical Biofeedback Device using Conscious Control of Electroencephalogram (EEG

    Directory of Open Access Journals (Sweden)

    Thomas A. Deuel

    2017-04-01

    Full Text Available A novel musical instrument and biofeedback device was created using electroencephalogram (EEG posterior dominant rhythm (PDR or mu rhythm to control a synthesized piano, which we call the Encephalophone. Alpha-frequency (8–12 Hz signal power from PDR in the visual cortex or from mu rhythm in the motor cortex was used to create a power scale which was then converted into a musical scale, which could be manipulated by the individual in real time. Subjects could then generate different notes of the scale by activation (event-related synchronization or de-activation (event-related desynchronization of the PDR or mu rhythms in visual or motor cortex, respectively. Fifteen novice normal subjects were tested in their ability to hit target notes presented within a 5-min trial period. All 15 subjects were able to perform more accurately (average of 27.4 hits, 67.1% accuracy for visual cortex/PDR signaling; average of 20.6 hits, 57.1% accuracy for mu signaling than a random note generation (19.03% accuracy. Moreover, PDR control was significantly more accurate than mu control. This shows that novice healthy individuals can control music with better accuracy than random, with no prior training on the device, and that PDR control is more accurate than mu control for these novices. Individuals with more years of musical training showed a moderate positive correlation with more PDR accuracy, but not mu accuracy. The Encephalophone may have potential applications both as a novel musical instrument without requiring movement, as well as a potential therapeutic biofeedback device for patients suffering from motor deficits (e.g., amyotrophic lateral sclerosis (ALS, brainstem stroke, traumatic amputation.

  12. Wireless-based portable EEG-EOG monitoring for real time drowsiness detection.

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    Arnin, J; Anopas, D; Horapong, M; Triponyuwasi, P; Yamsa-ard, T; Iampetch, S; Wongsawat, Y

    2013-01-01

    Drowsiness is one of the major risk factors causing accidents that result in a large number of damage. Drivers and industrial workers probably have a large effect on several mishaps occurring from drowsiness. Therefore, advanced technology to reduce these accidental rates is a very challenging problem. Nowadays, there have been many drowsiness detectors using electroencephalogram (EEG), however, the cost is still high and the use of this is uncomfortable in long-term monitoring because most of them require wiring and conventional wet electrodes. The purpose of this paper is to develop a portable wireless device that can automatically detect the drowsiness in real time by using the EEG and electrooculogram (EOG). The silver (Ag) conducting fabric consolidated in a headband used as dry electrodes can acquire signal from the user's forehead. The signal was sent via the wireless communication of XBee® 802.15.4 to a standalone microcontroller to analyze drowsiness using the proposed algorithm. The alarm will ring when the drowsiness occurs. Besides, the automatic drowsiness detection and alarm device yields the real-time detection accuracy of approximately 81%.

  13. Impact of dronabinol on quantitative electroencephalogram (qEEG) measures of sleep in obstructive sleep apnea syndrome.

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    Farabi, Sarah S; Prasad, Bharati; Quinn, Lauretta; Carley, David W

    2014-01-15

    To determine the effects of dronabinol on quantitative electroencephalogram (EEG) markers of the sleep process, including power distribution and ultradian cycling in 15 patients with obstructive sleep apnea (OSA). EEG (C4-A1) relative power (% total) in the delta, theta, alpha, and sigma bands was quantified by fast Fourier transformation (FFT) over 28-second intervals. An activation ratio (AR = [alpha + sigma] / [delta + theta]) also was computed for each interval. To assess ultradian rhythms, the best-fitting cosine wave was determined for AR and each frequency band in each polysomnogram (PSG). Fifteen subjects were included in the analysis. Dronabinol was associated with significantly increased theta power (p = 0.002). During the first half of the night, dronabinol decreased sigma power (p = 0.03) and AR (p = 0.03), and increased theta power (p = 0.0006). At increasing dronabinol doses, ultradian rhythms accounted for a greater fraction of EEG power variance in the delta band (p = 0.04) and AR (p = 0.03). Females had higher amplitude ultradian rhythms than males (theta: p = 0.01; sigma: p = 0.01). Decreasing AHI was associated with increasing ultradian rhythm amplitudes (sigma: p < 0.001; AR: p = 0.02). At the end of treatment, lower relative power in the theta band (p = 0.02) and lower AHI (p = 0.05) correlated with a greater decrease in sleepiness from baseline. This exploratory study demonstrates that in individuals with OSA, dronabinol treatment may yield a shift in EEG power toward delta and theta frequencies and a strengthening of ultradian rhythms in the sleep EEG.

  14. Distributed Signal Processing for Wireless EEG Sensor Networks.

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    Bertrand, Alexander

    2015-11-01

    Inspired by ongoing evolutions in the field of wireless body area networks (WBANs), this tutorial paper presents a conceptual and exploratory study of wireless electroencephalography (EEG) sensor networks (WESNs), with an emphasis on distributed signal processing aspects. A WESN is conceived as a modular neuromonitoring platform for high-density EEG recordings, in which each node is equipped with an electrode array, a signal processing unit, and facilities for wireless communication. We first address the advantages of such a modular approach, and we explain how distributed signal processing algorithms make WESNs more power-efficient, in particular by avoiding data centralization. We provide an overview of distributed signal processing algorithms that are potentially applicable in WESNs, and for illustration purposes, we also provide a more detailed case study of a distributed eye blink artifact removal algorithm. Finally, we study the power efficiency of these distributed algorithms in comparison to their centralized counterparts in which all the raw sensor signals are centralized in a near-end or far-end fusion center.

  15. Portable wireless neurofeedback system of EEG alpha rhythm enhances memory.

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    Wei, Ting-Ying; Chang, Da-Wei; Liu, You-De; Liu, Chen-Wei; Young, Chung-Ping; Liang, Sheng-Fu; Shaw, Fu-Zen

    2017-11-13

    Effect of neurofeedback training (NFT) on enhancement of cognitive function or amelioration of clinical symptoms is inconclusive. The trainability of brain rhythm using a neurofeedback system is uncertainty because various experimental designs are used in previous studies. The current study aimed to develop a portable wireless NFT system for alpha rhythm and to validate effect of the NFT system on memory with a sham-controlled group. The proposed system contained an EEG signal analysis device and a smartphone with wireless Bluetooth low-energy technology. Instantaneous 1-s EEG power and contiguous 5-min EEG power throughout the training were developed as feedback information. The training performance and its progression were kept to boost usability of our device. Participants were blinded and randomly assigned into either the control group receiving random 4-Hz power or Alpha group receiving 8-12-Hz power. Working memory and episodic memory were assessed by the backward digital span task and word-pair task, respectively. The portable neurofeedback system had advantages of a tiny size and long-term recording and demonstrated trainability of alpha rhythm in terms of significant increase of power and duration of 8-12 Hz. Moreover, accuracies of the backward digital span task and word-pair task showed significant enhancement in the Alpha group after training compared to the control group. Our tiny portable device demonstrated success trainability of alpha rhythm and enhanced two kinds of memories. The present study suggest that the portable neurofeedback system provides an alternative intervention for memory enhancement.

  16. Analyzing power spectral of electroencephalogram (EEG) signal to identify motoric arm movement using EMOTIV EPOC+

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    Bustomi, A.; Wijaya, S. K.; Prawito

    2017-07-01

    Rehabilitation of motoric dysfunction from the body becomes the main objective of developing Brain Computer Interface (BCI) technique, especially in the field of medical rehabilitation technology. BCI technology based on electrical activity of the brain, allow patient to be able to restore motoric disfunction of the body and help them to overcome the shortcomings mobility. In this study, EEG signal phenomenon was obtained from EMOTIV EPOC+, the signals were generated from the imagery of lifting arm, and look for any correlation between the imagery of motoric muscle movement against the recorded signals. The signals processing were done in the time-frequency domain, using Wavelet relative power (WRP) as feature extraction, and Support vector machine (SVM) as the classifier. In this study, it was obtained the result of maximum accuracy of 81.3 % using 8 channel (AF3, F7, F3, FC5, FC6, F4, F8, and AF4), 6 channel remaining on EMOTIV EPOC + does not contribute to the improvement of the accuracy of the classification system

  17. A Smartphone Interface for a Wireless EEG Headset with Real-Time 3D Reconstruction

    DEFF Research Database (Denmark)

    Stopczynski, Arkadiusz; Larsen, Jakob Eg; Stahlhut, Carsten

    2011-01-01

    We demonstrate a fully functional handheld brain scanner consisting of a low-cost 14-channel EEG headset with a wireless connec- tion to a smartphone, enabling minimally invasive EEG monitoring in naturalistic settings. The smartphone provides a touch-based interface with real-time brain state...

  18. Wearable, wireless EEG solutions in daily life applications: what are we missing?

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    Mihajlovic, Vojkan; Grundlehner, Bernard; Vullers, Ruud; Penders, Julien

    2015-01-01

    Monitoring human brain activity has great potential in helping us understand the functioning of our brain, as well as in preventing mental disorders and cognitive decline and improve our quality of life. Noninvasive surface EEG is the dominant modality for studying brain dynamics and performance in real-life interaction of humans with their environment. To take full advantage of surface EEG recordings, EEG technology has to be advanced to a level that it can be used in daily life activities. Furthermore, users have to see it as an unobtrusive option to monitor and improve their health. To achieve this, EEG systems have to be transformed from stationary, wired, and cumbersome systems used mostly in clinical practice today, to intelligent wearable, wireless, convenient, and comfortable lifestyle solutions that provide high signal quality. Here, we discuss state-of-the-art in wireless and wearable EEG solutions and a number of aspects where such solutions require improvements when handling electrical activity of the brain. We address personal traits and sensory inputs, brain signal generation and acquisition, brain signal analysis, and feedback generation. We provide guidelines on how these aspects can be advanced further such that we can develop intelligent wearable, wireless, lifestyle EEG solutions. We recognized the following aspects as the ones that need rapid research progress: application driven design, end-user driven development, standardization and sharing of EEG data, and development of sophisticated approaches to handle EEG artifacts.

  19. Electroencephalogram (EEG) (For Parents)

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    ... after a severe head injury or before a heart transplant or liver transplant . Preparation If your child is ... on this topic for: Parents Kids Teens Seizures First Aid: Seizures Epilepsy Epilepsy Special Needs Factsheet Encephalitis ...

  20. Consensus on the use of neurophysiological tests in the intensive care unit (ICU): electroencephalogram (EEG), evoked potentials (EP), and electroneuromyography (ENMG)

    DEFF Research Database (Denmark)

    Guørit, J.M.; Amantini, A.; Amodio, P.

    2009-01-01

    STUDY AIM: To provide a consensus of European leading authorities about the optimal use of clinical neurophysiological (CN) tests (electroencephalogram [EEG]; evoked potentials [EP]; electroneuromyography [ENMG]) in the intensive care unit (ICU) and, particularly, about the way to make these tests...... contribution to all other experts. A complete consensus has been reached when submitting the manuscript. RESULTS: What the group considered as the best classification systems for EEG and EP abnormalities in the ICU is first presented. CN tests are useful for diagnosis (epilepsy, brain death, and neuromuscular...... disorders), prognosis (anoxic ischemic encephalopathy, head trauma, and neurologic disturbances of metabolic and toxic origin), and follow-up, in the adult, paediatric, and neonatal ICU. Regarding prognosis, a clear distinction is made between these tests whose abnormalities are indicative of an ominous...

  1. Removal of eye blink artifacts in wireless EEG sensor networks using reduced-bandwidth canonical correlation analysis.

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    Somers, Ben; Bertrand, Alexander

    2016-12-01

    Chronic, 24/7 EEG monitoring requires the use of highly miniaturized EEG modules, which only measure a few EEG channels over a small area. For improved spatial coverage, a wireless EEG sensor network (WESN) can be deployed, consisting of multiple EEG modules, which interact through short-distance wireless communication. In this paper, we aim to remove eye blink artifacts in each EEG channel of a WESN by optimally exploiting the correlation between EEG signals from different modules, under stringent communication bandwidth constraints. We apply a distributed canonical correlation analysis (CCA-)based algorithm, in which each module only transmits an optimal linear combination of its local EEG channels to the other modules. The method is validated on both synthetic and real EEG data sets, with emulated wireless transmissions. While strongly reducing the amount of data that is shared between nodes, we demonstrate that the algorithm achieves the same eye blink artifact removal performance as the equivalent centralized CCA algorithm, which is at least as good as other state-of-the-art multi-channel algorithms that require a transmission of all channels. Due to their potential for extreme miniaturization, WESNs are viewed as an enabling technology for chronic EEG monitoring. However, multi-channel analysis is hampered in WESNs due to the high energy cost for wireless communication. This paper shows that multi-channel eye blink artifact removal is possible with a significantly reduced wireless communication between EEG modules.

  2. Consensus on the use of neurophysiological tests in the intensive care unit (ICU): electroencephalogram (EEG), evoked potentials (EP), and electroneuromyography (ENMG)

    DEFF Research Database (Denmark)

    Guørit, J.M.; Amantini, A.; Amodio, P.

    2009-01-01

    disorders), prognosis (anoxic ischemic encephalopathy, head trauma, and neurologic disturbances of metabolic and toxic origin), and follow-up, in the adult, paediatric, and neonatal ICU. Regarding prognosis, a clear distinction is made between these tests whose abnormalities are indicative of an ominous......STUDY AIM: To provide a consensus of European leading authorities about the optimal use of clinical neurophysiological (CN) tests (electroencephalogram [EEG]; evoked potentials [EP]; electroneuromyography [ENMG]) in the intensive care unit (ICU) and, particularly, about the way to make these tests...... clinically useful for the management of individual patients. METHODS: This study gathered together several European clinical neurophysiologists and neurointensivists whose leading contributions in the adult or paediatric ICU and in continuous neuromonitoring had been peer-acknowledged. It was based on both...

  3. Measurement of neural signals from inexpensive, wireless and dry EEG systems.

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    Grummett, T S; Leibbrandt, R E; Lewis, T W; DeLosAngeles, D; Powers, D M W; Willoughby, J O; Pope, K J; Fitzgibbon, S P

    2015-07-01

    Electroencephalography (EEG) is challenged by high cost, immobility of equipment and the use of inconvenient conductive gels. We compared EEG recordings obtained from three systems that are inexpensive, wireless, and/or dry (no gel), against recordings made with a traditional, research-grade EEG system, in order to investigate the ability of these 'non-traditional' systems to produce recordings of comparable quality to a research-grade system. The systems compared were: Emotiv EPOC (inexpensive and wireless), B-Alert (wireless), g.Sahara (dry) and g.HIamp (research-grade). We compared the ability of the systems to demonstrate five well-studied neural phenomena: (1) enhanced alpha activity with eyes closed versus open; (2) visual steady-state response (VSSR); (3) mismatch negativity; (4) P300; and (5) event-related desynchronization/synchronization. All systems measured significant alpha augmentation with eye closure, and were able to measure VSSRs (although these were smaller with g.Sahara). The B-Alert and g.Sahara were able to measure the three time-locked phenomena equivalently to the g.HIamp. The Emotiv EPOC did not have suitably located electrodes for two of the tasks and synchronization considerations meant that data from the time-locked tasks were not assessed. The results show that inexpensive, wireless, or dry systems may be suitable for experimental studies using EEG, depending on the research paradigm, and within the constraints imposed by their limited electrode placement and number.

  4. EEG activities during elicited sleep onset REM and NREM periods reflect different mechanisms of dream generation. Electroencephalograms. Rapid eye movement.

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    Takeuchi, Tomoka; Ogilvie, Robert D; Murphy, Timothy I; Ferrelli, Anthony V

    2003-02-01

    To be the first to compare EEG power spectra during sleep onset REM periods (SOREMP) and sleep onset NREM periods (NREMP) in normal individuals and relate this to dream appearance processes underlying these different types of sleep periods. Eight healthy undergraduates spent 7 consecutive nights in the sleep lab including 4 nights for SOREMP elicitation using the Sleep Interruption Technique. This enabled us to control preceding sleep processes between SOREMP and NREMP. EEG power spectra when participants did and did not report 'dreams' were compared between both types of sleep. Sleep stages, subjective measurements including dream property scores, sleepiness, mood, and tiredness after awakenings were also examined to determine their consistency with EEG findings. Increased alpha EEG activities (11.72-13.67 Hz) observed mainly in the central area were related to the absence of SOREMP dreams and appearance of NREMP dreams. Analyses of sleep stages combining two studies (16 participants) also supported the Fast Fourier Transform findings, showing that when dreams were reported there were decreased amounts of stage 2 and increased stage REM in SOREMP and increased stage W in NREMP. SOREMP dreams were more bizarre than NREMP dreams. Participants felt more tired after SOREMP with dreams than without dreams, while the opposite was observed after NREMP episodes. EEG power spectra patterns reflected different physiological mechanisms underlying generation of SOREMP and NREMP dreams. The same relationships were also reflected by sleep stage analyses as well as subjective measurements including dream properties and tiredness obtained after awakenings. This study not only supports the hypothesized relationships between REM mechanisms and REM dreams as well as arousal processes and NREM dreams, it also provides a new perspective to dream research due to its unique techniques to awaken participants and collect REM dreams during experimentally induced SOREMP.

  5. Wearable Battery-free Wireless 2-channel EEG Systems Powered by Energy Scavengers

    Directory of Open Access Journals (Sweden)

    Mieke VAN BAVEL

    2008-07-01

    Full Text Available Thermoelectric generators worn on a person’s body have demonstrated their capability to power a variety of wireless sensor nodes that are to improve his/her health or comfort. In this article, the design, fabrication and performance of two prototypes of a battery-free wireless 2-channel electroencephalography (EEG system are presented. The first system is powered solely by a thermoelectric generator that produces 2-2.5mW of power and is worn as a headband. The second system resembles a diadem or headphones and uses a hybrid power supply that combines a thermoelectric generator and photovoltaic cells in one device. This portable EEG headset considerably improves the comfort of patients in clinical as well as in non-clinical environments and opens perspectives for a new range of non-clinical applications.

  6. Test-retest reliability of frontal alpha electroencephalogram (EEG) and electrocardiogram (ECG) measures in adolescents: a pilot study.

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    Winegust, Adira K; Mathewson, Karen J; Schmidt, Louis A

    2014-12-01

    A number of studies have shown that the pattern of resting frontal EEG alpha power and asymmetry and heart rate are predictive of individual differences in affective style in children and adults. Although test-retest reliability of frontal electrocortical and autonomic measures has been established in adult and child and some clinical populations, few studies have examined test-retest reliability of these measures in adolescents. Here, we conducted a pilot study to examine the test-retest reliability of frontal EEG alpha power and asymmetry and heart period and heart rate in 10 typically developing adolescent participants (M age = 15.9 years) over a 1 month period. We found acceptable test-retest reliability using Pearson and intra-class correlations in left and right mid-frontal alpha power and asymmetry and heart period and heart rate over 1 month. These results provide initial evidence for acceptable levels of test-retest reliability in central and peripheral psychophysiological measures in adolescents used to index affective style in children and adults. Future studies are needed with a larger sample to ensure the reliability of these results.

  7. EEG Controlled Wheelchair

    Directory of Open Access Journals (Sweden)

    Swee Sim Kok

    2016-01-01

    Full Text Available This paper describes the development of a brainwave controlled wheelchair. The main objective of this project is to construct a wheelchair which can be directly controlled by the brain without requires any physical feedback as controlling input from the user. The method employed in this project is the Brain-computer Interface (BCI, which enables direct communication between the brain and the electrical wheelchair. The best method for recording the brain’s activity is electroencephalogram (EEG. EEG signal is also known as brainwaves signal. The device that used for capturing the EEG signal is the Emotiv EPOC headset. This headset is able to transmit the EEG signal wirelessly via Bluetooth to the PC (personal computer. By using the PC software, the EEG signals are processed and converted into mental command. According to the mental command (e.g. forward, left... obtained, the output electrical signal is sent out to the electrical wheelchair to perform the desired movement. Thus, in this project, a computer software is developed for translating the EEG signal into mental commands and transmitting out the controlling signal wirelessly to the electrical wheelchair.

  8. The electroencephalogram (EEG) as a research tool in human behavior genetics: psychological examinations in healthy males with various inherited EEG variants. II. Results.

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    Vogel, F; Schalt, E; Krüger, J

    1979-02-28

    The results of psychological examinations on 298 adult male probands with various inherited EEG variants are described. They may be summarized as follows: 1) The low-voltage (N) group scored high in intelligence tests, especially in spatial orientation. Personality scores revealed this group as 'normal', extravert, group-dependent, and not very energetic. 2) The borderline low-voltage (NG) group showed slight weakness in abstract thinking, short-time memory, and motor skills and a relatively strong tendency to have 'neurotic' complaints. Reaction time and motor skills were poor. 3) The occipital fast alpha-variants (BO) group performed very well in tests of abstract thinking and motor skills. 4) The monotonous alpha (R) group showed average performance in most intelligence scores but above-average in short-time memory and in precision under stress conditions. Personality scores indicated high spontaneous activity and toughmindedness. 5) The BG (fronto-precentral beta-group) category showed very low MMPI scores, indicating little neurotic tendency. Intelligence could be above average. 6) The diffuse beta (BD) group scored low in intelligence tests, especially in spatial orientation, and had a high error rate in tests measuring concentration and precision. Most differences were relatively small; the whole range of test scores could be found in all EEG groups.

  9. Toward a fully integrated wireless wearable EEG-NIRS bimodal acquisition system

    Science.gov (United States)

    Safaie, J.; Grebe, R.; Abrishami Moghaddam, H.; Wallois, F.

    2013-10-01

    Objective. Interactions between neuronal electrical activity and regional changes in microcirculation are assumed to play a major role in physiological brain activity and the development of pathological disorders, but have been poorly elucidated to date. There is a need for advanced diagnostic tools to investigate the relationships between these two physiological processes.Approach. To meet these needs, a wireless wearable system has been developed, which combines a near infrared spectroscopy (NIRS) system using light emitting diodes (LEDs) as a light source and silicon photodiodes as a detector with an integrated electroencephalography (EEG) system. Main results. The main advantages over currently available devices are miniaturization and integration of a real-time electrical and hemodynamic activity monitor into one wearable device. For patient distributed monitoring and creating a body-area network, up to seven same devices can be connected to a single base station (PC) synchronously. Each node presents enhanced portability due to the wireless communication and highly integrated components resulting in a small, lightweight signal acquisition device. Further progress includes the individual control of LEDs output to automatically or interactively adjust emitted light to the actual local situation online, the use of silicon photodiodes with a safe low-voltage power supply, and an integrated three dimensional accelerometer for movement detection for the identification of motion artifacts. Significance. The device was tested and validated using our enhanced EEG-NIRS tissue mimicking fluid phantom for sensitivity mapping. Typical somatotopic electrical evoked potential experiments were performed to verify clinical applicability.

  10. More attention, less deficit:wearable eeg-based serious game for focus improvement

    OpenAIRE

    Alchalabi, Alaa Eddin; Eddin, Amer Nour; Shirmohammadi, Shervin

    2017-01-01

    Attention Deficit Hyperactivity Disorder (ADHD) and Attention Deficit Disorders (ADD) are two of the most spread mental disorders characterized by the lack of attention and focus. One way to measure focus is through Electroencephalogram (EEG) signals that can be read using the new wireless EEG reading devices often used by Brain-computer Interface (BCI) researchers. In parallel, serious games have been recently providing opportunities for the rehabilitation of various cognitive and emotional ...

  11. Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG.

    Science.gov (United States)

    Zhang, Xiaoliang; Li, Jiali; Liu, Yugang; Zhang, Zutao; Wang, Zhuojun; Luo, Dianyuan; Zhou, Xiang; Zhu, Miankuan; Salman, Waleed; Hu, Guangdi; Wang, Chunbai

    2017-03-01

    The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver's brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety.

  12. Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG

    Directory of Open Access Journals (Sweden)

    Xiaoliang Zhang

    2017-03-01

    Full Text Available The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1 a wireless wearable EEG collection; (2 train driver vigilance detection; and (3 early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI device acquires the locomotive driver’s brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC via Bluetooth. In the second step, a support vector machine (SVM classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT to extract the EEG power spectrum density (PSD. In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety.

  13. Neural activity based biofeedback therapy for Autism spectrum disorder through wearable wireless textile EEG monitoring system

    Science.gov (United States)

    Sahi, Ahna; Rai, Pratyush; Oh, Sechang; Ramasamy, Mouli; Harbaugh, Robert E.; Varadan, Vijay K.

    2014-04-01

    Mu waves, also known as mu rhythms, comb or wicket rhythms are synchronized patterns of electrical activity involving large numbers of neurons, in the part of the brain that controls voluntary functions. Controlling, manipulating, or gaining greater awareness of these functions can be done through the process of Biofeedback. Biofeedback is a process that enables an individual to learn how to change voluntary movements for purposes of improving health and performance through the means of instruments such as EEG which rapidly and accurately 'feedback' information to the user. Biofeedback is used for therapeutic purpose for Autism Spectrum Disorder (ASD) by focusing on Mu waves for detecting anomalies in brain wave patterns of mirror neurons. Conventional EEG measurement systems use gel based gold cup electrodes, attached to the scalp with adhesive. It is obtrusive and wires sticking out of the electrodes to signal acquisition system make them impractical for use in sensitive subjects like infants and children with ASD. To remedy this, sensors can be incorporated with skull cap and baseball cap that are commonly used for infants and children. Feasibility of Textile based Sensor system has been investigated here. Textile based multi-electrode EEG, EOG and EMG monitoring system with embedded electronics for data acquisition and wireless transmission has been seamlessly integrated into fabric of these items for continuous detection of Mu waves. Textile electrodes were placed on positions C3, CZ, C4 according to 10-20 international system and their capability to detect Mu waves was tested. The system is ergonomic and can potentially be used for early diagnosis in infants and planning therapy for ASD patients.

  14. EEG

    African Journals Online (AJOL)

    2017-09-03

    Sep 3, 2017 ... In clinical psychiatry, EEG is also used in evaluating pres- ence of seizure especially the ones that produce complex behaviors (temporal lobe, frontal lobe and petit mal sei- zures). EEG can also be used during electroconvulsive therapy (ECT) to monitor whether or not the stimulus produces seizure activity.

  15. EEG

    Science.gov (United States)

    ... is also used to: Evaluate problems with sleep ( sleep disorders ) Monitor the brain during brain surgery An EEG may be done ... in some cases) Seizure disorder (such as epilepsy) Sleep disorder (such as narcolepsy ) Swelling of the brain (edema) Risks An EEG test is very safe. ...

  16. An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals

    Directory of Open Access Journals (Sweden)

    Simon Fauvel

    2014-01-01

    Full Text Available The use of wireless body sensor networks is gaining popularity in monitoring and communicating information about a person’s health. In such applications, the amount of data transmitted by the sensor node should be minimized. This is because the energy available in these battery powered sensors is limited. In this paper, we study the wireless transmission of electroencephalogram (EEG signals. We propose the use of a compressed sensing (CS framework to efficiently compress these signals at the sensor node. Our framework exploits both the temporal correlation within EEG signals and the spatial correlations amongst the EEG channels. We show that our framework is up to eight times more energy efficient than the typical wavelet compression method in terms of compression and encoding computations and wireless transmission. We also show that for a fixed compression ratio, our method achieves a better reconstruction quality than the CS-based state-of-the art method. We finally demonstrate that our method is robust to measurement noise and to packet loss and that it is applicable to a wide range of EEG signal types.

  17. Increased Prevalence of Intermittent Rhythmic Delta or Theta Activity (IRDA/IRTA) in the Electroencephalograms (EEGs) of Patients with Borderline Personality Disorder

    OpenAIRE

    Tebartz van Elst, Ludger; Fleck, Max; Bartels, Susanne; Altenm?ller, Dirk-Matthias; Riedel, Andreas; Bubl, Emanuel; Matthies, Swantje; Feige, Bernd; Perlov, Evgeniy; Endres, Dominique

    2016-01-01

    Introduction: An increased prevalence of pathological electroencephalography (EEG) signals has been reported in patients with borderline personality disorder (BPD). In an elaborative case description of such a patient with intermittent rhythmic delta and theta activity (IRDA/IRTA), the BPD symptoms where linked to the frequency of the IRDAs/IRTAs and vanished with the IRDAs/IRTAs following anticonvulsive therapy. This observation raised a question regarding the prevalence of such EEG abnormal...

  18. Increased Prevalence of Intermittent Rhythmic Delta or Theta Activity (IRDA/IRTA) in the Electroencephalograms (EEGs) of Patients with Borderline Personality Disorder.

    Science.gov (United States)

    Tebartz van Elst, Ludger; Fleck, Max; Bartels, Susanne; Altenmüller, Dirk-Matthias; Riedel, Andreas; Bubl, Emanuel; Matthies, Swantje; Feige, Bernd; Perlov, Evgeniy; Endres, Dominique

    2016-01-01

    An increased prevalence of pathological electroencephalography (EEG) signals has been reported in patients with borderline personality disorder (BPD). In an elaborative case description of such a patient with intermittent rhythmic delta and theta activity (IRDA/IRTA), the BPD symptoms where linked to the frequency of the IRDAs/IRTAs and vanished with the IRDAs/IRTAs following anticonvulsive therapy. This observation raised a question regarding the prevalence of such EEG abnormalities in BPD patients. The aim of this retrospective study was to identify the frequency of EEG abnormalities in a carefully analyzed psychiatric collective. Following earlier reports, we hypothesized an increased prevalence of EEG abnormalities in BPD patients. We recruited 96 consecutive patients with BPD from the archive of a university clinic for psychiatry and psychotherapy, and compared the prevalence of EEG abnormalities to those of 76 healthy controls subjects. The EEGs were rated by three different blinded clinicians, including a consultant specializing in epilepsy from the local epilepsy center. We found a significant increase in the prevalence of IRDAs and IRTAs in BPD patients (14.6%) compared to the control subjects (3.9%; p = 0.020). In this blinded retrospective case-control study, we were able to confirm an increased prevalence of pathological EEG findings (IRDAs/IRTAs only) in BPD patients. The major limitation of this study is that the control group was not matched on age and gender. Therefore, the results should be regarded as preliminary findings of an open uncontrolled, retrospective study. Future research performing prospective, controlled studies is needed to verify our findings and answer the question of whether such EEG findings might predict a positive response to anticonvulsive pharmacological treatment.

  19. Increased prevalence of intermittent rhythmic delta or theta activity (IRDA/IRTA in the electroencephalograms (EEGs of patients with borderline personality disorder

    Directory of Open Access Journals (Sweden)

    Ludger eTebartz Van Elst

    2016-02-01

    Full Text Available Introduction: An increased prevalence of pathological electroencephalography (EEG signals has been reported in patients with borderline personality disorder (BPD. In an elaborative case description of such a patient with intermittent rhythmic delta and theta activity (IRDA/IRTA, the BPD symptoms where linked to the frequency of the IRDAs/IRTAs and vanished with the IRDAs/IRTAs following anticonvulsive therapy. This observation raised a question regarding the prevalence of such EEG abnormalities in BPD patients. The aim of this retrospective study was to identify the frequency of EEG abnormalities in a carefully analyzed psychiatric collective. Following earlier reports, we hypothesized an increased prevalence of EEG abnormalities in BPD patients.Participants and Methods: We recruited 96 consecutive patients with BPD from the archive of a university clinic for psychiatry and psychotherapy, and compared the prevalence of EEG abnormalities to those of 76 healthy controls subjects. The EEGs were rated by three different blinded clinicians, including a consultant specializing in epilepsy from the local epilepsy center.Results: We found a significant increase in the prevalence of IRDAs and IRTAs in BPD patients (14.6% compared to the control subjects (3.9%; p=0.020. Discussion: In this blinded retrospective case-control study, we were able to confirm an increased prevalence of pathological EEG findings (IRDAs/IRTAs only in BPD patients. The major limitation of this study is that the control group was not matched on age and gender. Therefore, the results should be regarded as preliminary findings of an open uncontrolled, retrospective study. Future research performing prospective, controlled studies is needed to verify our findings and answer the question of whether such EEG findings might predict a positive response to anticonvulsive pharmacological treatment.

  20. CLINICAL SIGNIFICANCE OF EPILEPTIFORM ACTIVITY IN ELECTROENCEPHALOGRAM

    Directory of Open Access Journals (Sweden)

    L. Yu. Glukhova

    2016-01-01

    Full Text Available The article is devoted to some issues of sensitivity and specificity of epileptiform activity in the electroencephalogram (EEG. Epileptiform activity – it is sharp waves and spikes on EEG. Normal EEG does not exclude the diagnosis of epilepsy and viсe versa: presence of epileptiform activity on EEG is not necessarily caused by epilepsy. Several EEGs may be needed to detect epileptiform activity in patients with epilepsy. EEG recording during sleep with the use of different activation methods (hyperventilation, rhythmic photic stimulation, sleep deprivation can increase the probability of epileptiform activity detection. Clinical presentation should be taken into account while interpreting EEG results with registered epileptiform activity. The issues of epileptiform activity classification and differential interpretation of other electrical activity types are also discussed in the article. Main epileptiform patterns, their neurophysiological basis and correlation with clinical manifestations are described.

  1. Assessment of anaesthetic depth by clustering analysis and autoregressive modelling of electroencephalograms

    DEFF Research Database (Denmark)

    Thomsen, C E; Rosenfalck, A; Nørregaard Christensen, K

    1991-01-01

    The brain activity electroencephalogram (EEG) was recorded from 30 healthy women scheduled for hysterectomy. The patients were anaesthetized with isoflurane, halothane or etomidate/fentanyl. A multiparametric method was used for extraction of amplitude and frequency information from the EEG...

  2. STUDI AWAL: PENGARUH GAME KEKERASAN TERHADAP AKTIVITAS OTAK ANAK MELALUI PEMETAAN SINYAL OTAK (BRAIN MAPPING MENGGUNAKAN WIRELESS EEG

    Directory of Open Access Journals (Sweden)

    Nita Handayani

    2017-06-01

    Full Text Available Brain mapping adalah pemetaan aktivitas kelistrikan otak untuk mempelajari fungsional otak manusia. Pada studi ini, brain mapping digunakan untuk mempelajari pengaruh game kekerasan terhadap aktivitas fungsional otak anak dengan menggunakan wireless EEG (electroencephalography berupa Emotiv Epoc 14-channel. Subjek penelitian ini adalah anak-anak pecandu game kekerasan (10 anak dengan rentang usia antara 12-15 tahun. Aktivitas otak pada saat bermain game akan dibandingkan dengan kondisi rileks. Waktu perekaman EEG selama 42 menit untuk setiap subjek. Dari hasil analisis spektral daya menggunakan periodogram Welch menunjukkan bahwa pada saat bermain game, frekuensi gelombang delta dan theta meningkat terutama pada area frontal (F7, F3, FC5, FC6, F4, F8, dan AF4. Spektral daya gelombang alpha mengalami penurunan sedangkan gelombang beta mengalami peningkatan pada saat bermain game. Hal ini mengindikasikan bahwa anak mengalami beban mental dan berada pada kondisi stres pada saat bermain game kekerasan.

  3. Classification of Single-Trial Auditory Events Using Dry-Wireless EEG During Real and Motion Simulated Flight

    Directory of Open Access Journals (Sweden)

    Daniel eCallan

    2015-02-01

    Full Text Available Application of neuro-augmentation technology based on dry-wireless EEG may be considerably beneficial for aviation and space operations because of the inherent dangers involved. In this study we evaluate classification performance of perceptual events using a dry-wireless EEG system during motion platform based flight simulation and actual flight in an open cockpit biplane to determine if the system can be used in the presence of considerable environmental and physiological artifacts. A passive task involving 200 random auditory presentations of a chirp sound was used for evaluation. The advantage of this auditory task is that it does not interfere with the perceptual motor processes involved with piloting the plane. Classification was based on identifying the presentation of a chirp sound versus silent periods. Evaluation of Independent component analysis and Kalman filtering to enhance classification performance by extracting brain activity related to the auditory event from other non-task related brain activity and artifacts was assessed. The results of permutation testing revealed that single trial classification of presence or absence of an auditory event was significantly above chance for all conditions on a novel test set. The best performance could be achieved with both ICA and Kalman filtering relative to no processing: Platform Off (83.4% vs 78.3%, Platform On (73.1% vs 71.6%, Biplane Engine Off (81.1% vs 77.4%, and Biplane Engine On (79.2% vs 66.1%. This experiment demonstrates that dry-wireless EEG can be used in environments with considerable vibration, wind, acoustic noise, and physiological artifacts and achieve good single trial classification performance that is necessary for future successful application of neuro-augmentation technology based on brain-machine interfaces.

  4. Classification of single-trial auditory events using dry-wireless EEG during real and motion simulated flight.

    Science.gov (United States)

    Callan, Daniel E; Durantin, Gautier; Terzibas, Cengiz

    2015-01-01

    Application of neuro-augmentation technology based on dry-wireless EEG may be considerably beneficial for aviation and space operations because of the inherent dangers involved. In this study we evaluate classification performance of perceptual events using a dry-wireless EEG system during motion platform based flight simulation and actual flight in an open cockpit biplane to determine if the system can be used in the presence of considerable environmental and physiological artifacts. A passive task involving 200 random auditory presentations of a chirp sound was used for evaluation. The advantage of this auditory task is that it does not interfere with the perceptual motor processes involved with piloting the plane. Classification was based on identifying the presentation of a chirp sound vs. silent periods. Evaluation of Independent component analysis (ICA) and Kalman filtering to enhance classification performance by extracting brain activity related to the auditory event from other non-task related brain activity and artifacts was assessed. The results of permutation testing revealed that single trial classification of presence or absence of an auditory event was significantly above chance for all conditions on a novel test set. The best performance could be achieved with both ICA and Kalman filtering relative to no processing: Platform Off (83.4% vs. 78.3%), Platform On (73.1% vs. 71.6%), Biplane Engine Off (81.1% vs. 77.4%), and Biplane Engine On (79.2% vs. 66.1%). This experiment demonstrates that dry-wireless EEG can be used in environments with considerable vibration, wind, acoustic noise, and physiological artifacts and achieve good single trial classification performance that is necessary for future successful application of neuro-augmentation technology based on brain-machine interfaces.

  5. Consensus on the use of neurophysiological tests in the intensive care unit (ICU): electroencephalogram (EEG), evoked potentials (EP), and electroneuromyography (ENMG)

    DEFF Research Database (Denmark)

    Guérit, J-M; Amantini, A; Amodio, P

    2009-01-01

    a literature review and each participant's own experience. Given the methodological impossibility to gather studies fulfilling criteria of evidence-based medicine, this article essentially relies on expert opinions that were gained after several rounds, in which each expert was invited to communicate his own...... contribution to all other experts. A complete consensus has been reached when submitting the manuscript. RESULTS: What the group considered as the best classification systems for EEG and EP abnormalities in the ICU is first presented. CN tests are useful for diagnosis (epilepsy, brain death, and neuromuscular...

  6. Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems.

    Science.gov (United States)

    Chai, Rifai; Naik, Ganesh R; Ling, Sai Ho; Nguyen, Hung T

    2017-01-07

    One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia. Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s.

  7. EEG: Origin and measurement

    NARCIS (Netherlands)

    Lopes da Silva, F.; Mulert, C.; Lemieux, L.

    2010-01-01

    The existence of the electrical activity of the brain (i.e. the electroencephalogram or EEG) was discovered more than a century ago by Caton. After the demonstration that the EEG could be recorded from the human scalp by Berger in the 1920s, it made a slow start before it became accepted as a method

  8. Signal Quality Evaluation of Emerging EEG Devices

    Directory of Open Access Journals (Sweden)

    Thea Radüntz

    2018-02-01

    Full Text Available Electroencephalogram (EEG registration as a direct measure of brain activity has unique potentials. It is one of the most reliable and predicative indicators when studying human cognition, evaluating a subject's health condition, or monitoring their mental state. Unfortunately, standard signal acquisition procedures limit the usability of EEG devices and narrow their application outside the lab. Emerging sensor technology allows gel-free EEG registration and wireless signal transmission. Thus, it enables quick and easy application of EEG devices by users themselves. Although a main requirement for the interpretation of an EEG is good signal quality, there is a lack of research on this topic in relation to new devices. In our work, we compared the signal quality of six very different EEG devices. On six consecutive days, 24 subjects wore each device for 60 min and completed tasks and games on the computer. The registered signals were evaluated in the time and frequency domains. In the time domain, we examined the percentage of artifact-contaminated EEG segments and the signal-to-noise ratios. In the frequency domain, we focused on the band power variation in relation to task demands. The results indicated that the signal quality of a mobile, gel-based EEG system could not be surpassed by that of a gel-free system. However, some of the mobile dry-electrode devices offered signals that were almost comparable and were very promising. This study provided a differentiated view of the signal quality of emerging mobile and gel-free EEG recording technology and allowed an assessment of the functionality of the new devices. Hence, it provided a crucial prerequisite for their general application, while simultaneously supporting their further development.

  9. Video electroencephalogram telemetry in temporal lobe epilepsy

    Directory of Open Access Journals (Sweden)

    Jayanti Mani

    2014-01-01

    Full Text Available Temporal lobe epilepsy (TLE is the most commonly encountered medically refractory epilepsy. It is also the substrate of refractory epilepsy that gives the most gratifying results in any epilepsy surgery program, with a minimum use of resources. Correlation of clinical behavior and the ictal patterns during ictal behavior is mandatory for success at epilepsy surgery. Video electroencephalogram (EEG telemetry achieves this goal and hence plays a pivotal role in pre-surgical assessment. The role of telemetry is continuously evolving with the advent of digital EEG technology, of high-resolution volumetric magnetic resonance imaging and other functional imaging techniques. Most of surgical selection in patients with TLE can be done with a scalp video EEG monitoring. However, the limitations of the scalp EEG technique demand invasive recordings in a selected group of TLE patients. This subset of the patients can be a challenge to the epileptologist.

  10. Evaluation of a Prototype Low-Cost, Modular, Wireless Electroencephalography (EEG) Headset Design for Widespread Application

    Science.gov (United States)

    2016-06-01

    this study to consist of minimal- risk testing and evaluation of materiel. Therefore, the participants were not required to sign an informed-consent...2013b. Report No.: ARL-SR- 266. Oostenveld R, Praamstra P. The five percent electrode system for high-resolution EEG and ERP measurements

  11. Monitoring and diagnosis of Alzheimer's disease using noninvasive compressive sensing EEG

    Science.gov (United States)

    Morabito, F. C.; Labate, D.; Morabito, G.; Palamara, I.; Szu, H.

    2013-05-01

    The majority of elderly with Alzheimer's Disease (AD) receive care at home from caregivers. In contrast to standard tethered clinical settings, a wireless, real-time, body-area smartphone-based remote monitoring of electroencephalogram (EEG) can be extremely advantageous for home care of those patients. Such wearable tools pave the way to personalized medicine, for example giving the opportunity to control the progression of the disease and the effect of drugs. By applying Compressive Sensing (CS) techniques it is in principle possible to overcome the difficulty raised by smartphones spatial-temporal throughput rate bottleneck. Unfortunately, EEG and other physiological signals are often non-sparse. In this paper, it is instead shown that the EEG of AD patients becomes actually more compressible with the progression of the disease. EEG of Mild Cognitive Impaired (MCI) subjects is also showing clear tendency to enhanced compressibility. This feature favor the use of CS techniques and ultimately the use of telemonitoring with wearable sensors.

  12. Interobserver reliability of visual interpretation of electroencephalograms in children with newly diagnosed seizures

    NARCIS (Netherlands)

    Stroink, H; Schimsheimer, RJ; de Weerd, AW; Geerts, AT; Arts, WF; Peeters, EA; Brouwer, OF; van Donselaar, CA

    The reliability of visual interpretation of electroencephalograms (EEG) is of great importance in assessing the value of this diagnostic tool. We prospectively obtained 50 standard EEGs and 61 EEGs after partial sleep deprivation from 93 children (56 males, 37 females) with a mean age of 6 years 10

  13. Testing the validity of wireless EEG for cognitive research with auditory and visual paradigms

    DEFF Research Database (Denmark)

    Weed, Ethan; Kratschmer, Alexandra Regina; Pedersen, Michael Nygaard

    and smaller cognitive components. To test the feasibility of these headsets for cognitive research, we compared performance of the Emotiv Epoc wireless headset (EM) with Brain Products ActiCAP (BP) active electrodes on two well-studied components: the auditory mismatch negativity (MMN) and the visual face...

  14. Preliminary study of Alzheimer's Disease diagnosis based on brain electrical signals using wireless EEG

    Science.gov (United States)

    Handayani, N.; Akbar, Y.; Khotimah, S. N.; Haryanto, F.; Arif, I.; Taruno, W. P.

    2016-03-01

    This research aims to study brain's electrical signals recorded using EEG as a basis for the diagnosis of patients with Alzheimer's Disease (AD). The subjects consisted of patients with AD, and normal subjects are used as the control. Brain signals are recorded for 3 minutes in a relaxed condition and with eyes closed. The data is processed using power spectral analysis, brain mapping and chaos test to observe the level of complexity of EEG's data. The results show a shift in the power spectral in the low frequency band (delta and theta) in AD patients. The increase of delta and theta occurs in lobus frontal area and lobus parietal respectively. However, there is a decrease of alpha activity in AD patients where in the case of normal subjects with relaxed condition, brain alpha wave dominates the posterior area. This is confirmed by the results of brain mapping. While the results of chaos analysis show that the average value of MMLE is lower in AD patients than in normal subjects. The level of chaos associated with neural complexity in AD patients with lower neural complexity is due to neuronal damage caused by the beta amyloid plaques and tau protein in neurons.

  15. Adaptive filtering of electroencephalogram signals using the empirical-modes method

    Science.gov (United States)

    Grubov, V. V.; Runnova, A. E.; Koronovskii, A. A.; Hramov, A. E.

    2017-07-01

    A new method for the removal of physiological artifacts in the experimental signals of human electroencephalograms (EEGs) has been developed. The method is based on decomposition of the signal in terms of empirical modes. The algorithm involves EEG signal decomposition in terms of empirical modes, searching for modes with artifacts, removing these modes, and restoration of the EEG signal. The method was tested on experimental data and showed high efficiency in the removal of various physiological artifacts in EEGs.

  16. Development of a Mobile EEG-based Biometric Authentication System

    DEFF Research Database (Denmark)

    Klonovs, Juris; Petersen, Christoffer Kjeldgaard; Olesen, Henning

    In recent years the need for greater security for storing personal and business data or accessing corporate networks on mobile devices is growing rapidly, and one of the potential solutions is to employ the innovative biometric authentication techniques. This paper presents the development...... of a mobile biometric authentication system based on electroencephalogram (EEG) recordings in combination with already proven technologies such as facial detection and near-field communication (NFC). The overall goal of this work is to fill the gap between mobile web technologies and wireless EEG devices...... and present a mobile prototype system capable of authenticating users based on the uniqueness of their brainwaves. Furthermore, we implement a novel authentication process, which leads the authentication system to be more secure. We also give suggestions for future improvements of the system....

  17. The impact of an interdisciplinary electroencephalogram educational initiative for critical care trainees.

    Science.gov (United States)

    Chau, Destiny; Bensalem-Owen, Meriem; Fahy, Brenda G

    2014-12-01

    The purpose of this study is to evaluate the effectiveness of an interdisciplinary electroencephalogram (EEG) educational module for critical care training. Electroencephalogram is increasingly used for diagnosis, monitoring, and treatment decisions in critically ill patients with neurologic and nonneurologic disorders. Continuous EEG monitoring has an expanded role in the intensive care unit as an additional evaluation tool for critically ill patients with altered mental status. During a neurosurgical intensive care rotation, pulmonary critical care fellows participated in an EEG curriculum covering didactics, clinical exposure, and EEG interpretations. Using 25-question evaluation tools, including EEG interpretations, participants were assessed before EEG instruction and after curriculum completion. Nine fellows completed the pilot study. Evaluation scores increased from 7.56±2.24 to 16.67±2.96 (Pinstitutions for trainees of other specialties interested in critical care. Copyright © 2014 Elsevier Inc. All rights reserved.

  18. Wireless behind-the-ear EEG recording device with wireless interface to a mobile device (iPhone/iPod touch).

    Science.gov (United States)

    Do Valle, Bruno G; Cash, Sydney S; Sodini, Charlie G

    2014-01-01

    EEG remains the mainstay test for the diagnosis and treatment of patients with epilepsy. Unfortunately, ambulatory EEG systems are far from ideal for patients that have infrequent seizures. The systems only last up to 3 days and if a seizure is not captured during the recordings, the doctor cannot give a definite diagnosis of the patient's condition. The ambulatory systems also suffers from being too bulky and posing some constraints on the patient, such as not being able to shower during the recordings. This paper presents a novel behind-the-ear EEG recording device that uses an iPhone or iPod Touch to continuously upload the patient's data to a secure server. This device not only gives the doctors access to the EEG data in real time but it can be easily removed and re-applied by the patient at any time, thus reducing the interference with quality of life.

  19. Chaos based encryption system for encrypting electroencephalogram signals.

    Science.gov (United States)

    Lin, Chin-Feng; Shih, Shun-Han; Zhu, Jin-De

    2014-05-01

    In the paper, we use the Microsoft Visual Studio Development Kit and C# programming language to implement a chaos-based electroencephalogram (EEG) encryption system involving three encryption levels. A chaos logic map, initial value, and bifurcation parameter for the map were used to generate Level I chaos-based EEG encryption bit streams. Two encryption-level parameters were added to these elements to generate Level II chaos-based EEG encryption bit streams. An additional chaotic map and chaotic address index assignment process was used to implement the Level III chaos-based EEG encryption system. Eight 16-channel EEG Vue signals were tested using the encryption system. The encryption was the most rapid and robust in the Level III system. The test yielded superior encryption results, and when the correct deciphering parameter was applied, the EEG signals were completely recovered. However, an input parameter error (e.g., a 0.00001 % initial point error) causes chaotic encryption bit streams, preventing the recovery of 16-channel EEG Vue signals.

  20. Technical standards for recording and interpretation of neonatal electroencephalogram in clinical practice

    Directory of Open Access Journals (Sweden)

    Cherian Perumpillichira

    2009-01-01

    Full Text Available Neonatal electroencephalogram (EEG, though often perceived as being difficult to record and interpret, is relatively easy to study due to the immature nature of the brain, which expresses only a few well-defined set of patterns. The EEG interpreter needs to be aware of the maturational changes as well as the effect of pathological processes and medication on brain activity. It gives valuable information for the treatment and prognostication in encephalopathic neonates. In this group, serial EEGs or EEG monitoring often gives additional information regarding deterioration/improvement of the brain function or occurrence of seizures.

  1. An EEG Data Investigation Using Only Artifacts

    Science.gov (United States)

    2017-02-22

    EEG Data Investigation Using Only Artifacts 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 1 Chelsey...ORGANIZATION NAME(S) AND ADDRESS(ES) AND ADDRESS(ES) 1 Ball Aerospace – 2875 Presidential Drive , Fairborn, Ohio 45324 2 Oak Ridge Institute for Science...electroencephalogram (EEG) is a positive indicator of mental workload. However, EEG signals are easily affected by artifacts . An artifact mediation

  2. EEG monitoring in postanoxic coma

    NARCIS (Netherlands)

    Cloostermans, M.C.

    2014-01-01

    The electroencephalogram (EEG) contains information that is useful for the prediction of both poor and good neurological outcome in patients with postanoxic encephalopathy after cardiac arrest treated with mild hypothermia. The combined group of iso-electric, low voltage or burst-suppression

  3. Utility of the electroencephalogram in attention deficit hyperactivity disorder.

    Science.gov (United States)

    Millichap, J Gordon; Millichap, John J; Stack, Cynthia V

    2011-07-01

    An electroencephalogram (EEG) has not been routinely utilized in the evaluation of children with attention deficit hyperactivity disorder (ADHD). The utility of the EEG in ADHD is unclear. A recent study in our laboratory using sleep and sleep deprivation routinely found one in four non-epileptic children evaluated for attention deficit disorder has epileptiform discharges in the EEG, more than half focal. The majority of abnormalities (97.5%) occur in sleep and sleep-deprived records compared to 7% in prior wake only records. A review of eight publications showed that laboratories using awake only as routine EEG recordings report a relatively low prevalence of epileptiform discharges, whereas the higher prevalence of epileptiform discharges is seen in those with more prolonged sleep recordings. We have determined that sleep deprivation and sleep are essential to rule out an abnormal EEG in attention deficit disorder. In patients with attention deficit disorder complicated by epilepsy, stimulant therapy is generally safe, provided seizures are controlled by antiepileptic medication. Patients with epilepsy or subclinical electrographic abnormalities not treated with anticonvulsants are at increased risk of seizures when stimulant therapy is introduced, especially extended-release methylphenidate. Apart from an increase in risk of seizures and need for caution in use of stimulants, studies show that epileptiform discharges in the electroencephalogram are linked to a better response of attention deficit to methylphenidate and a higher cognitive performance. Transient cognitive impairment related to subclinical electrographic abnormalities responds to antiepileptic medication. An EEG is important in selected cases of attention deficit disorder and is useful in choice of medication, especially in children with lack of awareness and transient cognitive impairment.

  4. Improving EEG signal peak detection using feature weight learning ...

    Indian Academy of Sciences (India)

    Asrul Adam

    Neural network with random weights (NNRW); feature weight learning (FWL); electroencephalogram (EEG); peak detection algorithm; pattern recognition; particle swarm optimization (PSO). 1. Introduction. The utilization of peak detection algorithms has emerged as a useful tool in several physiological signal applications,.

  5. Preliminary study of Alzheimer's Disease diagnosis based on brain electrical signals using wireless EEG

    International Nuclear Information System (INIS)

    Handayani, N; Akbar, Y; Khotimah, S N; Haryanto, F; Arif, I; Taruno, W P

    2016-01-01

    This research aims to study brain's electrical signals recorded using EEG as a basis for the diagnosis of patients with Alzheimer's Disease (AD). The subjects consisted of patients with AD, and normal subjects are used as the control. Brain signals are recorded for 3 minutes in a relaxed condition and with eyes closed. The data is processed using power spectral analysis, brain mapping and chaos test to observe the level of complexity of EEG's data. The results show a shift in the power spectral in the low frequency band (delta and theta) in AD patients. The increase of delta and theta occurs in lobus frontal area and lobus parietal respectively. However, there is a decrease of alpha activity in AD patients where in the case of normal subjects with relaxed condition, brain alpha wave dominates the posterior area. This is confirmed by the results of brain mapping. While the results of chaos analysis show that the average value of MMLE is lower in AD patients than in normal subjects. The level of chaos associated with neural complexity in AD patients with lower neural complexity is due to neuronal damage caused by the beta amyloid plaques and tau protein in neurons. (paper)

  6. Computerized Electroencephalogram. A model of understanding the brain function in childhood psychosis and its treatment.

    Science.gov (United States)

    Simeon, J; Itil, T M

    1975-09-01

    Computer analysis of the electroencephalogram (CEEG) in psychotic children before and after pharmacotherapy, normal children of schizophrenic mothers, and matched normal children of normal parents indicated significant intergroup differences. The psychotic children had more slow, as well as very fast, EEG waves. With drug therapy the EEG showed a partial "normalization," as fast EEG activity decreased. The EEG and auditory evoked potential of children of schizophrenic mothers were strikingly similar to those of psychotic children and schizophrenic adults, with significant decreases of the average EEG amplitude and the evoked potential latencies. Psychotic children were distinctly differentiated from the normal children by discriminant function analysis of the EEG and EP. Quantitative analysis of brain functions in the mentally ill can help determine the neurophysiological correlates of behavior, a more scientific diagnostic classification, prognosis, and selection of therapy.

  7. Remote monitoring of electroencephalogram, electrocardiogram, and behavior during controlled atmosphere stunning in broilers: Implications for welfare

    NARCIS (Netherlands)

    Coenen, A.M.L.; Lankhaar, J.A.C.; Lowe, J.C.; McKeegan, D.

    2009-01-01

    This study examined the welfare implications of euthanizing broilers with 3 gas mixtures relevant to the commercial application of controlled atmosphere stunning (CAS). Birds were implanted/equipped with electrodes to measure brain activity (electroencephalogram, EEG) and heart rate. These signals

  8. The influence of diazepam on the electroencephalogram-evoked potential interrelation in rats

    NARCIS (Netherlands)

    Jongsma, M.L.A.; Rijn, C.M. van; Egmond, J. van; Schaijk, W.J. van; Sambeth, A.; Coenen, A.M.L.

    2000-01-01

    Though being a sedative increases b-activity in the electroencephalogram (EEG). Diazepam also affects auditory evoked potentials (AEPs). We investigated if the effect of diazepam on AEPs could be ascribed to its b-increasing effect. Eight rats received vehicle and diazepam counterbalanced. AEPs were

  9. Neurodevelopment in newborns: a sample entropy analysis of electroencephalogram

    International Nuclear Information System (INIS)

    Zhang, Dandan; Ding, Haiyan; Liu, Yunfeng; Ding, Haishu; Zhou, Congle; Ye, Datian

    2009-01-01

    The present paper investigates the neural ontogeny of newborns in view of electroencephalogram (EEG) complexity during active sleep (AS) and quiet sleep (QS). Sample entropy (SampEn) is applied to EEG recordings from 168 newborns with postmenstrual age (PMA) ranging from 25 to 60 weeks. The relationship between neurodevelopment and PMA is then explored according to the statistical analysis of the median and interquartile range of SampEn curves. It is found that SampEn of EEG during AS is higher than that during QS. SampEn increases during both AS and QS before about 42 weeks in PMA while it ceases its increase in QS and even decreases in AS after newborns reaching term age. A distinct decrease in the interquartile range of SampEn is found with increasing PMA (from 25 to about 50 weeks), followed by maintenance of low fluctuation in SampEn curves. The study in this paper sets the stage for exhaustive investigation of the SampEn of EEG during brain maturation in newborns. And it could be hoped that SampEn in sleep EEG might be a useful parameter against which delays and aberrations in brain maturation might be tested. The SampEn changes during brain maturation also offer functional clues about neurodevelopment, based on which further explorations could be done. The significance of this paper is the discovery of the decrease in EEG complexity after newborns reaching term. Although some potential neurophysiologic reasons are given, this new discovery might require more study to investigate. In addition, the fluctuation of EEG complexity is analyzed for the first time, which helps to understand the EEG maturation in neurodevelopment

  10. Detrended fluctuation analysis and Kolmogorov–Sinai entropy of electroencephalogram signals

    International Nuclear Information System (INIS)

    Lim, Jung Ho; Khang, Eun Joo; Lee, Tae Hyun; Kim, In Hye; Maeng, Seong Eun; Lee, Jae Woo

    2013-01-01

    We measured the electroencephalogram (EEG) of young students in the relaxed state and in the state of the mathematical activities. We applied the detrended fluctuation analysis and Kolmogorov–Sinai entropy (KSE) in the EEG signals. We found that the detrended fluctuation functions follow a power law with Hurst exponents larger than 1/2. The Hurst exponents enhanced at all EEG channels in the state of mathematical activities. The KSE in the relaxed state is larger than those in the state of the mathematical activities. These indicate that the entropy is enhanced in the disorder state of the brain.

  11. EEG Signal Classification With Super-Dirichlet Mixture Model

    DEFF Research Database (Denmark)

    Ma, Zhanyu; Tan, Zheng-Hua; Prasad, Swati

    2012-01-01

    Classification of the Electroencephalogram (EEG) signal is a challengeable task in the brain-computer interface systems. The marginalized discrete wavelet transform (mDWT) coefficients extracted from the EEG signals have been frequently used in researches since they reveal features related to the...

  12. Assessment of anaesthetic depth by clustering analysis and autoregressive modelling of electroencephalograms

    DEFF Research Database (Denmark)

    Thomsen, C E; Rosenfalck, A; Nørregaard Christensen, K

    1991-01-01

    The brain activity electroencephalogram (EEG) was recorded from 30 healthy women scheduled for hysterectomy. The patients were anaesthetized with isoflurane, halothane or etomidate/fentanyl. A multiparametric method was used for extraction of amplitude and frequency information from the EEG....... The method applied autoregressive modelling of the signal, segmented in 2 s fixed intervals. The features from the EEG segments were used for learning and for classification. The learning process was unsupervised and hierarchical clustering analysis was used to construct a learning set of EEG amplitude......-frequency patterns for each of the three anaesthetic drugs. These EEG patterns were assigned to a colour code corresponding to similar clinical states. A common learning set could be used for all patients anaesthetized with the same drug. The classification process could be performed on-line and the results were...

  13. Wavelet Packet Transform Based Driver Distraction Level Classification Using EEG

    Directory of Open Access Journals (Sweden)

    Mousa Kadhim Wali

    2013-01-01

    Full Text Available We classify the driver distraction level (neutral, low, medium, and high based on different wavelets and classifiers using wireless electroencephalogram (EEG signals. 50 subjects were used for data collection using 14 electrodes. We considered for this research 4 distraction stimuli such as Global Position Systems (GPS, music player, short message service (SMS, and mental tasks. Deriving the amplitude spectrum of three different frequency bands theta, alpha, and beta of EEG signals was based on fusion of discrete wavelet packet transform (DWPT and FFT. Comparing the results of three different classifiers (subtractive fuzzy clustering probabilistic neural network, -nearest neighbor was based on spectral centroid, and power spectral features extracted by different wavelets (db4, db8, sym8, and coif5. The results of this study indicate that the best average accuracy achieved by subtractive fuzzy inference system classifier is 79.21% based on power spectral density feature extracted by sym8 wavelet which gave a good class discrimination under ANOVA test.

  14. Estimating mental fatigue based on electroencephalogram and heart rate variability

    Science.gov (United States)

    Zhang, Chong; Yu, Xiaolin

    2010-01-01

    The effects of long term mental arithmetic task on psychology are investigated by subjective self-reporting measures and action performance test. Based on electroencephalogram (EEG) and heart rate variability (HRV), the impacts of prolonged cognitive activity on central nervous system and autonomic nervous system are observed and analyzed. Wavelet packet parameters of EEG and power spectral indices of HRV are combined to estimate the change of mental fatigue. Then wavelet packet parameters of EEG which change significantly are extracted as the features of brain activity in different mental fatigue state, support vector machine (SVM) algorithm is applied to differentiate two mental fatigue states. The experimental results show that long term mental arithmetic task induces the mental fatigue. The wavelet packet parameters of EEG and power spectral indices of HRV are strongly correlated with mental fatigue. The predominant activity of autonomic nervous system of subjects turns to the sympathetic activity from parasympathetic activity after the task. Moreover, the slow waves of EEG increase, the fast waves of EEG and the degree of disorder of brain decrease compared with the pre-task. The SVM algorithm can effectively differentiate two mental fatigue states, which achieves the maximum classification accuracy (91%). The SVM algorithm could be a promising tool for the evaluation of mental fatigue. Fatigue, especially mental fatigue, is a common phenomenon in modern life, is a persistent occupational hazard for professional. Mental fatigue is usually accompanied with a sense of weariness, reduced alertness, and reduced mental performance, which would lead the accidents in life, decrease productivity in workplace and harm the health. Therefore, the evaluation of mental fatigue is important for the occupational risk protection, productivity, and occupational health.

  15. EEG analyses with SOBI.

    Energy Technology Data Exchange (ETDEWEB)

    Glickman, Matthew R.; Tang, Akaysha (University of New Mexico, Albuquerque, NM)

    2009-02-01

    The motivating vision behind Sandia's MENTOR/PAL LDRD project has been that of systems which use real-time psychophysiological data to support and enhance human performance, both individually and of groups. Relevant and significant psychophysiological data being a necessary prerequisite to such systems, this LDRD has focused on identifying and refining such signals. The project has focused in particular on EEG (electroencephalogram) data as a promising candidate signal because it (potentially) provides a broad window on brain activity with relatively low cost and logistical constraints. We report here on two analyses performed on EEG data collected in this project using the SOBI (Second Order Blind Identification) algorithm to identify two independent sources of brain activity: one in the frontal lobe and one in the occipital. The first study looks at directional influences between the two components, while the second study looks at inferring gender based upon the frontal component.

  16. Burst suppression in sleep in a routine outpatient EEG

    Directory of Open Access Journals (Sweden)

    Ammar Kheder

    2014-01-01

    Full Text Available Burst suppression (BS is an electroencephalogram (EEG pattern that is characterized by brief bursts of spikes, sharp waves, or slow waves of relatively high amplitude alternating with periods of relatively flat EEG or isoelectric periods. The pattern is usually associated with coma, severe encephalopathy of various etiologies, or general anesthesia. We describe an unusual case of anoxic brain injury in which a BS pattern was seen during behaviorally defined sleep during a routine outpatient EEG study.

  17. EEG-based characterization of flicker perception

    NARCIS (Netherlands)

    Lazo, M.; Tsoneva, T.; Garcia Molina, G.

    2013-01-01

    Steady-State Visual Evoked Potential (SSVEP) is an oscillatory electrical response appearing in the electroencephalogram (EEG) in response to flicker stimulation. The SSVEP manifests more prominently in electrodes located near the visual cortex and has oscillatory components at the stimulation

  18. Wireless and batteryless biomedical microsystem for neural recording and epilepsy suppression based on brain focal cooling.

    Science.gov (United States)

    Hou, K-C; Chang, C-W; Chiou, J-C; Huang, Y-H; Shaw, F-Z

    2011-12-01

    This work presents a biomedical microsystem with a wireless radiofrequency (RF)-powered electronics and versatile sensors/actuators for use in nanomedicinal diagnosis and therapy. The cooling of brain tissue has the potential to reduce the frequency and severity of epilepsy. Miniaturised spiral coils as a wireless power module with low-dropout linear regulator circuit convert RF signals into a DC voltage, can be implanted without a battery in monitoring free behaviour. A thermoelectric (TE) cooler is an actuator that is employed to cool down brain tissue to suppress epilepsy. Electroencephalogram (EEG) electrodes and TE coolers are integrated to form module that is placed inside the head of a rat and fastened with a bio-compatible material. EEG signals are used to identify waveforms associated with epilepsy and are measured using readout circuits. The wireless part of the presented design achieves a low quiescent current and line/load regulation and high antenna/current efficiency with thermal protection to avoid damage to the implanted tissue. Epilepsy is suppressed by reducing the temperature to reduce the duration of this epileptic episode. Related characterisations demonstrate that the proposed design can be adopted in an effective nanomedicine microsystem.

  19. Assessing the Quality of Steady-state Visual-evoked Potentials for Moving Humans Using a Mobile Electroencephalogram Headset

    Directory of Open Access Journals (Sweden)

    Yuan-Pin eLin

    2014-03-01

    Full Text Available Recent advances in mobile electroencephalogram (EEG systems, featuring non-prep dry electrodes and wireless telemetry, have urged the needs of mobile brain-computer interfaces (BCIs for applications in our daily life. Since the brain may behave differently while people are actively situated in ecologically-valid environments versus highly-controlled laboratory environments, it remains unclear how well the current laboratory-oriented BCI demonstrations can be translated into operational BCIs for users with naturalistic movements. Understanding inherent links between natural human behaviors and brain activities is the key to ensuring the applicability and stability of mobile BCIs. This study aims to assess the quality of steady-state visual-evoked potentials (SSVEPs, which is one of promising channels for functioning BCI systems, recorded using a mobile EEG system under challenging recording conditions, e.g., walking. To systemati-cally explore the effects of walking locomotion on the SSVEPs, this study instructed subjects to stand or walk on a treadmill running at speeds of 1, 2, and 3 mile (s per hour (MPH while con-currently perceiving visual flickers (11 and 12 Hz. Empirical results of this study showed that the SSVEP amplitude tended to deteriorate when subjects switched from standing to walking. Such SSVEP suppression could be attributed to the walking locomotion, leading to distinctly deteriorated SSVEP detectability from standing (84.87±13.55% to walking (1 MPH: 83.03±13.24%, 2 MPH: 79.47±13.53%, and 3 MPH: 75.26±17.89%. These findings not only demonstrated the applicability and limitations of SSVEPs recorded from freely behaving humans in realistic environments, but also provide useful methods and techniques for boosting the translation of the BCI technology from laboratory demonstrations to practical applications.

  20. Study on non-linear bistable dynamics model based EEG signal discrimination analysis method.

    Science.gov (United States)

    Ying, Xiaoguo; Lin, Han; Hui, Guohua

    2015-01-01

    Electroencephalogram (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations generating from ionic current flows within the neurons of the brain. EEG signal is looked as one of the most important factors that will be focused in the next 20 years. In this paper, EEG signal discrimination based on non-linear bistable dynamical model was proposed. EEG signals were processed by non-linear bistable dynamical model, and features of EEG signals were characterized by coherence index. Experimental results showed that the proposed method could properly extract the features of different EEG signals.

  1. An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System

    Directory of Open Access Journals (Sweden)

    Hamza Djelouat

    2017-01-01

    Full Text Available The last decade has witnessed tremendous efforts to shape the Internet of things (IoT platforms to be well suited for healthcare applications. These platforms are comprised of a network of wireless sensors to monitor several physical and physiological quantities. For instance, long-term monitoring of brain activities using wearable electroencephalogram (EEG sensors is widely exploited in the clinical diagnosis of epileptic seizures and sleeping disorders. However, the deployment of such platforms is challenged by the high power consumption and system complexity. Energy efficiency can be achieved by exploring efficient compression techniques such as compressive sensing (CS. CS is an emerging theory that enables a compressed acquisition using well-designed sensing matrices. Moreover, system complexity can be optimized by using hardware friendly structured sensing matrices. This paper quantifies the performance of a CS-based multichannel EEG monitoring. In addition, the paper exploits the joint sparsity of multichannel EEG using subspace pursuit (SP algorithm as well as a designed sparsifying basis in order to improve the reconstruction quality. Furthermore, the paper proposes a modification to the SP algorithm based on an adaptive selection approach to further improve the performance in terms of reconstruction quality, execution time, and the robustness of the recovery process.

  2. Slowed EEG rhythmicity in patients with chronic pancreatitis: evidence of abnormal cerebral pain processing?

    DEFF Research Database (Denmark)

    Olesen, Søren Schou; Hansen, Tine Maria; Gravesen, Carina

    Intractable pain usually dominates the clinical presentation of chronic pancreatitis (CP). Slowing of electroencephalogram (EEG) rhythmicity has been associated with abnormal cortical pain processing in other chronic pain disorders. The aim of this study was to investigate the spectral distribution...

  3. Emergency electroencephalogram: Usefulness in the diagnosis of nonconvulsive status epilepticus by the on-call neurologist.

    Science.gov (United States)

    Máñez Miró, J U; Díaz de Terán, F J; Alonso Singer, P; Aguilar-Amat Prior, M J

    2018-03-01

    We aim to describe the use of emergency electroencephalogram (EmEEG) by the on-call neurologist when nonconvulsive status epilepticus (NCSE) is suspected, and in other indications, in a tertiary hospital. Observational retrospective cohort study of emergency EEG (EmEEG) recordings with 8-channel systems performed and analysed by the on-call neurologist in the emergency department and in-hospital wards between July 2013 and May 2015. Variables recorded were sex, age, symptoms, first diagnosis, previous seizure and cause, previous stroke, cancer, brain computed tomography, diagnosis after EEG, treatment, patient progress, routine control EEG (rEEG), and final diagnosis. We analysed frequency data, sensitivity, and specificity in the diagnosis of NCSE. The study included 135 EEG recordings performed in 129 patients; 51.4% were men and their median age was 69 years. In 112 cases (83%), doctors ruled out suspected NCSE because of altered level of consciousness in 42 (37.5%), behavioural abnormalities in 38 (33.9%), and aphasia in 32 (28.5%). The EmEEG diagnosis was NCSE in 37 patients (33%), and this was confirmed in 35 (94.6%) as the final diagnosis. In 3 other cases, NCSE was the diagnosis on discharge as confirmed by rEEG although the EmEEG missed this condition at first. EmEEG performed to rule out NCSE showed 92.1% sensitivity, 97.2% specificity, a positive predictive value of 94.6%, and a negative predictive value of 96%. Our experience finds that, in an appropriate clinical context, EmEEG performed by the on-call neurologist is a sensitive and specific tool for diagnosing NCSE. Copyright © 2016 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.

  4. Electroencephalogram and electrocardiograph assessment of mental fatigue in a driving simulator.

    Science.gov (United States)

    Zhao, Chunlin; Zhao, Min; Liu, Jianpin; Zheng, Chongxun

    2012-03-01

    Mental fatigue is a contributing factor to some serious transportation crashes. In this study, we measured mental fatigue in drivers using electroencephalogram (EEG) and electrocardiograph (ECG). Together, thirteen healthy subjects performed a continuous simulated driving task for 90 min with simultaneous ECG and multi-channel EEG recording of each subject. Several important physiological parameters were investigated using preprocessed ECG and EEG signals. The results show that the EEG alpha and beta, the relative power, the amplitude of P300 wave of event-related potential (ERP), the approximated entropy of the ECG, and the lower and upper bands of power of heart rate variability (HRV) are significantly different before and after finishing the driving task (p<0.05). These metrics are possible indices for measuring simulated driving mental fatigue. Copyright © 2011 Elsevier Ltd. All rights reserved.

  5. Enhanced Template Matching Using Dynamic Positional Warping for Identification of Specific Patterns in Electroencephalogram

    Directory of Open Access Journals (Sweden)

    Won-Du Chang

    2014-01-01

    Full Text Available Template matching is an approach for signal pattern recognition, often used for biomedical signals including electroencephalogram (EEG. Since EEG is often severely contaminated by various physiological or pathological artifacts, identification and rejection of these artifacts with improved template matching algorithms would enhance the overall quality of EEG signals. In this paper, we propose a novel approach to improve the accuracy of conventional template matching methods by adopting the dynamic positional warping (DPW technique, developed recently for handwriting pattern analysis. To validate the feasibility and superiority of the proposed method, eye-blink artifacts in the EEG signals were detected, and the results were then compared to those from conventional methods. DPW was found to outperform the conventional methods in terms of artifact detection accuracy, demonstrating the power of DPW in identifying specific one-dimensional data patterns.

  6. Electroencephalogram measurement using polymer-based dry microneedle electrode

    Science.gov (United States)

    Arai, Miyako; Nishinaka, Yuya; Miki, Norihisa

    2015-06-01

    In this paper, we report a successful electroencephalogram (EEG) measurement using polymer-based dry microneedle electrodes. The electrodes consist of needle-shaped substrates of SU-8, a silver film, and a nanoporous parylene protective film. Differently from conventional wet electrodes, microneedle electrodes do not require skin preparation and a conductive gel. SU-8 is superior as a structural material to poly(dimethylsiloxane) (PDMS; Dow Corning Toray Sylgard 184) in terms of hardness, which was used in our previous work, and facilitates the penetration of needles through the stratum corneum. SU-8 microneedles can be successfully inserted into the skin without breaking and could maintain a sufficiently low skin-electrode contact impedance for EEG measurement. The electrodes successfully measured EEG from the frontal pole, and the quality of acquired signals was verified to be as high as those obtained using commercially available wet electrodes without any skin preparation or a conductive gel. The electrodes are readily applicable to record brain activities for a long period with little stress involved in skin preparation to the users.

  7. Remote detection of human electroencephalograms using ultrahigh input impedance electric potential sensors

    Science.gov (United States)

    Harland, C. J.; Clark, T. D.; Prance, R. J.

    2002-10-01

    In this letter, we demonstrate the use of very high performance, ultrahigh impedance, electric potential probes in the detection of electrical activity in the brain. We show that these sensors, requiring no electrical or physical contact with the body, can be used to monitor the human electroencephalogram (EEG) revealing, as examples, the α and β rhythms and the α blocking phenomenon. We suggest that the advantages offered by these sensors compared with the currently used contact (Ag/AgCl) electrodes may act to stimulate new developments in multichannel EEG monitoring and in real-time electrical imaging of the brain.

  8. Periodic electroencephalogram discharges in a case of Lafora body disease: An unusual finding

    Directory of Open Access Journals (Sweden)

    Rajendra Singh Jain

    2016-01-01

    Full Text Available Lafora body disease (LBD is a form of progressive myoclonic epilepsy, characterized by seizures, myoclonic jerks, cognitive decline, ataxia, and intracellular polyglucosan inclusion bodies (Lafora bodies in the neurons, heart, skeletal muscle, liver, and sweat gland duct cells. Electroencephalogram (EEG findings in LBD may include multiple spikes and wave discharges, photosensitivity, multifocal epileptiform discharges, and progressive slowing in background activity. Periodicity in epileptiform discharges has not been frequently depicted in LBD. We herein report an unusual case of LBD who showed generalized periodic epileptiform discharges in EEG.

  9. Aetiology and prognosis of encephalopathic patterns on electroencephalogram in a general hospital.

    LENUS (Irish Health Repository)

    O'Sullivan, S S

    2012-02-03

    The purpose of this study was to investigate the frequency and clinical outcome of patients with encephalopathic electroencephalograms (EEGs) in a neurophysiology department based in a general hospital. We performed a retrospective review of all EEGs obtained during an 18-month period in a large tertiary referral hospital. The referral reasons for EEG, the diagnoses reached, and patient outcomes were reviewed according to EEG severity. One hundred and twenty-three patients with encephalopathic EEGs were reviewed. The most common referral reason found was for an assessment of a possible first-onset seizure. The most common diagnosis found was one of dementia or learning disability. Of patients who were followed-up for a median of 19 months, 20.7% had died. The mortality rate generally increased according to the severity of the encephalopathy on EEG. However, 21.4% of those patients with excessive theta activity only on EEG had died. This study highlights an increased mortality even in the apparently \\'milder\\' degrees of EEG abnormalities.

  10. EEG use in a tertiary referral centre.

    LENUS (Irish Health Repository)

    O'Toole, O

    2011-11-15

    The aim of this study was to retrospectively audit all electroencephalograms (EEGs) done over a 2-month period in 2009 by the Neurophysiology Department at Cork University Hospital. There were 316 EEGs performed in total, of which 176\\/316 (56%) were done within 24 hours of request. Out of 316 EEGs, 208 (66%) were considered \\'appropriate\\' by SIGN and NICE guidelines; 79\\/208 (38%) had abnormal EEGs and 28 of these abnormal EEGs had epileptiform features. There were 108\\/316 (34%) \\'inappropriate\\' requests for EEG; of these 15\\/108 (14%) were abnormal. Of the 67\\/316 (21%) patients who had EEGs requested based on a history of syncope\\/funny turns: none of these patients had epileptiform abnormalities on their EEGs. Our audit demonstrates that EEGs are inappropriately over-requested in our institution in particular for cases with reported \\'funny turns\\' and syncope. The yield from EEGs in this cohort of patients was low as would be expected.

  11. Fast entrainment of human electroencephalogram to a theta-band photic flicker during successful memory encoding

    Directory of Open Access Journals (Sweden)

    Naoyuki eSato

    2013-05-01

    Full Text Available Theta band power (4-8Hz in the scalp electroencephalogram (EEG is thought to be stronger during memory encoding for subsequently remembered items than for forgotten items. According to simultaneous EEG-functional magnetic resonance imaging (fMRI measurements, the memory-dependent EEG theta is associated with multiple regions of the brain. This suggests that the multiple regions cooperate with EEG theta synchronization during successful memory encoding. However, a question still remains: What kind of neural dynamic organizes such a memory-dependent global network? In this study, the modulation of the EEG theta entrainment property during successful encoding was hypothesized to lead to EEG theta synchronization among a distributed network. Then, a transient response of EEG theta to a theta-band photic flicker with a short duration was evaluated during memory encoding. In the results, flicker-induced EEG power increased and decreased with a time constant of several hundred milliseconds following the onset and the offset of the flicker, respectively. Importantly, the offset response of EEG power was found to be significantly decreased during successful encoding. Moreover, the offset response of the phase locking index was also found to associate with memory performance. According to computational simulations, the results are interpreted as a smaller time constant (i.e., faster response of a driven harmonic oscillator rather than a change in the spontaneous oscillatory input. This suggests that the fast response of EEG theta forms a global EEG theta network among memory-related regions during successful encoding, and it contributes to a flexible formation of the network along the time course.

  12. Fast entrainment of human electroencephalogram to a theta-band photic flicker during successful memory encoding

    Science.gov (United States)

    Sato, Naoyuki

    2013-01-01

    Theta band power (4–8 Hz) in the scalp electroencephalogram (EEG) is thought to be stronger during memory encoding for subsequently remembered items than for forgotten items. According to simultaneous EEG-functional magnetic resonance imaging (fMRI) measurements, the memory-dependent EEG theta is associated with multiple regions of the brain. This suggests that the multiple regions cooperate with EEG theta synchronization during successful memory encoding. However, a question still remains: What kind of neural dynamic organizes such a memory-dependent global network? In this study, the modulation of the EEG theta entrainment property during successful encoding was hypothesized to lead to EEG theta synchronization among a distributed network. Then, a transient response of EEG theta to a theta-band photic flicker with a short duration was evaluated during memory encoding. In the results, flicker-induced EEG power increased and decreased with a time constant of several hundred milliseconds following the onset and the offset of the flicker, respectively. Importantly, the offset response of EEG power was found to be significantly decreased during successful encoding. Moreover, the offset response of the phase locking index was also found to associate with memory performance. According to computational simulations, the results are interpreted as a smaller time constant (i.e., faster response) of a driven harmonic oscillator rather than a change in the spontaneous oscillatory input. This suggests that the fast response of EEG theta forms a global EEG theta network among memory-related regions during successful encoding, and it contributes to a flexible formation of the network along the time course. PMID:23730282

  13. Acute effects of traditional Thai massage on electroencephalogram in patients with scapulocostal syndrome.

    Science.gov (United States)

    Buttagat, Vitsarut; Eungpinichpong, Wichai; Kaber, David; Chatchawan, Uraiwon; Arayawichanon, Preeda

    2012-08-01

    To investigate acute effects of traditional Thai massage (TTM) on brain electrical activity (electroencephalogram (EEG) signals), anxiety and pain in patients with scapulocostal syndrome (SCS). A single-blind, randomized clinical trial. The School of Physical Therapy, Faculty of Associated Medical Sciences, Khon Kaen University, Thailand. Forty patients, who were diagnosed with SCS, were randomly allocated to receive a 30-min session of either TTM or physical therapy (PT) using ultrasound therapy and hot packs. Electroencephalogram (EEG), State Anxiety Inventory (STAI), and pain intensity rating. Results showed that both TTM and PT were associated with significant decreases in anxiety and pain intensity (p<0.01). However, there was a significantly greater reduction in anxiety and pain intensity for the TTM group when compared with the PT group. Analysis of EEG in the TTM group showed a significant increase in relaxation, manifested as an increase in delta activity (p<0.05) and a decrease in theta, alpha and beta activity (p<0.01). Similar changes were not found in the PT group. The EEG measures were also significantly different when compared between the groups (p<0.01), except for delta activity (p=0.051), indicating lower states of arousal with the TTM treatment. It is suggested that TTM provides acute neural effects that increase relaxation and decrease anxiety and pain intensity in patients with SCS. Copyright © 2012 Elsevier Ltd. All rights reserved.

  14. Controller design for Robotic hand through Electroencephalogram

    OpenAIRE

    Pandelidis P.; Kiriazis N.; Orgianelis K.; Koulios N.

    2016-01-01

    - This paper deals with the designing, the construction and the control of a robotic hand via an electroencephalogram sensor. First a robotic device that is able to mimic a real human hand is constructed. A PID controller is designed in order to improve the performance of the robotic arm for grabbing objects. Furthermore, a novel design approach is presented for controlling the motion of the robotic arm using signals produced from an innovative electroencephalogram sensor that detects the con...

  15. Frequency of EEG abnormalities in age-matched siblings of autistic children with abnormal sleep EEG patterns.

    Science.gov (United States)

    Chez, Michael G; Buchanan, Thomas; Aimonovitch, Mary; Mrazek, Susan; Krasne, Valerie; Langburt, Wayne; Memon, Shoaib

    2004-04-01

    Epileptiform activity in sleep has been described even in the absence of clinical seizures in 43-68% of patients with autistic spectrum disorders (ASDs). Genetic factors may play a significant role in the frequency of epilepsy, yet the frequency in normal age-matched controls is unknown. We studied overnight ambulatory electroencephalograms (EEGs) in 12 nonepileptic, nonautistic children with a sibling with both ASDs and an abnormal EEG. EEG studies were read and described independently by two pediatric epileptologists; 10 were normal studies and 2 were abnormal. The occurrence of abnormal EEGs in our sample (16.6%) was lower than the reported occurrence in children with ASDs. Further, the two abnormal EEGs were of types typically found in childhood and were different from those found in the ASD-affected siblings. The lack of similarity between sibling EEGs suggests that genetic factors alone do not explain the higher frequency of EEG abnormalities reported in ASDs.

  16. System front-end design for concurrent acquisition of electroencephalograms and EIT data

    Science.gov (United States)

    Guardo, R.; Jehanne-Lacasse, J.; Moumbe, A. P.; Gagnon, H.

    2010-04-01

    There is recently considerable interest in medical imaging to combine recording of bioelectrical signals with imaging procedures. For example, electroencephalograms (EEGs) recorded during functional magnetic resonance imaging are increasingly being used for neurological and behavioural research. Concurrent acquisition of EEGs and electrical impedance tomography (EIT) data have been suggested as a non invasive technique that could help localize the area of the brain responsible for seizures in epileptic patients awaiting resective surgery. Despite reasonably distinct spectra, EEGs and EIT signals are difficult to record simultaneously because of their very different amplitudes. In this paper, we describe the front-end of a 24-channel system designed to acquire both signals from the same set of scalp electrodes using time-division multiplexing. We have developped a 10-layer 20×15 cm printed circuit board of the front-end and are currently performing circuit characterization tests. System performance parameters and in vivo images will be presented at the conference.

  17. Permanency analysis on human electroencephalogram signals for pervasive Brain-Computer Interface systems.

    Science.gov (United States)

    Sadeghi, Koosha; Junghyo Lee; Banerjee, Ayan; Sohankar, Javad; Gupta, Sandeep K S

    2017-07-01

    Brain-Computer Interface (BCI) systems use some permanent features of brain signals to recognize their corresponding cognitive states with high accuracy. However, these features are not perfectly permanent, and BCI system should be continuously trained over time, which is tedious and time consuming. Thus, analyzing the permanency of signal features is essential in determining how often to repeat training. In this paper, we monitor electroencephalogram (EEG) signals, and analyze their behavior through continuous and relatively long period of time. In our experiment, we record EEG signals corresponding to rest state (eyes open and closed) from one subject everyday, for three and a half months. The results show that signal features such as auto-regression coefficients remain permanent through time, while others such as power spectral density specifically in 5-7 Hz frequency band are not permanent. In addition, eyes open EEG data shows more permanency than eyes closed data.

  18. Effects of Drawing on Alpha Activity: A Quantitative EEG Study with Implications for Art Therapy

    Science.gov (United States)

    Belkofer, Christopher M.; Van Hecke, Amy Vaughan; Konopka, Lukasz M.

    2014-01-01

    Little empirical evidence exists as to how materials used in art therapy affect the brain and its neurobiological functioning. This pre/post within-groups study utilized the quantitative electroencephalogram (qEEG) to measure residual effects in the brain after 20 minutes of drawing. EEG recordings were conducted before and after participants (N =…

  19. Telephone transmission of 20-channel digital electroencephalogram using lossless data compression.

    Science.gov (United States)

    Rozza, L; Tonella, P; Bertamini, C; Orrico, D; Antoniol, G; Castellaro, L

    1996-01-01

    The use of telecommunications for computer-assisted transmission of neurophysiological signals is a relatively new practice. With the development of digital technology, it is now possible to record electroencephalograms (EEGs) in digital form. Previous reports have demonstrated the possibility of real-time telephone transmission of a limited number of EEG channels. To assess the effectiveness of specific data-compression software to improve the transmission of digital 20-channel EEG records over ordinary public telephone lines. A prototype system was built to transmit digital EEG signals from one computer to another using two 14.4-kbps modems and proprietary lossless data-compression software. Forty compressed digital EEG records of 20 channels each were sent from different locations at variable distances using "plain old telephone service" (POTS). The mean compression ratio was 2.2 to 2.8:1 using a sampling frequency of 128 Hz and 2.8:1 at a sampling rate of 256 Hz. Transmission time was reduced proportionately. Although this study used a store-and-forward approach, the results suggest that it may be possible to transmit a large number of compressed EEG channels in real time using data compression.

  20. Burst-suppression pattern in the electroencephalogram of newborns and infants. Its clinical expression

    Directory of Open Access Journals (Sweden)

    Cervantes Blanco Jorge Mauricio

    2014-07-01

    Full Text Available Burst-suppression pattern in the electroencephalogram (EEG is associated with severe brain damage and has a bad prognosis in 85% of the cases. Objectives. To identify the prevalence of the EEG burst-suppression pattern (BSP in fullterm newborns and infants, determine its etiol- ogy, clinical features and course. Methods. A retrospective study was conducted. Between January 2008 and December 2012, 4,891 EEGs were reviewed. The EEGs of newborns and infants (< 3 months of age with BSP were selected. Results. 11 cases identified with burst suppression pattern. The overall prevalence of which was 3.5%; 8.1% among the newborns and 1.2% among infants. Seizures were the main reason for doing an EEG in the newborn period in 7 patients and after day 28 in three. The clinical manifestations were abnormal level of consciousness (n=8, hypotonia (n=2, and spasticity (n=6. The main causes were hypoxic ischemic injury, stroke and kernicterus. There were two cases of early infantile epileptic encephalopathy. Two patients died before the third month of age; 8 survived an average of 13 months. All had epilepsy, neurologic retardation and disability. Two patients had persistent EEG burst-suppression pattern; 1 and 3 months after the neonatal period respectively; 7 had focal spikes and an asymmetric pattern. Conclusions. Electroencephalographic burst-suppression pat- tern predicts a severe neurologic injury in fullterm newborns and infants.

  1. Nonlinear analysis of EEG signals at different mental states

    OpenAIRE

    Natarajan, Kannathal; Acharya U, Rajendra; Alias, Fadhilah; Tiboleng, Thelma; Puthusserypady, Sadasivan K

    2004-01-01

    Abstract Background The EEG (Electroencephalogram) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about the state of the brain. However, the human observer can not directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in di...

  2. EEG biofeedback improves attentional bias in high trait anxiety individuals

    OpenAIRE

    Wang, Sheng; Zhao, Yan; Chen, Sijuan; Lin, Guiping; Sun, Peng; Wang, Tinghuai

    2013-01-01

    Background Emotion-related attentional bias is implicated in the aetiology and maintenance of anxiety disorders. Electroencephalogram (EEG) biofeedback can obviously improve the anxiety disorders and reduce stress level, and can also enhance attention performance in healthy subjects. The present study examined the effects and mechanisms of EEG biofeedback training on the attentional bias of high trait anxiety (HTA) individuals toward negative stimuli. Results Event-related potentials were rec...

  3. Electroencephalogram approximate entropy influenced by both age and sleep

    Directory of Open Access Journals (Sweden)

    Gerick M. H. Lee

    2013-12-01

    Full Text Available The use of information-based measures to assess changes in conscious state is an increasingly popular topic. Though recent results have seemed to justify the merits of such methods, little has been done to investigate the applicability of such measures to children. For our work, we used the approximate entropy (ApEn, a measure previously shown to correlate with changes in conscious state when applied to the electroencephalogram (EEG, and sought to confirm whether previously reported trends in adult ApEn values across wake and sleep were present in children. Besides validating the prior findings that ApEn decreases from wake to sleep (including wake, rapid eye movement [REM] sleep, and non-REM sleep in adults, we found that previously reported ApEn decreases across vigilance states in adults were also present in children (ApEn trends for both age groups: wake > REM sleep > non-REM sleep. When comparing ApEn values between age groups, adults had significantly larger ApEn values than children during wakefulness. After the application of an 8 Hz high-pass filter to the EEG signal, ApEn values were recalculated. The number of electrodes with significant vigilance state effects dropped from all 109 electrodes with the original 1 Hz filter to 1 electrode with the 8 Hz filter. The number of electrodes with significant age effects dropped from ten to four. Our results support the notion that ApEn can reliably distinguish between vigilance states, with low-frequency sleep-related oscillations implicated as the driver of changes between vigilance states. We suggest that the observed differences between adult and child ApEn values during wake may reflect differences in connectivity between age groups, a factor which may be important in the use of EEG to measure consciousness.

  4. Two channel EEG thought pattern classifier.

    Science.gov (United States)

    Craig, D A; Nguyen, H T; Burchey, H A

    2006-01-01

    This paper presents a real-time electro-encephalogram (EEG) identification system with the goal of achieving hands free control. With two EEG electrodes placed on the scalp of the user, EEG signals are amplified and digitised directly using a ProComp+ encoder and transferred to the host computer through the RS232 interface. Using a real-time multilayer neural network, the actual classification for the control of a powered wheelchair has a very fast response. It can detect changes in the user's thought pattern in 1 second. Using only two EEG electrodes at positions O(1) and C(4) the system can classify three mental commands (forward, left and right) with an accuracy of more than 79 %

  5. Alteration in Memory and Electroencephalogram Waves with Sub-acute Noise Stress in Albino Rats and Safeguarded by Scoparia dulcis.

    Science.gov (United States)

    Loganathan, Sundareswaran; Rathinasamy, Sheeladevi

    2016-01-01

    Noise stress has different effects on memory and novelty and the link between them with an electroencephalogram (EEG) has not yet been reported. To find the effect of sub-acute noise stress on the memory and novelty along with EEG and neurotransmitter changes. Eight-arm maze (EAM) and Y-maze to analyze the memory and novelty by novel object test. Four groups of rats were used: Control, control treated with Scoparia dulcis extract, noise exposed, and noise exposed which received Scoparia extract. The results showed no marked difference observed between control and control treated with Scoparia extract on EAM, Y-maze, novel object test, and EEG in both prefrontal and occipital region, however, noise stress exposed rats showed significant increase in the reference memory and working memory error in EAM and latency delay, triad errors in Y-maze, and prefrontal and occipital EEG frequency rate with the corresponding increase in plasma corticosterone and epinephrine, and significant reduction in the novelty test, and significant reduction in the novelty test, amplitude of prefrontal, occipital EEG, and acetylcholine. These noise stress induced changes in EAM, Y-maze, novel object test, and neurotransmitters were significantly prevented when treated with Scoparia extract and these changes may be due to the normalizing action of Scoparia extract on the brain, which altered due to noise stress. Noise stress exposure causes EEG, behavior, and neurotransmitter alteration in the frontoparietal and occipital regions mainly involved in planning and recognition memoryOnly the noise stress exposed animals showed the significant alteration in the EEG, behavior, and neurotransmittersHowever, these noise stress induced changes in EEG behavior and neurotransmitters were significantly prevented when treated with Scoparia extractThese changes may be due to the normalizing action of Scoparia dulcis (adoptogen) on the brain which altered by noise stress. Abbreviations used: EEG

  6. Chaos analysis of EEG during isoflurane-induced loss of righting in rats

    OpenAIRE

    MacIver, M. B.; Bland, Brian H.

    2014-01-01

    It has long been known that electroencephalogram (EEG) signals generate chaotic strange attractors and the shape of these attractors correlate with depth of anesthesia. We applied chaos analysis to frontal cortical and hippocampal micro-EEG signals from implanted microelectrodes (layer 4 and CA1, respectively). Rats were taken to and from loss of righting reflex (LORR) with isoflurane and behavioral measures were compared to attractor shape. Resting EEG signals at LORR differed markedly from ...

  7. A statistically robust EEG re-referencing procedure to mitigate reference effect

    OpenAIRE

    Lepage, Kyle Q.; Kramer, Mark A.; Chu, Catherine J.

    2014-01-01

    Background: The electroencephalogram (EEG) remains the primary tool for diagnosis of abnormal brain activity in clinical neurology and for in vivo recordings of human neurophysiology in neuroscience research. In EEG data acquisition, voltage is measured at positions on the scalp with respect to a reference electrode. When this reference electrode responds to electrical activity or artifact all electrodes are affected. Successful analysis of EEG data often involves re-referencing procedures th...

  8. Acute toxicity and sleep-wake EEG analysis of Stachtarpheta ...

    African Journals Online (AJOL)

    The effect of systemic administration of TASC on sleep architecture in rats was also evaluated in Sprague-Dawley rats that were chronically implanted with electrodes for electroencephalogram (EEG) and electromyogram (EMG) recording. The acute toxicity test revealed no lethal effect with doses of SCCR (up to 2000 ...

  9. Expression of behaviour and psychoactive drugs in the rat EEG

    NARCIS (Netherlands)

    Lier, Hester van

    2004-01-01

    Brain activity and behaviour are related to each other. Psychoactive drugs can influence both brain activity and behaviour. In order to be able to understand the interplay between brain activity as measured by the electroencephalogram (EEG), behaviour, and psychoactive drugs, it is not sufficient to

  10. Expression of behaviour and psychoactive Drugs in the Rat EEG

    NARCIS (Netherlands)

    Lier, H. van

    2004-01-01

    Brain activity and behaviour are related to each other. Psychoactive drugs can influence both brain activity and behaviour. In order to be able to understand the interplay between brain activity as measured by the electroencephalogram (EEG), behaviour, and psychoactive drugs, it is not sufficient to

  11. Continuous emotion detection using EEG signals and facial expressions

    NARCIS (Netherlands)

    Soleymani, Mohammad; Asghari-Esfeden, Sadjad; Pantic, Maja; Fu, Yun

    Emotions play an important role in how we select and consume multimedia. Recent advances on affect detection are focused on detecting emotions continuously. In this paper, for the first time, we continuously detect valence from electroencephalogram (EEG) signals and facial expressions in response to

  12. Prompt recognition of brain states by their EEG signals

    DEFF Research Database (Denmark)

    Peters, B.O.; Pfurtscheller, G.; Flyvbjerg, H.

    1997-01-01

    Brain states corresponding to intention of movement of left and right index finger and right foot are classified by a ''committee'' of artificial neural networks processing individual channels of 56-electrode electroencephalograms (EEGs). Correct recognition is achieved in 83% of cases not previo...

  13. Prognostic accuracy of electroencephalograms in preterm infants

    DEFF Research Database (Denmark)

    Fogtmann, Emilie Pi; Plomgaard, Anne Mette; Greisen, Gorm

    2017-01-01

    CONTEXT: Brain injury is common in preterm infants, and predictors of neurodevelopmental outcome are relevant. OBJECTIVE: To assess the prognostic test accuracy of the background activity of the EEG recorded as amplitude-integrated EEG (aEEG) or conventional EEG early in life in preterm infants...... for predicting neurodevelopmental outcome. DATA SOURCES: The Cochrane Library, PubMed, Embase, and the Cumulative Index to Nursing and Allied Health Literature. STUDY SELECTION: We included observational studies that had obtained an aEEG or EEG within 7 days of life in preterm infants and reported...... neurodevelopmental outcomes 1 to 10 years later. DATA EXTRACTION: Two reviewers independently performed data extraction with regard to participants, prognostic testing, and outcomes. RESULTS: Thirteen observational studies with a total of 1181 infants were included. A metaanalysis was performed based on 3 studies...

  14. A Case of Habitual Neck Compression Induced Electroencephalogram Abnormalities: Differentiating from Epileptic Seizures Using a Tc-99m HMPAO SPECT

    International Nuclear Information System (INIS)

    Choi, Hongyoon; Seo, Minseok; Lee, Hoyoung; Kim, Youngsoo; Yun, Changho; Kim, Sangeun; Park, Sungho

    2014-01-01

    Self-induced hypoxia has been reported particularly in adolescents, and it can result in neurological injury. Here, we present a case of electroencephalogram (EEG) abnormalities induced by habitual neck compression differentiated from epileptic seizures by Tc-99m HMPAO SPECT. A 19-year-old male was admitted for evaluation of recurrent generalized tonic-clonic seizures. No interictal EEG abnormality was detected; however, abnormal slow delta waves were found immediately after habitual right neck compression. To differentiate EEG abnormalities due to a hemodynamic deficit induced by habitual neck compression from an epileptic seizure, Tc-99m HMPAO SPECT was performed immediately after right carotid artery compression. Abnormal delta waves were triggered, and cerebral hypoperfusion in the right internal carotid artery territory was detected on Tc-99m HMPAO SPECT. The slow delta wave detected on the EEG resulted from the cerebral hypoperfusion because of the habitual neck compression

  15. Electro-encephalogram disturbances in different sleep-wake states following exposure to high environmental heat.

    Science.gov (United States)

    Sinha, R K

    2004-05-01

    In this study, cerebral electrical activity or electro-encephalogram (EEG) was studied following exposure to high environmental heat, in three different age groups of freely moving rats. Each age group was subdivided into three groups: the acute heat stress group, subjected to a single exposure of 4 h at 38 degrees C in the biological oxygen demand incubator; the chronic heat stress group, exposed for 21 days, for 1 h each day, at 38 degrees C in the incubator; and the handling control group. The polygraphic sleep-wake recordings involved simultaneous recordings of cortical EEG, electro-oculogram (EOG), and electromyogram (EMG), on paper and in digital form on computer hard disk, just after the heat exposure for the acute stressed rats and on the 22nd day for the chronic stressed rats. The power spectrum was calculated for 2s epochs of the EEG signals. Quantitative analyses of EEG (qEEG) showed that, in all three age groups, changes in higher-frequency components (beta2) were significant in all sleep-wake states following both acute and chronic heat stress conditions. The power of beta2 activity in all three age groups after acute heat exposure was significantly decreased during slow wave sleep (SWS) (p weaning group and p < 0.05 for other data). Thus the study demonstrated that the cortical EEG is sensitive to environmental heat, and alterations in EEG frequencies in different states of mental consciousness due to high heat can be differentiated efficiently by EEG power spectrum analysis.

  16. Electroencephalogram abnormalities in full term infants with history of severe asphyxia

    Directory of Open Access Journals (Sweden)

    Susanti Halim

    2016-11-01

    Full Text Available Background An electroencephalogram (EEG is an electroimaging tool used to determine developmental and electrical problems in the brain. A history of severe asphyxia is a risk factor for these brain problems in infants. Objective To evaluate the prevalence of abnormal EEGs in full term neonates and to assess for an association with severe asphyxia, hypoxic ischemic encephalopathy (HIE, and spontaneous delivery. Methods This cross-sectional study was conducted at the Pediatric Outpatient Department of Sanglah Hospital, Denpasar, from November 2013 to January 2014. Subjects were fullterm infants aged 1 month who were delivered and/or hospitalized at Sanglah Hospital. All subjects underwent EEG. The EEGs were interpreted by a pediatric neurology consultant, twice, with a week interval between readings. Clinical data were obtained from medical records. Association between abnormal ECG and severe asphyxia were analyzed by Chi-square and multivariable logistic analyses. Results Of 55 subjects, 27 had a history of severe asphyxia and 28 were vigorous babies. Forty percent (22/55 of subjects had abnormal EEG findings, 19/22 of these subjects having history of severe asphyxia, 15/22 had history of hypoxic-ischemic encephalopathy (HIE, and 20/22 were delievered vaginally. There were strong correlations between the prevalence of abnormal EEG and history of severe asphyxia, HIE, and spontaneous delivery. Conclusion Prevalence of abnormal EEG among full-term neonates referred to neurology/growth development clinic is around 40%, with most of them having a history of severe asphyxia. Abnormal EEG is significantly associated to severe asphyxia, HIE, and spontaneous delivery.

  17. Controller design for Robotic hand through Electroencephalogram

    Directory of Open Access Journals (Sweden)

    Pandelidis P.

    2016-01-01

    Full Text Available - This paper deals with the designing, the construction and the control of a robotic hand via an electroencephalogram sensor. First a robotic device that is able to mimic a real human hand is constructed. A PID controller is designed in order to improve the performance of the robotic arm for grabbing objects. Furthermore, a novel design approach is presented for controlling the motion of the robotic arm using signals produced from an innovative electroencephalogram sensor that detects the concentration of the brain

  18. Alteration in Memory and Electroencephalogram Waves with Sub-acute Noise Stress in Albino Rats and Safeguarded by Scoparia dulcis

    OpenAIRE

    Loganathan, Sundareswaran; Rathinasamy, Sheeladevi

    2016-01-01

    Background: Noise stress has different effects on memory and novelty and the link between them with an electroencephalogram (EEG) has not yet been reported. Objective: To find the effect of sub-acute noise stress on the memory and novelty along with EEG and neurotransmitter changes. Materials and Methods: Eight-arm maze (EAM) and Y-maze to analyze the memory and novelty by novel object test. Four groups of rats were used: Control, control treated with Scoparia dulcis extract, noise exposed, a...

  19. [A wireless smart home system based on brain-computer interface of steady state visual evoked potential].

    Science.gov (United States)

    Zhao, Li; Xing, Xiao; Guo, Xuhong; Liu, Zehua; He, Yang

    2014-10-01

    Brain-computer interface (BCI) system is a system that achieves communication and control among humans and computers and other electronic equipment with the electroencephalogram (EEG) signals. This paper describes the working theory of the wireless smart home system based on the BCI technology. We started to get the steady-state visual evoked potential (SSVEP) using the single chip microcomputer and the visual stimulation which composed by LED lamp to stimulate human eyes. Then, through building the power spectral transformation on the LabVIEW platform, we processed timely those EEG signals under different frequency stimulation so as to transfer them to different instructions. Those instructions could be received by the wireless transceiver equipment to control the household appliances and to achieve the intelligent control towards the specified devices. The experimental results showed that the correct rate for the 10 subjects reached 100%, and the control time of average single device was 4 seconds, thus this design could totally achieve the original purpose of smart home system.

  20. Long-range correlations of electroencephalogram in rats irradiated by millimeter wave

    International Nuclear Information System (INIS)

    Xie Taorong; Pei Jian; Li Fen; Zhang Jie; Qi Hongxing; Chen Shude; Qiao Dengjiang

    2011-01-01

    A quantitative study was conducted on stress reaction in rat induced by 35 GHz millimeter wave. Long-range correlations analysis of the rat electroencephalogram(EEG) was investigated. The scaling exponents α 1 and α 2 were calculated by de-trended fluctuation analysis (DFA) method. The exponent α 1 shows that the high frequency EEG component is characterized by Brownian noise before irradiated by 35 GHz millimeter wave while it has long-range correlations during irradiation. The exponent α 2 shows that the low frequency EEG component has long-range correlations before irradiation while it is characterized by Brownian noise during irradiation. Introducing stress parameter k(k=α 2 /α 1 ), the average change rate of k was used to evaluate the intensity of stress in rat evoked by 35 GHz millimeter wave. The k increases 49.9%±13.6% during irradiation, which indicates that the high frequency EEG component becomes more ordered and the low frequency EEG component becomes more disordered, showing the acute stress in rat induced by 35 GHz millimeter wave. (authors)

  1. An ensemble with the Chinese pentatonic scale using electroencephalogram from both hemispheres.

    Science.gov (United States)

    Wu, Dan; Li, Chao-Yi; Yao, De-Zhong

    2013-10-01

    To listen to brain activity as a piece of music, we previously proposed scale-free brainwave music (SFBM) technology, which translated the scalp electroencephalogram (EEG) into musical notes according to the power law of both the EEG and music. In this study, the methodology was further extended to ensemble music on two channels from the two hemispheres. EEG data from two channels symmetrically located on the left and right hemispheres were translated into MIDI sequences by SFBM, and the EEG parameters modulated the pitch, duration and volume of each note. Then, the two sequences were filtered into an ensemble with two voices: the pentatonic scale (traditional Chinese music) or the heptatonic scale (standard Western music). We demonstrated differences in harmony between the two scales generated at different sleep stages, with the pentatonic scale being more harmonious. The harmony intervals of this brain ensemble at various sleep stages followed the power law. Compared with the heptatonic scale, it was easier to distinguish the different stages using the pentatonic scale. These results suggested that the hemispheric ensemble can represent brain activity by variations in pitch, tempo and harmony. The ensemble with the pentatonic scale sounds more consonant, and partially reflects the relations of the two hemispheres. This can be used to distinguish the different states of brain activity and provide a new perspective on EEG analysis.

  2. Changes in the electroencephalogram in acute uremia. Effects of parathyroid hormone and brain electrolytes.

    Science.gov (United States)

    Guisado, R; Arieff, A I; Massry, S G; Lazarowitz, V; Kerian, A

    1975-04-01

    Studies were carried out in order to evaluate the effects of changes in brain calcium and the influence of parathyroidectomy and administration of parathyroid extract on the electroencephalogram (EEG) of normal and uremic dogs. Manual analysis of frequency and power distribution of the EEG in uremic dogs revealed a significant increase in both the percentage distribution and the area or power occupied by frequencies below 5 Hz. In addition, high amplitude bursts of delta activity were apparent in the uremic dog. These changes were largely prevented by parathyroidectomy before the induction of uremia, but the administration of parathyroid extract to either normal dogs, or to previously parathyroidectomized uremic dogs, induced EEG changes similar to those noted in uremic animals with intact parathyroid glands. In all groups of animals which showed EEG changes, brain content of calcium was significantly higher than in either normal dogs or previously parathyroidectomized uremic dogs. Changes in arterial pH and bicarbonate, or in the concentrations of Na+, K+, urea, or creatinine in plasma or cerebrospinal fluid were similar in uremic animals with intact parthyroid glands and in previously parathyroidectomized uremia dogs. The results indicate that the EEG changes found in dogs with acute renal failure require the presence of excess parathyroid hormone in blood, and they may be related to the observed changes in brain content of calcium.

  3. EEG Power During Waking and NREM Sleep in Primary Insomnia

    Science.gov (United States)

    Wu, You Meme; Pietrone, Regina; Cashmere, J. David; Begley, Amy; Miewald, Jean M.; Germain, Anne; Buysse, Daniel J.

    2013-01-01

    Objective: Pathophysiological models of insomnia invoke the concept of 24-hour hyperarousal, which could lead to symptoms and physiological findings during waking and sleep. We hypothesized that this arousal could be seen in the waking electroencephalogram (EEG) of individuals with primary insomnia (PI), and that waking EEG power would correlate with non-REM (NREM) EEG. Methods: Subjects included 50 PI and 32 good sleeper controls (GSC). Five minutes of eyes closed waking EEG were collected at subjects' usual bedtimes, followed by polysomnography (PSG) at habitual sleep times. An automated algorithm and visual editing were used to remove artifacts from waking and sleep EEGs, followed by power spectral analysis to estimate power from 0.5–32 Hz. Results: We did not find significant differences in waking or NREM EEG spectral power of PI and GSC. Significant correlations between waking and NREM sleep power were observed across all frequency bands in the PI group and in most frequency bands in the GSC group. Conclusions: The absence of significant differences between groups in waking or NREM EEG power suggests that our sample was not characterized by a high degree of cortical arousal. The consistent correlations between waking and NREM EEG power suggest that, in samples with elevated NREM EEG beta activity, waking EEG power may show a similar pattern. Citation: Wu YM; Pietrone R; Cashmere JD; Begley A; Miewald JM; Germain A; Buysse DJ. EEG power during waking and NREM sleep in primary insomnia. J Clin Sleep Med 2013;9(10):1031-1037. PMID:24127147

  4. A physiology-based seizure detection system for multichannel EEG.

    Directory of Open Access Journals (Sweden)

    Chia-Ping Shen

    Full Text Available BACKGROUND: Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. Electroencephalogram (EEG signals play a critical role in the diagnosis of epilepsy. Multichannel EEGs contain more information than do single-channel EEGs. Automatic detection algorithms for spikes or seizures have traditionally been implemented on single-channel EEG, and algorithms for multichannel EEG are unavailable. METHODOLOGY: This study proposes a physiology-based detection system for epileptic seizures that uses multichannel EEG signals. The proposed technique was tested on two EEG data sets acquired from 18 patients. Both unipolar and bipolar EEG signals were analyzed. We employed sample entropy (SampEn, statistical values, and concepts used in clinical neurophysiology (e.g., phase reversals and potential fields of a bipolar EEG to extract the features. We further tested the performance of a genetic algorithm cascaded with a support vector machine and post-classification spike matching. PRINCIPAL FINDINGS: We obtained 86.69% spike detection and 99.77% seizure detection for Data Set I. The detection system was further validated using the model trained by Data Set I on Data Set II. The system again showed high performance, with 91.18% detection of spikes and 99.22% seizure detection. CONCLUSION: We report a de novo EEG classification system for seizure and spike detection on multichannel EEG that includes physiology-based knowledge to enhance the performance of this type of system.

  5. EEG Artifact Removal Using a Wavelet Neural Network

    Science.gov (United States)

    Nguyen, Hoang-Anh T.; Musson, John; Li, Jiang; McKenzie, Frederick; Zhang, Guangfan; Xu, Roger; Richey, Carl; Schnell, Tom

    2011-01-01

    !n this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We. compared the WNN algorithm with .the ICA technique ,and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data.

  6. [DESCRIPTION AND PRESENTATION OF THE RESULTS OF ELECTROENCEPHALOGRAM PROCESSING USING AN INFORMATION MODEL].

    Science.gov (United States)

    Myznikov, I L; Nabokov, N L; Rogovanov, D Yu; Khankevich, Yu R

    2016-01-01

    The paper proposes to apply the informational modeling of correlation matrix developed by I.L. Myznikov in early 1990s in neurophysiological investigations, such as electroencephalogram recording and analysis, coherence description of signals from electrodes on the head surface. The authors demonstrate information models built using the data from studies of inert gas inhalation by healthy human subjects. In the opinion of the authors, information models provide an opportunity to describe physiological processes with a high level of generalization. The procedure of presenting the EEG results holds great promise for the broad application.

  7. A randomized controlled trial into the effects of neurofeedback, methylphenidate, and physical activity on EEG power spectra in children with ADHD

    NARCIS (Netherlands)

    Janssen, T.W.P.; Bink, M.; Gelade, K.; van Mourik, R.; Maras, A.; Oosterlaan, J.

    2016-01-01

    Background The clinical and neurophysiological effects of neurofeedback (NF) as treatment for children with ADHD are still unclear. This randomized controlled trial (RCT) examined electroencephalogram (EEG) power spectra before and after NF compared to methylphenidate (MPH) treatment and physical

  8. Improved Diagnosis in Children with Partial Epilepsy Using a Multivariable Prediction Model Based on EEG Network Characteristics

    NARCIS (Netherlands)

    van Diessen, E.; Otte, W.M.; Braun, K.P.J.; Stam, C.J.; Jansen, F.E.

    2013-01-01

    Background:Electroencephalogram (EEG) acquisition is routinely performed to support an epileptic origin of paroxysmal events in patients referred with a possible diagnosis of epilepsy. However, in children with partial epilepsies the interictal EEGs are often normal. We aimed to develop a

  9. A new electroencephalogram classification with reduced recording time in asphyxiated term infants.

    Science.gov (United States)

    Kato, Toru; Tsuji, Takeshi; Hayakawa, Fumio; Kubota, Tetsuo; Kidokoro, Hiroyuki; Natsume, Jun; Watanabe, Kazuyoshi; Okumura, Akihisa

    2014-05-01

    Conventional electroencephalogram (cEEG) is a reliable predictor of outcome in term infants with hypoxic ischemic encephalopathy (HIE). Early therapeutic hypothermia initiated within 6h after birth is a beneficial treatment in these infants. However, a classification system with reduced cEEG recording time to determine early intervention has not been reported. The aim of this study is to propose a new classification of depression on cEEG with reduced recording time in infants with HIE and to examine the correlation between the classification and short-term outcome. We retrospectively investigated 20 term infants with HIE in whom cEEG was performed within 12h after birth, and deaths or outcomes at 18months of age were assessed. We determined grades 0-3 EEG depression in each 10-min epoch based on the most common EEG patterns of each 20s epoch defined by our criteria. Eighteen infants could be assessed by depression grade. The Spearman's rank correlation coefficient Rs between the maximum depression grade in 10-min epochs and three-grade outcomes was 0.68 (P=0.002), and that between the minimum one and outcomes was 0.66 (P=0.003). The area under the receiver operating characteristic curve of the maximum and minimum depression grades for predicting abnormal outcome were 0.885 and 0.869, respectively. We demonstrated a new cEEG depression classification with a recording time of at least 10min in term infants with HIE and a good correlation with short-term outcome. Copyright © 2013 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

  10. ``Seeing'' electroencephalogram through the skull: imaging prefrontal cortex with fast optical signal

    Science.gov (United States)

    Medvedev, Andrei V.; Kainerstorfer, Jana M.; Borisov, Sergey V.; Gandjbakhche, Amir H.; Vanmeter, John

    2010-11-01

    Near-infrared spectroscopy is a novel imaging technique potentially sensitive to both brain hemodynamics (slow signal) and neuronal activity (fast optical signal, FOS). The big challenge of measuring FOS noninvasively lies in the presumably low signal-to-noise ratio. Thus, detectability of the FOS has been controversially discussed. We present reliable detection of FOS from 11 individuals concurrently with electroencephalogram (EEG) during a Go-NoGo task. Probes were placed bilaterally over prefrontal cortex. Independent component analysis (ICA) was used for artifact removal. Correlation coefficient in the best correlated FOS-EEG ICA pairs was highly significant (p prefrontal cortex in rapid object recognition. EROS is highly localized and can provide cost-effective imaging tools for cortical mapping of cognitive processes.

  11. EEG-guided meditation: A personalized approach.

    Science.gov (United States)

    Fingelkurts, Andrew A; Fingelkurts, Alexander A; Kallio-Tamminen, Tarja

    2015-12-01

    The therapeutic potential of meditation for physical and mental well-being is well documented, however the possibility of adverse effects warrants further discussion of the suitability of any particular meditation practice for every given participant. This concern highlights the need for a personalized approach in the meditation practice adjusted for a concrete individual. This can be done by using an objective screening procedure that detects the weak and strong cognitive skills in brain function, thus helping design a tailored meditation training protocol. Quantitative electroencephalogram (qEEG) is a suitable tool that allows identification of individual neurophysiological types. Using qEEG screening can aid developing a meditation training program that maximizes results and minimizes risk of potential negative effects. This brief theoretical-conceptual review provides a discussion of the problem and presents some illustrative results on the usage of qEEG screening for the guidance of mediation personalization. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. Sleep EEG of Microcephaly in Zika Outbreak.

    Science.gov (United States)

    Kanda, Paulo Afonso Medeiros; Aguiar, Aline de Almeida Xavier; Miranda, Jose Lucivan; Falcao, Alexandre Loverde; Andrade, Claudia Suenia; Reis, Luigi Neves Dos Santos; Almeida, Ellen White R Bacelar; Bello, Yanes Brum; Monfredinho, Arthur; Kanda, Rafael Guimaraes

    2018-01-01

    Microcephaly (MC), previously considered rare, is now a health emergency of international concern because of the devastating Zika virus pandemic outbreak of 2015. The authors describe the electroencephalogram (EEG) findings in sleep EEG of epileptic children who were born with microcephaly in areas of Brazil with active Zika virus transmission between 2014 and 2017. The authors reviewed EEGs from 23 children. Nine were females (39.2%), and the age distribution varied from 4 to 48 months. MC was associated with mother positive serology to toxoplasmosis (toxo), rubella (rub), herpes, and dengue (1 case); toxo (1 case); chikungunya virus (CHIKV) (1 case); syphilis (1 case); and Zika virus (ZIKV) (10 cases). In addition, 1 case was associated with perinatal hypoxia and causes of 9 cases remain unknown. The main background EEG abnormality was diffuse slowing (10 cases), followed by classic (3 cases) and modified (5 cases) hypsarrhythmia. A distinct EEG pattern was seen in ZIKV (5 cases), toxo (2 cases), and undetermined cause (1 case). It was characterized by runs of frontocentrotemporal 4.5-13 Hz activity (7 cases) or diffuse and bilateral runs of 18-24 Hz (1 case). In ZIKV, this rhythmic activity was associated with hypsarrhythmia or slow background. Further studies are necessary to determine if this association is suggestive of ZIKV infection. The authors believe that EEG should be included in the investigation of all newly diagnosed congenital MC, especially those occurring in areas of autochthonous transmission of ZIKV.

  13. Cognitive neuroscience of creativity: EEG based approaches.

    Science.gov (United States)

    Srinivasan, Narayanan

    2007-05-01

    Cognitive neuroscience of creativity has been extensively studied using non-invasive electrical recordings from the scalp called electroencephalograms (EEGs) and event related potentials (ERPs). The paper discusses major aspects of performing research using EEG/ERP based experiments including the recording of the signals, removing noise, estimating ERP signals, and signal analysis for better understanding of the neural correlates of processes involved in creativity. Important factors to be kept in mind to record clean EEG signal in creativity research are discussed. The recorded EEG signal can be corrupted by various sources of noise and methodologies to handle the presence of unwanted artifacts and filtering noise are presented followed by methods to estimate ERPs from the EEG signals from multiple trials. The EEG and ERP signals are further analyzed using various techniques including spectral analysis, coherence analysis, and non-linear signal analysis. These analysis techniques provide a way to understand the spatial activations and temporal development of large scale electrical activity in the brain during creative tasks. The use of this methodology will further enhance our understanding the processes neural and cognitive processes involved in creativity.

  14. Emergence EEG pattern classification in sevoflurane anesthesia.

    Science.gov (United States)

    Liang, Zhenhu; Huang, Cheng; Li, Yongwang; Hight, Darren F; Voss, Logan J; Sleigh, Jamie W; Li, Xiaoli; Bai, Yang

    2018-03-07

    Objective. Significant spectral characteristics of electroencephalogram (EEG) patterns exist in individual patients during re-establishing consciousness after general anesthesia. However, these EEG patterns cannot be quantitatively identified using commercially available depth of anesthesia (DoA) monitors. This study proposed an effective classification method and indices to classify these patterns among patients. Approach. Four types of emergence EEG patterns were identified based on EEG data set from 52patients undergoing sevoflurane general anesthesia from two hospitals. Then, the relative power spectrum density (RPSD) of five frequency sub-bands of clinical interest (delta, theta, alpha, beta, and gamma) were selected for emergence state analysis. Finally, the genetic algorithm support vector machine (GA-SVM) was used to identify the emergence EEG patterns. Performance was reported in terms of sensitivity (SE), specificity (SP) and accuracy (AC). Main results. The combination of the mean and mode of RPSD in delta and alpha band (P (delta)/P (alpha) performed the best with the GA-SVM classification. AC indices obtained by GA-SVM across the four patterns were 90.64±7.61, 81.79±5.84, 82.14±7.99, and 72.86±11.11 respectively. Furthermore, the emergence time of the patients with EEG emergence pattern I and III increased with the increasing of patients' age. While for the patients with EEG emergence pattern IV, the emergence time positively correlates with the patients' age which less than 50, and negatively correlates with the patients' age which more than 50. Significance. The mean and mode of P (delta)/P (alpha) is a useful index to classify the different emergence EEG patterns. In addition, the EEG emergence patterns may correlate with underlying neural substrate which related with patients' age. © 2018 Institute of Physics and Engineering in Medicine.

  15. Distribution entropy analysis of epileptic EEG signals.

    Science.gov (United States)

    Li, Peng; Yan, Chang; Karmakar, Chandan; Liu, Changchun

    2015-01-01

    It is an open-ended challenge to accurately detect the epileptic seizures through electroencephalogram (EEG) signals. Recently published studies have made elaborate attempts to distinguish between the normal and epileptic EEG signals by advanced nonlinear entropy methods, such as the approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, etc. Most recently, a novel distribution entropy (DistEn) has been reported to have superior performance compared with the conventional entropy methods for especially short length data. We thus aimed, in the present study, to show the potential of DistEn in the analysis of epileptic EEG signals. The publicly-accessible Bonn database which consisted of normal, interictal, and ictal EEG signals was used in this study. Three different measurement protocols were set for better understanding the performance of DistEn, which are: i) calculate the DistEn of a specific EEG signal using the full recording; ii) calculate the DistEn by averaging the results for all its possible non-overlapped 5 second segments; and iii) calculate it by averaging the DistEn values for all the possible non-overlapped segments of 1 second length, respectively. Results for all three protocols indicated a statistically significantly increased DistEn for the ictal class compared with both the normal and interictal classes. Besides, the results obtained under the third protocol, which only used very short segments (1 s) of EEG recordings showed a significantly (p entropy algorithm. The capability of discriminating between the normal and interictal EEG signals is of great clinical relevance since it may provide helpful tools for the detection of a seizure onset. Therefore, our study suggests that the DistEn analysis of EEG signals is very promising for clinical and even portable EEG monitoring.

  16. Brain damage and addictive behavior: a neuropsychological and electroencephalogram investigation with pathologic gamblers.

    Science.gov (United States)

    Regard, Marianne; Knoch, Daria; Gütling, Eva; Landis, Theodor

    2003-03-01

    Gambling is a form of nonsubstance addiction classified as an impulse control disorder. Pathologic gamblers are considered healthy with respect to their cognitive status. Lesions of the frontolimbic systems, mostly of the right hemisphere, are associated with addictive behavior. Because gamblers are not regarded as "brain-lesioned" and gambling is nontoxic, gambling is a model to test whether addicted "healthy" people are relatively impaired in frontolimbic neuropsychological functions. Twenty-one nonsubstance dependent gamblers and nineteen healthy subjects underwent a behavioral neurologic interview centered on incidence, origin, and symptoms of possible brain damage, a neuropsychological examination, and an electroencephalogram. Seventeen gamblers (81%) had a positive medical history for brain damage (mainly traumatic head injury, pre- or perinatal complications). The gamblers, compared with the controls, were significantly more impaired in concentration, memory, and executive functions, and evidenced a higher prevalence of non-right-handedness (43%) and, non-left-hemisphere language dominance (52%). Electroencephalogram (EEG) revealed dysfunctional activity in 65% of the gamblers, compared with 26% of controls. This study shows that the "healthy" gamblers are indeed brain-damaged. Compared with a matched control population, pathologic gamblers evidenced more brain injuries, more fronto-temporo-limbic neuropsychological dysfunctions and more EEG abnormalities. The authors thus conjecture that addictive gambling may be a consequence of brain damage, especially of the frontolimbic systems, a finding that may well have medicolegal consequences.

  17. Noninvasive Electroencephalogram Based Control of a Robotic Arm for Writing Task Using Hybrid BCI System.

    Science.gov (United States)

    Gao, Qiang; Dou, Lixiang; Belkacem, Abdelkader Nasreddine; Chen, Chao

    2017-01-01

    A novel hybrid brain-computer interface (BCI) based on the electroencephalogram (EEG) signal which consists of a motor imagery- (MI-) based online interactive brain-controlled switch, "teeth clenching" state detector, and a steady-state visual evoked potential- (SSVEP-) based BCI was proposed to provide multidimensional BCI control. MI-based BCI was used as single-pole double throw brain switch (SPDTBS). By combining the SPDTBS with 4-class SSEVP-based BCI, movement of robotic arm was controlled in three-dimensional (3D) space. In addition, muscle artifact (EMG) of "teeth clenching" condition recorded from EEG signal was detected and employed as interrupter, which can initialize the statement of SPDTBS. Real-time writing task was implemented to verify the reliability of the proposed noninvasive hybrid EEG-EMG-BCI. Eight subjects participated in this study and succeeded to manipulate a robotic arm in 3D space to write some English letters. The mean decoding accuracy of writing task was 0.93 ± 0.03. Four subjects achieved the optimal criteria of writing the word "HI" which is the minimum movement of robotic arm directions (15 steps). Other subjects had needed to take from 2 to 4 additional steps to finish the whole process. These results suggested that our proposed hybrid noninvasive EEG-EMG-BCI was robust and efficient for real-time multidimensional robotic arm control.

  18. Adaptive tracking of discriminative frequency components in electroencephalograms for a robust brain-computer interface.

    Science.gov (United States)

    Thomas, Kavitha P; Guan, Cuntai; Lau, Chiew Tong; Vinod, A P; Ang, Kai Keng

    2011-06-01

    In an electroencephalogram (EEG)-based brain-computer interface (BCI), motor imagery has been successfully used as a communication strategy. Motor imagery causes detectable amplitude changes in certain frequency bands of EEGs, which are dubbed event-related desynchronization\\synchronization. The frequency components that give effective discrimination between different types of motor imagery are subject specific and identification of these subject-specific discriminative frequency components (DFCs) is important for the accurate classification of motor imagery activities. In this paper, we propose a new method to estimate the DFC using the Fisher criterion and investigate the variability of these DFCs over multiple sessions of EEG recording. Observing the variability of DFC over sessions in the analysis, a new BCI approach called the Adaptively Weighted Spectral-Spatial Patterns (AWSSP) algorithm is proposed. AWSSP tracks the variation in DFC over time adaptively based on the deviation of discriminative weight values of frequency components. The classification performance of the proposed AWSSP is compared with a static BCI approach that employs fixed DFCs. In the offline and online experiments, AWSSP offers better classification performance than the static approach, emphasizing the significance of tracking the variability of DFCs in EEGs for developing robust motor imagery-based BCI systems. A study of the effect of feedback on the variation in DFCs is also performed in online experiments and it is found that the presence of visual feedback results in increased variation in DFCs.

  19. Quantitative electroencephalogram utility in predicting conversion of mild cognitive impairment to dementia with Lewy bodies☆

    Science.gov (United States)

    Bonanni, Laura; Perfetti, Bernardo; Bifolchetti, Stefania; Taylor, John-Paul; Franciotti, Raffaella; Parnetti, Lucilla; Thomas, Astrid; Onofrj, Marco

    2015-01-01

    Mild cognitive impairment (MCI) as a precursor of dementia with Lewy bodies (DLB) is the focus of recent research, trying to explore the early mechanisms and possible biomarkers of DLB. Quantitative electroencephalogram (QEEG) methods are able to differentiate early DLB from Alzheimer's disease (AD). The aim of the present study was to assess whether QEEG abnormalities, characterized by dominant frequency 1.5 Hz, typical of early DLB, are already present at the stage of MCI and to evaluate whether EEG abnormalities can predict the development of DLB. Forty-seven MCI subjects were followed for 3 years. EEG recordings were obtained at admission and at the end of the study. At the end of follow-up, 20 subjects had developed probable DLB (MCI-DLB), 14 had probable AD (MCI-AD), 8 did not convert to dementia, 5 developed a non-AD/DLB dementia. One hundred percent of MCI-DLB showed EEG abnormalities at admission. Ninety three percent of MCI-AD maintained a normal EEG throughout the study. QEEG may represent a powerful tool to predict the progression from MCI to DLB with a sensitivity and specificity close to 100%. PMID:25129239

  20. Quantitative electroencephalogram utility in predicting conversion of mild cognitive impairment to dementia with Lewy bodies.

    Science.gov (United States)

    Bonanni, Laura; Perfetti, Bernardo; Bifolchetti, Stefania; Taylor, John-Paul; Franciotti, Raffaella; Parnetti, Lucilla; Thomas, Astrid; Onofrj, Marco

    2015-01-01

    Mild cognitive impairment (MCI) as a precursor of dementia with Lewy bodies (DLB) is the focus of recent research, trying to explore the early mechanisms and possible biomarkers of DLB. Quantitative electroencephalogram (QEEG) methods are able to differentiate early DLB from Alzheimer's disease (AD). The aim of the present study was to assess whether QEEG abnormalities, characterized by dominant frequency 1.5 Hz, typical of early DLB, are already present at the stage of MCI and to evaluate whether EEG abnormalities can predict the development of DLB. Forty-seven MCI subjects were followed for 3 years. EEG recordings were obtained at admission and at the end of the study. At the end of follow-up, 20 subjects had developed probable DLB (MCI-DLB), 14 had probable AD (MCI-AD), 8 did not convert to dementia, 5 developed a non-AD/DLB dementia. One hundred percent of MCI-DLB showed EEG abnormalities at admission. Ninety three percent of MCI-AD maintained a normal EEG throughout the study. QEEG may represent a powerful tool to predict the progression from MCI to DLB with a sensitivity and specificity close to 100%. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  1. Comparing the effect of hypercapnia and hypoxia on the electroencephalogram during wakefulness.

    Science.gov (United States)

    Wang, David; Yee, Brendon J; Wong, Keith K; Kim, Jong Won; Dijk, Derk-Jan; Duffin, James; Grunstein, Ronald R

    2015-01-01

    Hypoxia has been postulated as a key mechanism for neurocognitive impairment in sleep-disordered breathing. However, the effect of hypoxia on the electroencephalogram (EEG) is not clear. We examined quantitative EEG recordings from 20 normal volunteers under three 5-min ventilatory control protocols: progressive hypercapnia with iso-hyperoxia (pO2=150mmHg) (Protocol 1), progressive hypercapnia with iso-hypoxia (pO2=50mmHg) (Protocol 2), and progressive hypoxia with a CO2 scrubber in the circuit (Protocol 3). Each protocol started with a 5-min session of breathing room air as baseline. In Protocol 1, compared to its baseline, iso-hyperoxia hypercapnia led to a lower Alpha% and higher Delta/Alpha (D/A) ratio. Similarly, in Protocol 2, the iso-hypoxia hypercapnia induced a higher Delta%, a lower Alpha% and higher D/A ratio. No difference was found in any EEG spectral band including the D/A ratio when Protocols 1 & 2 were compared. In Protocol 3, the Delta%, Alpha% and D/A ratio recorded during hypoxia were not significantly different from baseline. We found that hypercapnia, but not hypoxia, may play a key role in slowing of the EEG in healthy humans. Hypercapnia may be a greater influence than hypoxia on brain neuroelectrical activities. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  2. An exploratory data analysis of electroencephalograms using the functional boxplots approach

    KAUST Repository

    Ngo, Duy

    2015-08-19

    Many model-based methods have been developed over the last several decades for analysis of electroencephalograms (EEGs) in order to understand electrical neural data. In this work, we propose to use the functional boxplot (FBP) to analyze log periodograms of EEG time series data in the spectral domain. The functional bloxplot approach produces a median curve—which is not equivalent to connecting medians obtained from frequency-specific boxplots. In addition, this approach identifies a functional median, summarizes variability, and detects potential outliers. By extending FBPs analysis from one-dimensional curves to surfaces, surface boxplots are also used to explore the variation of the spectral power for the alpha (8–12 Hz) and beta (16–32 Hz) frequency bands across the brain cortical surface. By using rank-based nonparametric tests, we also investigate the stationarity of EEG traces across an exam acquired during resting-state by comparing the spectrum during the early vs. late phases of a single resting-state EEG exam.

  3. Quantitative EEG in assessment of anaesthetic depth: comparative study of methodology

    DEFF Research Database (Denmark)

    Thomsen, C. E.; Prior, P. F.

    1996-01-01

    ) and (4) a depth of anaesthesia monitor based on EEG pattern recognition (ADAM). Dose-response curves are presented for stepwise increases in stable end-tidal concentrations of each agent. Results indicated considerable inter-patient variability and the limitations of single EEG measures, particularly......Methodology for assessment of depth of anaesthesia based on analysis of the electroencephalogram (EEG) is controversial. Techniques range from display of single measures, for example median value of the frequency spectrum, to dedicated pattern recognition systems based on measures of several EEG...

  4. Deep Neural Architectures for Mapping Scalp to Intracranial EEG.

    Science.gov (United States)

    Antoniades, Andreas; Spyrou, Loukianos; Martin-Lopez, David; Valentin, Antonio; Alarcon, Gonzalo; Sanei, Saeid; Took, Clive Cheong

    2018-03-19

    Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance. Here, an ensemble deep learning architecture for nonlinearly mapping scalp to iEEG data is proposed. The proposed architecture exploits the information from a limited number of joint scalp-intracranial recording to establish a novel methodology for detecting the epileptic discharges from the sEEG of a general population of subjects. Statistical tests and qualitative analysis have revealed that the generated pseudo-intracranial data are highly correlated with the true intracranial data. This facilitated the detection of IEDs from the scalp recordings where such waveforms are not often visible. As a real-world clinical application, these pseudo-iEEGs are then used by a convolutional neural network for the automated classification of intracranial epileptic discharges (IEDs) and non-IED of trials in the context of epilepsy analysis. Although the aim of this work was to circumvent the unavailability of iEEG and the limitations of sEEG, we have achieved a classification accuracy of 68% an increase of 6% over the previously proposed linear regression mapping.

  5. Alzheimer's disease: relationship between cognitive aspects and power and coherence EEG measures

    Directory of Open Access Journals (Sweden)

    Lineu C. Fonseca

    2011-12-01

    Full Text Available OBJECTIVE: To evaluate the relationship between specific cognitive aspects and quantitative EEG measures, in patients with mild or moderate Alzheimer's disease (AD. METHOD: Thirty-eight AD patients and 31 controls were assessed by CERAD neuropsychological battery (Consortium to Establish a Registry for AD and the electroencephalogram (EEG. The absolute power and coherences EEG measures were calculated at rest. The correlations between the cognitive variables and the EEG were evaluated. RESULTS: In the AD group there were significant correlations between different coherence EEG measures and Mini-Mental State Examination, verbal fluency, modified Boston naming, word list memory with repetition, word list recall and recognition, and constructional praxis (p<0.01. These correlations were all negative for the delta and theta bands and positive for alpha and beta. There were no correlations between cognitive aspects and absolute EEG power. CONCLUSION: The coherence EEG measures reflect different forms in the relationship between regions related to various cognitive dysfunctions.

  6. The spectrum of the non-rapid eye movement sleep electroencephalogram following total sleep deprivation is trait-like.

    Science.gov (United States)

    Tarokh, Leila; Rusterholz, Thomas; Achermann, Peter; Van Dongen, Hans P A

    2015-08-01

    The sleep electroencephalogram (EEG) spectrum is unique to an individual and stable across multiple baseline recordings. The aim of this study was to examine whether the sleep EEG spectrum exhibits the same stable characteristics after acute total sleep deprivation. Polysomnography (PSG) was recorded in 20 healthy adults across consecutive sleep periods. Three nights of baseline sleep [12 h time in bed (TIB)] following 12 h of wakefulness were interleaved with three nights of recovery sleep (12 h TIB) following 36 h of sustained wakefulness. Spectral analysis of the non-rapid eye movement (NREM) sleep EEG (C3LM derivation) was used to calculate power in 0.25 Hz frequency bins between 0.75 and 16.0 Hz. Intraclass correlation coefficients (ICCs) were calculated to assess stable individual differences for baseline and recovery night spectra separately and combined. ICCs were high across all frequencies for baseline and recovery and for baseline and recovery combined. These results show that the spectrum of the NREM sleep EEG is substantially different among individuals, highly stable within individuals and robust to an experimental challenge (i.e. sleep deprivation) known to have considerable impact on the NREM sleep EEG. These findings indicate that the NREM sleep EEG represents a trait. © 2015 European Sleep Research Society.

  7. A Distributed Classification Procedure for Automatic Sleep Stage Scoring Based on Instantaneous Electroencephalogram Phase and Envelope Features.

    Science.gov (United States)

    Karimzadeh, Foroozan; Boostani, Reza; Seraj, Esmaeil; Sameni, Reza

    2018-02-01

    During the past decades, a great body of research has been devoted to automatic sleep stage scoring using the electroencephalogram (EEG). However, the results are not yet satisfactory to be used as a standard procedure in clinical studies. In this study, using recent developments in robust EEG phase extraction, a novel set of EEG-based features containing the Shannon entropy of the instantaneous analytical form envelope and frequencies of the EEG are proposed for sleep stage scoring. The proposed feature set is used to construct a distributed decision-tree classifier, with binary K-nearest neighbor classifiers at each decision node. The decision-tree structure is designed by brute-force-search over various combinations of the proposed feature set. The performance of the proposed approach is evaluated over two available sleep EEG data sets acquired using single-channel EEG. The first set contains 20 healthy young subjects containing equal number of male and female, and the second one has been acquired from 140 adult subjects from both genders, with sleep disorder. The performance of the proposed method is tested versus state-of-the-art classifiers. The results demonstrate that the proposed method, resulted in overall accuracies of 88.97% and 83.17% over the two data sets, respectively. Considering the high performance and simplicity of the proposed scheme, the method can be of interest for clinical sleep disorder studies.

  8. [Specific language impairment and electroencephalogram: which recommendations in clinical practice? A cohort of 24 children].

    Science.gov (United States)

    Billard, C; Hassairi, I; Delteil, F

    2010-04-01

    Electroencephalographic recording (electroencephalogram [EEG]) is frequent in specific language impairment (SLI), whereas the relations between epileptiform activity (EA) and language disorders remain uncertain and the therapeutic approach undetermined. The aim of this prospective study was to clarify EEG indications and interpretation in SLI. We present a prospective study of cognitive (speech-language measures, psychological assessments) and electroencephalographic data on 24 children (20 males, 4 females; mean age: 4 years 5 months; range: 3 years to 4 years 8 months) with a diagnosis of SLI, defined as a pathologic score on at least 2 speech-language measures and IQ performance of at least 80 points, within epileptic seizures. All participants had an EEG after partial deprivation of sleep at night. When nonsporadic EA was found, 24-h EEG was performed. Antiepileptic treatment was prescribed depending on the frequency of discharges and the SLI profile. The follow-up lasted 2 years. All patients reached stage II sleep during their EEG. Seven children had abnormal electroencephalography results, including 5 children with EA. Two patients with mixed SLI prevailing on expression presented a left centrotemporal spike focus on EEG becoming subcontinuous during sleep. In the first case, the language progressed without antiepileptic treatment. The 2nd case was treated with ethosuximide; the EEG normalized on subsequent recordings, but the language disorder remained severe. The lexical and syntactic understanding and syntactic production scores were not different for children presented EA (5 cases) or without (19 cases) (Wilcoxon's test). Finally, the progression of the various linguistic skills was similar whether or not the children had EA (pactivity is more frequent in SLI than in normal children. It can be seen in all types of SLI but preferentially in the mixed forms. The longitudinal systematic evaluation of all the children with or without EA has never been

  9. A Novel Approach Based on Data Redundancy for Feature Extraction of EEG Signals.

    Science.gov (United States)

    Amin, Hafeez Ullah; Malik, Aamir Saeed; Kamel, Nidal; Hussain, Muhammad

    2016-03-01

    Feature extraction and classification for electroencephalogram (EEG) in medical applications is a challenging task. The EEG signals produce a huge amount of redundant data or repeating information. This redundancy causes potential hurdles in EEG analysis. Hence, we propose to use this redundant information of EEG as a feature to discriminate and classify different EEG datasets. In this study, we have proposed a JPEG2000 based approach for computing data redundancy from multi-channels EEG signals and have used the redundancy as a feature for classification of EEG signals by applying support vector machine, multi-layer perceptron and k-nearest neighbors classifiers. The approach is validated on three EEG datasets and achieved high accuracy rate (95-99 %) in the classification. Dataset-1 includes the EEG signals recorded during fluid intelligence test, dataset-2 consists of EEG signals recorded during memory recall test, and dataset-3 has epileptic seizure and non-seizure EEG. The findings demonstrate that the approach has the ability to extract robust feature and classify the EEG signals in various applications including clinical as well as normal EEG patterns.

  10. Use of Electroencephalography (EEG) to Assess CNS Changes Produced by Pesticides with different Modes of Action: Effects of Permethrin, Deltamethrin, Fipronil, Imidacloprid, Carbaryl, and Triadimefon

    Science.gov (United States)

    The electroencephalogram (EEG) is an apical measure, capable of detecting changes in brain neuronal activity produced by internal or external stimuli. We assessed whether pesticides with different modes of action produced different changes in the EEG of adult male Long-Evans rats...

  11. Sleep spindles and spike-wave discharges in EEG: Their generic features, similarities and distinctions disclosed with Fourier transform and continuous wavelet analysis

    NARCIS (Netherlands)

    Sitnikova, E.Y.; Hramov, A.E.; Koronovskii, A.A.; Luijtelaar, E.L.J.M. van

    2009-01-01

    Epileptic activity in the form of spike-wave discharges (SWD) appears in the electroencephalogram (EEG) during absence seizures. A relationship between SWD and normal sleep spindles is often assumed. This study compares time-frequency parameters of SW and sleep spindles as recorded in the EEG in the

  12. Electroencephalographic precursors of spike-wave discharges in a genetic rat model of absence epilepsy: Power spectrum and coherence EEG analyses

    NARCIS (Netherlands)

    Sitnikova, E.Y.; Luijtelaar, E.L.J.M. van

    2009-01-01

    Periods in the electroencephalogram (EEG) that immediately precede the onset of spontaneous spike-wave discharges (SWD) were examined in WAG/Rij rat model of absence epilepsy. Precursors of SWD (preSWD) were classified based on the distribution of EEG power in delta-theta-alpha frequency bands as

  13. Differences between state entropy and bispectral index during analysis of identical electroencephalogram signals: a comparison with two randomised anaesthetic techniques.

    Science.gov (United States)

    Pilge, Stefanie; Kreuzer, Matthias; Karatchiviev, Veliko; Kochs, Eberhard F; Malcharek, Michael; Schneider, Gerhard

    2015-05-01

    It is claimed that bispectral index (BIS) and state entropy reflect an identical clinical spectrum, the hypnotic component of anaesthesia. So far, it is not known to what extent different devices display similar index values while processing identical electroencephalogram (EEG) signals. To compare BIS and state entropy during analysis of identical EEG data. Inspection of raw EEG input to detect potential causes of erroneous index calculation. Offline re-analysis of EEG data from a randomised, single-centre controlled trial using the Entropy Module and an Aspect A-2000 monitor. Klinikum rechts der Isar, Technische Universität München, Munich. Forty adult patients undergoing elective surgery under general anaesthesia. Blocked randomisation of 20 patients per anaesthetic group (sevoflurane/remifentanil or propofol/remifentanil). Isolated forearm technique for differentiation between consciousness and unconsciousness. Prediction probability (PK) of state entropy to discriminate consciousness from unconsciousness. Correlation and agreement between state entropy and BIS from deep to light hypnosis. Analysis of raw EEG compared with index values that are in conflict with clinical examination, with frequency measures (frequency bands/Spectral Edge Frequency 95) and visual inspection for physiological EEG patterns (e.g. beta or delta arousal), pathophysiological features such as high-frequency signals (electromyogram/high-frequency EEG or eye fluttering/saccades), different types of electro-oculogram or epileptiform EEG and technical artefacts. PK of state entropy was 0.80 and of BIS 0.84; correlation coefficient of state entropy with BIS 0.78. Nine percent BIS and 14% state entropy values disagreed with clinical examination. Highest incidence of disagreement occurred after state transitions, in particular for state entropy after loss of consciousness during sevoflurane anaesthesia. EEG sequences which led to false 'conscious' index values often showed high

  14. Fractal Dimension in Epileptic EEG Signal Analysis

    Science.gov (United States)

    Uthayakumar, R.

    Fractal Analysis is the well developed theory in the data analysis of non-linear time series. Especially Fractal Dimension is a powerful mathematical tool for modeling many physical and biological time signals with high complexity and irregularity. Fractal dimension is a suitable tool for analyzing the nonlinear behaviour and state of the many chaotic systems. Particularly in analysis of chaotic time series such as electroencephalograms (EEG), this feature has been used to identify and distinguish specific states of physiological function.Epilepsy is the main fatal neurological disorder in our brain, which is analyzed by the biomedical signal called Electroencephalogram (EEG). The detection of Epileptic seizures in the EEG Signals is an important tool in the diagnosis of epilepsy. So we made an attempt to analyze the EEG in depth for knowing the mystery of human consciousness. EEG has more fluctuations recorded from the human brain due to the spontaneous electrical activity. Hence EEG Signals are represented as Fractal Time Series.The algorithms of fractal dimension methods have weak ability to the estimation of complexity in the irregular graphs. Divider method is widely used to obtain the fractal dimension of curves embedded into a 2-dimensional space. The major problem is choosing initial and final step length of dividers. We propose a new algorithm based on the size measure relationship (SMR) method, quantifying the dimensional behaviour of irregular rectifiable graphs with minimum time complexity. The evidence for the suitability (equality with the nature of dimension) of the algorithm is illustrated graphically.We would like to demonstrate the criterion for the selection of dividers (minimum and maximum value) in the calculation of fractal dimension of the irregular curves with minimum time complexity. For that we design a new method of computing fractal dimension (FD) of biomedical waveforms. Compared to Higuchi's algorithm, advantages of this method include

  15. Changes in EEG indices and serotonin concentrations in depression and anxiety disorders

    OpenAIRE

    I. V. Kichuk; E. A. Petrova; A. A. Mitrofanov; N. V. Solovyeva; V. B. Vilyanov

    2016-01-01

    Electroencephalogram (EEG) is an important tool to study brain function. EEG can evaluate the current functional state of the brain with high temporal resolution and identify metabolic and ion disorders that cannot be detected by magnetic resonance imaging.Objective: to analyze the relationship between some neurophysiological and biochemical parameters with a Neuro-KM hardware-software complex for the topographic mapping of brain electrical activity.Patients and methods. 75 patients with depr...

  16. An Investigation on the Effect of Various Noises on Human Sensibility by using EEG Signal

    OpenAIRE

    Wonhak Cho; Jongkwan Lee; Taeyoon Son; Hyeonki Choi

    2012-01-01

    Noise causes significant sensibility changes on a human. This study investigated the effect of five different noises on electroencephalogram (EEG) and subjective evaluation. Six human subjects were exposed to classic piano, ocean wave, alarm in army, ambulance, mosquito noise and EEG data were collected during the experimental session. Alpha band activity in the mosquito noise was smaller than that in the classic piano. Alpha band activity decreased 43.4 ± 8.2 % in the mosquito noise. On the ...

  17. Cortical plasticity induced by transcranial magnetic stimulation during wakefulness affects electroencephalogram activity during sleep.

    Directory of Open Access Journals (Sweden)

    Luigi De Gennaro

    Full Text Available BACKGROUND: Sleep electroencephalogram (EEG brain oscillations in the low-frequency range show local signs of homeostatic regulation after learning. Such increases and decreases of slow wave activity are limited to the cortical regions involved in specific task performance during wakefulness. Here, we test the hypothesis that reorganization of motor cortex produced by long-term potentiation (LTP affects EEG activity of this brain area during subsequent sleep. METHODOLOGY/PRINCIPAL FINDINGS: By pairing median nerve stimulation with transcranial magnetic stimulation over the contralateral motor cortex, one can potentiate the motor output, which is presumed to reflect plasticity of the neural circuitry. This paired associative stimulation increases M1 cortical excitability at interstimulus intervals of 25 ms. We compared the scalp distribution of sleep EEG power following paired associative stimulation at 25 ms to that following a control paradigm with 50 ms intervals. It is shown that the experimental manipulation by paired associative stimulation at 25 ms induces a 48% increase in amplitude of motor evoked potentials. This LTP-like potentiation, induced during waking, affects delta and theta EEG power in both REM and non-REM sleep, measured during the following night. Slow-wave activity increases in some frontal and prefrontal derivations and decreases at sites neighboring and contralateral to the stimulated motor cortex. The magnitude of increased amplitudes of motor evoked potentials by the paired associative stimulation at 25 ms predicts enhancements of slow-wave activity in prefrontal regions. CONCLUSIONS/SIGNIFICANCE: An LTP-like paradigm, presumably inducing increased synaptic strength, leads to changes in local sleep regulation, as indexed by EEG slow-wave activity. Enhancement and depression of slow-wave activity are interpreted in terms of a simultaneous activation of both excitatory and inhibitory circuits consequent to the paired

  18. Epileptiform activity in the electroencephalogram of 6-year-old children of women with epilepsy

    Directory of Open Access Journals (Sweden)

    Unnikrishnan Krishnan Syam

    2016-01-01

    Full Text Available Purpose: To study the epileptiform discharges (EDs in the electroencephalogram (EEG of 6-8-year-old children of women with epilepsy (WWE. Materials and Methods: All children born to women with epilepsy and prospectively followed up through the Kerala Registry of Epilepsy and Pregnancy (KREP, aged 6-8 years, were invited (n = 532. Out of the 254 children who responded, clinical evaluations and a 30-min digital 18 channel EEG were completed in 185 children. Results: Of the 185 children examined, 37 (20% children (19 males, 18 females had ED in their EEG. The EDs were generalized in 7 children, and focal in 30 children. The EDs were present in the sleep record only of 16 (43% children and in the awake record only of 6 (16% children. Out of the 94 children for whom seizure history was available, 7 children (7.4% had seizures (neonatal seizures: 4, febrile seizure: 1, and single nonfebrile seizure: 2 and none had history of epilepsy or recurrent nonfebrile seizures. The odds ratio (OR for occurrence of ED in the EEG was significantly higher for children of WWE [OR = 3.5, 95% confidence interval (CI 2.3-6.0] when compared to the published data for age-matched children of mothers without epilepsy. There was no association between the occurrence of ED and the children′s maternal characteristics [epilepsy syndrome, seizures during pregnancy, maternal intelligence quotient (IQ] or the children′s characteristics [antenatal exposure to specific antiepileptic drugs (AEDs, birth weight, malformations, IQ]. Conclusion: Children of WWE have a higher risk of epileptiform activity in their EEG when compared to healthy children in the community though none had recurrent seizures.

  19. Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia

    Science.gov (United States)

    Timashev, Serge F.; Panischev, Oleg Yu.; Polyakov, Yuriy S.; Demin, Sergey A.; Kaplan, Alexander Ya.

    2012-02-01

    We apply flicker-noise spectroscopy (FNS), a time series analysis method operating on structure functions and power spectrum estimates, to study the clinical electroencephalogram (EEG) signals recorded in children/adolescents (11 to 14 years of age) with diagnosed schizophrenia-spectrum symptoms at the National Center for Psychiatric Health (NCPH) of the Russian Academy of Medical Sciences. The EEG signals for these subjects were compared with the signals for a control sample of chronically depressed children/adolescents. The purpose of the study is to look for diagnostic signs of subjects' susceptibility to schizophrenia in the FNS parameters for specific electrodes and cross-correlations between the signals simultaneously measured at different points on the scalp. Our analysis of EEG signals from scalp-mounted electrodes at locations F3 and F4, which are symmetrically positioned in the left and right frontal areas of cerebral cortex, respectively, demonstrates an essential role of frequency-phase synchronization, a phenomenon representing specific correlations between the characteristic frequencies and phases of excitations in the brain. We introduce quantitative measures of frequency-phase synchronization and systematize the values of FNS parameters for the EEG data. The comparison of our results with the medical diagnoses for 84 subjects performed at NCPH makes it possible to group the EEG signals into 4 categories corresponding to different risk levels of subjects' susceptibility to schizophrenia. We suggest that the introduced quantitative characteristics and classification of cross-correlations may be used for the diagnosis of schizophrenia at the early stages of its development.

  20. EEG feature selection method based on decision tree.

    Science.gov (United States)

    Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

    2015-01-01

    This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

  1. SVM detection of epileptiform activity in routine EEG.

    LENUS (Irish Health Repository)

    Kelleher, Daniel

    2010-01-01

    Routine electroencephalogram (EEG) is an important test in aiding the diagnosis of patients with suspected epilepsy. These recordings typically last 20-40 minutes, during which signs of abnormal activity (spikes, sharp waves) are looked for in the EEG trace. It is essential that events of short duration are detected during the routine EEG test. The work presented in this paper examines the effect of changing a range of input values to the detection system on its ability to distinguish between normal and abnormal EEG activity. It is shown that the length of analysis window in the range of 0.5s to 1s are well suited to the task. Additionally, it is reported that patient specific systems should be used where possible due to their better performance.

  2. Diagnostic Role of ECG Recording Simultaneously With EEG Testing.

    Science.gov (United States)

    Kendirli, Mustafa Tansel; Aparci, Mustafa; Kendirli, Nurten; Tekeli, Hakan; Karaoglan, Mustafa; Senol, Mehmet Guney; Togrol, Erdem

    2015-07-01

    Arrhythmia is not uncommon in the etiology of syncope which mimics epilepsy. Data about the epilepsy induced vagal tonus abnormalities have being increasingly reported. So we aimed to evaluate what a neurologist may gain by a simultaneous electrocardiogram (ECG) and electroencephalogram (EEG) recording in the patients who underwent EEG testing due to prediagnosis of epilepsy. We retrospectively evaluated and detected ECG abnormalities in 68 (18%) of 376 patients who underwent EEG testing. A minimum of 20 of minutes artifact-free recording were required for each patient. Standard 1-channel ECG was simultaneously recorded in conjunction with the EEG. In all, 28% of females and 14% of males had ECG abnormalities. Females (mean age 49 years, range 18-88 years) were older compared with the male group (mean age 28 years, range 16-83 years). Atrial fibrillation was more frequent in female group whereas bradycardia and respiratory sinus arrhythmia was higher in male group. One case had been detected a critical asystole indicating sick sinus syndrome in the female group and treated with a pacemaker implantation in the following period. Simultaneous ECG recording in conjunction with EEG testing is a clinical prerequisite to detect and to clarify the coexisting ECG and EEG abnormalities and their clinical relevance. Potentially rare lethal causes of syncope that mimic seizure or those that could cause resistance to antiepileptic therapy could effectively be distinguished by detecting ECG abnormalities coinciding with the signs and abnormalities during EEG recording. © EEG and Clinical Neuroscience Society (ECNS) 2014.

  3. Automatic detection of ischemic stroke based on scaling exponent electroencephalogram using extreme learning machine

    Science.gov (United States)

    Adhi, H. A.; Wijaya, S. K.; Prawito; Badri, C.; Rezal, M.

    2017-03-01

    Stroke is one of cerebrovascular diseases caused by the obstruction of blood flow to the brain. Stroke becomes the leading cause of death in Indonesia and the second in the world. Stroke also causes of the disability. Ischemic stroke accounts for most of all stroke cases. Obstruction of blood flow can cause tissue damage which results the electrical changes in the brain that can be observed through the electroencephalogram (EEG). In this study, we presented the results of automatic detection of ischemic stroke and normal subjects based on the scaling exponent EEG obtained through detrended fluctuation analysis (DFA) using extreme learning machine (ELM) as the classifier. The signal processing was performed with 18 channels of EEG in the range of 0-30 Hz. Scaling exponents of the subjects were used as the input for ELM to classify the ischemic stroke. The performance of detection was observed by the value of accuracy, sensitivity and specificity. The result showed, performance of the proposed method to classify the ischemic stroke was 84 % for accuracy, 82 % for sensitivity and 87 % for specificity with 120 hidden neurons and sine as the activation function of ELM.

  4. Multiscale Entropy of Electroencephalogram as a Potential Predictor for the Prognosis of Neonatal Seizures.

    Directory of Open Access Journals (Sweden)

    Wen-Yu Lu

    Full Text Available Increasing animal studies supported the harmful effects of prolonged or frequent neonatal seizures in developing brain, including increased risk of later epilepsy. Various nonlinear analytic measures had been applied to investigate the change of brain complexity with age. This study focuses on clarifying the relationship between later epilepsy and the changes of electroencephalogram (EEG complexity in neonatal seizures.EEG signals from 19 channels of the whole brain from 32 neonates below 2 months old were acquired. The neonates were classified into 3 groups: 9 were normal controls, 9 were neonatal seizures without later epilepsy, and 14 were neonatal seizures with later epilepsy. Sample entropy (SamEn, multiscale entropy (MSE and complexity index (CI were analyzed.Although there was no significant change in SamEn, the CI values showed significantly decreased over Channels C3, C4, and Cz in patients with neonatal seizures and later epilepsy compared with control group. More multifocal epileptiform discharges in EEG, more abnormal neuroimaging findings, and higher incidence of future developmental delay were noted in the group with later epilepsy.Decreased MSE and CI values in patients with neonatal seizures and later epilepsy may reflect the mixed effects of acute insults, underlying brain immaturity, and prolonged seizures-related injuries. The analysis of MSE and CI can therefore provide a quantifiable and accurate way to decrypt the mystery of neonatal seizures, and could be a promising predictor.

  5. Cerebral monitoring during carotid endarterectomy using near-infrared diffuse optical spectroscopies and electroencephalogram

    Energy Technology Data Exchange (ETDEWEB)

    Shang Yu; Cheng Ran; Dong Lixin; Yu Guoqiang [Center for Biomedical Engineering, University of Kentucky, KY (United States); Ryan, Stephen J [Department of Neurology, University of Kentucky, KY (United States); Saha, Sibu P, E-mail: guoqiang.yu@uky.edu [Division of Cardiothoracic Surgery, University of Kentucky, KY (United States)

    2011-05-21

    Intraoperative monitoring of cerebral hemodynamics during carotid endarterectomy (CEA) provides essential information for detecting cerebral hypoperfusion induced by temporary internal carotid artery (ICA) clamping and post-CEA hyperperfusion syndrome. This study tests the feasibility and sensitivity of a novel dual-wavelength near-infrared diffuse correlation spectroscopy technique in detecting cerebral blood flow (CBF) and cerebral oxygenation in patients undergoing CEA. Two fiber-optic probes were taped on both sides of the forehead for cerebral hemodynamic measurements, and the instantaneous decreases in CBF and electroencephalogram (EEG) alpha-band power during ICA clamping were compared to test the measurement sensitivities of the two techniques. The ICA clamps resulted in significant CBF decreases (-24.7 {+-} 7.3%) accompanied with cerebral deoxygenation at the surgical sides (n = 12). The post-CEA CBF were significantly higher (+43.2 {+-} 16.9%) than the pre-CEA CBF. The CBF responses to ICA clamping were significantly faster, larger and more sensitive than EEG responses. Simultaneous monitoring of CBF, cerebral oxygenation and EEG power provides a comprehensive evaluation of cerebral physiological status, thus showing potential for the adoption of acute interventions (e.g., shunting, medications) during CEA to reduce the risks of severe cerebral ischemia and cerebral hyperperfusion syndrome.

  6. Influence of vertical dimension of occlusion changes on the electroencephalograms of complete denture wearers.

    Science.gov (United States)

    Matsuda, Risa; Yoneyama, Yoshikazu; Morokuma, Masakazu; Ohkubo, Chikahiro

    2014-04-01

    The present study was conducted to identify how changes in the vertical dimension of occlusion (VDO) affect the sensory perception and activity of the brain in complete denture wearers using an electroencephalogram (EEG). Subjects were 21 individuals wearing complete dentures who regularly visited the Division of Prosthodontics at Tsurumi University Dental Hospital for checkups (12 males and 9 females, average age: 76.6). Based on their original dentures, two duplicate dentures with different VDO (-3mm and +5mm) were fabricated. EEG activity and occlusal force were measured before and after gum chewing with each denture in all subjects. Negative indicator scores for psychological conditions and stable neuronal activity (Dα) were calculated using EEG data. Statistical analysis was performed using the Wilcoxon test to compare changes in the sensory perception, activity of the brain, and occlusal force (α=0.05). After gum chewing with the +5-mm denture, a significant increase was observed in the negative indicator score (p0.05). A significant decrease was observed in the occlusal force between the original denture and the -3-mm denture (pocclusal force were influenced by immediate changes in the VDO of the complete denture. Copyright © 2014 Japan Prosthodontic Society. Published by Elsevier Ltd. All rights reserved.

  7. [Study on Sleep Staging Based on Support Vector Machines and Feature Selection in Single Channel Electroencephalogram].

    Science.gov (United States)

    Lin, Xiujing; Xia, Yongming; Qian, Songrong

    2015-06-01

    Sleep electroencephalogram (EEG) is an important index in diagnosing sleep disorders and related diseases. Manual sleep staging is time-consuming and often influenced by subjective factors. Existing automatic sleep staging methods have high complexity and a low accuracy rate. A sleep staging method based on support vector machines (SVM) and feature selection using single channel EEG single is proposed in this paper. Thirty-eight features were extracted from the single channel EEG signal. Then based on the feature selection method F-Score's definition, it was extended to multiclass with an added eliminate factor in order to find proper features, which were used as SVM classifier inputs. The eliminate factor was adopted to reduce the negative interaction of features to the result. Research on the F-Score with an added eliminate factor was further accomplished with the data from a standard open source database and the results were compared with none feature selection and standard F-Score feature selection. The results showed that the present method could effectively improve the sleep staging accuracy and reduce the computation time.

  8. Usability of four commercially-oriented EEG systems

    Science.gov (United States)

    Hairston, W. David; Whitaker, Keith W.; Ries, Anthony J.; Vettel, Jean M.; Cortney Bradford, J.; Kerick, Scott E.; McDowell, Kaleb

    2014-08-01

    Electroencephalography (EEG) holds promise as a neuroimaging technology that can be used to understand how the human brain functions in real-world, operational settings while individuals move freely in perceptually-rich environments. In recent years, several EEG systems have been developed that aim to increase the usability of the neuroimaging technology in real-world settings. Here, the usability of three wireless EEG systems from different companies are compared to a conventional wired EEG system, BioSemi’s ActiveTwo, which serves as an established laboratory-grade ‘gold standard’ baseline. The wireless systems compared include Advanced Brain Monitoring’s B-Alert X10, Emotiv Systems’ EPOC and the 2009 version of QUASAR’s Dry Sensor Interface 10-20. The design of each wireless system is discussed in relation to its impact on the system’s usability as a potential real-world neuroimaging system. Evaluations are based on having participants complete a series of cognitive tasks while wearing each of the EEG acquisition systems. This report focuses on the system design, usability factors and participant comfort issues that arise during the experimental sessions. In particular, the EEG systems are assessed on five design elements: adaptability of the system for differing head sizes, subject comfort and preference, variance in scalp locations for the recording electrodes, stability of the electrical connection between the scalp and electrode, and timing integration between the EEG system, the stimulus presentation computer and other external events.

  9. An Experiment of Ocular Artifacts Elimination from EEG Signals using ICA and PCA Methods

    Directory of Open Access Journals (Sweden)

    Arjon Turnip

    2014-12-01

    Full Text Available In the modern world of automation, biological signals, especially Electroencephalogram (EEG is gaining wide attention as a source of biometric information. Eye-blinks and movement of the eyeballs produce electrical signals (contaminate the EEG signals that are collectively known as ocular artifacts. These noise signals are required to be separated from the EEG signals to obtain the accurate results. This paper reports an experiment of ocular artifacts elimination from EEG signal using blind source separation algorithm based on independent component analysis and principal component analysis. EEG signals are recorded on three conditions, which are normal conditions, closed eyes, and blinked eyes. After processing, the dominant frequency of EEG signals in the range of 12-14 Hz either on normal, closed, and blinked eyes conditions is obtained. 

  10. Hypoglycemia-Associated EEG Changes in Prepubertal Children With Type 1 Diabetes

    DEFF Research Database (Denmark)

    Hansen, Grith Lærkholm; Foli-Andersen, Pia; Fredheim, Siri

    2016-01-01

    BACKGROUND: The purpose of this study was to explore the possible difference in the electroencephalogram (EEG) pattern between euglycemia and hypoglycemia in children with type 1 diabetes (T1D) during daytime and during sleep. The aim is to develop a hypoglycemia alarm based on continuous EEG...... measurement and real-time signal processing. METHOD: Eight T1D patients aged 6-12 years were included. A hyperinsulinemic hypoglycemic clamp was performed to induce hypoglycemia both during daytime and during sleep. Continuous EEG monitoring was performed. For each patient, quantitative EEG (qEEG) measures...... in specific bands comparing hypoglycemia to euglycemia both during daytime and during sleep. In daytime the EEG-based algorithm identified hypoglycemia in all children on average at a blood glucose (BG) level of 2.5 ± 0.5 mmol/l and 18.4 (ranging from 0 to 55) minutes prior to blood glucose nadir. During...

  11. A social conflict increases EEG slow-wave activity during subsequent sleep

    NARCIS (Netherlands)

    Meerlo, P; de Bruin, EA; Strijkstra, AM; Daan, S

    2001-01-01

    Electroencephalogram (EEG) slow-wave activity (SWA) during non-rapid eye movement (NREM) sleep is widely viewed as an indicator of sleep debt and sleep intensity. In a previous study, we reported a strong increase in SWA during NREM sleep after a social conflict in rats. To test whether this

  12. Ambient temperature during torpor affects NREM sleep EEG during arousal episodes in hibernating European ground squirrels

    NARCIS (Netherlands)

    Strijkstra, AM; Daan, S

    1997-01-01

    Ambient temperature (T-a) systematically affects the frequency of arousal episodes in mammalian hibernation. This variation might hypothetically be attributed to temperature effects on the rate of sleep debt increase in torpor. We studied this rate by recording sleep electroencephalogram (EEG) in

  13. EEG theta and beta power spectra in adolescents with ADHD versus adolescents with ASD plus ADHD

    NARCIS (Netherlands)

    Bink, M.; van Boxtel, G.J.M.; Popma, A.; Bongers, I.L.; Denissen, A.J.M.; van Nieuwenhuizen, Ch.

    2015-01-01

    Attention problems are common in youngsters with attention deficit hyperactivity disorder (ADHD) as well as in adolescents with combined autism spectrum disorder (ASD) and ADHD. However, it is unknown whether there is psychophysiological overlap and/or a difference in electroencephalogram (EEG)

  14. The effect of a prospected reward on semantic processing : An N400 EEG study

    NARCIS (Netherlands)

    van der Ven, Sanne H. G.; van Touw, Sven A. C.; van Hoogmoed, Anne H.; Janssen, Eva M.; Leseman, Paul P. M.

    2016-01-01

    Promised rewards are often used in education to stimulate learning behaviour. The present study tested whether a reward prospect affects semantic processing and recall of learned materials. Thirty-nine females participated in an electroencephalogram (EEG) task measuring semantic processing using the

  15. The prognostic value of serial EEG recordings following acute neonatal asphyxia in full-term infants

    NARCIS (Netherlands)

    Zeinstra, Esther; Fock, Johanna M; Begeer, Jacobus H; van Weerden, Tiemen W; Maurits, Natascha M; Zweens, Mariana J

    2001-01-01

    Perinatal asphyxia is one of the major causes of non-progressive neurological deficits seen in children. It is reported that currently no set of parameters allowing for accurate prediction of prognosis following severe perinatal asphyxia is available. Even electroencephalogram (EEG) recordings,

  16. Sex Differences in the Sleep EEG of Young Adults : Visual Scoring and Spectral Analysis

    NARCIS (Netherlands)

    Dijk, Derk Jan; Beersma, Domien G.M.; Bloem, Gerda M.

    1989-01-01

    Baseline sleep of 13 men (mean age of 23.5 years) and 15 women (21.9 years) was analyzed. Visual scoring of the electroencephalograms (EEGs) revealed no significant differences between the sexes in the amounts of slow-wave sleep and rapid-eye-movement (REM) sleep. Spectral analysis, however,

  17. EEG theta and beta power spectra in adolescents with ADHD versus adolescents with ASD + ADHD.

    NARCIS (Netherlands)

    Bink, M.; van Boxtel, G.J.M.; Popma, A.; Bongers, I.L.; Denissen, A.J.; van Nieuwenhuizen, Ch.

    2015-01-01

    Attention problems are common in youngsters with attention deficit hyperactivity disorder (ADHD) as well as in adolescents with combined autism spectrum disorder (ASD) and ADHD. However, it is unknown whether there is psychophysiological overlap and/or a difference in electroencephalogram (EEG)

  18. Atypical EEG Power Correlates with Indiscriminately Friendly Behavior in Internationally Adopted Children

    Science.gov (United States)

    Tarullo, Amanda R.; Garvin, Melissa C.; Gunnar, Megan R.

    2011-01-01

    While effects of institutional care on behavioral development have been studied extensively, effects on neural systems underlying these socioemotional and attention deficits are only beginning to be examined. The current study assessed electroencephalogram (EEG) power in 18-month-old internationally adopted, postinstitutionalized children (n = 37)…

  19. Multichannel EEG Visualization

    NARCIS (Netherlands)

    Caat, Michael ten

    2008-01-01

    Electroencephalography (EEG) measures electrical brain activity by electrodes attached to the scalp. Multichannel EEG refers to a measurement with a large number of electrodes. EEG has clinical as well as scientific applications, including neurology, psychology, pharmacy, linguistics, and biology.

  20. Comparative analysis of cognitive tasks for modeling mental workload with electroencephalogram.

    Science.gov (United States)

    Hwang, Taeho; Kim, Miyoung; Hwangbo, Minsu; Oh, Eunmi

    2014-01-01

    Previous electroencephalogram (EEG) studies have shown that cognitive workload can be estimated by using several types of cognitive tasks. In this study, we attempted to characterize cognitive tasks that have been used to manipulate workload for generating classification models. We carried out a comparative analysis between two representative types of working memory tasks: the n-back task and the mental arithmetic task. Based on experiments with 7 healthy subjects using Emotiv EPOC, we compared the consistency, robustness, and efficiency of each task in determining cognitive workload in a short training session. The mental arithmetic task seems consistent and robust in manipulating clearly separable high and low levels of cognitive workload with less training. In addition, the mental arithmetic task shows consistency despite repeated usage over time and without notable task adaptation in users. The current study successfully quantifies the quality and efficiency of cognitive workload modeling depending on the type and configuration of training tasks.

  1. Illumination influences working memory: an EEG study.

    Science.gov (United States)

    Park, Jin Young; Min, Byoung-Kyong; Jung, Young-Chul; Pak, Hyensou; Jeong, Yeon-Hong; Kim, Eosu

    2013-09-05

    Illumination conditions appear to influence working efficacy in everyday life. In the present study, we obtained electroencephalogram (EEG) correlates of working-memory load, and investigated how these waveforms are modulated by illumination conditions. We hypothesized that illumination conditions may affect cognitive performance. We designed an EEG study to monitor and record participants' EEG during the Sternberg working memory task under four different illumination conditions. Illumination conditions were generated with a factorial design of two color-temperatures (3000 and 7100 K) by two illuminance levels (150 and 700 lx). During a working memory task, we observed that high illuminance led to significantly lower frontal EEG theta activity than did low illuminance. These differences persisted despite no significant difference in task performance between illumination conditions. We found that the latency of an early event-related potential component, such as N1, was significantly modulated by the illumination condition. The fact that the illumination condition affects brain activity but not behavioral performance suggests that the lighting conditions used in the present study did not influence the performance stage of behavioral processing. Nevertheless, our findings provide objective evidence that illumination conditions modulate brain activity. Further studies are necessary to refine the optimal lighting parameters for facilitating working memory. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

  2. Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling.

    Science.gov (United States)

    Kawashima, Issaku; Kumano, Hiroaki

    2017-01-01

    Mind-wandering (MW), task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG) variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR) to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

  3. Electroencephalogram Similarity Analysis Using Temporal and Spectral Dynamics Analysis for Propofol and Desflurane Induced Unconsciousness

    Directory of Open Access Journals (Sweden)

    Quan Liu

    2018-01-01

    Full Text Available Important information about the state dynamics of the brain during anesthesia is unraveled by Electroencephalogram (EEG approaches. Patterns that are observed through EEG related to neural circuit mechanism under different molecular targets dependent anesthetics have recently attracted much attention. Propofol, a Gamma-amino butyric acid, is known with evidently increasing alpha oscillation. Desflurane shares the same receptor action and should be similar to propofol. To explore their dynamics, EEG under routine surgery level anesthetic depth is analyzed using multitaper spectral method from two groups: propofol (n = 28 and desflurane (n = 23. The time-varying spectrum comparison was undertaken to characterize their properties. Results show that both of the agents are dominated by slow and alpha waves. Especially, for increased alpha band feature, propofol unconsciousness shows maximum power at about 10 Hz (mean ± SD; frequency: 10.2 ± 1.4 Hz; peak power, −14.0 ± 1.6 dB, while it is approximate about 8 Hz (mean ± SD; frequency: 8.3 ± 1.3 Hz; peak power, −13.8 ± 1.6 dB for desflurane with significantly lower frequency-resolved spectra for this band. In addition, the mean power of propofol is much higher from alpha to gamma band, including slow oscillation than that of desflurane. The patterns might give us an EEG biomarker for specific anesthetic. This study suggests that both of the anesthetics exhibit similar spectral dynamics, which could provide insight into some common neural circuit mechanism. However, differences between them also indicate their uniqueness where relevant.

  4. Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling

    Directory of Open Access Journals (Sweden)

    Issaku Kawashima

    2017-07-01

    Full Text Available Mind-wandering (MW, task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

  5. Higher-Order Spectrum in Understanding Nonlinearity in EEG Rhythms

    Directory of Open Access Journals (Sweden)

    Cauchy Pradhan

    2012-01-01

    Full Text Available The fundamental nature of the brain's electrical activities recorded as electroencephalogram (EEG remains unknown. Linear stochastic models and spectral estimates are the most common methods for the analysis of EEG because of their robustness, simplicity of interpretation, and apparent association with rhythmic behavioral patterns in nature. In this paper, we extend the use of higher-order spectrum in order to indicate the hidden characteristics of EEG signals that simply do not arise from random processes. The higher-order spectrum is an extension Fourier spectrum that uses higher moments for spectral estimates. This essentially nullifies all Gaussian random effects, therefore, can reveal non-Gaussian and nonlinear characteristics in the complex patterns of EEG time series. The paper demonstrates the distinguishing features of bispectral analysis for chaotic systems, filtered noises, and normal background EEG activity. The bispectrum analysis detects nonlinear interactions; however, it does not quantify the coupling strength. The squared bicoherence in the nonredundant region has been estimated to demonstrate nonlinear coupling. The bicoherence values are minimal for white Gaussian noises (WGNs and filtered noises. Higher bicoherence values in chaotic time series and normal background EEG activities are indicative of nonlinear coupling in these systems. The paper shows utility of bispectral methods as an analytical tool in understanding neural process underlying human EEG patterns.

  6. Resonance detection of EEG signals using two-layer wavelet analysis

    International Nuclear Information System (INIS)

    Abdallah, H. M; Odeh, F.S.

    2000-01-01

    This paper presents the hybrid quadrature mirror filter (HQMF) algorithm applied to the electroencephalogram (EEG) signal during mental activity. The information contents of this signal, i.e., its medical diagnosis, lie in its power spectral density (PSD). The HQMF algorithm is a modified technique that is based on the shape and the details of the signal. If applied efficiently, the HQMF algorithm will produce much better results than conventional wavelet methods in detecting (diagnosing) the information of the EEG signal from its PSD. This technique is applicable not only to EEG signals, but is highly recommended to compression analysis and de noising techniques. (authors). 16 refs., 9 figs

  7. Battery-Less Electroencephalogram System Architecture Optimization

    Science.gov (United States)

    2016-12-01

    6 2.3.3 Rule 3 − Bitdepth Determination 7 3. Experimental Setup 8 3.1 Architectures Under Consideration 9 3.2 Power Modeling 9 4. Results and...72-h mission. The EEG system desired to achieve this task must amplify a target signal with a resulting minimum resolution below 1 µV while handling 10...specifications of commercial systems/products EMOTIV Neuroelectronics TI Epoc+2 ENOBIO3 ADS12991 Channels 14 8, 20, or 32 8 Bandwidth 0.2–43 Hz 0–250 Hz

  8. Spreading depression: evidence of five electroencephalogram phases.

    Science.gov (United States)

    Broberg, Marita; Pope, Kenneth J; Olsson, Torsten; Shuttleworth, C William; Willoughby, John O

    2014-10-01

    Spreading depression (SD), a self-propagating wave of astroglial and neuronal depolarization, is an accompaniment of several neurological disorders including epilepsy. Its well-described features are initial depolarization, followed by EEG flattening. In this in vivo study in awake animals, the relationship of SDs to epileptiform activity was re-examined. We assessed SDs generated by mechanical stimulation and by metabolic inhibition with fluorocitrate. In addition to identifying prolonged EEG depression, we identified two periods, one prior to and another during depression, characterized by increases in power of specific frequencies that were sometimes associated with epileptiform discharges. The first period was characterized by ripple activity close to the induction site (88% of SDs with intracortical electrodes). The second period was characterized by localized low-frequency spikes (100% with dural screw electrodes, 65% with intracortical electrodes). By using fluorocitrate to induce SDs, the initial period was also characterized by runs of spikes (52%). Finally, with SDs induced by both methods, there was a period at the end of depression when additional, unprovoked SDs occurred (20%). Five stages of SD were defined by these phenomena, in the order: excitation, depression, excitation, depression, SD, with metabolic inhibition enhancing the expression of epileptiform spiking. © 2014 Wiley Periodicals, Inc.

  9. Comparative EEG mapping studies in Huntington's disease patients and controls.

    Science.gov (United States)

    Painold, Annamaria; Anderer, Peter; Holl, Anna K; Letmaier, Martin; Saletu-Zyhlarz, Gerda M; Saletu, Bernd; Bonelli, Raphael M

    2010-11-01

    Huntington's disease (HD) is a devastating neurodegenerative disorder with prominent motor and cognitive decline. Previous studies with small sample sizes and methodological limitations have described abnormal electroencephalograms (EEG) in this cohort. The aim of the present study was to investigate objectively and quantitatively the neurophysiological basis of the disease in HD patients as compared to normal controls, utilizing EEG mapping. In 55 HD patients and 55 healthy controls, a 3-min vigilance-controlled EEG (V-EEG) was recorded during midmorning hours. Evaluation of 36 EEG variables was carried out by spectral analysis and visualized by EEG mapping techniques. To elucidate drug interference, the analysis was performed for the total group, unmedicated patients only and between treated and untreated patients. Statistical overall analysis by the omnibus significance test demonstrated significant (p < 0.01 and p < 0.05) EEG differences between HD patients and controls. Subsequent univariate analysis revealed a general decrease in total power and absolute alpha and beta power, an increase in delta/theta power, and a slowing of the centroids of delta/theta, beta and total power. The slowing of the EEG in HD reflects a disturbed brain function in the sense of a vigilance decrement, electrophysiologically characterized by inhibited cortical areas (increased delta/theta power) and a lack of normal routine and excitatory activity (decreased alpha and beta power). The results are similar to those found in other dementing disorders. Medication did not affect the overall interpretation of the quantitative EEG analysis, but certain differences might be due to drug interaction, predominantly with antipsychotics. Spearman rank correlations revealed significant correlations between EEG mapping and cognitive and motor impairment in HD patients.

  10. Effect of negative and positive emotions on EEG spectral asymmetry.

    Science.gov (United States)

    Orgo, L; Bachmann, M; Lass, J; Hinrikus, H

    2015-08-01

    The aim of the study was to evaluate the applicability of electroencephalogram (EEG) spectral asymmetry index (SASI) for discrimination of the effect of negative and positive emotions on human brain bioelectrical activity. SASI has been previously proposed as a method to detect depression based on the balance of EEG theta and beta frequency band powers. Emotions were evoked on 22 healthy subjects using emotional pictures portraying humans from International Affective Picture System (IAPS) and late response to stimuli was examined (1700-2200 ms). Electroencephalogram (EEG) was recorded in 30 channels divided into 10 brain regions: left frontal, right frontal, left temporal, right temporal, frontal, frontocentral, central, centroparietal, parietal and occipital. Negative stimuli, compared to neutral stimuli, significantly increased SASI in frontocentral, central, centroparietal, parietal and occipital areas. Positive stimuli, compared to neutral stimuli, significantly decreased SASI in left temporal, centroparietal, parietal and occipital areas. The results indicate that SASI provides a good discrimination between the effects of negative, neutral and positive emotions on human EEG.

  11. Correlation between perceived stigma and EEG paroxysmal abnormality in childhood epilepsy.

    Science.gov (United States)

    Kanemura, Hideaki; Sano, Fumikazu; Ohyama, Tetsuo; Sugita, Kanji; Aihara, Masao

    2015-11-01

    We investigated the relationship between abnormal electroencephalogram (EEG) findings such as localized EEG paroxysmal abnormality (PA) and the perception of stigma to determine EEG factors associated with perceived stigma in childhood epilepsy. Participants comprised 40 patients (21 boys, 19 girls; mean age, 14.6 years) with epilepsy at enrollment. The criteria for inclusion were as follows: 1) age of 12-18 years, inclusive; 2) ≥6 months after epilepsy onset; 3) the ability to read and speak Japanese; and 4) the presence of EEG PA. Fifteen healthy seizure-free children were included as a control group. Participants were asked to rate how often they felt or acted in the ways described in the items of the Child Stigma Scale using a 5-point scale. Electroencephalogram paroxysms were classified based on the presence of spikes, sharp waves, or spike-wave complexes, whether focal or generalized. Participants showed significantly higher stigma scores than healthy subjects (pstigma. The average total scores of patients presenting with EEG PA at generalized, frontal, RD, midtemporal, and occipital regions were 2.3, 4.0, 2.4, 3.2, and 2.2, respectively. The scores of all questions were higher in the frontal group than those in other regions (pstigma than children presenting with nonfrontal EEG PA (pstigma. Further studies are needed to confirm whether frontal EEG PA may function as a mediator of emotional responses such as perceived stigma in childhood epilepsy. Copyright © 2015 Elsevier Inc. All rights reserved.

  12. Long-Term Evolution Electromagnetic Fields Exposure Modulates the Resting State EEG on Alpha and Beta Bands.

    Science.gov (United States)

    Yang, Lei; Chen, Qinghua; Lv, Bin; Wu, Tongning

    2017-05-01

    Long-term evolution (LTE) wireless telecommunication systems are widely used globally, which has raised a concern that exposure to electromagnetic fields (EMF) emitted from LTE devices can change human neural function. To date, few studies have been conducted on the effect of exposure to LTE EMF. Here, we evaluated the changes in electroencephalogram (EEG) due to LTE EMF exposure. An LTE EMF exposure system with a stable power emission, which was equivalent to the maximum emission from an LTE mobile phone, was used to radiate the subjects. Numerical simulations were conducted to ensure that the specific absorption rate in the subject's head was below the safety limits. Exposure to LTE EMF reduced the spectral power and the interhemispheric coherence in the alpha and beta bands of the frontal and temporal brain regions. No significant change was observed in the spectral power and the inter-hemispheric coherence in different timeslots during and after the exposure. These findings also corroborated those of our previous study using functional magnetic resonant imaging.

  13. Validation of a smartphone-based EEG among people with epilepsy: A prospective study

    DEFF Research Database (Denmark)

    Mckenzie, Erica D.; Lim, Andrew S P; Leung, Edward C W

    2017-01-01

    Our objective was to assess the ability of a smartphone-based electroencephalography (EEG) application, the Smartphone Brain Scanner-2 (SBS2), to detect epileptiform abnormalities compared to standard clinical EEG. The SBS2 system consists of an Android tablet wirelessly connected to a 14-electrode...

  14. Wavelet-based study of valence?arousal model of emotions on EEG signals with LabVIEW

    OpenAIRE

    Guzel Aydin, Seda; Kaya, Turgay; Guler, Hasan

    2016-01-01

    This paper illustrates the wavelet-based feature extraction for emotion assessment using electroencephalogram (EEG) signal through graphical coding design. Two-dimensional (valence?arousal) emotion model was studied. Different emotions (happy, joy, melancholy, and disgust) were studied for assessment. These emotions were stimulated by video clips. EEG signals obtained from four subjects were decomposed into five frequency bands (gamma, beta, alpha, theta, and delta) using ?db5? wavelet functi...

  15. Brain-computer interfaces for EEG neurofeedback: peculiarities and solutions.

    Science.gov (United States)

    Huster, René J; Mokom, Zacharais N; Enriquez-Geppert, Stefanie; Herrmann, Christoph S

    2014-01-01

    Neurofeedback training procedures designed to alter a person's brain activity have been in use for nearly four decades now and represent one of the earliest applications of brain-computer interfaces (BCI). The majority of studies using neurofeedback technology relies on recordings of the electroencephalogram (EEG) and applies neurofeedback in clinical contexts, exploring its potential as treatment for psychopathological syndromes. This clinical focus significantly affects the technology behind neurofeedback BCIs. For example, in contrast to other BCI applications, neurofeedback BCIs usually rely on EEG-derived features with only a minimum of additional processing steps being employed. Here, we highlight the peculiarities of EEG-based neurofeedback BCIs and consider their relevance for software implementations. Having reviewed already existing packages for the implementation of BCIs, we introduce our own solution which specifically considers the relevance of multi-subject handling for experimental and clinical trials, for example by implementing ready-to-use solutions for pseudo-/sham-neurofeedback. © 2013.

  16. SYNDROMES OF BEHAVIORAL AND SPEECH DISORDERS ASSOCIATED WITH BENIGN EPILEPTIFORM DISCHARGES OF CHILDHOOD ON ELECTROENCEPHALOGRAM

    Directory of Open Access Journals (Sweden)

    I. A. Sadekov

    2017-01-01

    Full Text Available Objective: to assess the role and significance of benign epileptiform discharges of childhood (BEDC on electroencephalogram (EEG in development of speech and behaviorial disorders in children.Materials and methods. 90 children aged 3–7 years were included in the study: 30 of them were healthy, 30 had attention deficit hyperactivity disorder (ADHD, and 30 had expressive language disorder (ELD. We analyzed the role of persistent epileptiform activity (BEDC type in EEG as well as frontal intermittent rhythmic delta activity in the development of some neuropsychiatric disorders and speech disorders in children.Results. We suggest to allocate a special variant of ADHD – epileptiform disintegration of behavior; we also propose the strategies for its therapeutic correction.Conclusion. Detection of epileptiform activity (BEDC type on EEG in children with ELD is a predictor of cognitive disorders development and requires therapeutic correction, which should be aimed at stimulation of brain maturation. Detection of frontal intermittent rhythmic delta activity in children with ELD requires neurovisualization with further determining of treatment strategy.

  17. Electroencephalogram complexity analysis in children with attention-deficit/hyperactivity disorder during a visual cognitive task.

    Science.gov (United States)

    Zarafshan, Hadi; Khaleghi, Ali; Mohammadi, Mohammad Reza; Moeini, Mahdi; Malmir, Nastaran

    2016-01-01

    The aim of this study was to investigate electroencephalogram (EEG) dynamics using complexity analysis in children with attention-deficit/hyperactivity disorder (ADHD) compared with healthy control children when performing a cognitive task. Thirty 7-12-year-old children meeting Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5) criteria for ADHD and 30 healthy control children underwent an EEG evaluation during a cognitive task, and Lempel-Ziv complexity (LZC) values were computed. There were no significant differences between ADHD and control groups on age and gender. The mean LZC of the ADHD children was significantly larger than healthy children over the right anterior and right posterior regions during the cognitive performance. In the ADHD group, complexity of the right hemisphere was higher than that of the left hemisphere, but the complexity of the left hemisphere was higher than that of the right hemisphere in the normal group. Although fronto-striatal dysfunction is considered conclusive evidence for the pathophysiology of ADHD, our arithmetic mental task has provided evidence of structural and functional changes in the posterior regions and probably cerebellum in ADHD.

  18. Artifact removal from EEG signals using adaptive filters in cascade

    International Nuclear Information System (INIS)

    Garces Correa, A; Laciar, E; Patino, H D; Valentinuzzi, M E

    2007-01-01

    Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records

  19. Rapidly Learned Identification of Epileptic Seizures from Sonified EEG

    Directory of Open Access Journals (Sweden)

    Psyche eLoui

    2014-10-01

    Full Text Available Sonification refers to a process by which data are converted into sound, providing an auditory alternative to visual display. Currently, the prevalent method for diagnosing seizures in epilepsy is by visually reading a patient’s electroencephalogram (EEG. However, sonification of the EEG data provides certain advantages due to the nature of human auditory perception. We hypothesized that human listeners will be able to identify seizures from EEGs using the auditory modality alone, and that accuracy of seizure identification will increase after a short training session. Here we describe an algorithm we have used to sonify EEGs of both seizure and non-seizure activity, followed by a training study in which subjects listened to short clips of sonified EEGs and determine whether each clip was of seizure or normal activity, both before and after a short training session. Results show that before training subjects performed at chance level in differentiating seizures vs. non-seizures, but there was a significant improvement of accuracy after the training session. After training, subjects successfully distinguished seizures from non-seizures using the auditory modality alone. Further analyses using signal detection theory demonstrated improvement in sensitivity and reduction in response bias as a result of training. This study demonstrates the potential of sonified EEGs to be used for the detection of seizures. Future studies will attempt to increase accuracy using novel training and sonification modifications, with the goals of managing, predicting, and ultimately controlling seizures using sonification as a possible biofeedback-based intervention for epilepsy.

  20. Wireless and mobile systems in telemedicine

    Directory of Open Access Journals (Sweden)

    Reza Safdari

    2012-12-01

    Full Text Available Background: It is necessary to deploy mobile and wireless systems in healthcare, because they have many benefits for healthcare systems. The objectives of this article were introducing various systems, applications, and standards of the wireless and mobile telemedicine. Material and Methods: This review study was conducted in 2010. To conduct the study, published articles in the years 2005 to 2012, in English with an emphasis on wireless and mobile technologies in health were studied. Search was done with key words include telemedicine, wireless health systems, health and telecommunications technology in databases including Pubmed, Science Direct, Google Scholar, Web of Sciences, Proquest. The collected data were analyzed. Results: Telemedicine system in the ambulance, telemedicine systems in space, telecardiology systems, EEG system, ultrasound system are some types of wireless and mobile systems in telemedicine. PDA-based mobile and wireless telemedicine application, based PDA drug application, and patient tracking application are some of wireless and mobile applications of telemedicine. The most important standards of wireless and mobile telemedicine are HL7, DICOM, SNOMed, and ICD-9-CM. Conclusion: There are many challenges in the wireless and mobile systems in telemedicine, despite the many benefits. Slow speed in sending pictures and video, lack of attention to the privacy in the design of these systems, environmental variables and the number of users during the day are some of these challenges. It is recommended to consider these challenges during the planning and designing of wireless and mobile systems in telemedicine.

  1. Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism.

    Science.gov (United States)

    Kumar, Surendra; Ghosh, Subhojit; Tetarway, Suhash; Sinha, Rakesh Kumar

    2015-07-01

    In this study, the magnitude and spatial distribution of frequency spectrum in the resting electroencephalogram (EEG) were examined to address the problem of detecting alcoholism in the cerebral motor cortex. The EEG signals were recorded from chronic alcoholic conditions (n = 20) and the control group (n = 20). Data were taken from motor cortex region and divided into five sub-bands (delta, theta, alpha, beta-1 and beta-2). Three methodologies were adopted for feature extraction: (1) absolute power, (2) relative power and (3) peak power frequency. The dimension of the extracted features is reduced by linear discrimination analysis and classified by support vector machine (SVM) and fuzzy C-mean clustering. The maximum classification accuracy (88 %) with SVM clustering was achieved with the EEG spectral features with absolute power frequency on F4 channel. Among the bands, relatively higher classification accuracy was found over theta band and beta-2 band in most of the channels when computed with the EEG features of relative power. Electrodes wise CZ, C3 and P4 were having more alteration. Considering the good classification accuracy obtained by SVM with relative band power features in most of the EEG channels of motor cortex, it can be suggested that the noninvasive automated online diagnostic system for the chronic alcoholic condition can be developed with the help of EEG signals.

  2. Remote monitoring of electroencephalogram, electrocardiogram, and behavior during controlled atmosphere stunning in broilers: implications for welfare.

    Science.gov (United States)

    Coenen, A M L; Lankhaar, J; Lowe, J C; McKeegan, D E F

    2009-01-01

    This study examined the welfare implications of euthanizing broilers with 3 gas mixtures relevant to the commercial application of controlled atmosphere stunning (CAS). Birds were implanted/equipped with electrodes to measure brain activity (electroencephalogram, EEG) and heart rate. These signals were recorded using a purpose-built telemetry-logging system, small enough to be worn by each bird in a spandex backpack. The birds were euthanized in a scaled-down CAS apparatus consisting of a conveyor belt passing through 2 compartments. Three gas environments were applied (8 birds per treatment): 1) anoxia (N(2) with <2% residual O(2), in both compartments), 2) hypercapnic anoxia (N(2) with 30% CO(2) and <2% residual O(2), in both compartments), and 3) a 2-phase approach with a hypercapnic hyperoxygenated anesthetic phase (40% CO(2), 30% O(2), and 30% N(2), in the first compartment, 80 s) followed by a second euthanasia phase (80% CO(2) in air, in the second compartment). All 3 CAS approaches effectively achieved nonrecovery states, and time to loss of consciousness for each bird was determined by visual determination of isoelectric EEG and by calculation of the correlation dimension of the EEG. Hypercapnic anoxia resulted in rapid unconsciousness and death; both anoxic treatments were associated with early onset prolonged wing flapping and sustained tonic convulsions as displayed in the electrophysiological recordings. These responses were seen in the period when consciousness remained a possibility. Hypercapnic hyperoxygenation (the 2-phase approach) was associated with respiratory disruption, but this treatment eliminated initial clonic convulsions in the stunning process, and tonic convulsions were not seen. These results suggest that the presence of O(2) in the first stage of CAS is associated with an absence of potentially distressing behavioral responses. The respiratory discomfort associated with hypercapnic hyperoxygenation is an issue. We propose that this

  3. Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing.

    Science.gov (United States)

    Buteneers, Pieter; Verstraeten, David; van Mierlo, Pieter; Wyckhuys, Tine; Stroobandt, Dirk; Raedt, Robrecht; Hallez, Hans; Schrauwen, Benjamin

    2011-11-01

    In this paper we propose a technique based on reservoir computing (RC) to mark epileptic seizures on the intra-cranial electroencephalogram (EEG) of rats. RC is a recurrent neural networks training technique which has been shown to possess good generalization properties with limited training. The system is evaluated on data containing two different seizure types: absence seizures from genetic absence epilepsy rats from Strasbourg (GAERS) and tonic-clonic seizures from kainate-induced temporal-lobe epilepsy rats. The dataset consists of 452hours from 23 GAERS and 982hours from 15 kainate-induced temporal-lobe epilepsy rats. During the preprocessing stage, several features are extracted from the EEG. A feature selection algorithm selects the best features, which are then presented as input to the RC-based classification algorithm. To classify the output of this algorithm a two-threshold technique is used. This technique is compared with other state-of-the-art techniques. A balanced error rate (BER) of 3.7% and 3.5% was achieved on the data from GAERS and kainate rats, respectively. This resulted in a sensitivity of 96% and 94% and a specificity of 96% and 99% respectively. The state-of-the-art technique for GAERS achieved a BER of 4%, whereas the best technique to detect tonic-clonic seizures achieved a BER of 16%. Our method outperforms up-to-date techniques and only a few parameters need to be optimized on a limited training set. It is therefore suited as an automatic aid for epilepsy researchers and is able to eliminate the tedious manual review and annotation of EEG. Copyright © 2011 Elsevier B.V. All rights reserved.

  4. [Early evaluation of patients with amplitude-integrated electroencephalogram on brain function prognosis after cardiopulmonary cerebral resuscitation].

    Science.gov (United States)

    Dong, Xin; Shao, Huanzhang; Yang, Yanan; Qin, Lijie; Guo, Zhisong; Zhang, Huifeng; Zhang, Xueyan; Qin, Bingyu

    2017-10-01

    To explore the characteristic of early evaluation of patients with amplitude-integrated electroencephalogram (aEEG) on brain function prognosis after cardiopulmonary cerebral resuscitation (CPCR). A retrospective analysis of the clinical data of patients with adult CPCR in intensive care unit (ICU) of Henan Provincial People's Hospital from March 2016 to March 2017 was performed. The length of stay, recovery time, acute physiology and chronic health evaluation II (APACHE II) score, aEEG and Glasgow coma scale (GCS) within 72 hours were recorded. The main clinical outcome was the prognosis of brain function (Glasgow-Pittsburgh cerebral performance category, CPC) in patients with CPCR after 3 months. Relationship between aEEG and GCS and their correlation with brain function prognosis was analyzed by Spearman rank correlation analysis. The effects of aEEG and GCS on prognosis of brain function were evaluated by Logistic regression analysis. The predictive ability of aEEG and GCS for brain function prognosis was evaluated by receiver operating characteristic (ROC) curve. A total of 31 patients with CPCR were enrolled, with 18 males and 13 females; mean age was (41.84±16.96) years old; recovery time average was (19.42±10.79) minutes; the length of stay was (14.84±10.86) days; APACHE II score 19.29±6.42; aEEG grade I (normal amplitude) in 7 cases, grade II (mild to moderate abnormal amplitude) in 13 cases, grade III (severe abnormal amplitude) in 11 cases; GCS grade I (9-14 scores) in 7 cases, grade II (4-8 scores) in 14 cases, grade III (3 scores) in 10 cases; 19 survivals, 12 deaths; the prognosis of brain function was good (CPC 1-2) in 8 cases, and the prognosis of brain function was poor (CPC 3-5) in 23 cases. There was no significant difference in age, gender, recovery time, length of stay and APACHE II score between two groups with different brain function prognosis, while aEEG grade and GCS grade were significantly different. Cochran-Armitage trend test

  5. Adaptive extraction of emotion-related EEG segments using multidimensional directed information in time-frequency domain.

    Science.gov (United States)

    Petrantonakis, Panagiotis C; Hadjileontiadis, Leontios J

    2010-01-01

    Emotion discrimination from electroencephalogram (EEG) has gained attention the last decade as a user-friendly and effective approach to EEG-based emotion recognition (EEG-ER) systems. Nevertheless, challenging issues regarding the emotion elicitation procedure, especially its effectiveness, raise. In this work, a novel method, which not only evaluates the degree of emotion elicitation but localizes the emotion information in the time-frequency domain, as well, is proposed. The latter, incorporates multidimensional directed information at the time-frequency EEG representation, extracted using empirical mode decomposition, and introduces an asymmetry index for adaptive emotion-related EEG segment selection. Experimental results derived from 16 subjects visually stimulated with pictures from the valence/arousal space drawn from the International Affective Picture System database, justify the effectiveness of the proposed approach and its potential contribution to the enhancement of EEG-ER systems.

  6. Wireless Access

    Indian Academy of Sciences (India)

    First page Back Continue Last page Overview Graphics. Wireless Access. Wireless connect to the Base station. Easy and Convenient access. Costlier as compared to the wired technology. Reliability challenges. We see it as a complementary technology to the DSL.

  7. Statistical maps for EEG dipolar source localization.

    Science.gov (United States)

    Bénar, Christian G; Gunn, Roger N; Grova, Christophe; Champagne, Benoît; Gotman, Jean

    2005-03-01

    We present a method that estimates three-dimensional statistical maps for electroencephalogram (EEG) source localization. The maps assess the likelihood that a point in the brain contains a dipolar source, under the hypothesis of one, two or three activated sources. This is achieved by examining all combinations of one to three dipoles on a coarse grid and attributing to each combination a score based on an F statistic. The probability density function of the statistic under the null hypothesis is estimated nonparametrically, using bootstrap resampling. A theoretical F distribution is then fitted to the empirical distribution in order to allow correction for multiple comparisons. The maps allow for the systematic exploration of the solution space for dipolar sources. They permit to test whether the data support a given solution. They do not rely on the assumption of uncorrelated source time courses. They can be compared to other statistical parametric maps such as those used in functional magnetic resonance imaging (fMRI). Results are presented for both simulated and real data. The maps were compared with LORETA and MUSIC results. For the real data consisting of an average of epileptic spikes, we observed good agreement between the EEG statistical maps, intracranial EEG recordings, and fMRI activations.

  8. Application of linear graph embedding as a dimensionality reduction technique and sparse representation classifier as a post classifier for the classification of epilepsy risk levels from EEG signals

    Science.gov (United States)

    Prabhakar, Sunil Kumar; Rajaguru, Harikumar

    2015-12-01

    The most common and frequently occurring neurological disorder is epilepsy and the main method useful for the diagnosis of epilepsy is electroencephalogram (EEG) signal analysis. Due to the length of EEG recordings, EEG signal analysis method is quite time-consuming when it is processed manually by an expert. This paper proposes the application of Linear Graph Embedding (LGE) concept as a dimensionality reduction technique for processing the epileptic encephalographic signals and then it is classified using Sparse Representation Classifiers (SRC). SRC is used to analyze the classification of epilepsy risk levels from EEG signals and the parameters such as Sensitivity, Specificity, Time Delay, Quality Value, Performance Index and Accuracy are analyzed.

  9. Meditation and the EEG

    OpenAIRE

    West, Michael

    1980-01-01

    Previous research on meditation and the EEG is described, and findings relating to EEG patterns during meditation are discussed. Comparisons of meditation with other altered states are reviewed and it is concluded that, on the basis of existing EEG evidence, there is some reason for differentiating between meditation and drowsing. Research on alpha-blocking and habituation of the blocking response during meditation is reviewed, and the effects of meditation on EEG patterns outside of meditati...

  10. Mobile EEG in epilepsy

    NARCIS (Netherlands)

    Askamp, Jessica; van Putten, Michel Johannes Antonius Maria

    2014-01-01

    The sensitivity of routine EEG recordings for interictal epileptiform discharges in epilepsy is limited. In some patients, inpatient video-EEG may be performed to increase the likelihood of finding abnormalities. Although many agree that home EEG recordings may provide a cost-effective alternative

  11. Low β2 Main Peak Frequency in the Electroencephalogram Signs Vulnerability To Depression.

    Directory of Open Access Journals (Sweden)

    Damien Claverie

    2016-11-01

    Full Text Available Objective:After an intense and repeated stress some rats become vulnerable to depression. This state is characterized by persistent low serum BDNF concentration. Our objective was to determine whether electrophysiological markers can sign vulnerability to depression. Methods:Forty-three Sprague Dawley rats were recorded with supradural electrodes above hippocampus and connected to wireless EEG transmitters. Twenty-nine animals experienced four daily social defeats (SD followed by one month recovery. After SD, 14 rats had persistent low serum BDNF level and were considered as vulnerable (V while the 15 others were considered as non-vulnerable (NV. EEG signals were analyzed during active waking before SD (Baseline, just after SD (Post-Stress and 1 month after SD (Recovery.Results:We found that V animals are characterized by higher high θ and α spectral relative powers and lower β2 main peak frequency before SD. These differences are maintained at Post-Stress and Recovery for α spectral relative powers and β2 main peak frequency. Using ROC analysis, we show that low β2 main peak frequency assessed during Baseline is a good predictor of the future state of vulnerability to depression.Conclusion:Given the straightforwardness of EEG recordings, these results open the way to prospective studies in humans aiming to identify population at-risk for depression.

  12. Decoding English Alphabet Letters Using EEG Phase Information

    Science.gov (United States)

    Wang, YiYan; Wang, Pingxiao; Yu, Yuguo

    2018-01-01

    Increasing evidence indicates that the phase pattern and power of the low frequency oscillations of brain electroencephalograms (EEG) contain significant information during the human cognition of sensory signals such as auditory and visual stimuli. Here, we investigate whether and how the letters of the alphabet can be directly decoded from EEG phase and power data. In addition, we investigate how different band oscillations contribute to the classification and determine the critical time periods. An English letter recognition task was assigned, and statistical analyses were conducted to decode the EEG signal corresponding to each letter visualized on a computer screen. We applied support vector machine (SVM) with gradient descent method to learn the potential features for classification. It was observed that the EEG phase signals have a higher decoding accuracy than the oscillation power information. Low-frequency theta and alpha oscillations have phase information with higher accuracy than do other bands. The decoding performance was best when the analysis period began from 180 to 380 ms after stimulus presentation, especially in the lateral occipital and posterior temporal scalp regions (PO7 and PO8). These results may provide a new approach for brain-computer interface techniques (BCI) and may deepen our understanding of EEG oscillations in cognition. PMID:29467615

  13. Nonlinear analysis of EEG signals at different mental states

    Directory of Open Access Journals (Sweden)

    Tiboleng Thelma

    2004-03-01

    Full Text Available Abstract Background The EEG (Electroencephalogram is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about the state of the brain. However, the human observer can not directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. This work discusses the effect on the EEG signal due to music and reflexological stimulation. Methods In this work, nonlinear parameters like Correlation Dimension (CD, Largest Lyapunov Exponent (LLE, Hurst Exponent (H and Approximate Entropy (ApEn are evaluated from the EEG signals under different mental states. Results The results obtained show that EEG to become less complex relative to the normal state with a confidence level of more than 85% due to stimulation. Conclusions It is found that the measures are significantly lower when the subjects are under sound or reflexologic stimulation as compared to the normal state. The dimension increases with the degree of the cognitive activity. This suggests that when the subjects are under sound or reflexologic stimuli, the number of parallel functional processes active in the brain is less and the brain goes to a more relaxed state

  14. EEG biofeedback improves attentional bias in high trait anxiety individuals.

    Science.gov (United States)

    Wang, Sheng; Zhao, Yan; Chen, Sijuan; Lin, Guiping; Sun, Peng; Wang, Tinghuai

    2013-10-07

    Emotion-related attentional bias is implicated in the aetiology and maintenance of anxiety disorders. Electroencephalogram (EEG) biofeedback can obviously improve the anxiety disorders and reduce stress level, and can also enhance attention performance in healthy subjects. The present study examined the effects and mechanisms of EEG biofeedback training on the attentional bias of high trait anxiety (HTA) individuals toward negative stimuli. Event-related potentials were recorded while HTA (n=24) and nonanxious (n=21) individuals performed the color-word emotional Stroop task. During the emotional Stroop task, HTA participants showed longer reaction times and P300 latencies induced by negative words, compared to nonanxious participants.The EEG biofeedback significantly decreased the trait anxiety inventory score and reaction time in naming the color of negative words in the HTA group. P300 latencies evoked by negative stimuli in the EEG biofeedback group were significantly reduced after the alpha training, while no significant changes were observed in the sham biofeedback group after the intervention. The prolonged P300 latency is associated with attentional bias to negative stimuli in the HTA group. EEG biofeedback training demonstrated a significant improvement of negative emotional attentional bias in HTA individuals, which may be due to the normalization of P300 latency.

  15. Localised astroglial dysfunction disrupts high-frequency EEG rhythms.

    Science.gov (United States)

    Willoughby, J O; Mackenzie, L; Pope, K J; Broberg, M; Nilsson, M

    2005-02-01

    We used cerebral cortex injections of fluorocitrate to determine if selective astrocytic disturbances affect the electroencephalogram (EEG). Rats were halothane-anaesthetized and 0.8 nmol of sodium fluorocitrate was injected into hindlimb (motor-sensory) cortex. Extra-dural EEG electrodes were implanted after which the anaesthesia was ceased. EEG was recorded at 1, 3, 5, 7, 24 and 48 hours. There was a broad-band reduction in frequencies in the EEG between 20 and 100 Hz commencing within 1 hour of injection and largely restricted to the side of injection and to frontal cortex, and maximal at 3 hours. Halothane had a suppressive effect on gamma power after citrate injection, but also prevented EEG suppression caused by fluorocitrate, consistent with the hypothesis that some of the action of fluorocitrate depended on gap-junctions. The findings are consistent with the hypothesis that primary astroglial dysfunction leads to reduced neuronal transmission and further supports gap-junctions as mediating fluorocitrate-induced astroglial effects.

  16. EEG upper/low alpha frequency power ratio relates to temporo-parietal brain atrophy and memory performances in mild cognitive impairment

    OpenAIRE

    Moretti, Davide V.; Paternic?, Donata; Binetti, Giuliano; Zanetti, Orazio; Frisoni, Giovanni B.

    2013-01-01

    Objective: Temporo-parietal cortex thinning is associated to mild cognitive impairment (MCI) due to Alzheimer disease (AD). The increase of EEG upper/low alpha power ratio has been associated with AD-converter MCI subjects. We investigated the association of alpha3/alpha2 ratio with patterns of cortical thickness in MCI. Materials and Methods: Seventy-four adult subjects with MCI underwent clinical and neuropsychological evaluation, electroencephalogram (EEG) recording and high resolution ...

  17. Development of nonlinear techniques based on time-frequency representation and information theory for the analysis of EEG signals to assess different states of consciousness

    OpenAIRE

    Melia, Umberto

    2014-01-01

    Tesi per compendi de publicacions. La consulta íntegra de la tesi, inclosos els articles no comunicats públicament per drets d'autor, es pot realitzar prèvia petició a l'Arxiu de la UPC Electroencephalogram (EEG) recordings provide insight into the changes in brain activity associated with various states of anesthesia, epilepsy, brain attentiveness, sleep disorders, brain disorders, etc. EEG's are complex signals whose statistical properties depend on both space and time. Their randomness ...

  18. The sleep-deprived electroencephalogram: evidence and practice.

    Science.gov (United States)

    Glick, Thomas H

    2002-08-01

    Sleep deprivation for the initial electroencephalogram for suspected seizures is a widespread but inconsistent practice not informed by balanced evidence. Daily practice suggests that nonneurologists are confused by the meaning and value of, and indications for, "sleep" (tracing) vs "sleep deprivation" (and other alternatives). They need specific, informed guidance from general neurologists on best practices. To document illustratively the variability of neurologists' practices, the level of relevant information among nonneurologists, and the current state of published evidence; and to stimulate formulation of consensus advisories. I surveyed knowledge and practices of (1) nonneurologists in a community teaching hospital; (2) local and national neurologists and epileptologists; (3) electroencephalogram laboratory protocols; and (4) textbook accounts and recommendations and the relevant journal literature. National professional organizations were contacted for advisories or guidelines. Most nonneurologists surveyed misunderstood "sleep" vs "sleep-deprived" electroencephalograms and their actual protocols. They are unaware of evidence on benefits vs burdens. Neurologists' practices are inconsistent. Experts generally agree that sleep deprivation produces substantial activation of interictal epileptiform discharges beyond the activation of sleep per se. However, most published recommendations and interviewed epileptologists do not suggest sleep deprivation for the initial electroencephalogram because of "inconvenience" (burdens) for the patient. Evidence-based or reasoned guidance is minimal, and professional societies have not issued advisories. Confusion over sleep deprivation, disparities between evidence and recommendations, and inconsistent practices create a need for expert consensus for guidance, as well as comparative research on alternative methods of increasing diagnostic yield.

  19. The Performance of EEG-P300 Classification using Backpropagation Neural Networks

    Directory of Open Access Journals (Sweden)

    Arjon Turnip

    2013-12-01

    Full Text Available Electroencephalogram (EEG recordings signal provide an important function of brain-computer communication, but the accuracy of their classification is very limited in unforeseeable signal variations relating to artifacts. In this paper, we propose a classification method entailing time-series EEG-P300 signals using backpropagation neural networks to predict the qualitative properties of a subject’s mental tasks by extracting useful information from the highly multivariate non-invasive recordings of brain activity. To test the improvement in the EEG-P300 classification performance (i.e., classification accuracy and transfer rate with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis (BLDA. Finally, the result of the experiment showed that the average of the classification accuracy was 97% and the maximum improvement of the average transfer rate is 42.4%, indicating the considerable potential of the using of EEG-P300 for the continuous classification of mental tasks.

  20. Quantitative EEG Brain Mapping In Psychotropic Drug Development, Drug Treatment Selection, and Monitoring.

    Science.gov (United States)

    Itil, Turan M.; Itil, Kurt Z.

    1995-05-01

    Quantification of standard electroencephalogram (EEG) by digital computers [computer-analyzed EEG (CEEG)] has transformed the subjective analog EEG into an objective scientific method. Until a few years ago, CEEG was only used to assist in the development of psychotropic drugs by means of the quantitative pharmaco EEG. Thanks to the computer revolution and the accompanying reductions in cost of quantification, CEEG can now also be applied in psychiatric practice. CEEG can assist the physician in confirming clinical diagnoses, selecting psychotropic drugs for treatment, and drug treatment monitoring. Advancements in communications technology allow physicians and researchers to reduce the costs of acquiring a high-technology CEEG brain mapping system by utilizing the more economical telephonic services.

  1. An Approach for Pattern Recognition of EEG Applied in Prosthetic Hand Drive

    Directory of Open Access Journals (Sweden)

    Xiao-Dong Zhang

    2011-12-01

    Full Text Available For controlling the prosthetic hand by only electroencephalogram (EEG, it has become the hot spot in robotics research to set up a direct communication and control channel between human brain and prosthetic hand. In this paper, the EEG signal is analyzed based on multi-complicated hand activities. And then, two methods of EEG pattern recognition are investigated, a neural prosthesis hand system driven by BCI is set up, which can complete four kinds of actions (arm’s free state, arm movement, hand crawl, hand open. Through several times of off-line and on-line experiments, the result shows that the neural prosthesis hand system driven by BCI is reasonable and feasible, the C-support vector classifiers-based method is better than BP neural network on the EEG pattern recognition for multi-complicated hand activities.

  2. Wireless virtualization

    CERN Document Server

    Wen, Heming; Le-Ngoc, Tho

    2013-01-01

    This SpringerBriefs is an overview of the emerging field of wireless access and mobile network virtualization. It provides a clear and relevant picture of the current virtualization trends in wireless technologies by summarizing and comparing different architectures, techniques and technologies applicable to a future virtualized wireless network infrastructure. The readers are exposed to a short walkthrough of the future Internet initiative and network virtualization technologies in order to understand the potential role of wireless virtualization in the broader context of next-generation ubiq

  3. Resting frontal EEG asymmetry and shyness and sociability in schizophrenia: a pilot study of community-based outpatients.

    Science.gov (United States)

    Jetha, Michelle K; Schmidt, Louis A; Goldberg, Joel O

    2009-01-01

    We conducted a pilot study to examine the relations among the patterns of resting regional electroencephalogram (EEG) alpha activity, trait shyness and sociability, and positive and negative symptoms scores in 20 adults with schizophrenia, attending a community-based treatment and rehabilitation center. As predicted, patients' positive symptoms were related to greater relative resting left frontal EEG activity, replicating earlier work. When only adults with low to no positive symptoms were considered, trait shyness was related to greater relative resting right frontal EEG activity, whereas trait sociability was related to greater relative resting left frontal EEG activity. This finding is similar to what is consistently noted in healthy adults. These pilot data suggest that positive symptoms in patients with schizophrenia may obscure the relations between personality and frontal EEG asymmetry measures observed in healthy adults.

  4. Real-time inference of word relevance from electroencephalogram and eye gaze

    Science.gov (United States)

    Wenzel, M. A.; Bogojeski, M.; Blankertz, B.

    2017-10-01

    Objective. Brain-computer interfaces can potentially map the subjective relevance of the visual surroundings, based on neural activity and eye movements, in order to infer the interest of a person in real-time. Approach. Readers looked for words belonging to one out of five semantic categories, while a stream of words passed at different locations on the screen. It was estimated in real-time which words and thus which semantic category interested each reader based on the electroencephalogram (EEG) and the eye gaze. Main results. Words that were subjectively relevant could be decoded online from the signals. The estimation resulted in an average rank of 1.62 for the category of interest among the five categories after a hundred words had been read. Significance. It was demonstrated that the interest of a reader can be inferred online from EEG and eye tracking signals, which can potentially be used in novel types of adaptive software, which enrich the interaction by adding implicit information about the interest of the user to the explicit interaction. The study is characterised by the following novelties. Interpretation with respect to the word meaning was necessary in contrast to the usual practice in brain-computer interfacing where stimulus recognition is sufficient. The typical counting task was avoided because it would not be sensible for implicit relevance detection. Several words were displayed at the same time, in contrast to the typical sequences of single stimuli. Neural activity was related with eye tracking to the words, which were scanned without restrictions on the eye movements.

  5. Exercise benefits for the aging brain depend on the accompanying cognitive load: insights from sleep electroencephalogram.

    Science.gov (United States)

    Horne, Jim

    2013-11-01

    Although exercise clearly offsets aging effects on the body, its benefits for the aging brain are likely to depend on the extent that physical activity (especially locomotion) facilitates multisensory encounters, curiosity, and interactions with novel environments; this is especially true for exploratory activity, which occupies much of wakefulness for most mammals in the wild. Cognition is inseparable from physical activity, with both interlinked to promote neuroplasticity and more successful brain aging. In these respects and for humans, exercising in a static, featureless, artificially lit indoor setting contrasts with exploratory outdoor walking within a novel environment during daylight. However, little is known about the comparative benefits for the aging brain of longer-term daily regimens of this latter nature including the role of sleep, to the extent that sleep enhances neuroplasticity as shown in short-term laboratory studies. More discerning analyses of sleep electroencephalogram (EEG) slow-wave activity especially 0.5-2-Hz activity would provide greater insights into use-dependent recovery processes during longer-term tracking of these regimens and complement slower changing waking neuropsychologic and resting functional magnetic resonance imaging (fMRI) measures, including those of the brain's default mode network. Although the limited research only points to ephemeral small sleep EEG effects of pure exercise, more enduring effects seem apparent when physical activity incorporates cognitive challenges. In terms of "use it or lose it," curiosity-driven "getting out and about," encountering, interacting with, and enjoying novel situations may well provide the brain with its real exercise, further reflected in changes to the dynamics of sleep. Copyright © 2013 Elsevier B.V. All rights reserved.

  6. Phase synchronization for classification of spontaneous EEG signals in brain-computer interfaces

    OpenAIRE

    Gysels, Elly; Kunt, Murat; Celka, Patrick

    2007-01-01

    By directly analyzing brain activity, Brain-Computer Interfaces (BCIs) allow for communication that does not rely on any muscular control and therefore constitute a possible communication channel for the completely paralyzed. Typically, the user performs different mental tasks, that correspond to different output commands as recognized by the system. From the recorded brain signals (Electroencephalogram, EEG), features that characterize the mental tasks and allow their discrimination by a cla...

  7. Phase synchronization for classification of spontaneous EEG signals in brain-computer interfaces

    OpenAIRE

    Gysels, Elly

    2005-01-01

    By directly analyzing brain activity, Brain-Computer Interfaces (BCIs) allow for communication that does not rely on any muscular control and therefore constitute a possible communication channel for the completely paralyzed. Typically, the user performs different mental tasks, that correspond to different output commands as recognized by the system. From the recorded brain signals (Electroencephalogram, EEG), features that characterize the mental tasks and allow their discrimination by a cla...

  8. Brain order disorder 2nd group report of f-EEG

    Science.gov (United States)

    Lalonde, Francois; Gogtay, Nitin; Giedd, Jay; Vydelingum, Nadarajen; Brown, David; Tran, Binh Q.; Hsu, Charles; Hsu, Ming-Kai; Cha, Jae; Jenkins, Jeffrey; Ma, Lien; Willey, Jefferson; Wu, Jerry; Oh, Kenneth; Landa, Joseph; Lin, C. T.; Jung, T. P.; Makeig, Scott; Morabito, Carlo Francesco; Moon, Qyu; Yamakawa, Takeshi; Lee, Soo-Young; Lee, Jong-Hwan; Szu, Harold H.; Kaur, Balvinder; Byrd, Kenneth; Dang, Karen; Krzywicki, Alan; Familoni, Babajide O.; Larson, Louis; Harkrider, Susan; Krapels, Keith A.; Dai, Liyi

    2014-05-01

    Since the Brain Order Disorder (BOD) group reported on a high density Electroencephalogram (EEG) to capture the neuronal information using EEG to wirelessly interface with a Smartphone [1,2], a larger BOD group has been assembled, including the Obama BRAIN program, CUA Brain Computer Interface Lab and the UCSD Swartz Computational Neuroscience Center. We can implement the pair-electrodes correlation functions in order to operate in a real time daily environment, which is of the computation complexity of O(N3) for N=102~3 known as functional f-EEG. The daily monitoring requires two areas of focus. Area #(1) to quantify the neuronal information flow under arbitrary daily stimuli-response sources. Approach to #1: (i) We have asserted that the sources contained in the EEG signals may be discovered by an unsupervised learning neural network called blind sources separation (BSS) of independent entropy components, based on the irreversible Boltzmann cellular thermodynamics(ΔS Area #(2) applying EEG bio-feedback will improve collective decision making (TBD). Approach to #2: We introduce a novel performance quality metrics, in terms of the throughput rate of faster (Δt) & more accurate (ΔA) decision making, which applies to individual, as well as team brain dynamics. Following Nobel Laureate Daniel Kahnmen's novel "Thinking fast and slow", through the brainwave biofeedback we can first identify an individual's "anchored cognitive bias sources". This is done in order to remove the biases by means of individually tailored pre-processing. Then the training effectiveness can be maximized by the collective product Δt * ΔA. For Area #1, we compute a spatiotemporally windowed EEG in vitro average using adaptive time-window sampling. The sampling rate depends on the type of neuronal responses, which is what we seek. The averaged traditional EEG measurements and are further improved by BSS decomposition into finer stimulus-response source mixing matrix [A] having finer & faster

  9. Emotional responses as independent components in EEG

    DEFF Research Database (Denmark)

    Jensen, Camilla Birgitte Falk; Petersen, Michael Kai; Larsen, Jakob Eg

    2014-01-01

    Combine wireless neuroheadsets with smartphones that enable mobile brain imaging can potentially allow us to design cognitive interfaces which adapt to our affective responses. Neuroimaging experiments using electroencephalography (EEG) initially identified two components elicited by pleasant...... or unpleasant images; early posterior negativity (EPN) and late positive potential (LPP). Recent studies suggest that several time course components may be modulated by emotional content in images or text. However these neural signatures are characterized by small voltage changes that would be highly...... susceptible to noise if captured in a mobile context. Hypothesizing that retrieval of emotional responses in mobile usage scenarios could be enhanced through spatial filtering, we compare a standard EEG electrode based analysis against an approach based on independent component analysis (ICA). By clustering...

  10. Endosulfan and cholinergic (muscarinic) transmission: effect on electroencephalograms and [3H]quinuclidinyl benzilate in pigeon brain

    International Nuclear Information System (INIS)

    Anand, M.; Agrawal, A.K.; Gopal, K.; Sur, R.N.; Seth, P.K.

    1986-01-01

    Single exposure of endosulfan (5 mg/kg) to pigeons (Columbia livia) caused neuronal hyperexcitability as evidence by spike discharges of 200-500 μV in the electroencephalograms (EEG) from the telencephalon and hyperstriatum, but there was not effect on the ectostriatal area. Cholinergic (muscarinic) receptor binding study using [ 3 H]quinuclidinyl benzilate ([ 3 H]QNB) as a specific ligand indicated that a single exposure to 5 mg/kg of endosulfan caused a significant increase in [ 3 H]QNB binding to the striatal membrane. Behavior study further indicated that a single dose of 200 μg/kg of oxotremorine produced a significant induction in the tremor in endosulfan-pretreated pigeons. The results of this behavioral and biochemical study indicate the involvement of a cholinergic (muscarinic) transmitter system in endosulfan-induced neurotoxicity

  11. Monitoring alert and drowsy states by modeling EEG source nonstationarity

    Science.gov (United States)

    Hsu, Sheng-Hsiou; Jung, Tzyy-Ping

    2017-10-01

    Objective. As a human brain performs various cognitive functions within ever-changing environments, states of the brain characterized by recorded brain activities such as electroencephalogram (EEG) are inevitably nonstationary. The challenges of analyzing the nonstationary EEG signals include finding neurocognitive sources that underlie different brain states and using EEG data to quantitatively assess the state changes. Approach. This study hypothesizes that brain activities under different states, e.g. levels of alertness, can be modeled as distinct compositions of statistically independent sources using independent component analysis (ICA). This study presents a framework to quantitatively assess the EEG source nonstationarity and estimate levels of alertness. The framework was tested against EEG data collected from 10 subjects performing a sustained-attention task in a driving simulator. Main results. Empirical results illustrate that EEG signals under alert versus drowsy states, indexed by reaction speeds to driving challenges, can be characterized by distinct ICA models. By quantifying the goodness-of-fit of each ICA model to the EEG data using the model deviation index (MDI), we found that MDIs were significantly correlated with the reaction speeds (r  =  ‑0.390 with alertness models and r  =  0.449 with drowsiness models) and the opposite correlations indicated that the two models accounted for sources in the alert and drowsy states, respectively. Based on the observed source nonstationarity, this study also proposes an online framework using a subject-specific ICA model trained with an initial (alert) state to track the level of alertness. For classification of alert against drowsy states, the proposed online framework achieved an averaged area-under-curve of 0.745 and compared favorably with a classic power-based approach. Significance. This ICA-based framework provides a new way to study changes of brain states and can be applied to

  12. Characterisation of the Effects of Sleep Deprivation on the Electroencephalogram Using Permutation Lempel–Ziv Complexity, a Non-Linear Analysis Tool

    Directory of Open Access Journals (Sweden)

    Pinar Deniz Tosun

    2017-12-01

    Full Text Available Specific patterns of brain activity during sleep and waking are recorded in the electroencephalogram (EEG. Time-frequency analysis methods have been widely used to analyse the EEG and identified characteristic oscillations for each vigilance state (VS, i.e., wakefulness, rapid-eye movement (REM and non-rapid-eye movement (NREM sleep. However, other aspects such as change of patterns associated with brain dynamics may not be captured unless a non-linear-based analysis method is used. In this pilot study, Permutation Lempel–Ziv complexity (PLZC, a novel symbolic dynamics analysis method, was used to characterise the changes in the EEG in sleep and wakefulness during baseline and recovery from sleep deprivation (SD. The results obtained with PLZC were contrasted with a related non-linear method, Lempel–Ziv complexity (LZC. Both measure the emergence of new patterns. However, LZC is dependent on the absolute amplitude of the EEG, while PLZC is only dependent on the relative amplitude due to symbolisation procedure and thus, more resistant to noise. We showed that PLZC discriminates activated brain states associated with wakefulness and REM sleep, which both displayed higher complexity, compared to NREM sleep. Additionally, significantly lower PLZC values were measured in NREM sleep during the recovery period following SD compared to baseline, suggesting a reduced emergence of new activity patterns in the EEG. These findings were validated using PLZC on surrogate data. By contrast, LZC was merely reflecting changes in the spectral composition of the EEG. Overall, this study implies that PLZC is a robust non-linear complexity measure, which is not dependent on amplitude variations in the signal, and which may be useful to further assess EEG alterations induced by environmental or pharmacological manipulations.

  13. Research on the Characteristics of Alzheimer's Disease Using EEG

    Science.gov (United States)

    Ueda, Taishi; Musha, Toshimitsu; Yagi, Tohru

    In this paper, we proposed a new method for diagnosing Alzheimer's disease (AD) on the basis of electroencephalograms (EEG). The method, which is termed Power Variance Function (PVF) method, indicates the variance of the power at each frequency. By using the proposed method, the power of EEG at each frequency was calculated using Wavelet transform, and the corresponding variances were defined as PVF. After the PVF histogram of 55 healthy people was approximated as a Generalized Extreme Value (GEV) distribution, we evaluated the PVF of 22 patients with AD and 25 patients with mild cognitive impairment (MCI). As a result, the values for all AD and MCI subjects were abnormal. In particular, the PVF in the θ band for MCI patients was abnormally high, and the PVF in the α band for AD patients was low.

  14. EEG and Sonic Platforms to Enhance Mindfulness Meditation

    Directory of Open Access Journals (Sweden)

    Caitilin de Berigny

    2016-09-01

    Full Text Available This paper explores interactive applications that encourage mindfulness through sensors and novel input technology. Research in psychology and neuroscience demonstrating the benefits of mindfulness is initiating a new movement in interactive design. As cutting edge technologies become more accessible they are being employed to research and explore the practice of mindfulness. We examine three interactive installation artworks that promote mindfulness. In order to contextualize the interactive artworks discussed we first examine the historical background of the Electroencephalogram (EEG. We then discuss the physiological processes of meditation and the history behind the clinical practice of mindfulness. We show how artists and designers employ EEG sensors, to record the electrical activity of the brain to visualize mindfulness meditation practices. Lastly, we conclude the paper by discussing the future of the three artworks.

  15. Detecting mental EEG properties using detrended fluctuation analysis.

    Science.gov (United States)

    Jiang, Zhaohui; Ning, Yan; An, Bin; Li, Ao; Feng, Huanqing

    2005-01-01

    Based on detrended fluctuation analysis (DFA), we explore the characteristics of multichannel electroencephalogram (EEG), which is recorded from many subjects performing different mental tasks. The results show that mental EEG exhibits long-range power-law correlations by calculating its scaling exponents (alpha), which can reflect the kinds of mental tasks. The scaling exponent of letter-composing is different from that of multiplication especially at positions C3 and C4, and at positions O1 and O2 the scaling exponent of rotation is also different distinctively from that of multiplication. Detrended fluctuation analysis exhibits its robustness against noises in our works. We could benefit more from the results of this paper in designing mental tasks and selecting brain areas in brain-computer interface systems.

  16. EEG changes during sequences of visual and kinesthetic motor imagery.

    Science.gov (United States)

    Stecklow, Marcus Vinicius; Infantosi, Antonio Fernando Catelli; Cagy, Maurício

    2010-08-01

    The evoked cerebral electric response when sequences of complex motor imagery (MI) task are executed several times is still unclear. This work aims at investigating the existence of habituation in the cortical response, more specifically in the alpha band peak of parietal and occipital areas (10-20 international system electroencephalogram, EEG, protocol). The EEG signals were acquired during sequences of MI of volleyball spike movement in kinesthetic and visual modalities and also at control condition. Thirty right-handed male subjects (18 to 40 years) were assigned to either an 'athlete' or a 'non-athlete' group, both containing 15 volunteers. Paired Wilcoxon tests (with alpha=0.05) indicates that sequential MI of complex tasks promotes cortical changes, mainly in the power vicinity of the alpha peak. This finding is more pronounced along the initial trials and also for the athletes during the modality of kinesthetic motor imagery.

  17. Analysis of music-brain interaction with simultaneous measurement of regional cerebral blood flow and electroencephalogram beta rhythm in human subjects.

    Science.gov (United States)

    Nakamura, S; Sadato, N; Oohashi, T; Nishina, E; Fuwamoto, Y; Yonekura, Y

    1999-11-19

    To elucidate the neural substrates of the receptive aspect of music, we measured regional cerebral blood flow (rCBF) with positron emission tomography (PET) and simultaneously recorded the electroencephalogram (EEG) in eight normal volunteers. Compared with the rest condition, listening to music caused a significant increase in EEG beta power spectrum (13-30 Hz) averaged over the posterior two third of the scalp. The averaged beta power spectrum was positively correlated with rCBF in the premotor cortex and adjacent prefrontal cortices bilaterally, the anterior portion of the precuneus and the anterior cingulate cortex in both the rest and the music conditions. Listening to music newly recruited the posterior portion of the precuneus bilaterally. This may reflect the interaction of the music with the cognitive processes, such as music-evoked memory recall or visual imagery.

  18. EEG-based "serious" games and monitoring tools for pain management.

    Science.gov (United States)

    Sourina, Olga; Wang, Qiang; Nguyen, Minh Khoa

    2011-01-01

    EEG-based "serious games" for medical applications attracted recently more attention from the research community and industry as wireless EEG reading devices became easily available on the market. EEG-based technology has been applied in anesthesiology, psychology, etc. In this paper, we proposed and developed EEG-based "serious" games and doctor's monitoring tools that could be used for pain management. As EEG signal is considered to have a fractal nature, we proposed and develop a novel spatio-temporal fractal based algorithm for brain state quantification. The algorithm is implemented with blobby visualization tools for patient monitoring and in EEG-based "serious" games. Such games could be used by patient even at home convenience for pain management as an alternative to traditional drug treatment.

  19. Wireless Internet

    NARCIS (Netherlands)

    el Zarki, M.; Heijenk, Geert; Lee, Kenneth S.; Bidgoli, H.

    This chapter addresses the topic of wireless Internet, the extension of the wireline Internet architecture to the wireless domain. As such the chapter introduces the reader to the dominant characteristics of the Internet, from its structure to the protocols that control the forwarding of data and

  20. Corticotropin-releasing hormone induces depression-like changes of sleep electroencephalogram in healthy women.

    Science.gov (United States)

    Schüssler, P; Kluge, M; Gamringer, W; Wetter, T C; Yassouridis, A; Uhr, M; Rupprecht, R; Steiger, A

    2016-12-01

    We reported previously that repetitive intravenous injections of corticotropin-releasing hormone (CRH) around sleep onset prompt depression-like changes in certain sleep and endocrine activity parameters (e.g. decrease of slow-wave sleep during the second half of the night, blunted growth hormone peak, elevated cortisol concentration during the first half of the night). Furthermore a sexual dimorphism of the sleep-endocrine effects of the hormones growth hormone-releasing hormone and ghrelin was observed. In the present placebo-controlled study we investigated the effect of pulsatile administration of 4×50μg CRH on sleep electroencephalogram (EEG) and nocturnal cortisol and GH concentration in young healthy women. After CRH compared to placebo, intermittent wakefulness increased during the total night and the sleep efficiency index decreased. During the first third of the night, REM sleep and stage 2 sleep increased and sleep stage 3 decreased. Cortisol concentration was elevated throughout the night and during the first and second third of the night. GH secretion remained unchanged. Our data suggest that after CRH some sleep and endocrine activity parameters show also depression-like changes in healthy women. These changes are more distinct in women than in men. Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. EFFICACY OF ACTIVATION PROCEDURES TO ILLUSTRATE EEG CHANGES IN EPILEPSY

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    Rimpy Bhuyan

    2017-04-01

    Full Text Available BACKGROUND EEG or Electroencephalogram, which is the most important diagnostic procedure to evaluate Epilepsy patients, may sometimes fall short of accurate sensitivity and may require few Activation Procedures such as ‘Hyperventilation’ and ‘Sleep’ to bring out the active changes of an Epileptic brain. The present study was done with the aim of knowing the efficacy of such Activation Procedures like ‘Hyperventilation’ and ‘Sleep’ in illustrating the EEG wave pattern changes of an Epileptic brain during the interictal period. MATERIALS AND METHODS The present study was done in the Department of Physiology in association with the Department of Neurology, Assam Medical College & Hospital, Dibrugarh, Assam from June 2014 to May 2015. ‘113’ clinically diagnosed cases of Epilepsy were studied and analysed through Electroencephalogram using the internationally accepted 10-20 electrode placement method. Hyperventilation was used in 28 Epilepsy cases and Sleep was used in 14 Epilepsy cases. History & Physical examination findings were recorded in a Proforma. Chi-square analysis was done through GraphPad Prism 6 software to assess the significance of the activation procedures used. RESULTS Our study found that EEG of 42 cases out of the total 113 cases required Activation Procedures to elicit the wave pattern changes of the Epileptic brain. Hyperventilation was helpful in adult age group and sleep was useful in children age group. Hyperventilation had overall 53.57% sensitivity in detecting Epilepsy, and Sleep had 64.29% sensitivity in detecting Epilepsy. Hyperventilation was specifically helpful to elicit absence seizures where it had 75% sensitivity. CONCLUSION The sensitivity of EEG in detecting Epilepsy can thus be increased by using activation procedures like sleep & Hyperventilation to ensure that no epilepsy cases are missed out in diagnosis & treatment.

  2. To what extent can dry and water-based EEG electrodes replace conductive gel ones?: A Steady State Visual Evoked Potential Brain-Computer Interface Case Study

    NARCIS (Netherlands)

    Mihajlovic, V.; Garcia Molina, G.; Peuscher, J

    2011-01-01

    Recent technological advances in the field of skin electrodes and on-body sensors indicate a possibility of having an alternative to the traditionally used conductive gel electrodes for measuring electrical signals of the brain (electroencephalogram, EEG). This paper evaluates whether water-based

  3. Nonlinear analysis of EEG for epileptic seizures

    Energy Technology Data Exchange (ETDEWEB)

    Hively, L.M.; Clapp, N.E.; Daw, C.S.; Lawkins, W.F. [Oak Ridge National Lab., TN (United States); Eisenstadt, M.L. [Knoxville Neurology Clinic, St. Mary`s Medical Center, Knoxville, TN (United States)

    1995-04-01

    We apply chaotic time series analysis (CTSA) to human electroencephalogram (EEG) data. Three epoches were examined: epileptic seizure, non-seizure, and transition from non-seizure to seizure. The CTSA tools were applied to four forms of these data: raw EEG data (e-data), artifact data (f-data) via application of a quadratic zero-phase filter of the raw data, artifact-filtered data (g- data) and that was the residual after subtracting f-data from e-data, and a low-pass-filtered version (h-data) of g-data. Two different seizures were analyzed for the same patient. Several nonlinear measures uniquely indicate an epileptic seizure in both cases, including an abrupt decrease in the time per wave cycle in f-data, an abrupt increase in the Kolmogorov entropy and in the correlation dimension for e-h data, and an abrupt increase in the correlation dimension for e-h data. The transition from normal to seizure state also is characterized by distinctly different trends in the nonlinear measures for each seizure and may be potential seizure predictors for this patient. Surrogate analysis of e-data shows that statistically significant nonlinear structure is present during the non-seizure, transition , and seizure epoches.

  4. Epileptiform electroencephalogram abnormality in children with congenital sensorineural hearing loss.

    Science.gov (United States)

    El-Badry, Mohamed Mohamed; Hamdy, Nermin Aly; Sobhy, Sayed; Gamal, Reham

    2014-04-01

    This work was designed to study electroencephalogram findings in children with congenital sensorineural hearing loss and correlate these findings with the SNHL parameters as duration, etiology, severity, and type. Ninety children with bilateral congenital sensorineural hearing loss served as the study group. They were free from any neurological disorders or symptoms that are commonly associated with abnormal electroencephalogram as convulsions or loss of consciousness. Twenty children having normal hearing with no history of otological or neurological disorders served as the control group. All children participating in the study were subjected to full medical and audiological history, otological examination, neurological examination, audiological evaluation and electroencephalogram recording. Mean age of the children in the control group was 3.56 ± 2.1 years and mean age of the children in the study group was 3.8 ± 2.2 years. While none of the control children had abnormal electroencephalogram, 38 (42.2%) of children with congenital SNHL had epileptiform electroencephalogram abnormality. The epileptiform abnormality was generalized in 14 children (36.8%), focal temporal in 17 children (44.7%) and focal other than temporal in 7 children (18.4%). According to the hemispheric side affected, the abnormality was right in 14 children (36.8%), left in 10 children (26.3%) and bilateral in 14 children (36.8%). No statistically significant predominance of specific site or side of the epileptiform abnormality was found. Similarly, no statistical significant prevalent of the epileptiform abnormality was found in relation to the age or sex of children, duration of hearing loss or etiology of hearing loss (i.e., genetic vs. neonatal insults). On the other hand, the epileptiform abnormality was statistically prevalent in children with moderate degree of hearing loss, and in children with auditory neuropathy spectrum disorder. The epileptiform electroencephalogram abnormality is

  5. Using a Wireless Electroencephalography Device to Evaluate E-Health and E-Learning Interventions.

    Science.gov (United States)

    Mailhot, Tanya; Lavoie, Patrick; Maheu-Cadotte, Marc-André; Fontaine, Guillaume; Cournoyer, Alexis; Côté, José; Dupuis, France; Karsenti, Thierry; Cossette, Sylvie

    Measuring engagement and other reactions of patients and health professionals to e-health and e-learning interventions remains a challenge for researchers. The aim of this pilot study was to assess the feasibility and acceptability of using a wireless electroencephalography (EEG) device to measure affective (anxiety, enjoyment, relaxation) and cognitive (attention, engagement, interest) reactions of patients and healthcare professionals during e-health or e-learning interventions. Using a wireless EEG device, we measured patient (n = 6) and health professional (n = 7) reactions during a 10-minute session of an e-health or e-learning intervention. The following feasibility and acceptability indicators were assessed and compared for patients and healthcare professionals: number of eligible participants who consented to participate, reasons for refusal, time to install and calibrate the wireless EEG device, number of participants who completed the full 10-minute sessions, participant comfort when wearing the device, signal quality, and number of observations obtained for each reaction. The wireless EEG readings were compared to participant self-rating of their reactions. We obtained at least 75% of possible observations for attention, engagement, enjoyment, and interest. EEG scores were similar to self-reported scores, but they varied throughout the sessions, which gave information on participants' real-time reactions to the e-health/e-learning interventions. Results on the other indicators support the feasibility and acceptability of the wireless EEG device for both patients and professionals. Using the wireless EEG device was feasible and acceptable. Future studies must examine its use in other contexts of care and explore which components of the interventions affected participant reactions by combining wireless EEG and eye tracking.

  6. Investigating reading comprehension through EEG

    Directory of Open Access Journals (Sweden)

    Luciane Baretta

    2012-12-01

    Full Text Available http://dx.doi.org/10.5007/2175-8026.2012n63p69   Experimental studies point that different factors can influence reading comprehension, such as the topic, text type, reading task, and others. The advances in technologies for the past decades have provided researchers with several possibilities to investigate what goes on in one’s brain since their eyes meet the page until comprehension is achieved. Since the mid-80’s, numerous studies have been conducted with the use of the electroencephalogram (EEG to investigate the process of reading, through the analysis of different components – n400, n100 or n1, P2, among others. These components reveal, for example, how the brain integrates the meaning of a specific word in the semantic context of a given sentence.  based on previous studies, which demonstrate that different types of words affect cognitive load, this paper aims at investigating how the brain processes function and content words inserted in expository and narrative texts with suitable / unsuitable conclusions. results showed that the type of text and word influence the cognitive load in different scalp areas (midline, right and left hemispheres. The  n1s were more pronounced to the content words inserted in narrative texts and to the function words inserted in the expository type of texts, corroborating former studies.

  7. Reduction of the Dimensionality of the EEG Channels during Scoliosis Correction Surgeries Using a Wavelet Decomposition Technique

    Directory of Open Access Journals (Sweden)

    Mahmoud I. Al-Kadi

    2014-07-01

    Full Text Available This paper presents a comparison between the electroencephalogram (EEG channels during scoliosis correction surgeries. Surgeons use many hand tools and electronic devices that directly affect the EEG channels. These noises do not affect the EEG channels uniformly. This research provides a complete system to find the least affected channel by the noise. The presented system consists of five stages: filtering, wavelet decomposing (Level 4, processing the signal bands using four different criteria (mean, energy, entropy and standard deviation, finding the useful channel according to the criteria’s value and, finally, generating a combinational signal from Channels 1 and 2. Experimentally, two channels of EEG data were recorded from six patients who underwent scoliosis correction surgeries in the Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM (the Medical center of National University of Malaysia. The combinational signal was tested by power spectral density, cross-correlation function and wavelet coherence. The experimental results show that the system-outputted EEG signals are neatly switched without any substantial changes in the consistency of EEG components. This paper provides an efficient procedure for analyzing EEG signals in order to avoid averaging the channels that lead to redistribution of the noise on both channels, reducing the dimensionality of the EEG features and preparing the best EEG stream for the classification and monitoring stage.

  8. Reduction of the dimensionality of the EEG channels during scoliosis correction surgeries using a wavelet decomposition technique.

    Science.gov (United States)

    Al-Kadi, Mahmoud I; Reaz, Mamun Bin Ibne; Ali, Mohd Alauddin Mohd; Liu, Chian Yong

    2014-07-21

    This paper presents a comparison between the electroencephalogram (EEG) channels during scoliosis correction surgeries. Surgeons use many hand tools and electronic devices that directly affect the EEG channels. These noises do not affect the EEG channels uniformly. This research provides a complete system to find the least affected channel by the noise. The presented system consists of five stages: filtering, wavelet decomposing (Level 4), processing the signal bands using four different criteria (mean, energy, entropy and standard deviation), finding the useful channel according to the criteria's value and, finally, generating a combinational signal from Channels 1 and 2. Experimentally, two channels of EEG data were recorded from six patients who underwent scoliosis correction surgeries in the Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM) (the Medical center of National University of Malaysia). The combinational signal was tested by power spectral density, cross-correlation function and wavelet coherence. The experimental results show that the system-outputted EEG signals are neatly switched without any substantial changes in the consistency of EEG components. This paper provides an efficient procedure for analyzing EEG signals in order to avoid averaging the channels that lead to redistribution of the noise on both channels, reducing the dimensionality of the EEG features and preparing the best EEG stream for the classification and monitoring stage.

  9. Comparison of Quantitative Characteristics of Early Post-resuscitation EEG Between Asphyxial and Ventricular Fibrillation Cardiac Arrest in Rats.

    Science.gov (United States)

    Chen, Bihua; Chen, Gang; Dai, Chenxi; Wang, Pei; Zhang, Lei; Huang, Yuanyuan; Li, Yongqin

    2017-05-08

    Quantitative electroencephalogram (EEG) analysis has shown promising results in studying brain injury and functional recovery after cardiac arrest (CA). However, whether the quantitative characteristics of EEG, as potential indicators of neurological prognosis, are influenced by CA causes is unknown. The purpose of this study was designed to compare the quantitative characteristics of early post-resuscitation EEG between asphyxial CA (ACA) and ventricular fibrillation CA (VFCA) in rats. Thirty-two Sprague-Dawley rats of both sexes were randomized into either ACA or VFCA group. Cardiopulmonary resuscitation was initiated after 5-min untreated CA. Characteristics of early post-resuscitation EEG were compared, and the relationships between quantitative EEG features and neurological outcomes were investigated. Compared with VFCA, serum level of S100B, neurological deficit score and brain histopathologic damage score were dramatically higher in the ACA group. Quantitative measures of EEG, including onset time of EEG burst, time to normal trace, burst suppression ratio, and information quantity, were significantly lower for CA caused by asphyxia and correlated with the 96-h neurological outcome and survival. Characteristics of earlier post-resuscitation EEG differed between cardiac and respiratory causes. Quantitative measures of EEG not only predicted neurological outcome and survival, but also have the potential to stratify CA with different causes.

  10. [Wavelet entropy analysis of spontaneous EEG signals in Alzheimer's disease].

    Science.gov (United States)

    Zhang, Meiyun; Zhang, Benshu; Chen, Ying

    2014-08-01

    Wavelet entropy is a quantitative index to describe the complexity of signals. Continuous wavelet transform method was employed to analyze the spontaneous electroencephalogram (EEG) signals of mild, moderate and severe Alzheimer's disease (AD) patients and normal elderly control people in this study. Wavelet power spectrums of EEG signals were calculated based on wavelet coefficients. Wavelet entropies of mild, moderate and severe AD patients were compared with those of normal controls. The correlation analysis between wavelet entropy and MMSE score was carried out. There existed significant difference on wavelet entropy among mild, moderate, severe AD patients and normal controls (Pwavelet entropy for mild, moderate, severe AD patients was significantly lower than that for normal controls, which was related to the narrow distribution of their wavelet power spectrums. The statistical difference was significant (Pwavelet entropy of EEG and the MMSE score were significantly correlated (r= 0. 601-0. 799, PWavelet entropy is a quantitative indicator describing the complexity of EEG signals. Wavelet entropy is likely to be an electrophysiological index for AD diagnosis and severity assessment.

  11. Fusion of Facial Expressions and EEG for Multimodal Emotion Recognition

    Directory of Open Access Journals (Sweden)

    Yongrui Huang

    2017-01-01

    Full Text Available This paper proposes two multimodal fusion methods between brain and peripheral signals for emotion recognition. The input signals are electroencephalogram and facial expression. The stimuli are based on a subset of movie clips that correspond to four specific areas of valance-arousal emotional space (happiness, neutral, sadness, and fear. For facial expression detection, four basic emotion states (happiness, neutral, sadness, and fear are detected by a neural network classifier. For EEG detection, four basic emotion states and three emotion intensity levels (strong, ordinary, and weak are detected by two support vector machines (SVM classifiers, respectively. Emotion recognition is based on two decision-level fusion methods of both EEG and facial expression detections by using a sum rule or a production rule. Twenty healthy subjects attended two experiments. The results show that the accuracies of two multimodal fusion detections are 81.25% and 82.75%, respectively, which are both higher than that of facial expression (74.38% or EEG detection (66.88%. The combination of facial expressions and EEG information for emotion recognition compensates for their defects as single information sources.

  12. Electroencephalogram and magnetic resonance imaging comparison as a predicting factor for neurodevelopmental outcome in hypoxic ischemic encephalopathy infant treated with hypothermia

    Directory of Open Access Journals (Sweden)

    Francesca Del Balzo

    2014-10-01

    Full Text Available Hypoxic-ischemic encephalopathy (HIE is an important cause of acute neurological damage in newborns at (or near term. Several trials in recent years have shown that moderate hypothermia by total body cooling or selective head is an effective intervention to reduce mortality and major disability in infants survived a perinatal hypoxic-ischemic attack. Follow-up in these patients is very important to establish neurodevelopmental outcome, and specific markers can lead us to detect predicting sign for good or poor outcome. We reported a few cases of newborn with HIE treated with hypothermia, in whom the comparison between electroencephalogram (EEG and magnetic resonance imaging (MRI represents the first marker for neurodevelopment outcome prediction. The continuous EEG monitoring showed a depressed EEG activity with diffuse burst depression in 7 patients. No epileptic abnormalities were registered. In 10 out of 20 patients no abnormalities of the background activity and no epileptic abnormalities were observed. We found that a depressed EEG activity during the first 72 h of life and a diffused alteration of basal ganglia at MRI were correlated with a poor neurodevelopmental outcome at 18 months of follow-up.

  13. Effects of early intervention on EEG power and coherence in previously institutionalized children in Romania.

    Science.gov (United States)

    Marshall, Peter J; Reeb, Bethany C; Fox, Nathan A; Nelson, Charles A; Zeanah, Charles H

    2008-01-01

    Two groups of Romanian children were compared on spectral power and coherence in the electroencephalogram (EEG) in early childhood. One group consisted of previously institutionalized children who had been randomly assigned to a foster care intervention at a mean age of 23 months. The second group had been randomized to remain in institutional care. Because of a policy of noninterference, a number of these children also experienced placement into alternative family care environments. There were minimal group differences between the foster care and institutionalized groups in EEG power and coherence across all measured frequency bands at 42 months of age. However, age at foster care placement within the foster care group was correlated with certain measures of EEG power and coherence. Earlier age at foster care placement was associated with increased alpha power and decreased short-distance EEG coherence. Further analyses separating age at placement from duration of intervention suggest that this effect may be more robust for EEG coherence than EEG band power. Supplementary analyses examined whether the EEG measures mediated changes in intellectual abilities within the foster care children, but no clear evidence of mediation was observed.

  14. Epileptogenic developmental venous anomaly: insights from simultaneous EEG/fMRI.

    Science.gov (United States)

    Scheidegger, Olivier; Wiest, Roland; Jann, Kay; König, Thomas; Meyer, Klaus; Hauf, Martinus

    2013-04-01

    Developmental venous anomalies (DVAs) are associated with epileptic seizures; however, the role of DVA in the epileptogenesis is still not established. Simultaneous interictal electroencephalogram/functional magnetic resonance imaging (EEG/fMRI) recordings provide supplementary information to electroclinical data about the epileptic generators, and thus aid in the differentiation of clinically equivocal epilepsy syndromes. The main objective of our study was to characterize the epileptic network in a patient with DVA and epilepsy by simultaneous EEG/fMRI recordings. A 17-year-old woman with recently emerging generalized tonic-clonic seizures, and atypical generalized discharges, was investigated using simultaneous EEG/fMRI at the university hospital. Previous high-resolution MRI showed no structural abnormalities, except a DVA in the right frontal operculum. Interictal EEG recordings showed atypical generalized discharges, corresponding to positive focal blood oxygen level dependent (BOLD) correlates in the right frontal operculum, a region drained by the DVA. Additionally, widespread cortical bilateral negative BOLD correlates in the frontal and parietal lobes were delineated, resembling a generalized epileptic network. The EEG/fMRI recordings support a right frontal lobe epilepsy, originating in the vicinity of the DVA, propagating rapidly to both frontal and parietal lobes, as expressed on the scalp EEG by secondary bilateral synchrony. The DVA may be causative of focal epilepsies in cases where no concomitant epileptogenic lesions can be detected. Advanced imaging techniques, such as simultaneous EEG/fMRI, may thus aid in the differentiation of clinically equivocal epilepsy syndromes.

  15. EEG in non-clinical drug safety assessments: Current and emerging considerations.

    Science.gov (United States)

    Authier, Simon; Delatte, Marcus S; Kallman, Mary-Jeanne; Stevens, Joanne; Markgraf, Carrie

    2016-01-01

    Electroencephalogram (EEG) data in nonclinical species can play a critical role in the successful evaluation of a compound during drug development, particularly in the evaluation of seizure potential and for monitoring changes in sleep. Yet, while non-invasive electrocardiogram (ECG) monitoring is commonly included in preclinical safety studies, pre-dose or post-dose EEG assessments are not. Industry practices as they relate to preclinical seizure liability and sleep assessments are not well characterized and the extent of preclinical EEG testing varies between organizations. In the current paper, we discuss the various aspects of preclinical EEG to characterize drug-induced seizure risk and sleep disturbances, as well as describe the use of these data in a regulatory context. An overview of EEG technology-its correct application and its limitations, as well as best practices for setting up the animal models is presented. Sleep and seizure detection are discussed in detail. A regulatory perspective on the use of EEG data is provided and, tying together the previous topics is a discussion of the translational aspects of EEG. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  16. Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm

    Directory of Open Access Journals (Sweden)

    E. Parvinnia

    2014-01-01

    Full Text Available Electroencephalogram (EEG signals are often used to diagnose diseases such as seizure, alzheimer, and schizophrenia. One main problem with the recorded EEG samples is that they are not equally reliable due to the artifacts at the time of recording. EEG signal classification algorithms should have a mechanism to handle this issue. It seems that using adaptive classifiers can be useful for the biological signals such as EEG. In this paper, a general adaptive method named weighted distance nearest neighbor (WDNN is applied for EEG signal classification to tackle this problem. This classification algorithm assigns a weight to each training sample to control its influence in classifying test samples. The weights of training samples are used to find the nearest neighbor of an input query pattern. To assess the performance of this scheme, EEG signals of thirteen schizophrenic patients and eighteen normal subjects are analyzed for the classification of these two groups. Several features including, fractal dimension, band power and autoregressive (AR model are extracted from EEG signals. The classification results are evaluated using Leave one (subject out cross validation for reliable estimation. The results indicate that combination of WDNN and selected features can significantly outperform the basic nearest-neighbor and the other methods proposed in the past for the classification of these two groups. Therefore, this method can be a complementary tool for specialists to distinguish schizophrenia disorder.

  17. Classification of EEG Signals Based on Pattern Recognition Approach.

    Science.gov (United States)

    Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

    2017-01-01

    Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a "pattern recognition" approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90-7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11-89.63% and 91.60-81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy.

  18. Data mining EEG signals in depression for their diagnostic value.

    Science.gov (United States)

    Mohammadi, Mahdi; Al-Azab, Fadwa; Raahemi, Bijan; Richards, Gregory; Jaworska, Natalia; Smith, Dylan; de la Salle, Sara; Blier, Pierre; Knott, Verner

    2015-12-23

    Quantitative electroencephalogram (EEG) is one neuroimaging technique that has been shown to differentiate patients with major depressive disorder (MDD) and non-depressed healthy volunteers (HV) at the group-level, but its diagnostic potential for detecting differences at the individual level has yet to be realized. Quantitative EEGs produce complex data sets derived from digitally analyzed electrical activity at different frequency bands, at multiple electrode locations, and under different vigilance (eyes open vs. closed) states, resulting in potential feature patterns which may be diagnostically useful, but detectable only with advanced mathematical models. This paper uses a data mining methodology for classifying EEGs of 53 MDD patients and 43 HVs. This included: (a) pre-processing the data, including cleaning and normalization, applying Linear Discriminant Analysis (LDA) to map the features into a new feature space; and applying Genetic Algorithm (GA) to identify the most significant features; (b) building predictive models using the Decision Tree (DT) algorithm to discover rules and hidden patterns based on the reduced and mapped features; and (c) evaluating the models based on the accuracy and false positive values on the EEG data of MDD and HV participants. Two categories of experiments were performed. The first experiment analyzed each frequency band individually, while the second experiment analyzed the bands together. Application of LDA and GA markedly reduced the total number of utilized features by ≥ 50 % and, with all frequency bands analyzed together, the model showed average classification accuracy (MDD vs. HV) of 80 %. The best results from model testing with additional test EEG recordings from 9 MDD patients and 35 HV individuals demonstrated an accuracy of 80 % and showed an average sensitivity of 70 %, a specificity of 76 %, and a positive (PPV) and negative predictive value (NPV) of 74 and 75 %, respectively. These initial findings

  19. Classification of EEG Signals Based on Pattern Recognition Approach

    Directory of Open Access Journals (Sweden)

    Hafeez Ullah Amin

    2017-11-01

    Full Text Available Feature extraction is an important step in the process of electroencephalogram (EEG signal classification. The authors propose a “pattern recognition” approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR and principal component analysis (PCA. A high density EEG dataset validated the proposed method (128-channels by identifying two classifications: (1 EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM test; (2 EEG signals recorded during a baseline task (eyes open. Classifiers such as, K-nearest neighbors (KNN, Support Vector Machine (SVM, Multi-layer Perceptron (MLP, and Naïve Bayes (NB were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5 of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5 deriving from the sub-band range (3.90–7.81 Hz. Accuracy rates for MLP and NB classifiers were comparable at 97.11–89.63% and 91.60–81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy.

  20. Nanoparticle formulation improves the anticonvulsant effect of clonazepam on the pentylenetetrazole-induced seizures: behavior and electroencephalogram.

    Science.gov (United States)

    Leyva-Gómez, Gerardo; González-Trujano, María Eva; López-Ruiz, Edith; Couraud, Pierre-Olivier; Wekslerg, Babette; Romero, Ignacio; Miller, Florence; Delie, Florence; Allémann, Eric; Quintanar-Guerrero, David

    2014-08-01

    To document the efficacy of clonazepam (CLZ) either free as a solution or loaded in solid lipid nanoparticles (CLZ-SLN) or mixed micelles (CLZ-MM), the in vitro blood-brain barrier permeability of the formulations was determined. Behavior and/or electroencephalograms (EEGs) of rodents receiving treatments were also studied. The in vitro permeability of CLZ increased when associated with SLN, but decreased in the case of MM. The occurrence of the pentylenetetrazole (PTZ)-induced seizures in mice was significantly prevented by CLZ, even when exposed a lower dose of CLZ-SLN after administration by the oral route. The behavioral severity and EEGs showing the PTZ-induced paroxystic activity in rats diminished significantly in the presence of CLZ alone (0.3 mg/kg), and were almost totally prevented in the rats treated with CLZ-SLN (equivalent to 0.3 mg/kg). The frequency, duration, and spreading of the spikes-wave of rats treated with CLZ-SLN decreased significantly as compared with CLZ alone, CLZ-MM, or the vehicle. These results show an in vitro-in vivo correlation in the enhanced blood-brain barrier permeability of SLN formulation, and a contribution of MM to the carrier effect of drugs toward the bloodstream and brain, where this pharmaceutical formulation of CLZ-SLN improves the anticonvulsant effect of this benzodiazepine, thus offering additional advantages after oral administration. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

  1. Bluetooth Communication Interface for EEG Signal Recording in Hyperbaric Chambers.

    Science.gov (United States)

    Pastena, Lucio; Formaggio, Emanuela; Faralli, Fabio; Melucci, Massimo; Rossi, Marco; Gagliardi, Riccardo; Ricciardi, Lucio; Storti, Silvia F

    2015-07-01

    Recording biological signals inside a hyperbaric chamber poses technical challenges (the steel walls enclosing it greatly attenuate or completely block the signals as in a Faraday cage), practical (lengthy cables creating eddy currents), and safety (sparks hazard from power supply to the electronic apparatus inside the chamber) which can be overcome with new wireless technologies. In this technical report we present the design and implementation of a Bluetooth system for electroencephalographic (EEG) recording inside a hyperbaric chamber and describe the feasibility of EEG signal transmission outside the chamber. Differently from older systems, this technology allows the online recording of amplified signals, without interference from eddy currents. In an application of this technology, we measured EEG activity in professional divers under three experimental conditions in a hyperbaric chamber to determine how oxygen, assumed at a constant hyperbaric pressure of 2.8 ATA , affects the bioelectrical activity. The EEG spectral power estimated by fast Fourier transform and the cortical sources of the EEG rhythms estimated by low-resolution brain electromagnetic analysis were analyzed in three different EEG acquisitions: breathing air at sea level; breathing oxygen at a simulated depth of 18 msw, and breathing air at sea level after decompression.

  2. Increased frontal electroencephalogram theta amplitude in patients with anorexia nervosa compared to healthy controls

    Directory of Open Access Journals (Sweden)

    Hestad KA

    2016-09-01

    Full Text Available Knut A Hestad,1–3 Siri Weider,3,4 Kristian Bernhard Nilsen,5–7 Marit Sæbø Indredavik,8,9 Trond Sand7,10 1Department of Research, Innlandet Hospital Trust, Brumunddal, Norway; 2Department of Public Health, Hedmark University of Applied Sciences, Elverum, Norway; 3Department of Psychology, Faculty of Social Sciences and Technology Management, Norwegian University of Science and Technology (NTNU, Trondheim, Norway; 4Department of Psychiatry, Specialised Unit for Eating Disorder Patients, Levanger Hospital, Health Trust Nord-Trøndelag, Levanger, Norway; 5Department of Neuroscience, Norwegian University of Science and Technology (NTNU, Trondheim, Norway; 6Department of Work Psychology and Physiology, National Institute of Occupational Health, Oslo, Norway; 7Department of Neurology, Section for Clinical Neurophysiology, Oslo University Hospital, Ullevål, Oslo, Norway; 8Regional Centre for Child and Youth Mental Health and Child Welfare, Faculty of Medicine, Norwegian University of Science and Technology (NTNU, Trondheim, Norway; 9Department of Child and Adolescent Psychiatry, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway; 10Department of Neurology and Clinical Neurophysiology, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway Objective: To conduct a blind study of quantitative electroencephalogram-band amplitudes in patients with anorexia nervosa (AN and healthy controls.Methods: Twenty-one patients with AN and 24 controls were examined with eyes-closed 16-channel electroencephalogram. Main variables were absolute alpha, theta, and delta amplitudes in frontal, temporal, and posterior regions.Results: There were no significant differences between the AN patients and controls regarding absolute regional band amplitudes in µV. Borderline significance was found for anterior theta (P=0.051. Significantly increased left and right frontal electrode theta amplitude was found in AN patients (F3, P=0.014; F4, P

  3. Sleep/Wake Physiology and Quantitative Electroencephalogram Analysis of the Neuroligin-3 Knockout Rat Model of Autism Spectrum Disorder.

    Science.gov (United States)

    Thomas, Alexia M; Schwartz, Michael D; Saxe, Michael D; Kilduff, Thomas S

    2017-10-01

    Neuroligin-3 (NLGN3) is one of the many genes associated with autism spectrum disorder (ASD). Sleep dysfunction is highly prevalent in ASD, but has not been rigorously examined in ASD models. Here, we evaluated sleep/wake physiology and behavioral phenotypes of rats with genetic ablation of Nlgn3. Male Nlgn3 knockout (KO) and wild-type (WT) rats were assessed using a test battery for ASD-related behaviors and also implanted with telemeters to record the electroencephalogram (EEG), electromyogram, body temperature, and locomotor activity. 24-h EEG recordings were analyzed for sleep/wake states and spectral composition. Nlgn3 KO rats were hyperactive, exhibited excessive chewing behavior, and had impaired prepulse inhibition to an auditory startle stimulus. KO rats also spent less time in non-rapid eye movement (NREM) sleep, more time in rapid eye movement (REM) sleep, exhibited elevated theta power (4-9 Hz) during wakefulness and REM, and elevated delta power (0.5-4 Hz) during NREM. Beta (12-30 Hz) power and gamma (30-50 Hz) power were suppressed across all vigilance states. The sleep disruptions in Nlgn3 KO rats are consistent with observations of sleep disturbances in ASD patients. The EEG provides objective measures of brain function to complement rodent behavioral analyses and therefore may be a useful tool to study ASD. © Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

  4. Single-Trial EEG-EMG coherence analysis reveals muscle fatigue-related progressive alterations in corticomuscular coupling.

    Science.gov (United States)

    Siemionow, Vlodek; Sahgal, Vinod; Yue, Guang H

    2010-04-01

    Voluntary muscle fatigue is a progressive process. A recent study demonstrated muscle fatigue-induced weakening of functional corticomuscular coupling measured by coherence between the brain [electroencephalogram (EEG)] and muscle [electromyogram (EMG)] signals after a relatively long-duration muscle contraction. Comparing the EEG-EMG coherence before versus after fatigue or between data of two long-duration time blocks is not adequate to reveal the dynamic nature of the fatigue process. The purpose of this study was to address this issue by quantifying single-trial EEG-EMG coherence and EEG, EMG power based on wavelet transform. Eight healthy subjects performed 200 maximal intermittent handgrip contractions in a single session with handgrip force, EEG and EMG signals acquired simultaneously. The EEG and EMG data during each 2-s handgrip was subjected to single trial EEG-EMG wavelet energy spectrum and coherence computation. The EEG-EMG coherence and energy spectrum at beta (15 ~ 35 Hz) and gamma (35-50 Hz) frequency bands were statistically analyzed in 2-block (75 trials per block), 5-block (30 trials/block), and 10-block (15 trials/block) data settings. The energy of both the EEG and EMG signals decreased significantly with muscle fatigue. The EEG-EMG coherence had a significant reduction for the 2-block comparison. More detailed dynamical changing and inter-subject variation of the EEG-EMG coherence and energy were revealed by 5- and 10-block comparisons. These results show feasibility of wavelet transform-based measurement of the EEG-EMG coherence and corresponding energy based on single-trial data, which provides extra information to demonstrate a time course of dynamic adaptations of the functional corticomuscular coupling, as well as brain and muscle signals during muscle fatigue.

  5. Correlation of Tc-99m HMPAO SPECT with surface EEG, MR, and CT for noninvasive localization of seizure foci

    International Nuclear Information System (INIS)

    Engelstad, B.L.; Laxer, K.D.; Dickson, H.S.; Cooper, K.E.; Huberty, J.P.; White, D.L.

    1987-01-01

    Some patients with refractory seizure disorders are candidates for surgical management. Correct preoperative lateralization is essential. Of 19 patients with seizure disorders who underwent Tc-99m HMPAO SPECT, 14 were considered to have ultimately had definitive localization by other means: consistently abnormal surface electroencephalogram (EEG), subdural or deep electrode EEG, EEG response to resection, abnormal histopatholgy, or grossly abnormal MR image. Lateralization with SPECT was (1) focal or regional hypoperfusion (11 patients) or (2) discrete focal hyperperfusion (one patient). Correct lateralization was obtained in ten of 14 with SPECT, nine of 14 with surface EEG, seven of 12 with MR, and one of eight with CT. Preoperative evaluation of patients with medically refractory seizures can be aided by Tc-99m HMPAO SPECT

  6. Electroretinogram and visual evoked response in a form of `neuronal lipidosis' with diagnostic EEG features

    Science.gov (United States)

    Harden, Ann; Pampiglione, G.; Picton-Robinson, N.

    1973-01-01

    Combined recordings of the electroretinogram (ERG) and the cortical visual evoked response (VER) have been made together with the electroencephalogram (EEG) in 16 children suffering from a `late infantile' form of `neuronal lipidosis'. The ERG was not usually recordable, in keeping with a gross loss of function of the retinal receptor elements. However, in all the 16 children, at whatever stage of the disease, a grossly enlarged VER was seen (some 12 to 20 times higher in amplitude than in a control group) and visible as a discharge on the primary EEG tracing. The first components of the VER were, however, of fairly similar wave form and latency to those seen in the control children. This unusual combination of ERG and VER findings together with the EEG features have not been found in other groups of diseases. PMID:4691692

  7. Fractals properties of EEG during event-related desynchronization of motor imagery.

    Science.gov (United States)

    Nguyen, Ngoc Quang; Truong, Quang Dang Khoa; Kondo, Toshiyuki

    2015-01-01

    Chaos and fractal dimension are emerging modalities for the research of electroencephalogram (EEG) signal processing. The capability of measuring non-linear characteristics of the fractal dimension enables new methodologies to identify distinct brain activities. Recent studies on the topic focus on utilizing various types of fractals as features in order to design better brain state classification system. However, we have little insight about the EEG signals projected in fractal dimension. In this paper, we investigate the relationship between the non-linear characteristics of ongoing EEG signals and event-related desynchronization (ERD) during motor imagery. We observed a considerable synchronization between ERD and fractal dimension. This finding suggests further usage of chaos and fractal theory in investigating brain activities.

  8. Frontal EEG alpha activity and obsessive-compulsive behaviors in nonclinical young adults: A pilot study

    Directory of Open Access Journals (Sweden)

    Michael eWong

    2015-09-01

    Full Text Available Previous studies have shown that the resting electroencephalogram (EEG alpha patterns of nonclinical participants who score high on measures of negative affect, such as depression and shyness, are different from those who score low. However, we know relatively little about patterns of resting EEG alpha patterns in a nonclinical sample of individuals with high levels of obsessive-compulsive behaviors indicative of OCD. Here we measured resting EEG alpha activity in frontal and parietal regions of nonclinical participants who scored high and low on the Padua-R, a measure of the severity of OCD-related behaviors. We found that participants who scored high on the Padua-R exhibited decreased overall activity in frontal regions relative to individuals who scored low on the measure. We speculate that frontal hypoactivity may be a possible marker and/or index of risk for OCD.

  9. Qualitative and quantitative EEG abnormalities in violent offenders with antisocial personality disorder.

    Science.gov (United States)

    Reyes, Ana Calzada; Amador, Alfredo Alvarez

    2009-02-01

    Resting eyes closed electroencephalogram was studied in a group of violent offenders evaluated at Psychiatric Department of the Legal Medicine Institute in Cuba (18 with antisocial personality disorder, ASPD, and 10 without psychiatric diagnosis). Characteristics of the EEG visual inspection and the use of frequency domain quantitative analysis techniques (narrow band spectral parameters) are described. Both groups were compared to Cuban normative database. High incidences of electroencephalographic abnormalities were found in both groups of violent offenders. The most frequent were: electrogenesis alterations, attenuated alpha rhythm and theta and delta activities increase in the frontal lobe. In the quantitative analysis theta and delta frequencies were increased and alpha activity was decreased in both groups. Differences appear for the topographical patterns present in subjects of both groups. EEG abnormalities were more severe in ASPD than in control group. Results suggest that EEG abnormalities in violent offenders should reflect aspects of brain dysfunction related to antisocial behaviour.

  10. Single trial EEG classification applied to a face recognition experiment using different feature extraction methods.

    Science.gov (United States)

    Li, Yudu; Ma, Sen; Hu, Zhongze; Chen, Jiansheng; Su, Guangda; Dou, Weibei

    2015-01-01

    Research on brain machine interface (BMI) has been developed very fast in recent years. Numerous feature extraction methods have successfully been applied to electroencephalogram (EEG) classification in various experiments. However, little effort has been spent on EEG based BMI systems regarding familiarity of human faces cognition. In this work, we have implemented and compared the classification performances of four common feature extraction methods, namely, common spatial pattern, principal component analysis, wavelet transform and interval features. High resolution EEG signals were collected from fifteen healthy subjects stimulated by equal number of familiar and novel faces. Principal component analysis outperforms other methods with average classification accuracy reaching 94.2% leading to possible real life applications. Our findings thereby may contribute to the BMI systems for face recognition.

  11. A Quantitative Analysis of an EEG Epileptic Record Based on MultiresolutionWavelet Coefficients

    Directory of Open Access Journals (Sweden)

    Mariel Rosenblatt

    2014-11-01

    Full Text Available The characterization of the dynamics associated with electroencephalogram (EEG signal combining an orthogonal discrete wavelet transform analysis with quantifiers originated from information theory is reviewed. In addition, an extension of this methodology based on multiresolution quantities, called wavelet leaders, is presented. In particular, the temporal evolution of Shannon entropy and the statistical complexity evaluated with different sets of multiresolution wavelet coefficients are considered. Both methodologies are applied to the quantitative EEG time series analysis of a tonic-clonic epileptic seizure, and comparative results are presented. In particular, even when both methods describe the dynamical changes of the EEG time series, the one based on wavelet leaders presents a better time resolution.

  12. Extraction of features from sleep EEG for Bayesian assessment of brain development.

    Directory of Open Access Journals (Sweden)

    Vitaly Schetinin

    Full Text Available Brain development can be evaluated by experts analysing age-related patterns in sleep electroencephalograms (EEG. Natural variations in the patterns, noise, and artefacts affect the evaluation accuracy as well as experts' agreement. The knowledge of predictive posterior distribution allows experts to estimate confidence intervals within which decisions are distributed. Bayesian approach to probabilistic inference has provided accurate estimates of intervals of interest. In this paper we propose a new feature extraction technique for Bayesian assessment and estimation of predictive distribution in a case of newborn brain development assessment. The new EEG features are verified within the Bayesian framework on a large EEG data set including 1,100 recordings made from newborns in 10 age groups. The proposed features are highly correlated with brain maturation and their use increases the assessment accuracy.

  13. Transverse patterning dissociates human EEG theta power and hippocampal BOLD activation.

    Science.gov (United States)

    Meltzer, Jed A; Fonzo, Greg A; Constable, R Todd

    2009-01-01

    Theta oscillations (4-8 Hz) are often modulated in human electroencephalogram (EEG) studies of memory, whereas overlapping frequencies dominate rodent hippocampal EEG. An emerging parallelism between theta reactivity and hippocampal functional magnetic resonance imaging activation has suggested a homology between theta activity in humans and rodents, representing a process of cortico-hippocampal interaction involved in memory. In the present study, we investigated EEG reactivity during performance of a relational memory task that induces a negative hippocampal blood oxygenation level dependent (BOLD) signal change, compared to a nonrelational control condition. Relational trials induced theta increases and alpha decreases. Low Resolution Electromagnetic Brain Tomography estimates localized theta and alpha modulation to frontal midline and parietal midline cortices, respectively, both of which exhibit negative BOLD responses in this task. Thus, theta and alpha dynamics are dissociable from positive BOLD activation, and may, in fact, colocalize with negative BOLD responses.

  14. Quantitative Electroencephalogram and psychopathy. A case study report

    Directory of Open Access Journals (Sweden)

    O. Ortega-Noriega

    2015-01-01

    Full Text Available Our study shows the case of a psychopath prisoner who has been in jail for three years accused of rape. The subject was evaluated through the Psychopathy Checklist-Revised (PCL-R and Quantitative Electroencephalogram (QEEG. QEEG measures used in this study were absolute power and occipital Alpha medium frequency. Values were transformed into Z score and compared with the QEEG normative database. Results showed a Theta excess and Alpha decrease; moreover occipital Alpha medium frequency was below the norm for the subject's age. Findings suggest a cortical hypoactivation; some theories suggest that some psychopath's features can be explained by this low activation.

  15. Hypoglycemia-Related Electroencephalogram Changes Assessed by Multiscale Entropy

    DEFF Research Database (Denmark)

    Fabris, C.; Sparacino, G.; Sejling, A. S.

    2014-01-01

    physiopathological conditions have never been assessed in hypoglycemia. The present study investigates if properties of the EEG signal measured by nonlinear entropy-based algorithms are altered in a significant manner when a state of hypoglycemia is entered. Subjects and Methods: EEG was acquired from 19 patients...... derivation in the two glycemic intervals was assessed using the multiscale entropy (MSE) approach, obtaining measures of sample entropy (SampEn) at various temporal scales. The comparison of how signal irregularity measured by SampEn varies as the temporal scale increases in the two glycemic states provides...

  16. EEG and Spelling Disabilities

    OpenAIRE

    J Gordon Millichap

    1991-01-01

    The EEGs in 23 13-year-old Finnish-speaking boys with spelling disabilities and in 21 matched controls were studied in the Departments of Child Neurology, Paediatrics, Clinical Neurophysiology and Psychology, University of Helsinki, Finland.

  17. Removal of Muscle Artifacts from Single-Channel EEG Based on Ensemble Empirical Mode Decomposition and Multiset Canonical Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Xun Chen

    2014-01-01

    Full Text Available Electroencephalogram (EEG recordings are often contaminated with muscle artifacts. This disturbing muscular activity strongly affects the visual analysis of EEG and impairs the results of EEG signal processing such as brain connectivity analysis. If multichannel EEG recordings are available, then there exist a considerable range of methods which can remove or to some extent suppress the distorting effect of such artifacts. Yet to our knowledge, there is no existing means to remove muscle artifacts from single-channel EEG recordings. Moreover, considering the recently increasing need for biomedical signal processing in ambulatory situations, it is crucially important to develop single-channel techniques. In this work, we propose a simple, yet effective method to achieve the muscle artifact removal from single-channel EEG, by combining ensemble empirical mode decomposition (EEMD with multiset canonical correlation analysis (MCCA. We demonstrate the performance of the proposed method through numerical simulations and application to real EEG recordings contaminated with muscle artifacts. The proposed method can successfully remove muscle artifacts without altering the recorded underlying EEG activity. It is a promising tool for real-world biomedical signal processing applications.

  18. EEG spectral phenotypes: heritability and association with marijuana and alcohol dependence in an American Indian community study

    OpenAIRE

    Ehlers, Cindy L.; Phillips, Evelyn; Gizer, Ian R.; Gilder, David A.; Wilhelmsen, Kirk C.

    2009-01-01

    Native Americans have some of the highest rates of marijuana and alcohol use and abuse, yet neurobiological measures associated with dependence on these substances in this population remain unknown. The present investigation evaluated the heritability of spectral characteristics of the electroencephalogram (EEG) and their correlation with marijuana and alcohol dependence in an American Indian community. Participants (n=626) were evaluated for marijuana (MJ) and alcohol (ALC) dependence, as we...

  19. A framework to reconcile frequency scaling measurements, from intracellular recordings, local-field potentials, up to EEG and MEG signals

    OpenAIRE

    Bedard, Claude; Gomes, Jean-Marie; Bal, Thierry; Destexhe, Alain

    2016-01-01

    In this viewpoint article, we discuss the electric properties of the medium around neurons, which are important to correctly interpret extracellular potentials or electric field effects in neural tissue. We focus on how these electric properties shape the frequency scaling of brain signals at different scales, such as intracellular recordings, the local field potential (LFP), the electroencephalogram (EEG) or the magnetoencephalogram (MEG). These signals display frequency-scaling properties w...

  20. Comparison of ambulatory electroencephagraphy in the diagnosis of epilepsy in children with sleep-derivation electroencephalogram

    Directory of Open Access Journals (Sweden)

    Ying Yang

    2016-01-01

    Full Text Available Objective: To compare the value of aEEG with sdEEG in the diagnosis of epilepsy in children, improve diagnostic accuracy. Methods: 204 suspected epilepsy children were dedected by MRI, rEEG and aEEG, sdEEG, routine examination results as the gold standard computing aEEG, sdEEG diagnostic accuracy, and analyze the impact aEEG, factors sdEEG test results. Results: 204 cases of suspected epileptic children diagnosed with epilepsy routine 189 cases, aEEG diagnostic sensitivity, specificity, positive predictive value, negative predictive value of 94.18%, 93.33%, 99.44%, 56.00%, sensitivity sdEEG diagnosis, specificity, positive predictive value, negative predictive value of 91.01%, 73.33%, 97.73%, 39.29%; aEEG epileptic discharge detection rate of 62.54%, higher than 51.95% sdEEG of; sdEEG in 1 to 3 years old suffering children epileptiform discharges detected positive rate accounted for 69.81%, higher than the 4 to 7 years old and > years the proportion of children, and in three age aEEG epileptiform discharge detection rate more balanced. Conclusions: aEEG inspection sensitivity, specificity for the diagnosis of epilepsy in children is higher than sdEEG, and for the diagnosis of all ages of children, and sdEEG use more flexible, suitable for children under 3 years of diagnosis.

  1. Extracting message inter-departure time distributions from the human electroencephalogram.

    Directory of Open Access Journals (Sweden)

    Bratislav Mišić

    2011-06-01

    Full Text Available The complex connectivity of the cerebral cortex is a topic of much study, yet the link between structure and function is still unclear. The processing capacity and throughput of information at individual brain regions remains an open question and one that could potentially bridge these two aspects of neural organization. The rate at which information is emitted from different nodes in the network and how this output process changes under different external conditions are general questions that are not unique to neuroscience, but are of interest in multiple classes of telecommunication networks. In the present study we show how some of these questions may be addressed using tools from telecommunications research. An important system statistic for modeling and performance evaluation of distributed communication systems is the time between successive departures of units of information at each node in the network. We describe a method to extract and fully characterize the distribution of such inter-departure times from the resting-state electroencephalogram (EEG. We show that inter-departure times are well fitted by the two-parameter Gamma distribution. Moreover, they are not spatially or neurophysiologically trivial and instead are regionally specific and sensitive to the presence of sensory input. In both the eyes-closed and eyes-open conditions, inter-departure time distributions were more dispersed over posterior parietal channels, close to regions which are known to have the most dense structural connectivity. The biggest differences between the two conditions were observed at occipital sites, where inter-departure times were significantly more variable in the eyes-open condition. Together, these results suggest that message departure times are indicative of network traffic and capture a novel facet of neural activity.

  2. Acquisition technology research of EEG and related physiological signals under +Gz acceleration.

    Science.gov (United States)

    Li, Y; Zhang, T; Deng, L; Wang, B

    2014-06-01

    With the continuous improvement of maneuvering performance of modern high-performance aircraft, the protection problem of flight personnel under high G acceleration, the development as well as research on monitoring system and the equipment for human physiological signals processing which include electroencephalogram (EEG) have become more and more important. Due to the particularity of +Gz experimental conditions, the high-risk of human experiments and the great difficulty of dynamic measurement, there is little research on the synchronous acquisition technology of EEG and related physiological signals under +Gz acceleration environment. We propose a framework to execute human experiments using the three-axial high-performance human centrifuge, develop reasonable operation mode and design a new experimental research method for EEG signal acquisition and variation characteristics on three-axial high-performance human centrifuge under the environment of +Gz acceleration. We also propose to build the synchronous real-time acquisition plan of EEG, electrocardiogram, brain blood pressure, ear pulse and related physiological signals under centrifuge +Gz acceleration with different equipments and methods. The good profiles of EEG, heart rate, brain blood pressure and ear pulse are obtained and analyzed comparatively. In addition, the FMS hop-by-hop continuous blood pressure and hemodynamic measurement system Portapres are successfully applied to the ambulatory blood pressure measure under centrifuge +Gz acceleration environment. The proposed methods establish the basis and have an important guiding significance for follow-up experiment development, EEG features spectral analysis and correlation research of all signals.

  3. EEG biomarkers in major depressive disorder: discriminative power and prediction of treatment response.

    Science.gov (United States)

    Olbrich, Sebastian; Arns, Martijn

    2013-10-01

    Major depressive disorder (MDD) has high population prevalence and is associated with substantial impact on quality of life, not least due to an unsatisfactory time span of sometimes several weeks from initiation of treatment to clinical response. Therefore extensive research focused on the identification of cost-effective and widely available electroencephalogram (EEG)-based biomarkers that not only allow distinguishing between patients and healthy controls but also have predictive value for treatment response for a variety of treatments. In this comprehensive overview on EEG research on MDD, biomarkers that are either assessed at baseline or during the early course of treatment and are helpful in discriminating patients from healthy controls and assist in predicting treatment outcome are reviewed, covering recent decades up to now. Reviewed markers include quantitative EEG (QEEG) measures, connectivity measures, EEG vigilance-based measures, sleep-EEG-related measures and event-related potentials (ERPs). Further, the value and limitations of these different markers are discussed. Finally, the need for integrated models of brain function and the necessity for standardized procedures in EEG biomarker research are highlighted to enhance future research in this field.

  4. EEG power and glucose fluctuations are coupled during sleep in young adults with type 1 diabetes.

    Science.gov (United States)

    Farabi, Sarah S; Carley, David W; Quinn, Lauretta

    2016-08-01

    To determine the coupling between brain activity and glucose variations during sleep in young adults with type 1 diabetes mellitus (T1DM). 27 participants, age 18-30, wore a continuous glucose monitoring system (CGMS) and underwent in-laboratory overnight polysomnography (PSG). Quantitative electroencephalogram (qEEG) metrics were determined from the PSG and included Delta, Theta, Alpha, Sigma, Beta and Gamma Band power at 5-min intervals. Wavelet Coherence Analysis was employed to determine the time varying and frequency specific coupling between glucose and EEG Band power. ANOVA was used to compare differences across fluctuation speeds and EEG bands. There was a high degree of time varying and frequency specific coupling between glucose variations and EEG power in all EEG Bands during sleep. The average number of intervals of statistically significant coherence was highest for fluctuations periods between 10 and 30min in all Bands (p<0.0001 for each). Mean significant coherence was negatively correlated with hemoglobin A1c, a marker of glycemic control. The relationship between glucose and EEG power during sleep is time varying and frequency dependent in young adults with T1DM. Understanding the time varying mutual relationship between glucose changes and brain activity during sleep may have implications for disease management in T1DM. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  5. An EEG-Based Person Authentication System with Open-Set Capability Combining Eye Blinking Signals

    Directory of Open Access Journals (Sweden)

    Qunjian Wu

    2018-01-01

    Full Text Available The electroencephalogram (EEG signal represents a subject’s specific brain activity patterns and is considered as an ideal biometric given its superior forgery prevention. However, the accuracy and stability of the current EEG-based person authentication systems are still unsatisfactory in practical application. In this paper, a multi-task EEG-based person authentication system combining eye blinking is proposed, which can achieve high precision and robustness. Firstly, we design a novel EEG-based biometric evoked paradigm using self- or non-self-face rapid serial visual presentation (RSVP. The designed paradigm could obtain a distinct and stable biometric trait from EEG with a lower time cost. Secondly, the event-related potential (ERP features and morphological features are extracted from EEG signals and eye blinking signals, respectively. Thirdly, convolutional neural network and back propagation neural network are severally designed to gain the score estimation of EEG features and eye blinking features. Finally, a score fusion technology based on least square method is proposed to get the final estimation score. The performance of multi-task authentication system is improved significantly compared to the system using EEG only, with an increasing average accuracy from 92.4% to 97.6%. Moreover, open-set authentication tests for additional imposters and permanence tests for users are conducted to simulate the practical scenarios, which have never been employed in previous EEG-based person authentication systems. A mean false accepted rate (FAR of 3.90% and a mean false rejected rate (FRR of 3.87% are accomplished in open-set authentication tests and permanence tests, respectively, which illustrate the open-set authentication and permanence capability of our systems.

  6. Emotion recognition from EEG using higher order crossings.

    Science.gov (United States)

    Petrantonakis, Panagiotis C; Hadjileontiadis, Leontios J

    2010-03-01

    Electroencephalogram (EEG)-based emotion recognition is a relatively new field in the affective computing area with challenging issues regarding the induction of the emotional states and the extraction of the features in order to achieve optimum classification performance. In this paper, a novel emotion evocation and EEG-based feature extraction technique is presented. In particular, the mirror neuron system concept was adapted to efficiently foster emotion induction by the process of imitation. In addition, higher order crossings (HOC) analysis was employed for the feature extraction scheme and a robust classification method, namely HOC-emotion classifier (HOC-EC), was implemented testing four different classifiers [quadratic discriminant analysis (QDA), k-nearest neighbor, Mahalanobis distance, and support vector machines (SVMs)], in order to accomplish efficient emotion recognition. Through a series of facial expression image projection, EEG data have been collected by 16 healthy subjects using only 3 EEG channels, namely Fp1, Fp2, and a bipolar channel of F3 and F4 positions according to 10-20 system. Two scenarios were examined using EEG data from a single-channel and from combined-channels, respectively. Compared with other feature extraction methods, HOC-EC appears to outperform them, achieving a 62.3% (using QDA) and 83.33% (using SVM) classification accuracy for the single-channel and combined-channel cases, respectively, differentiating among the six basic emotions, i.e., happiness, surprise, anger, fear, disgust, and sadness. As the emotion class-set reduces its dimension, the HOC-EC converges toward maximum classification rate (100% for five or less emotions), justifying the efficiency of the proposed approach. This could facilitate the integration of HOC-EC in human machine interfaces, such as pervasive healthcare systems, enhancing their affective character and providing information about the user's emotional status (e.g., identifying user's emotion

  7. Deep learning for EEG-Based preference classification

    Science.gov (United States)

    Teo, Jason; Hou, Chew Lin; Mountstephens, James

    2017-10-01

    Electroencephalogram (EEG)-based emotion classification is rapidly becoming one of the most intensely studied areas of brain-computer interfacing (BCI). The ability to passively identify yet accurately correlate brainwaves with our immediate emotions opens up truly meaningful and previously unattainable human-computer interactions such as in forensic neuroscience, rehabilitative medicine, affective entertainment and neuro-marketing. One particularly useful yet rarely explored areas of EEG-based emotion classification is preference recognition [1], which is simply the detection of like versus dislike. Within the limited investigations into preference classification, all reported studies were based on musically-induced stimuli except for a single study which used 2D images. The main objective of this study is to apply deep learning, which has been shown to produce state-of-the-art results in diverse hard problems such as in computer vision, natural language processing and audio recognition, to 3D object preference classification over a larger group of test subjects. A cohort of 16 users was shown 60 bracelet-like objects as rotating visual stimuli on a computer display while their preferences and EEGs were recorded. After training a variety of machine learning approaches which included deep neural networks, we then attempted to classify the users' preferences for the 3D visual stimuli based on their EEGs. Here, we show that that deep learning outperforms a variety of other machine learning classifiers for this EEG-based preference classification task particularly in a highly challenging dataset with large inter- and intra-subject variability.

  8. Changes in the Electro-encephalogram Minimal Cerebral Dysfunction

    African Journals Online (AJOL)

    1974-03-23

    Mar 23, 1974 ... Procedural inadequacies, however, detract from the value of several of these investigations. Paine,'" Paine et al.' and Kenny and Clemmens' all failed .... vity) and indices of cortical dysfunction on each occasion of testing. Both sets of EEGs were·rated independently and blindly by two experienced electro- ...

  9. Avian reflex and electroencephalogram responses in different states of consciousness.

    Science.gov (United States)

    Sandercock, Dale A; Auckburally, Adam; Flaherty, Derek; Sandilands, Victoria; McKeegan, Dorothy E F

    2014-06-22

    Defining states of clinical consciousness in animals is important in veterinary anaesthesia and in studies of euthanasia and welfare assessment at slaughter. The aim of this study was to validate readily observable reflex responses in relation to different conscious states, as confirmed by EEG analysis, in two species of birds under laboratory conditions (35-week-old layer hens (n=12) and 11-week-old turkeys (n=10)). We evaluated clinical reflexes and characterised electroencephalograph (EEG) activity (as a measure of brain function) using spectral analyses in four different clinical states of consciousness: conscious (fully awake), semi-conscious (sedated), unconscious-optimal (general anaesthesia), unconscious-sub optimal (deep hypnotic state), as well as assessment immediately following euthanasia. Jaw or neck muscle tone was the most reliable reflex measure distinguishing between conscious and unconscious states. Pupillary reflex was consistently observed until respiratory arrest. Nictitating membrane reflex persisted for a short time (power (PTOT) significantly increased, whereas median (F50) and spectral edge (F95) frequencies significantly decreased. This study demonstrates that EEG analysis can differentiate between clinical states (and loss of brain function at death) in birds and provides a unique integration of reflex responses and EEG activity. Copyright © 2014 Elsevier Inc. All rights reserved.

  10. Nonlinear analysis of the electroencephalogram in depth of anesthesia

    Directory of Open Access Journals (Sweden)

    Oscar Leonardo Mosquera-Dussan

    2015-01-01

    Full Text Available El procesamiento digital de la señal de electroencefalograma (EEG ha tomado importancia en el monitoreo de profundidad anestésica, contribuyendo a una mejor técnica anestésica. El objetivo es realizar una revisión de métodos matemáticos no lineales aplicados recientemente al análisis de EEG la cual presenta características no lineales y no estacionarias. Una revisión fue desarrollada abarcando métodos matemáticos no lineales en el dominio del tiempo y frecuencia, los cuales han sido aplicados recientemente al análisis de EEG: Entropía Aproximada, Entropía Muestral, Entropía Espectral, Entropía Permutada, Transformada Wavelet, Entropía Wavelet, Bispectro, Bicoherencia y Transformada Hilbert Huang. Los algoritmos implementados fueron probados en un registro EEG de un paciente en la Clínica Universidad de La Sabana. Resultados publicados en la literatura a fin del tema son discutidos. Técnicas no lineales como el análisis de entropía, y la combinación con transformadas Wavelet y Hilbert Huang en el dominio de la frecuencia han presentado resultados prometedores en clasificación de estados de profundidad anestésica.

  11. Electroencephalogram signals processing for topographic brain mapping and epilepsies classification.

    Science.gov (United States)

    Arab, Mohammad Reza; Suratgar, Amir Abolfazl; Ashtiani, Alireza Rezaei

    2010-09-01

    In this study, topographic brain mapping and wavelet transform-neural network method are used for the classification of grand mal (clonic stage) and petit mal (absence) epilepsies into healthy, ictal and interictal (EEGs). Preprocessing is included to remove artifacts occurred by blinking, wandering baseline (electrodes movement) and eyeball movement using the Discrete Wavelet Transformation (DWT). De-noising EEG signals from the AC power supply frequency with a suitable notch filter is another job of preprocessing. In experimental data, the preprocessing enhanced speed and accuracy of the processing stage (wavelet transform and neural network). The EEGs signals are categorized to normal and petit mal and clonic epilepsy by an expert neurologist. The categorization is confirmed by Fast Fourier Transform (FFT) analysis and brain mapping. The dataset includes waves such as sharp, spike and spike-slow wave. Through the Counties Wavelet Transform (CWT) of EEG records, transient features are accurately captured and separated and used as classifier input. We introduce a two-stage classifier based on the Learning Vector Quantization (LVQ) neural network location in both time and frequency contexts. The brain mapping used for finding the epilepsy locates in the brain. The simulation results are very promising and the accuracy of the proposed classifier in experimental clinical data is ∼80%. Copyright © 2010 Elsevier Ltd. All rights reserved.

  12. Recording EEG in immature rats with a novel miniature telemetry system

    Science.gov (United States)

    Zayachkivsky, A.; Lehmkuhle, M. J.; Fisher, J. H.; Ekstrand, J. J.

    2013-01-01

    Serial EEG recordings from immature rat pups are extremely difficult to obtain but important for analyzing animal models of neonatal seizures and other pediatric neurological conditions as well as normal physiology. In this report, we describe the features and applications of a novel miniature telemetry system designed to record EEG in rat pups as young as postnatal day 6 (P6). First, we have recorded electrographic seizure activity in two animal models of neonatal seizures, hypoxia- and kainate-induced seizures at P7. Second, we describe a viable approach for long-term continuous EEG monitoring of naturally reared rat pups implanted with EEG at P6. Third, we have used serial EEG recordings to record age-dependent changes in the background EEG signal as the animals matured from P7 to P11. The important advantages of using miniature wireless EEG technology are: 1) minimally invasive surgical implantation; 2) a device form-factor that is compatible with housing of rat pups with the dam and littermates; 3) serial recordings of EEG activity; and 4) low power consumption of the unit, theoretically allowing continuous monitoring for up to 2 yr without surgical reimplantation. The miniature EEG telemetry system provides a technical advance that allows researchers to record continuous and serial EEG recordings in neonatal rodent models of human neurological disorders, study the progression of the disease, and then assess possible therapies using quantitative EEG as an outcome measure. This new technical approach should improve animal models of human conditions that rely on EEG monitoring for diagnosis and therapy. PMID:23114207

  13. Progress in EEG-Based Brain Robot Interaction Systems

    Science.gov (United States)

    Li, Mengfan; Niu, Linwei; Xian, Bin; Zeng, Ming; Chen, Genshe

    2017-01-01

    The most popular noninvasive Brain Robot Interaction (BRI) technology uses the electroencephalogram- (EEG-) based Brain Computer Interface (BCI), to serve as an additional communication channel, for robot control via brainwaves. This technology is promising for elderly or disabled patient assistance with daily life. The key issue of a BRI system is to identify human mental activities, by decoding brainwaves, acquired with an EEG device. Compared with other BCI applications, such as word speller, the development of these applications may be more challenging since control of robot systems via brainwaves must consider surrounding environment feedback in real-time, robot mechanical kinematics, and dynamics, as well as robot control architecture and behavior. This article reviews the major techniques needed for developing BRI systems. In this review article, we first briefly introduce the background and development of mind-controlled robot technologies. Second, we discuss the EEG-based brain signal models with respect to generating principles, evoking mechanisms, and experimental paradigms. Subsequently, we review in detail commonly used methods for decoding brain signals, namely, preprocessing, feature extraction, and feature classification, and summarize several typical application examples. Next, we describe a few BRI applications, including wheelchairs, manipulators, drones, and humanoid robots with respect to synchronous and asynchronous BCI-based techniques. Finally, we address some existing problems and challenges with future BRI techniques. PMID:28484488

  14. Progress in EEG-Based Brain Robot Interaction Systems

    Directory of Open Access Journals (Sweden)

    Xiaoqian Mao

    2017-01-01

    Full Text Available The most popular noninvasive Brain Robot Interaction (BRI technology uses the electroencephalogram- (EEG- based Brain Computer Interface (BCI, to serve as an additional communication channel, for robot control via brainwaves. This technology is promising for elderly or disabled patient assistance with daily life. The key issue of a BRI system is to identify human mental activities, by decoding brainwaves, acquired with an EEG device. Compared with other BCI applications, such as word speller, the development of these applications may be more challenging since control of robot systems via brainwaves must consider surrounding environment feedback in real-time, robot mechanical kinematics, and dynamics, as well as robot control architecture and behavior. This article reviews the major techniques needed for developing BRI systems. In this review article, we first briefly introduce the background and development of mind-controlled robot technologies. Second, we discuss the EEG-based brain signal models with respect to generating principles, evoking mechanisms, and experimental paradigms. Subsequently, we review in detail commonly used methods for decoding brain signals, namely, preprocessing, feature extraction, and feature classification, and summarize several typical application examples. Next, we describe a few BRI applications, including wheelchairs, manipulators, drones, and humanoid robots with respect to synchronous and asynchronous BCI-based techniques. Finally, we address some existing problems and challenges with future BRI techniques.

  15. Observation on the Changes of Sleep Structure in 82 Patients with Epilepsy by Polysomnography Combined with Long-term Video Electroencephalogram

    Directory of Open Access Journals (Sweden)

    Hongliang Li

    2014-03-01

    Full Text Available Objective: To investigate the effect of epileptiform discharge on changes of sleep structure in patients with epilepsy. Methods: A total of 82 patients diagnosed with epilepsy were performed with polysomnography (PSG concomitant with long-term video electroencephalogram (LTV EEG to analyze their sleep structures and epileptic EEG. Results: The PSG in this study was marked by different levels of changes in sleep parameters with increased latency stage and decreased rapid eye movement (REM sleep as well as increased times of arousals at night, in which 8 patients had no REM sleep. During sleep, epileptiform discharges had evidently influence on phase Ⅰ and Ⅲ-Ⅳ sleep of non-REM (NREM and discharge group was more significant in the increase of phase Ⅰ sleep but decrease of phases Ⅲ - Ⅳ sleep of NREM. Conclusion: Patients with epilepsy is often accompanied with disorders of sleep structures, especially those with epileptiform discharges during sleep. Application of PSG concomitant with LTV EEG are more beneficial for the overall analysis of relationship between sleep structure and epileptiform discharges.

  16. Effects of electrical water bath stunning current frequencies on the spontaneous electroencephalogram and somatosensory evoked potentials in hens.

    Science.gov (United States)

    Raj, A B M; O'Callaghan, M

    2004-04-01

    1. The effectiveness of water bath electrical stunning of chickens with a constant root mean square (rms) current of 100 mA per bird delivered for 3 s using 100, 200, 400, 800 and 1500 Hz sine wave alternating current (AC) was investigated in layer hens. The quantitative changes occurring in the electroencephalogram (EEG) and somatosensory evoked potentials (SEPs) were used to determine the effectiveness of stunning. The changes occurring in the EEG were evaluated using Fast Fourier Transformations (FFT) and the SEPs were averaged to determine whether they were present or abolished. 2. The results of FFT indicated that stunning of chickens with a constant rms current of 100 mA per bird using 100 or 200 Hz induced epileptiform activity in all the hens, immediately followed by a reduction in the total (2 to 30 Hz) and relative (13 to 30 Hz) power contents in the EEG frequency bands indicative of unconsciousness and insensibility. The SEPs were abolished in the majority of hens stunned with 100 Hz and all the hens stunned with 200 Hz. 3. By contrast, stunning using 400, 800 or 1500 Hz failed to induce epileptiform activity in all the birds, the total and relative power contents in the EEG frequency bands showed a substantial increase, rather than reduction, and the SEPs were also retained in the majority of chickens. It is therefore suggested that stunning using these frequencies failed to stun them satisfactorily. In these birds, occurrence of a painful arousal, rather than unconsciousness, could not be ruled out. 4. It is therefore suggested that water bath electrical stunning of chickens with a minimum rms current of 100 mA per bird delivered using 100 or 200 Hz would be adequate to ensure bird welfare under commercial conditions, provided both the carotid arteries in the neck are severed at slaughter. On humanitarian and bird welfare grounds, a rms current of greater than 100 mA per bird should be applied whilst using frequencies of 400 Hz or more of sine wave AC

  17. Electroencephalogram of Age-Dependent Epileptic Encephalopathies in Infancy and Early Childhood

    OpenAIRE

    Wong-Kisiel, Lily C.; Nickels, Katherine

    2013-01-01

    Epileptic encephalopathy syndromes are disorders in which the epileptiform abnormalities are thought to contribute to a progressive cerebral dysfunction. Characteristic electroencephalogram findings have an important diagnostic value in classification of epileptic encephalopathy syndromes. In this paper, we focus on electroencephalogram findings of childhood epileptic encephalopathy syndromes and provide sample illustrations.

  18. Analysis of the influence of memory content of auditory stimuli on the memory content of EEG signal.

    Science.gov (United States)

    Namazi, Hamidreza; Khosrowabadi, Reza; Hussaini, Jamal; Habibi, Shaghayegh; Farid, Ali Akhavan; Kulish, Vladimir V

    2016-08-30

    One of the major challenges in brain research is to relate the structural features of the auditory stimulus to structural features of Electroencephalogram (EEG) signal. Memory content is an important feature of EEG signal and accordingly the brain. On the other hand, the memory content can also be considered in case of stimulus. Beside all works done on analysis of the effect of stimuli on human EEG and brain memory, no work discussed about the stimulus memory and also the relationship that may exist between the memory content of stimulus and the memory content of EEG signal. For this purpose we consider the Hurst exponent as the measure of memory. This study reveals the plasticity of human EEG signals in relation to the auditory stimuli. For the first time we demonstrated that the memory content of an EEG signal shifts towards the memory content of the auditory stimulus used. The results of this analysis showed that an auditory stimulus with higher memory content causes a larger increment in the memory content of an EEG signal. For the verification of this result, we benefit from approximate entropy as indicator of time series randomness. The capability, observed in this research, can be further investigated in relation to human memory.

  19. Chaos analysis of EEG during isoflurane-induced loss of righting in rats.

    Science.gov (United States)

    MacIver, M B; Bland, Brian H

    2014-01-01

    It has long been known that electroencephalogram (EEG) signals generate chaotic strange attractors and the shape of these attractors correlate with depth of anesthesia. We applied chaos analysis to frontal cortical and hippocampal micro-EEG signals from implanted microelectrodes (layer 4 and CA1, respectively). Rats were taken to and from loss of righting reflex (LORR) with isoflurane and behavioral measures were compared to attractor shape. Resting EEG signals at LORR differed markedly from awake signals, more similar to slow wave sleep signals, and easily discerned in raw recordings (high amplitude slow waves), and in fast Fourier transform analysis (FFT; increased delta power), in good agreement with previous studies. EEG activation stimulated by turning rats on their side, to test righting, produced signals quite similar to awake resting state EEG signals. That is, the high amplitude slow wave activity changed to low amplitude fast activity that lasted for several seconds, before returning to slow wave activity. This occurred regardless of whether the rat was able to right itself, or not. Testing paw pinch and tail clamp responses produced similar EEG activations, even from deep anesthesia when burst suppression dominated the spontaneous EEG. Chaotic attractor shape was far better at discerning between these awake-like signals, at loss of responses, than was FFT analysis. Comparisons are provided between FFT and chaos analysis of EEG during awake walking, slow wave sleep, and isoflurane-induced effects at several depths of anesthesia. Attractors readily discriminated between natural sleep and isoflurane-induced "delta" activity. Chaotic attractor shapes changed gradually through the transition from awake to LORR, indicating that this was not an on/off like transition, but rather a point along a continuum of brain states.

  20. Chaos analysis of EEG during isoflurane-induced loss of righting in rats

    Directory of Open Access Journals (Sweden)

    Bruce eMaciver

    2014-10-01

    Full Text Available It has long been known that electroencephalogram (EEG signals generate chaotic strange attractors and the shape of these attractors correlate with depth of anesthesia. We applied chaos analysis to frontal cortical and hippocampal micro-EEG signals from implanted microelectrodes (layer 4 and CA1, respectively. Rats were taken to and from loss of righting reflex (LORR with isoflurane and behavioral measures were compared to attractor shape. Resting EEG signals at LORR differed markedly from awake signals, more similar to slow wave sleep signals, and easily discerned in raw recordings (high amplitude slow waves, and in fast Fourier transform analysis (FFT; increased delta power, in good agreement with previous studies. EEG activation stimulated by turning rats on their side, to test righting, produced signals quite similar to awake resting state EEG signals. That is, the high amplitude slow wave activity changed to low amplitude fast activity that lasted for several seconds, before returning to slow wave activity. This occurred regardless of whether the rat was able to right itself, or not. Testing paw pinch and tail clamp responses produced similar EEG activations, even from deep anesthesia when burst suppression dominated the spontaneous EEG. Chaotic attractor shape was far better at discerning between these awake-like signals, at loss of responses, than was FFT analysis. Comparisons are provided between FFT and chaos analysis of EEG during awake walking, slow wave sleep, and isoflurane-induced effects at several depths of anesthesia. Attractors readily discriminated between natural sleep and isoflurane-induced ‘delta’ activity. Chaotic attractor shapes changed gradually through the transition from awake to LORR, indicating that this was not an on/off like transition, but rather a point along a continuum of brain states.

  1. Modulatory effects of aromatherapy massage intervention on electroencephalogram, psychological assessments, salivary cortisol and plasma brain-derived neurotrophic factor.

    Science.gov (United States)

    Wu, Jin-Ji; Cui, Yanji; Yang, Yoon-Sil; Kang, Moon-Seok; Jung, Sung-Cherl; Park, Hyeung Keun; Yeun, Hye-Young; Jang, Won Jung; Lee, Sunjoo; Kwak, Young Sook; Eun, Su-Yong

    2014-06-01

    Aromatherapy massage is commonly used for the stress management of healthy individuals, and also has been often employed as a therapeutic use for pain control and alleviating psychological distress, such as anxiety and depression, in oncological palliative care patients. However, the exact biological basis of aromatherapy massage is poorly understood. Therefore, we evaluated here the effects of aromatherapy massage interventions on multiple neurobiological indices such as quantitative psychological assessments, electroencephalogram (EEG) power spectrum pattern, salivary cortisol and plasma brain-derived neurotrophic factor (BDNF) levels. A control group without treatment (n = 12) and aromatherapy massage group (n = 13) were randomly recruited. They were all females whose children were diagnosed as attention deficit hyperactivity disorder and followed up in the Department of Psychiatry, Jeju National University Hospital. Participants were treated with aromatherapy massage for 40 min twice per week for 4 weeks (8 interventions). A 4-week-aromatherapy massage program significantly improved all psychological assessment scores in the Stat-Trait Anxiety Index, Beck Depression Inventory and Short Form of Psychosocial Well-being Index. Interestingly, plasma BDNF levels were significantly increased after a 4 week-aromatherapy massage program. Alpha-brain wave activities were significantly enhanced and delta wave activities were markedly reduced following the one-time aromatherapy massage treatment, as shown in the meditation and neurofeedback training. In addition, salivary cortisol levels were significantly reduced following the one-time aromatherapy massage treatment. These results suggest that aromatherapy massage could exert significant influences on multiple neurobiological indices such as EEG pattern, salivary cortisol and plasma BDNF levels as well as psychological assessments. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  2. The EEG in psychiatry

    African Journals Online (AJOL)

    Adele

    2004-05-20

    May 20, 2004 ... chiatric disorders have a neurobiological basis. Nevertheless, the typically marked focal or generalised slowing found in the. EEG in patients with acute or chronic encephalopathies due to metabolic changes, infections, toxins, trauma and tumours is useful to the clinician in the differentiation of these disor-.

  3. Decoding spatial attention with EEG and virtual acoustic space.

    Science.gov (United States)

    Dong, Yue; Raif, Kaan E; Determan, Sarah C; Gai, Yan

    2017-11-01

    Decoding spatial attention based on brain signals has wide applications in brain-computer interface (BCI). Previous BCI systems mostly relied on visual patterns or auditory stimulation (e.g., loudspeakers) to evoke synchronous brain signals. There would be difficulties to cover a large range of spatial locations with such a stimulation protocol. The present study explored the possibility of using virtual acoustic space and a visual-auditory matching paradigm to overcome this issue. The technique has the flexibility of generating sound stimulation from virtually any spatial location. Brain signals of eight human subjects were obtained with a 32-channel Electroencephalogram (EEG). Two amplitude-modulated noise or speech sentences carrying distinct spatial information were presented concurrently. Each sound source was tagged with a unique modulation phase so that the phase of the recorded EEG signals indicated the sound being attended to. The phase-tagged sound was further filtered with head-related transfer functions to create the sense of virtual space. Subjects were required to pay attention to the sound source that best matched the location of a visual target. For all the subjects, the phase of a single sound could be accurately reflected over the majority of electrodes based on EEG responses of 90 s or less. The electrodes providing significant decoding performance on auditory attention were fewer and may require longer EEG responses. The reliability and efficiency of decoding with a single electrode varied with subjects. Overall, the virtual acoustic space protocol has the potential of being used in practical BCI systems. © 2017 Saint Louis University. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society.

  4. Atypical EEG Power Correlates With Indiscriminately Friendly Behavior in Internationally Adopted Children

    Science.gov (United States)

    Tarullo, Amanda R.; Garvin, Melissa C.; Gunnar, Megan R.

    2012-01-01

    While effects of institutional care on behavioral development have been studied extensively, effects on neural systems underlying these socioemotional and attention deficits are only beginning to be examined. The current study assessed electroencephalogram (EEG) power in 18-month-old internationally adopted, post-institutionalized children (n = 37) and comparison groups of non-adopted children (n = 47) and children internationally adopted from foster care (n = 39). For their age, post-institutionalized children had an atypical EEG power distribution, with relative power concentrated in lower frequency bands compared to non-adopted children. Both internationally adopted groups had lower absolute alpha power than non-adopted children. EEG power was not related to growth at adoption or to global cognitive ability. Atypical EEG power distribution at 18 months predicted indiscriminate friendliness and poorer inhibitory control at 36 months. Both post-institutionalized and foster care children were more likely than non-adopted children to exhibit indiscriminate friendliness. Results are consistent with a cortical hypoactivation model of the effects of early deprivation on neural development and provide initial evidence associating this atypical EEG pattern with indiscriminate friendliness. Outcomes observed in the foster care children raise questions about the specificity of institutional rearing as a risk factor and emphasize the need for broader consideration of the effects of early deprivation and disruptions in care. PMID:21171750

  5. Prediction of advertisement preference by fusing EEG response and sentiment analysis.

    Science.gov (United States)

    Gauba, Himaanshu; Kumar, Pradeep; Roy, Partha Pratim; Singh, Priyanka; Dogra, Debi Prosad; Raman, Balasubramanian

    2017-08-01

    This paper presents a novel approach to predict rating of video-advertisements based on a multimodal framework combining physiological analysis of the user and global sentiment-rating available on the internet. We have fused Electroencephalogram (EEG) waves of user and corresponding global textual comments of the video to understand the user's preference more precisely. In our framework, the users were asked to watch the video-advertisement and simultaneously EEG signals were recorded. Valence scores were obtained using self-report for each video. A higher valence corresponds to intrinsic attractiveness of the user. Furthermore, the multimedia data that comprised of the comments posted by global viewers, were retrieved and processed using Natural Language Processing (NLP) technique for sentiment analysis. Textual contents from review comments were analyzed to obtain a score to understand sentiment nature of the video. A regression technique based on Random forest was used to predict the rating of an advertisement using EEG data. Finally, EEG based rating is combined with NLP-based sentiment score to improve the overall prediction. The study was carried out using 15 video clips of advertisements available online. Twenty five participants were involved in our study to analyze our proposed system. The results are encouraging and these suggest that the proposed multimodal approach can achieve lower RMSE in rating prediction as compared to the prediction using only EEG data. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Quantitative EEG and medial temporal lobe atrophy in Alzheimer′s dementia: Preliminary study

    Directory of Open Access Journals (Sweden)

    Soo-Ji Lee

    2015-01-01

    Full Text Available Backgrounds: The electroencephalogram (EEG abnormalities in Alzheimer′s disease (AD have been widely reported, and medial temporal lobe atrophy (MTLA is one of the hallmarks in early stage of AD. We aimed to assess the relationship between EEG abnormalities and MTLA and its clinical validity. Materials and Methods: A total of 18 patients with AD were recruited (the mean age: 77.83 years. Baseline EEGs were analyzed with quantitative spectral analysis. MTLA was assessed by a T1-axial visual rating scale (VRS. Results: In relative power spectrum analysis according to the right MTLA severity, the power of theta waves in C4, T4, F4, F8, and T5 increased significantly and the power of beta waves in T6, C4, T4, F8, T5, P3, T3, and F7 decreased significantly in severe atrophy group. In relative power spectrum analysis according to the left MTLA severity, the power of theta waves in T3 increased significantly and that of beta waves in P4, T6, C4, F4, F8, T5, P3, C3, T3, F3, and F7 decreased significantly in severe atrophy group. Conclusion: The severe MTLA group, regardless of laterality, showed more severe quantitative EEG alterations. These results suggest that quantitative EEG abnormalities are correlated with the MTLA, which may play an important role in AD process.

  7. A Biologically Inspired Approach to Frequency Domain Feature Extraction for EEG Classification

    Directory of Open Access Journals (Sweden)

    Nurhan Gursel Ozmen

    2018-01-01

    Full Text Available Classification of electroencephalogram (EEG signal is important in mental decoding for brain-computer interfaces (BCI. We introduced a feature extraction approach based on frequency domain analysis to improve the classification performance on different mental tasks using single-channel EEG. This biologically inspired method extracts the most discriminative spectral features from power spectral densities (PSDs of the EEG signals. We applied our method on a dataset of six subjects who performed five different imagination tasks: (i resting state, (ii mental arithmetic, (iii imagination of left hand movement, (iv imagination of right hand movement, and (v imagination of letter “A.” Pairwise and multiclass classifications were performed in single EEG channel using Linear Discriminant Analysis and Support Vector Machines. Our method produced results (mean classification accuracy of 83.06% for binary classification and 91.85% for multiclassification that are on par with the state-of-the-art methods, using single-channel EEG with low computational cost. Among all task pairs, mental arithmetic versus letter imagination yielded the best result (mean classification accuracy of 90.29%, indicating that this task pair could be the most suitable pair for a binary class BCI. This study contributes to the development of single-channel BCI, as well as finding the best task pair for user defined applications.

  8. Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering

    Directory of Open Access Journals (Sweden)

    Chin-Teng Lin

    2018-01-01

    Full Text Available Electroencephalogram (EEG signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA, feature extraction, and the Gaussian mixture model (GMM to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.

  9. EEG amplitude modulation analysis for semi-automated diagnosis of Alzheimer's disease

    Science.gov (United States)

    Falk, Tiago H.; Fraga, Francisco J.; Trambaiolli, Lucas; Anghinah, Renato

    2012-12-01

    Recent experimental evidence has suggested a neuromodulatory deficit in Alzheimer's disease (AD). In this paper, we present a new electroencephalogram (EEG) based metric to quantitatively characterize neuromodulatory activity. More specifically, the short-term EEG amplitude modulation rate-of-change (i.e., modulation frequency) is computed for five EEG subband signals. To test the performance of the proposed metric, a classification task was performed on a database of 32 participants partitioned into three groups of approximately equal size: healthy controls, patients diagnosed with mild AD, and those with moderate-to-severe AD. To gauge the benefits of the proposed metric, performance results were compared with those obtained using EEG spectral peak parameters which were recently shown to outperform other conventional EEG measures. Using a simple feature selection algorithm based on area-under-the-curve maximization and a support vector machine classifier, the proposed parameters resulted in accuracy gains, relative to spectral peak parameters, of 21.3% when discriminating between the three groups and by 50% when mild and moderate-to-severe groups were merged into one. The preliminary findings reported herein provide promising insights that automated tools may be developed to assist physicians in very early diagnosis of AD as well as provide researchers with a tool to automatically characterize cross-frequency interactions and their changes with disease.

  10. EEG and ocular correlates of circadian melatonin phase and human performance decrements during sleep loss

    Science.gov (United States)

    Cajochen, C.; Khalsa, S. B.; Wyatt, J. K.; Czeisler, C. A.; Dijk, D. J.

    1999-01-01

    The aim of this study was to quantify the associations between slow eye movements (SEMs), eye blink rate, waking electroencephalogram (EEG) power density, neurobehavioral performance, and the circadian rhythm of plasma melatonin in a cohort of 10 healthy men during up to 32 h of sustained wakefulness. The time course of neurobehavioral performance was characterized by fairly stable levels throughout the first 16 h of wakefulness followed by deterioration during the phase of melatonin secretion. This deterioration was closely associated with an increase in SEMs. Frontal low-frequency EEG activity (1-7 Hz) exhibited a prominent increase with time awake and little circadian modulation. EEG alpha activity exhibited circadian modulation. The dynamics of SEMs and EEG activity were phase locked to changes in neurobehavioral performance and lagged the plasma melatonin rhythm. The data indicate that frontal areas of the brain are more susceptible to sleep loss than occipital areas. Frontal EEG activity and ocular parameters may be used to monitor and predict changes in neurobehavioral performance associated with sleep loss and circadian misalignment.

  11. BNDF heterozygosity is associated with memory deficits and alterations in cortical and hippocampal EEG power.

    Science.gov (United States)

    Geist, Phillip A; Dulka, Brooke N; Barnes, Abigail; Totty, Michael; Datta, Subimal

    2017-08-14

    Brain derived neurotrophic factor (BDNF) plays a pivotal role in structural plasticity, learning, and memory. Electroencephalogram (EEG) spectral power in the cortex and hippocampus has also been correlated with learning and memory. In this study, we investigated the effect of globally reduced BDNF levels on learning behavior and EEG power via BDNF heterozygous (KO) rats. We employed several behavioral tests that are thought to depend on cortical and hippocampal plasticity to varying degrees: novel object recognition, a test that is reliant on a variety of cognitive systems; contextual fear, which is highly hippocampal-dependent; and cued fear, which has been shown to be amygdala-dependent. We also examined the effects of BDNF reduction on cortical and hippocampal EEG spectral power via chronically implanted electrodes in the motor cortex and dorsal hippocampus. We found that BDNF KO rats were impaired in novelty recognition and fear memory retention, while hippocampal EEG power was decreased in slow waves and increased in fast waves. Interestingly, our results, for the first time, show sexual dimorphism in each of our tests. These results support the hypothesis that BDNF drives both cognitive plasticity and coordinates EEG activity patterns, potentially serving as a link between the two. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. Serial identification of EEG patterns using adaptive wavelet-based analysis

    Science.gov (United States)

    Nazimov, A. I.; Pavlov, A. N.; Nazimova, A. A.; Grubov, V. V.; Koronovskii, A. A.; Sitnikova, E.; Hramov, A. E.

    2013-10-01

    A problem of recognition specific oscillatory patterns in the electroencephalograms with the continuous wavelet-transform is discussed. Aiming to improve abilities of the wavelet-based tools we propose a serial adaptive method for sequential identification of EEG patterns such as sleep spindles and spike-wave discharges. This method provides an optimal selection of parameters based on objective functions and enables to extract the most informative features of the recognized structures. Different ways of increasing the quality of patterns recognition within the proposed serial adaptive technique are considered.

  13. Comparative Study of Data Classification Methods Between EEG and ECoG Used to BCI

    Directory of Open Access Journals (Sweden)

    Yu Ge

    2014-09-01

    Full Text Available Effective decoding of the source signal is a key to improve Brain-computer interfaces (BCI performances. Two groups of motor imagery (MI data based on electroencephalograms (EEG and electrocorticograms (ECoG which provided by International Brain-Computer Interface Competition organization are analyzed, and concluded that ECoG signals processing is more suitable for model-driven approaches. Temporal-frequency features were extracted by model-driven method instead of data-driven method and compared, and classified by support vector machine (SVM. The results show 6 % improvement of motor imagery experiment classification accuracy on ECoG data, compared with of data-driven method.

  14. Hybrid EEG-EOG brain-computer interface system for practical machine control.

    Science.gov (United States)

    Punsawad, Yunyong; Wongsawat, Yodchanan; Parnichkun, Manukid

    2010-01-01

    Practical issues such as accuracy with various subjects, number of sensors, and time for training are important problems of existing brain-computer interface (BCI) systems. In this paper, we propose a hybrid framework for the BCI system that can make machine control more practical. The electrooculogram (EOG) is employed to control the machine in the left and right directions while the electroencephalogram (EEG) is employed to control the forword, no action, and complete stop motions of the machine. By using only 2-channel biosignals, the average classification accuracy of more than 95% can be achieved.

  15. Feature Selection Strategy for Classification of Single-Trial EEG Elicited by Motor Imagery

    DEFF Research Database (Denmark)

    Prasad, Swati; Tan, Zheng-Hua; Prasad, Ramjee

    2011-01-01

    Brain-Computer Interface (BCI) provides new means of communication for people with motor disabilities by utilizing electroencephalographic activity. Selection of features from Electroencephalogram (EEG) signals for classification plays a key part in the development of BCI systems. In this paper, we...... present a feature selection strategy consisting of channel selection by fisher ratio analysis in the frequency domain and time segment selection by visual inspection in time domain. The proposed strategy achieves an absolute improvement of 7.5% in the misclassification rate as compared with the baseline...

  16. [Influence of ketogenic diet on the clinical effects and electroencephalogram features in 31 children with pharmacoresistant epileptic encephalopathy].

    Science.gov (United States)

    Li, Bao-Min; Tong, Li-Li; Jia, Gui-Juan; Wang, Ji-Wen; Lei, Ge-Fei; Yin, Ping; Sun, Ruo-Peng

    2013-05-01

    To investigate the effect of ketogenic diet (KD) on the clinical and electroencephalogram features in children with pharmacoresistant epileptic encephalopathy. Thirty-one children (19 boys, 12 girls) aged 7 months to 7 years (mean 2 years 5 month) with epilepsy refractory to conventional antiepileptic drugs (AEDs) were included in this study. In addition to their original AED treatment, the children were assigned to different ketogenic diets based on their age. The prospective electro-clinical assessment was performed prior to the KD and then one week, one month and again 3 months after the initiation of therapy, respectively. The reduction of seizure frequency in 52%, 68% and 71% of all patients exceeded 50% one week, one month and three months after KD treatment respectively. KD is particularly effective in myoclonic astatic epilepsy (MAE; Doose Syndrome) and West syndrome with 100% and 81.25% of the patients having a greater than 50% seizure reduction, respectively. After 3 months of KD treatment, more than 2/3 patients experienced a reduction in interictal epileptiform discharges (IEDs) and improvement in EEG background. The clinical and electroencephalographic improvement confirms that KD is beneficial in children with refractory epilepsy.

  17. EEG alpha and level of response to alcohol in Hispanic- and non-Hispanic-American young adults with a family history of alcoholism.

    Science.gov (United States)

    Ehlers, Cindy L; Phillips, Evelyn; Wall, Tamara L; Wilhelmsen, Kirk; Schuckit, Marc A

    2004-05-01

    A person's level of response to alcohol is associated with family history of alcoholism, ethnic heritage and electroencephalogram (EEG) phenotype. The present study's aims were to investigate EEG alpha and response to alcohol in Hispanic-American and non-Hispanic-American young adults. EEG power in the slow and fast alpha frequency ranges (7.5-9 Hz, 9-12 Hz) was obtained at baseline and at 74 minutes following the administration of alcohol to Hispanic-American (n = 79) and white non-Hispanic-American (n = 208) young adult men and women (18-25 years old), all of whom had a family history but no personal history of alcohol dependence. Measures of breath alcohol concentrations and subjective responses to alcohol (Subjective High Assessment Scale [SHAS]) also were ascertained. Alcohol was found to produce significant effects on EEG power in the slow (F = 79.5, p SHAS scores (F = 5.2, 2/283 df, p SHAS.

  18. Quantitative EEG and neurofeedback in children and adolescents: anxiety disorders, depressive disorders, comorbid addiction and attention-deficit/hyperactivity disorder, and brain injury.

    Science.gov (United States)

    Simkin, Deborah R; Thatcher, Robert W; Lubar, Joel

    2014-07-01

    This article explores the science surrounding neurofeedback. Both surface neurofeedback (using 2-4 electrodes) and newer interventions, such as real-time z-score neurofeedback (electroencephalogram [EEG] biofeedback) and low-resolution electromagnetic tomography neurofeedback, are reviewed. The limited literature on neurofeedback research in children and adolescents is discussed regarding treatment of anxiety, mood, addiction (with comorbid attention-deficit/hyperactivity disorder), and traumatic brain injury. Future potential applications, the use of quantitative EEG for determining which patients will be responsive to medications, the role of randomized controlled studies in neurofeedback research, and sensible clinical guidelines are considered. Copyright © 2014 Elsevier Inc. All rights reserved.

  19. The effect of different EEG derivations on sleep staging in rats: the frontal midline–parietal bipolar electrode for sleep scoring

    International Nuclear Information System (INIS)

    Fang, Guangzhan; Zhang, Chunpeng; Xia, Yang; Lai, Yongxiu; Liu, Tiejun; You, Zili; Yao, Dezhong

    2009-01-01

    Most sleep-staging research has focused on developing and optimizing algorithms for sleep scoring, but little attention has been paid to the effect of different electroencephalogram (EEG) derivations on sleep staging. To explore the possible effects of EEG derivations, an automatic computer method was established and confirmed by agreement analysis between the computer and two independent raters, and four fronto-parietal bipolar leads were compared for sleep scoring in rats. The results demonstrated that different bipolar electrodes have significantly different sleep-staging accuracies, and that the optimal frontal electrode for sleep scoring is located at the anterior midline

  20. Spontaneous Slow Fluctuation of EEG Alpha Rhythm Reflects Activity in Deep-Brain Structures: A Simultaneous EEG-fMRI Study.

    Directory of Open Access Journals (Sweden)

    Kei Omata

    Full Text Available The emergence of the occipital alpha rhythm on brain electroencephalogram (EEG is associated with brain activity in the cerebral neocortex and deep brain structures. To further understand the mechanisms of alpha rhythm power fluctuation, we performed simultaneous EEGs and functional magnetic resonance imaging recordings in human subjects during a resting state and explored the dynamic relationship between alpha power fluctuation and blood oxygenation level-dependent (BOLD signals of the brain. Based on the frequency characteristics of the alpha power time series (APTS during 20-minute EEG recordings, we divided the APTS into two components: fast fluctuation (0.04-0.167 Hz and slow fluctuation (0-0.04 Hz. Analysis of the correlation between the MRI signal and each component revealed that the slow fluctuation component of alpha power was positively correlated with BOLD signal changes in the brain stem and the medial part of the thalamus and anterior cingulate cortex, while the fast fluctuation component was correlated with the lateral part of the thalamus and the anterior cingulate cortex, but not the brain stem. In summary, these data suggest that different subcortical structures contribute to slow and fast modulations of alpha spectra on brain EEG.

  1. Wireless Biological Electronic Sensors.

    Science.gov (United States)

    Cui, Yue

    2017-10-09

    The development of wireless biological electronic sensors could open up significant advances for both fundamental studies and practical applications in a variety of areas, including medical diagnosis, environmental monitoring, and defense applications. One of the major challenges in the development of wireless bioelectronic sensors is the successful integration of biosensing units and wireless signal transducers. In recent years, there are a few types of wireless communication systems that have been integrated with biosensing systems to construct wireless bioelectronic sensors. To successfully construct wireless biological electronic sensors, there are several interesting questions: What types of biosensing transducers can be used in wireless bioelectronic sensors? What types of wireless systems can be integrated with biosensing transducers to construct wireless bioelectronic sensors? How are the electrical sensing signals generated and transmitted? This review will highlight the early attempts to address these questions in the development of wireless biological electronic sensors.

  2. Low Cost Electrode Assembly for EEG Recordings in Mice

    Directory of Open Access Journals (Sweden)

    Emily C. Vogler

    2017-11-01

    Full Text Available Wireless electroencephalography (EEG of small animal subjects typically utilizes miniaturized EEG devices which require a robust recording and electrode assembly that remains in place while also being well-tolerated by the animal so as not to impair the ability of the animal to perform normal living activities or experimental tasks. We developed simple and fast electrode assembly and method of electrode implantation using electrode wires and wire-wrap technology that provides both higher survival and success rates in obtaining recordings from the electrodes than methods using screws as electrodes. The new wire method results in a 51% improvement in the number of electrodes that successfully record EEG signal. Also, the electrode assembly remains affixed and provides EEG signal for at least a month after implantation. Screws often serve as recording electrodes, which require either drilling holes into the skull to insert screws or affixing screws to the surface of the skull with adhesive. Drilling holes large enough to insert screws can be invasive and damaging to brain tissue, using adhesives may interfere with conductance and result in a poor signal, and soldering screws to wire leads results in fragile connections. The methods presented in this article provide a robust implant that is minimally invasive and has a significantly higher success rate of electrode implantation. In addition, the implant remains affixed and produces good recordings for over a month, while using economical, easily obtained materials and skills readily available in most animal research laboratories.

  3. Polymorphisms of ADORA2A modulate psychomotor vigilance and the effects of caffeine on neurobehavioural performance and sleep EEG after sleep deprivation

    OpenAIRE

    Bodenmann, S; Hohoff, C; Freitag, C; Deckert, J; Rétey, J V; Bachmann, V; Landolt, H-P

    2012-01-01

    Background and purpose:  Prolonged wakefulness impairs sustained vigilant attention as measured with the psychomotor vigilance task (PVT) and induces a compensatory increase in sleep intensity in recovery sleep as quantified by slow-wave activity (SWA) in the sleep electroencephalogram (EEG). These effects of sleep deprivation are counteracted by the adenosine receptor antagonist caffeine, suggesting that the adenosine neuromodulator/receptor system is importantly involved. To examine a role ...

  4. Local experience-dependent changes in the wake EEG after prolonged wakefulness.

    Science.gov (United States)

    Hung, Ching-Sui; Sarasso, Simone; Ferrarelli, Fabio; Riedner, Brady; Ghilardi, M Felice; Cirelli, Chiara; Tononi, Giulio

    2013-01-01

    Prolonged wakefulness leads to a progressive increase in sleep pressure, reflected in a global increase in slow wave activity (SWA, 0.5-4.5 Hz) in the sleep electroencephalogram (EEG). A global increase in wake theta activity (5-9 Hz) also occurs. Recently, it was shown that prolonged wakefulness in rodents leads to signs of "local sleep" in an otherwise awake brain, accompanied by a slow/theta wave (2-6 Hz) in the local EEG that occurs at different times in different cortical areas. Compelling evidence in animals and humans also indicates that sleep is locally regulated by the amount of experience-dependent plasticity. Here, we asked whether the extended practice of tasks that involve specific brain circuits results in increased occurrence of local intermittent theta waves in the human EEG, above and beyond the global EEG changes previously described. Participants recorded with high-density EEG completed 2 experiments during which they stayed awake ≥ 24 h practicing a language task (audiobook listening [AB]) or a visuomotor task (driving simulator [DS]). Sleep laboratory. 16 healthy participants (7 females). Two extended wake periods. Both conditions resulted in global increases in resting wake EEG theta power at the end of 24 h of wake, accompanied by increased sleepiness. Moreover, wake theta power as well as the occurrence and amplitude of theta waves showed regional, task-dependent changes, increasing more over left frontal derivations in AB, and over posterior parietal regions in DS. These local changes in wake theta power correlated with similar local changes in sleep low frequencies including SWA. Extended experience-dependent plasticity of specific circuits results in a local increase of the wake theta EEG power in those regions, followed by more intense sleep, as reflected by SWA, over the same areas.

  5. How do reference montage and electrodes setup affect the measured scalp EEG potentials?

    Science.gov (United States)

    Hu, Shiang; Lai, Yongxiu; Valdes-Sosa, Pedro A.; Bringas-Vega, Maria L.; Yao, Dezhong

    2018-04-01

    Objective. Human scalp electroencephalogram (EEG) is widely applied in cognitive neuroscience and clinical studies due to its non-invasiveness and ultra-high time resolution. However, the representativeness of the measured EEG potentials for the underneath neural activities is still a problem under debate. This study aims to investigate systematically how both reference montage and electrodes setup affect the accuracy of EEG potentials. Approach. First, the standard EEG potentials are generated by the forward calculation with a single dipole in the neural source space, for eleven channel numbers (10, 16, 21, 32, 64, 85, 96, 128, 129, 257, 335). Here, the reference is the ideal infinity implicitly determined by forward theory. Then, the standard EEG potentials are transformed to recordings with different references including five mono-polar references (Left earlobe, Fz, Pz, Oz, Cz), and three re-references (linked mastoids (LM), average reference (AR) and reference electrode standardization technique (REST)). Finally, the relative errors between the standard EEG potentials and the transformed ones are evaluated in terms of channel number, scalp regions, electrodes layout, dipole source position and orientation, as well as sensor noise and head model. Main results. Mono-polar reference recordings are usually of large distortions; thus, a re-reference after online mono-polar recording should be adopted in general to mitigate this effect. Among the three re-references, REST is generally superior to AR for all factors compared, and LM performs worst. REST is insensitive to head model perturbation. AR is subject to electrodes coverage and dipole orientation but no close relation with channel number. Significance. These results indicate that REST would be the first choice of re-reference and AR may be an alternative option for high level sensor noise case. Our findings may provide the helpful suggestions on how to obtain the EEG potentials as accurately as possible for

  6. Design of an online EEG based neurofeedback game for enhancing attention and memory.

    Science.gov (United States)

    Thomas, Kavitha P; Vinod, A P; Guan, Cuntai

    2013-01-01

    Brain-Computer Interface (BCI) is an alternative communication and control channel between brain and computer which finds applications in neuroprosthetics, brain wave controlled computer games etc. This paper proposes an Electroencephalogram (EEG) based neurofeedback computer game that allows the player to control the game with the help of attention based brain signals. The proposed game protocol requires the player to memorize a set of numbers in a matrix, and to correctly fill the matrix using his attention. The attention level of the player is quantified using sample entropy features of EEG. The statistically significant performance improvement of five healthy subjects after playing a number of game sessions demonstrates the effectiveness of the proposed game in enhancing their concentration and memory skills.

  7. Type III intermittency: a nonlinear dynamic model of EEG burst suppression.

    Science.gov (United States)

    Rae-Grant, A D; Kim, Y W

    1994-01-01

    Burst suppression electroencephalograms from 9 comatose patients have been studied using nonlinear dynamic techniques. These EEG records show many dynamical features characteristic of nonlinear systems, including sensitive dependence on initial conditions, self-organization, similarity across scales, and intermittency. Histograms of burst durations showed an asymmetric distribution with a decreasing tail of increasing duration. Interpreting the histograms from the standpoint of intermittency classifications of iterated dynamical maps, the absence of any conspicuous maximal cut-off duration suggests a type III intermittency. The power-law exponent of the decreasing tail is -3/2 for type III intermittency in the large scale sample size limit, and we have found the EEGs to be consistent with type III intermittency behavior. We have also developed a nonlinear algorithm which models burst suppression pattern based on a low dimensional return map. Burst suppression pattern appears to be the constrained activity of a nonlinear dynamical system at the transition to chaos.

  8. EEG oscillatory states as neuro-phenomenology of consciousness as revealed from patients in vegetative and minimally conscious states.

    Science.gov (United States)

    Fingelkurts, Alexander A; Fingelkurts, Andrew A; Bagnato, Sergio; Boccagni, Cristina; Galardi, Giuseppe

    2012-03-01

    The value of resting electroencephalogram (EEG) in revealing neural constitutes of consciousness (NCC) was examined. We quantified the dynamic repertoire, duration and oscillatory type of EEG microstates in eyes-closed rest in relation to the degree of expression of clinical self-consciousness. For NCC a model was suggested that contrasted normal, severely disturbed state of consciousness and state without consciousness. Patients with disorders of consciousness were used. Results suggested that the repertoire, duration and oscillatory type of EEG microstates in resting condition quantitatively related to the level of consciousness expression in brain-damaged patients and healthy-conscious subjects. Specifically, results demonstrated that (a) decreased number of EEG microstate types was associated with altered states of consciousness, (b) unawareness was associated with the lack of diversity in EEG alpha-rhythmic microstates, and (c) the probability for the occurrence and duration of delta-, theta- and slow-alpha-rhythmic microstates were associated with unawareness, whereas the probability for the occurrence and duration of fast-alpha-rhythmic microstates were associated with consciousness. In conclusion, resting EEG has a potential value in revealing NCC. This work may have implications for clinical care and medical-legal decisions in patients with disorders of consciousness. Copyright © 2011 Elsevier Inc. All rights reserved.

  9. Joint Maximum Likelihood Time Delay Estimation of Unknown Event-Related Potential Signals for EEG Sensor Signal Quality Enhancement

    Directory of Open Access Journals (Sweden)

    Kyungsoo Kim

    2016-06-01

    Full Text Available Electroencephalograms (EEGs measure a brain signal that contains abundant information about the human brain function and health. For this reason, recent clinical brain research and brain computer interface (BCI studies use EEG signals in many applications. Due to the significant noise in EEG traces, signal processing to enhance the signal to noise power ratio (SNR is necessary for EEG analysis, especially for non-invasive EEG. A typical method to improve the SNR is averaging many trials of event related potential (ERP signal that represents a brain’s response to a particular stimulus or a task. The averaging, however, is very sensitive to variable delays. In this study, we propose two time delay estimation (TDE schemes based on a joint maximum likelihood (ML criterion to compensate the uncertain delays which may be different in each trial. We evaluate the performance for different types of signals such as random, deterministic, and real EEG signals. The results show that the proposed schemes provide better performance than other conventional schemes employing averaged signal as a reference, e.g., up to 4 dB gain at the expected delay error of 10°.

  10. Simultaneous wireless electrophysiological and neurochemical monitoring

    Science.gov (United States)

    Murari, Kartikeya; Mollazadeh, Mohsen; Thakor, Nitish; Cauwenberghs, Gert

    2008-08-01

    Information processing and propagation in the central nervous system is mostly electrical in nature. At synapses, electrical signals cause the release of neurotransmitters like dopamine, glutamate etc., that are sensed by post-synaptic neurons resulting in signal propagation or inhibition. It can be very informative to monitor electrical and neurochemical signals simultaneously to understand the mechanisms underlying normal or abnormal brain function. We present an integrated system for the simultaneous wireless acquisition of neurophysiological and neurochemical activity. Applications of the system to neuroscience include monitoring EEG and glutamate in rat somatosensory cortex following global ischemia.

  11. Comparison of Amplitude-Integrated EEG and Conventional EEG in a Cohort of Premature Infants.

    Science.gov (United States)

    Meledin, Irina; Abu Tailakh, Muhammad; Gilat, Shlomo; Yogev, Hagai; Golan, Agneta; Novack, Victor; Shany, Eilon

    2017-03-01

    To compare amplitude-integrated EEG (aEEG) and conventional EEG (EEG) activity in premature neonates. Biweekly aEEG and EEG were simultaneously recorded in a cohort of infants born less than 34 weeks gestation. aEEG recordings were visually assessed for lower and upper border amplitude and bandwidth. EEG recordings were compressed for visual evaluation of continuity and assessed using a signal processing software for interburst intervals (IBI) and frequencies' amplitude. Ten-minute segments of aEEG and EEG indices were compared using regression analysis. A total of 189 recordings from 67 infants were made, from which 1697 aEEG/EEG pairs of 10-minute segments were assessed. Good concordance was found for visual assessment of continuity between the 2 methods. EEG IBI, alpha and theta frequencies' amplitudes were negatively correlated to the aEEG lower border while conceptional age (CA) was positively correlated to aEEG lower border ( P continuity and amplitude.

  12. Aesthetic preference recognition of 3D shapes using EEG.

    Science.gov (United States)

    Chew, Lin Hou; Teo, Jason; Mountstephens, James

    2016-04-01

    Recognition and identification of aesthetic preference is indispensable in industrial design. Humans tend to pursue products with aesthetic values and make buying decisions based on their aesthetic preferences. The existence of neuromarketing is to understand consumer responses toward marketing stimuli by using imaging techniques and recognition of physiological parameters. Numerous studies have been done to understand the relationship between human, art and aesthetics. In this paper, we present a novel preference-based measurement of user aesthetics using electroencephalogram (EEG) signals for virtual 3D shapes with motion. The 3D shapes are designed to appear like bracelets, which is generated by using the Gielis superformula. EEG signals were collected by using a medical grade device, the B-Alert X10 from advance brain monitoring, with a sampling frequency of 256 Hz and resolution of 16 bits. The signals obtained when viewing 3D bracelet shapes were decomposed into alpha, beta, theta, gamma and delta rhythm by using time-frequency analysis, then classified into two classes, namely like and dislike by using support vector machines and K-nearest neighbors (KNN) classifiers respectively. Classification accuracy of up to 80 % was obtained by using KNN with the alpha, theta and delta rhythms as the features extracted from frontal channels, Fz, F3 and F4 to classify two classes, like and dislike.

  13. Interictal spike EEG source analysis in hypothalamic hamartoma epilepsy.

    Science.gov (United States)

    Leal, Alberto J R; Passão, Vitorina; Calado, Eulália; Vieira, José P; Silva Cunha, João P

    2002-12-01

    The epilepsy associated with the hypothalamic hamartomas constitutes a syndrome with peculiar seizures, usually refractory to medical therapy, mild cognitive delay, behavioural problems and multifocal spike activity in the scalp electroencephalogram (EEG). The cortical origin of spikes has been widely assumed but not specifically demonstrated. We present results of a source analysis of interictal spikes from 4 patients (age 2-25 years) with epilepsy and hypothalamic hamartoma, using EEG scalp recordings (32 electrodes) and realistic boundary element models constructed from volumetric magnetic resonance imaging (MRIs). Multifocal spike activity was the most common finding, distributed mainly over the frontal and temporal lobes. A spike classification based on scalp topography was done and averaging within each class performed to improve the signal to noise ratio. Single moving dipole models were used, as well as the Rap-MUSIC algorithm. All spikes with good signal to noise ratio were best explained by initial deep sources in the neighbourhood of the hamartoma, with late sources located in the cortex. Not a single patient could have his spike activity explained by a combination of cortical sources. Overall, the results demonstrate a consistent origin of spike activity in the subcortical region in the neighbourhood of the hamartoma, with late spread to cortical areas.

  14. Music increases frontal EEG coherence during verbal learning.

    Science.gov (United States)

    Peterson, David A; Thaut, Michael H

    2007-02-02

    Anecdotal and some empirical evidence suggests that music can enhance learning and memory. However, the mechanisms by which music modulates the neural activity associated with learning and memory remain largely unexplored. We evaluated coherent frontal oscillations in the electroencephalogram (EEG) while subjects were engaged in a modified version of Rey's Auditory Verbal Learning Test (AVLT). Subjects heard either a spoken version of the AVLT or the conventional AVLT word list sung. Learning-related changes in coherence (LRCC) were measured by comparing the EEG during word encoding on correctly recalled trials to the immediately preceding trial on which the same word was not recalled. There were no significant changes in coherence associated with conventional verbal learning. However, musical verbal learning was associated with increased coherence within and between left and right frontal areas in theta, alpha, and gamma frequency bands. It is unlikely that the different patterns of LRCC reflect general performance differences; the groups exhibited similar learning performance. The results suggest that verbal learning with a musical template strengthens coherent oscillations in frontal cortical networks involved in verbal encoding.

  15. LORETA EEG phase reset of the default mode network.

    Science.gov (United States)

    Thatcher, Robert W; North, Duane M; Biver, Carl J

    2014-01-01

    The purpose of this study was to explore phase reset of 3-dimensional current sources in Brodmann areas located in the human default mode network (DMN) using Low Resolution Electromagnetic Tomography (LORETA) of the human electroencephalogram (EEG). The EEG was recorded from 19 scalp locations from 70 healthy normal subjects ranging in age from 13 to 20 years. A time point by time point computation of LORETA current sources were computed for 14 Brodmann areas comprising the DMN in the delta frequency band. The Hilbert transform of the LORETA time series was used to compute the instantaneous phase differences between all pairs of Brodmann areas. Phase shift and lock durations were calculated based on the 1st and 2nd derivatives of the time series of phase differences. Phase shift duration exhibited three discrete modes at approximately: (1) 25 ms, (2) 50 ms, and (3) 65 ms. Phase lock duration present primarily at: (1) 300-350 ms and (2) 350-450 ms. Phase shift and lock durations were inversely related and exhibited an exponential change with distance between Brodmann areas. The results are explained by local neural packing density of network hubs and an exponential decrease in connections with distance from a hub. The results are consistent with a discrete temporal model of brain function where anatomical hubs behave like a "shutter" that opens and closes at specific durations as nodes of a network giving rise to temporarily phase locked clusters of neurons for specific durations.

  16. Creutzfeldt-Jakob disease versus anti-LGI1 limbic encephalitis in a patient with progressive cognitive dysfunction, psychiatric symptoms, involuntary facio-brachio-crural movement, and an abnormal electroencephalogram: a case report

    Directory of Open Access Journals (Sweden)

    Sun L

    2015-06-01

    Full Text Available Li Sun, Jie Cao, Chang Liu, Yudan LvDepartment of Neurology, The First Hospital of JiLin University, ChangChun, People’s Republic of ChinaAbstract: Diagnosis of Creutzfeldt-Jakob disease (CJD is often challenging in elderly individuals, not only because of its variable clinical features but also because of nonspecific changes on the electroencephalogram (EEG in the early stages of the disease. Here we report on a patient who presented with progressive cognitive dysfunction, psychiatric symptoms, involuntary facio-brachio-crural movement, and an abnormal EEG. We provide a detailed analysis and differential diagnosis between anti-leucine-rich glioma inactivated 1 (LGI1 limbic encephalitis versus CJD, in the hope of providing a new understanding of CJD. A 65-year-old Chinese man presented with slowly progressive cognitive decline with psychiatric symptoms. On admission, he presented with facial grimacing and brief left upper limb dystonic posturing lasting 1–2 seconds, with hyponatremia that was difficult to rectify. Neurological examination showed increased muscle tension in the left limb but without pathological reflexes. His early EEG showed focal periodic wave complexes. Diffusion-weighted magnetic resonance imaging showed a suspected “lace sign” in the occipital cortex. His cerebrospinal fluid was negative for LGI1 antibodies and positive for 14-3-3 brain protein. Therefore, we made a presumptive diagnosis of CJD. At the following visit, a second EEG showed paroxysmal sharp wave complexes, but the patient had a poor prognosis. Atypical facio-brachio-crural movement and nonspecific EEG changes may occasionally be found in patients with CJD or anti-LGI1 encephalitis. Clinicians should not be dissuaded from a diagnosis of CJD where the EEG does not show paroxysmal sharp wave complexes in the early stages but abnormal facio-brachio-crural movement is present.Keywords: abnormal facio-brachio-crural movement, hyponatremia, Creutzfeldt

  17. On-off intermittency of thalamo-cortical neuronal network oscillations in the electroencephalogram of rodents with genetic predisposition to absence epilepsy

    Science.gov (United States)

    Hramov, Alexander E.; Grubov, Vadim V.; Pavlov, Alexey N.; Sitnikova, Evgenija Yu.; Koronovskii, Alexey A.; Runnova, Anastasija E.; Shurugina, Sveltlana A.; Ivanov, Alexey V.

    2013-02-01

    Spike-wave discharges are electroencephalographic hallmarks of absence epilepsy. Spike-wave discharges are known to originate from thalamo-cortical neuronal network that normally produces sleep spindle oscillations. Although both sleep spindles and spike-wave discharges are considered as thalamo-cortical oscillations, functional relationship between them is still uncertain. The present study describes temporal dynamics of spike-wave discharges and sleep spindles as determined in long-time electroencephalograms (EEG) recorded in WAG/Rij rat model of absence epilepsy. We have proposed the wavelet-based method for the automatic detection of spike-wave discharges, sleep spindles (10-15Hz) and 5-9Hz oscillations in EEG. It was found that non-linear dynamics of spike-wave discharges and sleep spindles fits well to the law of 'on-off intermittency'. Intermittency in sleep spindles and spike-wave discharges implies that (1) temporal dynamics of these oscillations are deterministic in nature, and (2) it might be controlled by a system-level mechanism responsible for circadian modulation of neuronal network activity.

  18. Emotion Recognition from Single-Trial EEG Based on Kernel Fisher’s Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Yi-Hung Liu

    2014-07-01

    Full Text Available Electroencephalogram-based emotion recognition (EEG-ER has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI. However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher’s discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher’s emotion pattern (KFEP, and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68% and arousal (84.79% among all testing methods.

  19. In the twinkling of an eye: synchronization of EEG and eye tracking based on blink signatures

    DEFF Research Database (Denmark)

    Bækgaard, Per; Petersen, Michael Kai; Larsen, Jakob Eg

    2014-01-01

    ACHIEVING ROBUST ADAPTIVE SYNCHRONIZATION OF MULTIMODAL BIOMETRIC INPUTS: The recent arrival of wireless EEG headsets that enable mobile real-time 3D brain imaging on smartphones, and low cost eye trackers that provide gaze control of tablets, will radically change how biometric sensors might...... be integrated into next generation user interfaces. In experimental lab settings EEG neuroimaging and eye tracking data are traditionally combined using external triggers to synchronize the signals. However, with biometric sensors increasingly being applied in everyday usage scenarios, there will be a need...

  20. Role of EEG as Biomarker in the Early Detection and Classification of Dementia

    Directory of Open Access Journals (Sweden)

    Noor Kamal Al-Qazzaz

    2014-01-01

    Full Text Available The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.

  1. Improving Cross-Day EEG-Based Emotion Classification Using Robust Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Yuan-Pin Lin

    2017-07-01

    Full Text Available Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability, arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18 and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40% as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing neither achieved the cross-day classification (the accuracy was around chance level nor replicated the encouraging improvement due to the inter-day EEG variability. This result

  2. A Wireless Sensor Enabled by Wireless Power

    Science.gov (United States)

    Lee, Da-Sheng; Liu, Yu-Hong; Lin, Chii-Ruey

    2012-01-01

    Through harvesting energy by wireless charging and delivering data by wireless communication, this study proposes the concept of a wireless sensor enabled by wireless power (WPWS) and reports the fabrication of a prototype for functional tests. One WPWS node consists of wireless power module and sensor module with different chip-type sensors. Its main feature is the dual antenna structure. Following RFID system architecture, a power harvesting antenna was designed to gather power from a standard reader working in the 915 MHz band. Referring to the Modbus protocol, the other wireless communication antenna was integrated on a node to send sensor data in parallel. The dual antenna structure integrates both the advantages of an RFID system and a wireless sensor. Using a standard UHF RFID reader, WPWS can be enabled in a distributed area with a diameter up to 4 m. Working status is similar to that of a passive tag, except that a tag can only be queried statically, while the WPWS can send dynamic data from the sensors. The function is the same as a wireless sensor node. Different WPWSs equipped with temperature and humidity, optical and airflow velocity sensors are tested in this study. All sensors can send back detection data within 8 s. The accuracy is within 8% deviation compared with laboratory equipment. A wireless sensor network enabled by wireless power should be a totally wireless sensor network using WPWS. However, distributed WPWSs only can form a star topology, the simplest topology for constructing a sensor network. Because of shielding effects, it is difficult to apply other complex topologies. Despite this limitation, WPWS still can be used to extend sensor network applications in hazardous environments. Further research is needed to improve WPWS to realize a totally wireless sensor network. PMID:23443370

  3. A Wireless Sensor Enabled by Wireless Power

    Directory of Open Access Journals (Sweden)

    Da-Sheng Lee

    2012-11-01

    Full Text Available Through harvesting energy by wireless charging and delivering data by wireless communication, this study proposes the concept of a wireless sensor enabled by wireless power (WPWS and reports the fabrication of a prototype for functional tests. One WPWS node consists of wireless power module and sensor module with different chip-type sensors. Its main feature is the dual antenna structure. Following RFID system architecture, a power harvesting antenna was designed to gather power from a standard reader working in the 915 MHz band. Referring to the Modbus protocol, the other wireless communication antenna was integrated on a node to send sensor data in parallel. The dual antenna structure integrates both the advantages of an RFID system and a wireless sensor. Using a standard UHF RFID reader, WPWS can be enabled in a distributed area with a diameter up to 4 m. Working status is similar to that of a passive tag, except that a tag can only be queried statically, while the WPWS can send dynamic data from the sensors. The function is the same as a wireless sensor node. Different WPWSs equipped with temperature and humidity, optical and airflow velocity sensors are tested in this study. All sensors can send back detection data within 8 s. The accuracy is within 8% deviation compared with laboratory equipment. A wireless sensor network enabled by wireless power should be a totally wireless sensor network using WPWS. However, distributed WPWSs only can form a star topology, the simplest topology for constructing a sensor network. Because of shielding effects, it is difficult to apply other complex topologies. Despite this limitation, WPWS still can be used to extend sensor network applications in hazardous environments. Further research is needed to improve WPWS to realize a totally wireless sensor network.

  4. A wireless sensor enabled by wireless power.

    Science.gov (United States)

    Lee, Da-Sheng; Liu, Yu-Hong; Lin, Chii-Ruey

    2012-11-22

    Through harvesting energy by wireless charging and delivering data by wireless communication, this study proposes the concept of a wireless sensor enabled by wireless power (WPWS) and reports the fabrication of a prototype for functional tests. One WPWS node consists of wireless power module and sensor module with different chip-type sensors. Its main feature is the dual antenna structure. Following RFID system architecture, a power harvesting antenna was designed to gather power from a standard reader working in the 915 MHz band. Referring to the Modbus protocol, the other wireless communication antenna was integrated on a node to send sensor data in parallel. The dual antenna structure integrates both the advantages of an RFID system and a wireless sensor. Using a standard UHF RFID reader, WPWS can be enabled in a distributed area with a diameter up to 4 m. Working status is similar to that of a passive tag, except that a tag can only be queried statically, while the WPWS can send dynamic data from the sensors. The function is the same as a wireless sensor node. Different WPWSs equipped with temperature and humidity, optical and airflow velocity sensors are tested in this study. All sensors can send back detection data within 8 s. The accuracy is within 8% deviation compared with laboratory equipment. A wireless sensor network enabled by wireless power should be a totally wireless sensor network using WPWS. However, distributed WPWSs only can form a star topology, the simplest topology for constructing a sensor network. Because of shielding effects, it is difficult to apply other complex topologies. Despite this limitation, WPWS still can be used to extend sensor network applications in hazardous environments. Further research is needed to improve WPWS to realize a totally wireless sensor network.

  5. Predicting seizure by modeling synaptic plasticity based on EEG signals - a case study of inherited epilepsy

    Science.gov (United States)

    Zhang, Honghui; Su, Jianzhong; Wang, Qingyun; Liu, Yueming; Good, Levi; Pascual, Juan M.

    2018-03-01

    This paper explores the internal dynamical mechanisms of epileptic seizures through quantitative modeling based on full brain electroencephalogram (EEG) signals. Our goal is to provide seizure prediction and facilitate treatment for epileptic patients. Motivated by an earlier mathematical model with incorporated synaptic plasticity, we studied the nonlinear dynamics of inherited seizures through a differential equation model. First, driven by a set of clinical inherited electroencephalogram data recorded from a patient with diagnosed Glucose Transporter Deficiency, we developed a dynamic seizure model on a system of ordinary differential equations. The model was reduced in complexity after considering and removing redundancy of each EEG channel. Then we verified that the proposed model produces qualitatively relevant behavior which matches the basic experimental observations of inherited seizure, including synchronization index and frequency. Meanwhile, the rationality of the connectivity structure hypothesis in the modeling process was verified. Further, through varying the threshold condition and excitation strength of synaptic plasticity, we elucidated the effect of synaptic plasticity to our seizure model. Results suggest that synaptic plasticity has great effect on the duration of seizure activities, which support the plausibility of therapeutic interventions for seizure control.

  6. Predicting seizure by modeling synaptic plasticity based on EEG signals - a case study of inherited epilepsy.

    Science.gov (United States)

    Zhang, Honghui; Su, Jianzhong; Wang, Qingyun; Liu, Yueming; Good, Levi; Pascual, Juan

    2018-03-01

    This paper explores the internal dynamical mechanisms of epileptic seizures through quantitative modeling based on full brain electroencephalogram (EEG) signals. Our goal is to provide seizure prediction and facilitate treatment for epileptic patients. Motivated by an earlier mathematical model with incorporated synaptic plasticity, we studied the nonlinear dynamics of inherited seizures through a differential equation model. First, driven by a set of clinical inherited electroencephalogram data recorded from a patient with diagnosed Glucose Transporter Deficiency, we developed a dynamic seizure model on a system of ordinary differential equations. The model was reduced in complexity after considering and removing redundancy of each EEG channel. Then we verified that the proposed model produces qualitatively relevant behavior which matches the basic experimental observations of inherited seizure, including synchronization index and frequency. Meanwhile, the rationality of the connectivity structure hypothesis in the modeling process was verified. Further, through varying the threshold condition and excitation strength of synaptic plasticity, we elucidated the effect of synaptic plasticity to our seizure model. Results suggest that synaptic plasticity has great effect on the duration of seizure activities, which support the plausibility of therapeutic interventions for seizure control.

  7. On the Use of EEG or MEG Brain Imaging Tools in Neuromarketing Research

    Directory of Open Access Journals (Sweden)

    Giovanni Vecchiato

    2011-01-01

    Full Text Available Here we present an overview of some published papers of interest for the marketing research employing electroencephalogram (EEG and magnetoencephalogram (MEG methods. The interest for these methodologies relies in their high-temporal resolution as opposed to the investigation of such problem with the functional Magnetic Resonance Imaging (fMRI methodology, also largely used in the marketing research. In addition, EEG and MEG technologies have greatly improved their spatial resolution in the last decades with the introduction of advanced signal processing methodologies. By presenting data gathered through MEG and high resolution EEG we will show which kind of information it is possible to gather with these methodologies while the persons are watching marketing relevant stimuli. Such information will be related to the memorization and pleasantness related to such stimuli. We noted that temporal and frequency patterns of brain signals are able to provide possible descriptors conveying information about the cognitive and emotional processes in subjects observing commercial advertisements. These information could be unobtainable through common tools used in standard marketing research. We also show an example of how an EEG methodology could be used to analyze cultural differences between fruition of video commercials of carbonated beverages in Western and Eastern countries.

  8. Recognizing of stereotypic patterns in epileptic EEG using empirical modes and wavelets

    Science.gov (United States)

    Grubov, V. V.; Sitnikova, E.; Pavlov, A. N.; Koronovskii, A. A.; Hramov, A. E.

    2017-11-01

    Epileptic activity in the form of spike-wave discharges (SWD) appears in the electroencephalogram (EEG) during absence seizures. This paper evaluates two approaches for detecting stereotypic rhythmic activities in EEG, i.e., the continuous wavelet transform (CWT) and the empirical mode decomposition (EMD). The CWT is a well-known method of time-frequency analysis of EEG, whereas EMD is a relatively novel approach for extracting signal's waveforms. A new method for pattern recognition based on combination of CWT and EMD is proposed. It was found that this combined approach resulted to the sensitivity of 86.5% and specificity of 92.9% for sleep spindles and 97.6% and 93.2% for SWD, correspondingly. Considering strong within- and between-subjects variability of sleep spindles, the obtained efficiency in their detection was high in comparison with other methods based on CWT. It is concluded that the combination of a wavelet-based approach and empirical modes increases the quality of automatic detection of stereotypic patterns in rat's EEG.

  9. On the use of EEG or MEG brain imaging tools in neuromarketing research.

    Science.gov (United States)

    Vecchiato, Giovanni; Astolfi, Laura; De Vico Fallani, Fabrizio; Toppi, Jlenia; Aloise, Fabio; Bez, Francesco; Wei, Daming; Kong, Wanzeng; Dai, Jounging; Cincotti, Febo; Mattia, Donatella; Babiloni, Fabio

    2011-01-01

    Here we present an overview of some published papers of interest for the marketing research employing electroencephalogram (EEG) and magnetoencephalogram (MEG) methods. The interest for these methodologies relies in their high-temporal resolution as opposed to the investigation of such problem with the functional Magnetic Resonance Imaging (fMRI) methodology, also largely used in the marketing research. In addition, EEG and MEG technologies have greatly improved their spatial resolution in the last decades with the introduction of advanced signal processing methodologies. By presenting data gathered through MEG and high resolution EEG we will show which kind of information it is possible to gather with these methodologies while the persons are watching marketing relevant stimuli. Such information will be related to the memorization and pleasantness related to such stimuli. We noted that temporal and frequency patterns of brain signals are able to provide possible descriptors conveying information about the cognitive and emotional processes in subjects observing commercial advertisements. These information could be unobtainable through common tools used in standard marketing research. We also show an example of how an EEG methodology could be used to analyze cultural differences between fruition of video commercials of carbonated beverages in Western and Eastern countries.

  10. Quantum neural network-based EEG filtering for a brain-computer interface.

    Science.gov (United States)

    Gandhi, Vaibhav; Prasad, Girijesh; Coyle, Damien; Behera, Laxmidhar; McGinnity, Thomas Martin

    2014-02-01

    A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.

  11. EEG biometric identification: a thorough exploration of the time-frequency domain

    Science.gov (United States)

    DelPozo-Banos, Marcos; Travieso, Carlos M.; Weidemann, Christoph T.; Alonso, Jesús B.

    2015-10-01

    Objective. Although interest in using electroencephalogram (EEG) activity for subject identification has grown in recent years, the state of the art still lacks a comprehensive exploration of the discriminant information within it. This work aims to fill this gap, and in particular, it focuses on the time-frequency representation of the EEG. Approach. We executed qualitative and quantitative analyses of six publicly available data sets following a sequential experimentation approach. This approach was divided in three blocks analysing the configuration of the power spectrum density, the representation of the data and the properties of the discriminant information. A total of ten experiments were applied. Main results. Results show that EEG information below 40 Hz is unique enough to discriminate across subjects (a maximum of 100 subjects were evaluated here), regardless of the recorded cognitive task or the sensor location. Moreover, the discriminative power of rhythms follows a W-like shape between 1 and 40 Hz, with the central peak located at the posterior rhythm (around 10 Hz). This information is maximized with segments of around 2 s, and it proved to be moderately constant across montages and time. Significance. Therefore, we characterize how EEG activity differs across individuals and detail the optimal conditions to detect subject-specific information. This work helps to clarify the results of previous studies and to solve some unanswered questions. Ultimately, it will serve as guide for the design of future biometric systems.

  12. EEG signal classification using PSO trained RBF neural network for epilepsy identification

    Directory of Open Access Journals (Sweden)

    Sandeep Kumar Satapathy

    Full Text Available The electroencephalogram (EEG is a low amplitude signal generated in the brain, as a result of information flow during the communication of several neurons. Hence, careful analysis of these signals could be useful in understanding many human brain disorder diseases. One such disease topic is epileptic seizure identification, which can be identified via a classification process of the EEG signal after preprocessing with the discrete wavelet transform (DWT. To classify the EEG signal, we used a radial basis function neural network (RBFNN. As shown herein, the network can be trained to optimize the mean square error (MSE by using a modified particle swarm optimization (PSO algorithm. The key idea behind the modification of PSO is to introduce a method to overcome the problem of slow searching in and around the global optimum solution. The effectiveness of this procedure was verified by an experimental analysis on a benchmark dataset which is publicly available. The result of our experimental analysis revealed that the improvement in the algorithm is significant with respect to RBF trained by gradient descent and canonical PSO. Here, two classes of EEG signals were considered: the first being an epileptic and the other being non-epileptic. The proposed method produced a maximum accuracy of 99% as compared to the other techniques. Keywords: Electroencephalography, Radial basis function neural network, Particle swarm optimization, Discrete wavelet transform, Machine learning

  13. Stage 2 Sleep EEG Sigma Activity and Motor Learning in Childhood ADHD: A Pilot Study.

    Science.gov (United States)

    Saletin, Jared M; Coon, William G; Carskadon, Mary A

    2017-01-01

    Attention deficit hyperactivity disorder (ADHD) is associated with deficits in motor learning and sleep. In healthy adults, overnight improvements in motor skills are associated with sleep spindle activity in the sleep electroencephalogram (EEG). This association is poorly characterized in children, particularly in pediatric ADHD. Polysomnographic sleep was monitored in 7 children with ADHD and 14 typically developing controls. All children were trained on a validated motor sequence task (MST) in the evening with retesting the following morning. Analyses focused on MST precision (speed-accuracy trade-off). NREM Stage 2 sleep EEG power spectral analyses focused on spindle-frequency EEG activity in the sigma (12-15 Hz) band. The ADHD group demonstrated a selective decrease in power within the sigma band. Evening MST precision was lower in ADHD, yet no difference in performance was observed following sleep. Moreover, ADHD status moderated the association between slow sleep spindle activity (12-13.5 Hz) and overnight improvement; spindle-frequency EEG activity was positively associated with performance improvements in children with ADHD but not in controls. These data highlight the importance of sleep in supporting next-day behavior in ADHD while indicating that differences in sleep neurophysiology may contribute to deficits in this population.

  14. REM Sleep EEG Instability in REM Sleep Behavior Disorder and Clonazepam Effects.

    Science.gov (United States)

    Ferri, Raffaele; Rundo, Francesco; Silvani, Alessandro; Zucconi, Marco; Bruni, Oliviero; Ferini-Strambi, Luigi; Plazzi, Giuseppe; Manconi, Mauro

    2017-08-01

    We aimed to analyze quantitatively rapid eye movement (REM) sleep electroencephalogram (EEG) in controls, drug-naïve idiopathic REM sleep behavior disorder patients (iRBD), and iRBD patients treated with clonazepam. Twenty-nine drug-naïve iRBD patients (mean age 68.2 years), 14 iRBD patients under chronic clonazepam therapy (mean age 66.3 years), and 21 controls (mean age 66.8 years) were recruited. Power spectra were obtained from sleep EEG (central derivation), using a 2-second sliding window, with 1-second steps. The power values of each REM sleep EEG spectral band (one every second) were normalized with respect to the average power value obtained during sleep stage 2 in the same individual. In drug-naïve patients, the normalized power values showed a less pronounced REM-related decrease of power in all bands with frequency sleep EEG structure changes found in this study disclose subtle but significant alterations in the cortical electrophysiology of RBD that might represent the early expression of the supposed neurodegenerative processes already taking place at this stage of the disease and might be the target of better and effective future therapeutic strategies for this condition. © Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

  15. Development of Brain EEG Connectivity across Early Childhood: Does Sleep Play a Role?

    Directory of Open Access Journals (Sweden)

    Monique K. LeBourgeois

    2013-11-01

    Full Text Available Sleep has beneficial effects on brain function and learning, which are reflected in plastic changes in the cortex. Early childhood is a time of rapid maturation in fundamental skills—e.g., language, cognitive control, working memory—that are predictive of future functioning. Little is currently known about the interactions between sleep and brain maturation during this developmental period. We propose coherent electroencephalogram (EEG activity during sleep may provide unique insight into maturational processes of functional brain connectivity. Longitudinal sleep EEG assessments were performed in eight healthy subjects at ages 2, 3 and 5 years. Sleep EEG coherence increased across development in a region- and frequency-specific manner. Moreover, although connectivity primarily decreased intra-hemispherically across a night of sleep, an inter-hemispheric overnight increase occurred in the frequency range of slow waves (0.8–2 Hz, theta (4.8–7.8 Hz and sleep spindles (10–14 Hz, with connectivity changes of up to 20% across a night of sleep. These findings indicate sleep EEG coherence reflects processes of brain maturation—i.e., programmed unfolding of neuronal networks—and moreover, sleep-related alterations of brain connectivity during the sensitive maturational window of early childhood.

  16. Review on solving the forward problem in EEG source analysis

    Directory of Open Access Journals (Sweden)

    Vergult Anneleen

    2007-11-01

    Full Text Available Abstract Background The aim of electroencephalogram (EEG source localization is to find the brain areas responsible for EEG waves of interest. It consists of solving forward and inverse problems. The forward problem is solved by starting from a given electrical source and calculating the potentials at the electrodes. These evaluations are necessary to solve the inverse problem which is defined as finding brain sources which are responsible for the measured potentials at the EEG electrodes. Methods While other reviews give an extensive summary of the both forward and inverse problem, this review article focuses on different aspects of solving the forward problem and it is intended for newcomers in this research field. Results It starts with focusing on the generators of the EEG: the post-synaptic potentials in the apical dendrites of pyramidal neurons. These cells generate an extracellular current which can be modeled by Poisson's differential equation, and Neumann and Dirichlet boundary conditions. The compartments in which these currents flow can be anisotropic (e.g. skull and white matter. In a three-shell spherical head model an analytical expression exists to solve the forward problem. During the last two decades researchers have tried to solve Poisson's equation in a realistically shaped head model obtained from 3D medical images, which requires numerical methods. The following methods are compared with each other: the boundary element method (BEM, the finite element method (FEM and the finite difference method (FDM. In the last two methods anisotropic conducting compartments can conveniently be introduced. Then the focus will be set on the use of reciprocity in EEG source localization. It is introduced to speed up the forward calculations which are here performed for each electrode position rather than for each dipole position. Solving Poisson's equation utilizing FEM and FDM corresponds to solving a large sparse linear system. Iterative

  17. EEG topography during sleep inertia upon awakening after a period of increased homeostatic sleep pressure.

    Science.gov (United States)

    Gorgoni, Maurizio; Ferrara, Michele; D'Atri, Aurora; Lauri, Giulia; Scarpelli, Serena; Truglia, Ilaria; De Gennaro, Luigi

    2015-07-01

    Behavioral and physiological indexes of high sleep inertia (SI) characterize the awakening from recovery (REC) sleep after prolonged wakefulness, but the associated electroencephalogram (EEG) topography has never been investigated. Here, we compare the EEG topography following the awakening from baseline (BSL) and REC sleep. We have recorded the EEG waking activity of 26 healthy subjects immediately after the awakening from BSL sleep and from REC sleep following 40 h of prolonged wakefulness. In both BSL and REC conditions, 12 subjects were awakened from stage 2 sleep, and 14 subjects from rapid eye movement (REM) sleep. The full-scalp waking EEG (eyes closed) was recorded after all awakenings. Subjects awakened from REC sleep showed a reduction of fronto-central alpha and beta-1 activities, while no significant effects of the sleep stage of awakening have been observed. Positive correlations between pre- and post-awakening EEG modifications following REC sleep have been found in the posterior and lateral cortices in the frequency ranges from theta to beta-2 and (only for REM awakenings) extending to the fronto-central regions in the beta-1 band, and in the midline central and parietal derivations for the alpha and delta bands, respectively. These findings suggest that the higher SI after REC sleep may be due to the fronto-central decrease of alpha and beta-1 activity and to the persistence of the sleep EEG features after awakening in the posterior, lateral, and fronto-central cortices, without influences of the sleep stage of awakening. Copyright © 2015 Elsevier B.V. All rights reserved.

  18. Adenosine deaminase polymorphism affects sleep EEG spectral power in a large epidemiological sample.

    Directory of Open Access Journals (Sweden)

    Diego Robles Mazzotti

    Full Text Available Slow wave oscillations in the electroencephalogram (EEG during sleep may reflect both sleep need and intensity, which are implied in homeostatic regulation. Adenosine is strongly implicated in sleep homeostasis, and a single nucleotide polymorphism in the adenosine deaminase gene (ADA G22A has been associated with deeper and more efficient sleep. The present study verified the association between the ADA G22A polymorphism and changes in sleep EEG spectral power (from C3-A2, C4-A1, O1-A2, and O2-A1 derivations in the Epidemiologic Sleep Study (EPISONO sample from São Paulo, Brazil. Eight-hundred individuals were subjected to full-night polysomnography and ADA G22A genotyping. Spectral analysis of the EEG was carried out in all individuals using fast Fourier transformation of the signals from each EEG electrode. The genotype groups were compared in the whole sample and in a subsample of 120 individuals matched according to ADA genotype for age, gender, body mass index, caffeine intake status, presence of sleep disturbance, and sleep-disturbing medication. When compared with homozygous GG genotype carriers, A allele carriers showed higher delta spectral power in Stage 1 and Stages 3+4 of sleep, and increased theta spectral power in Stages 1, 2 and REM sleep. These changes were seen both in the whole sample and in the matched subset. The higher EEG spectral power indicates that the sleep of individuals carrying the A allele may be more intense. Therefore, this polymorphism may be an important source of variation in sleep homeostasis in humans, through modulation of specific components of the sleep EEG.

  19. Adaptive Wireless Transceiver Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Many wireless technologies are already available for sensor applications. It is inevitable that many non-interoperable wireless technologies between 400 MHz and 5.8...

  20. Dry EEG Electrodes

    Directory of Open Access Journals (Sweden)

    M. A. Lopez-Gordo

    2014-07-01

    Full Text Available Electroencephalography (EEG emerged in the second decade of the 20th century as a technique for recording the neurophysiological response. Since then, there has been little variation in the physical principles that sustain the signal acquisition probes, otherwise called electrodes. Currently, new advances in technology have brought new unexpected fields of applications apart from the clinical, for which new aspects such as usability and gel-free operation are first order priorities. Thanks to new advances in materials and integrated electronic systems technologies, a new generation of dry electrodes has been developed to fulfill the need. In this manuscript, we review current approaches to develop dry EEG electrodes for clinical and other applications, including information about measurement methods and evaluation reports. We conclude that, although a broad and non-homogeneous diversity of approaches has been evaluated without a consensus in procedures and methodology, their performances are not far from those obtained with wet electrodes, which are considered the gold standard, thus enabling the former to be a useful tool in a variety of novel applications.

  1. INTELLIGENT EEG ANALYSIS

    Directory of Open Access Journals (Sweden)

    M. Murugesan

    2011-04-01

    Full Text Available Brain is the wonderful organ of human body. It is the agent of information collection and transformation. The neural activity of the human brain starts between the 17th and 23rd week of prenatal development. It is believed that from this early stage and throughout life electrical signals are generated by the brain function but also the status of the whole body. Understanding of neuronal functions and neurophysiologic properties of the brain function together with the mechanisms underlying the generation of signals and their recording is, however, vital for those who deal with these signals for detection, diagnosis, and treatment of brain disorders and the related diseases. This research paper concentrated only on brain tumor detection. Using minimum electrode location the brain tumor possibility is detected. This paper is separated into two parts: the First part deals with electrode location on the scalp and the second part deals with how the fuzzy logic rule based algorithm is applied for estimation of brain tumor from EEG. Basically 8 locations are identified. After acquiring the pure EEG signal Fuzzy Logic Rule is applied to predict the possibility of brain tumor.

  2. Wireless Communication Technologies

    Indian Academy of Sciences (India)

    First page Back Continue Last page Overview Graphics. Wireless Communication Technologies. Since 1999, the wireless LAN has experienced a tremendous growth. Reasons: Adoption of industry standards. Interoperability testing. The progress of wireless equipments to higher data rates. Rapid decrease in product ...

  3. EEG spectral phenotypes: heritability and association with marijuana and alcohol dependence in an American Indian community study.

    Science.gov (United States)

    Ehlers, Cindy L; Phillips, Evelyn; Gizer, Ian R; Gilder, David A; Wilhelmsen, Kirk C

    2010-01-15

    Native Americans have some of the highest rates of marijuana and alcohol use and abuse, yet neurobiological measures associated with dependence on these substances in this population remain unknown. The present investigation evaluated the heritability of spectral characteristics of the electroencephalogram (EEG) and their correlation with marijuana and alcohol dependence in an American Indian community. Participants (n=626) were evaluated for marijuana (MJ) and alcohol (ALC) dependence, as well as other psychiatric disorders. EEGs were collected from six cortical sites and spectral power determined in five frequency bands (delta 1.0-4.0 Hz, theta 4.0-7.5 Hz, alpha 7.5-12.0 Hz, low beta 12.0-20.0 Hz and high beta/gamma 20-50 Hz). The estimated heritability (h(2)) of the EEG phenotypes was calculated using SOLAR, and ranged from 0.16 to 0.67. Stepwise linear regression was used to detect correlations between MJ and ALC dependence and the spectral characteristics of the EEG using a model that took into account: age, gender, Native American Heritage (NAH) and a lifetime diagnosis of antisocial personality and/or conduct disorder (ASPD/CD). Increases in spectral power in the delta frequency range, were significantly correlated with gender (pEEG delta and high beta/gamma activity are correlated with MJ dependence and alcohol dependence, respectively, in this community sample of Native Americans. Copyright (c) 2009 Elsevier Ireland Ltd. All rights reserved.

  4. Time-frequency analysis of band-limited EEG with BMFLC and Kalman filter for BCI applications

    Science.gov (United States)

    2013-01-01

    Background Time-Frequency analysis of electroencephalogram (EEG) during different mental tasks received significant attention. As EEG is non-stationary, time-frequency analysis is essential to analyze brain states during different mental tasks. Further, the time-frequency information of EEG signal can be used as a feature for classification in brain-computer interface (BCI) applications. Methods To accurately model the EEG, band-limited multiple Fourier linear combiner (BMFLC), a linear combination of truncated multiple Fourier series models is employed. A state-space model for BMFLC in combination with Kalman filter/smoother is developed to obtain accurate adaptive estimation. By virtue of construction, BMFLC with Kalman filter/smoother provides accurate time-frequency decomposition of the bandlimited signal. Results The proposed method is computationally fast and is suitable for real-time BCI applications. To evaluate the proposed algorithm, a comparison with short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for both synthesized and real EEG data is performed in this paper. The proposed method is applied to BCI Competition data IV for ERD detection in comparison with existing methods. Conclusions Results show that the proposed algorithm can provide optimal time-frequency resolution as compared to STFT and CWT. For ERD detection, BMFLC-KF outperforms STFT and BMFLC-KS in real-time applicability with low computational requirement. PMID:24274109

  5. Epileptic seizure detection from EEG signals with phase-amplitude cross-frequency coupling and support vector machine

    Science.gov (United States)

    Liu, Yang; Wang, Jiang; Cai, Lihui; Chen, Yingyuan; Qin, Yingmei

    2018-03-01

    As a pattern of cross-frequency coupling (CFC), phase-amplitude coupling (PAC) depicts the interaction between the phase and amplitude of distinct frequency bands from the same signal, and has been proved to be closely related to the brain’s cognitive and memory activities. This work utilized PAC and support vector machine (SVM) classifier to identify the epileptic seizures from electroencephalogram (EEG) data. The entropy-based modulation index (MI) matrixes are used to express the strength of PAC, from which we extracted features as the input for classifier. Based on the Bonn database, which contains five datasets of EEG segments obtained from healthy volunteers and epileptic subjects, a 100% classification accuracy is achieved for identifying seizure ictal from healthy data, and an accuracy of 97.67% is reached in the classification of ictal EEG signals from inter-ictal EEGs. Based on the CHB-MIT database which is a group of continuously recorded epileptic EEGs by scalp electrodes, a 97.50% classification accuracy is obtained and a raising sign of MI value is found at 6s before seizure onset. The classification performance in this work is effective, and PAC can be considered as a useful tool for detecting and predicting the epileptic seizures and providing reference for clinical diagnosis.

  6. CCNA Wireless Study Guide

    CERN Document Server

    Lammle, Todd

    2010-01-01

    A complete guide to the CCNA Wireless exam by leading networking authority Todd Lammle. The CCNA Wireless certification is the most respected entry-level certification in this rapidly growing field. Todd Lammle is the undisputed authority on networking, and this book focuses exclusively on the skills covered in this Cisco certification exam. The CCNA Wireless Study Guide joins the popular Sybex study guide family and helps network administrators advance their careers with a highly desirable certification.: The CCNA Wireless certification is the most respected entry-level wireless certification

  7. Nocturnal electroencephalogram registrations in type 1 (insulin-dependent) diabetic patients with hypoglycaemia

    DEFF Research Database (Denmark)

    Bendtson, I; Gade, J; Rosenfalck, A M

    1991-01-01

    Eight Type 1 (insulin-dependent) diabetic patients with no diabetic complications were studied overnight for two consecutive and one subsequent night with continuous monitoring of electroencephalogram and serial hormone measurements. The aims were: 1) to evaluate the influence of spontaneous...... and insulin-induced hypoglycaemia on nocturnal electroencephalogram sleep-patterns and, 2) to evaluate counter-regulatory hormone responses. Spontaneous hypoglycaemia occurred on six nights (38%) with blood glucose concentrations less than 3.0 mmol/l and on four nights less than 2.0 mmol/l. All the patients...... experienced insulin-induced hypoglycaemia with a blood glucose nadir of 1.6 (range 1.4-1.9) mmol/l. The electroencephalogram was analysed by a new method developed for this purpose in contrast to the traditional definition of delta-, theta-, alpha- and beta-activity. The blood glucose concentration could...

  8. Sedation with dexmedetomidine for conducting electroencephalogram in a patient with Angelman syndrome: a case report

    Directory of Open Access Journals (Sweden)

    Magda Lourenço Fernandes

    2016-04-01

    Full Text Available ABSTRACT INTRODUCTION: Angelman syndrome is characterized by severe mental retardation and speech and seizure disorders. This rare genetic condition is associated with changes in GABAA receptor. Patients with Angelman syndrome need to be sedated during an electroencephalogram ordered for diagnostic purposes or evolutionary control. Dexmedetomidine, whose action is independent of GABA receptor, promotes a sleep similar to physiological sleep and can facilitate the performing of this examination in patients with Angelman syndrome. CASE REPORT: Female patient, 14 years old, with Angelman syndrome; electroencephalogram done under sedation with dexmedetomidine. The procedure was uneventful and bradycardia or respiratory depression was not recorded. The examination was successfully interpreted and epileptiform activity was not observed. CONCLUSION: Dexmedetomidine promoted satisfactory sedation, was well tolerated and enabled the interpretation of the electroencephalogram in a patient with Angelman syndrome and seizure disorder.

  9. Sedation with dexmedetomidine for conducting electroencephalogram in a patient with Angelman syndrome: a case report.

    Science.gov (United States)

    Fernandes, Magda Lourenço; do Carmo Santos, Maria; Gomez, Renato Santiago

    2016-01-01

    Angelman syndrome is characterized by severe mental retardation and speech and seizure disorders. This rare genetic condition is associated with changes in GABAA receptor. Patients with Angelman syndrome need to be sedated during an electroencephalogram ordered for diagnostic purposes or evolutionary control. Dexmedetomidine, whose action is independent of GABA receptor, promotes a sleep similar to physiological sleep and can facilitate the performing of this examination in patients with Angelman syndrome. Female patient, 14 years old, with Angelman syndrome; electroencephalogram done under sedation with dexmedetomidine. The procedure was uneventful and bradycardia or respiratory depression was not recorded. The examination was successfully interpreted and epileptiform activity was not observed. Dexmedetomidine promoted satisfactory sedation, was well tolerated and enabled the interpretation of the electroencephalogram in a patient with Angelman syndrome and seizure disorder. Copyright © 2014 Sociedade Brasileira de Anestesiologia. Published by Elsevier Editora Ltda. All rights reserved.

  10. Sleep EEG in growth disorders.

    OpenAIRE

    Taylor, B J; Brook, C G

    1986-01-01

    The sleep of 30 children with disorders of growth and development was studied because of the known association between sleep and the secretion of hormones. Thirty three normal children were studied for comparison. The sleep of two consecutive nights was monitored at home using a small portable electroencephalogram and electro-oculogram recorder. Within the normal group there were no significant differences between sexes nor between the first and second nights of recording. There was a signifi...

  11. Modulation of the COMT Val158Met polymorphism on resting-state EEG power in postmenopausal healthy women

    Directory of Open Access Journals (Sweden)

    Silvia eSolis-Ortiz

    2015-04-01

    Full Text Available The catechol-O-methyltransferase (COMT Val158Met polymorphism impacts cortical dopamine levels and may influence cortical electrical activity in the human brain. This study investigated whether COMT genotype influences resting-state electroencephalogram (EEG power in the frontal, parietal and midline regions in healthy volunteers. EEG recordings were conducted in the resting-state in 13 postmenopausal healthy woman carriers of the Val/Val genotype and 11 with the Met/Met genotype. The resting EEG spectral absolute power in the frontal (F3, F4, F7, F8, FC3 and FC4, parietal (CP3, CP4, P3 and P4 and midline (Fz, FCz, Cz, CPz, Pz and Oz was analyzed during the eyes-open and eyes-closed conditions. The frequency bands considered were the delta, theta, alpha1, alpha2, beta1 and beta2. EEG data of the Val/Val and Met/Met genotypes, brain regions and conditions were analyzed using a general linear model analysis. In the individuals with the Met/Met genotype, delta activity was increased in the eyes-closed condition, theta activity was increased in the eyes-closed and in the eyes-open conditions, and alpha1 band, alpha2 band and beta1band activity was increased in the eyes-closed condition.A significant interaction between COMT genotypes and spectral bands was observed. Met homozygote individuals exhibited more delta, theta and beta1 activity than individuals with the Val/Val genotype. No significant interaction between COMT genotypes and the resting-state EEG regional power and conditions were observed for the three brain regions studied. Our findings indicate that the COMT Val158Met polymorphism does not directly impact resting-state EEG regional power, but instead suggest that COMT genotype can modulate resting-state EEG spectral power in postmenopausal healthy women.

  12. Improving the inter-rater agreement of hypsarrhythmia using a simplified EEG grading scale for children with infantile spasms.

    Science.gov (United States)

    Mytinger, John R; Hussain, Shaun A; Islam, Monica P; Millichap, John J; Patel, Anup D; Ryan, Nicole R; Twanow, Jaime-Dawn E; Heyer, Geoffrey L

    2015-10-01

    There is poor inter-rater agreement in determining the presence or absence of hypsarrhythmia among patients with infantile spasms. Yet, remission of hypsarrhythmia has been used as a clinical and research outcome measure. Two important features of hypsarrhythmia are the burden of epileptiform discharges and the amplitudes of background slow waves. We hypothesized that an electroencephalogram (EEG) grading scale emphasizing epileptiform discharge burden and the amplitudes of background slow waves would improve inter-rater agreement in interpreting hypsarrhythmia. Our aim was to assess inter-rater agreement of hypsarrhythmia using a novel and simplified EEG grading scale called the 'BASED' (Burden of Amplitudes and Epileptiform Discharges) score and compare this to the traditional method of EEG analysis. Twenty patients with infantile spasms were prospectively evaluated and electroclinical outcomes were determined. Forty EEG clips (20 pre-treatment and 20 post-treatment), representing the most severely abnormal five minute sleep epoch of each study, were assessed by three reviewers blinded to treatment and clinical outcome. Fleiss' kappa (К) was used to assess the inter-rater agreement in the interpretation of hypsarrhythmia when using the BASED score compared to the traditional method of EEG analysis. Reviewers had favorable inter-rater agreement using the BASED score in interpreting hypsarrhythmia (К: 0.87) compared to when using the traditional method of EEG analysis to interpret hypsarrhythmia (К: 0.09). The three reviewers all agreed on the presence or absence of hypsarrhythmia in 37/40 (93%) epochs using the BASED score but in only 15/40 (38%) epochs using the traditional method of EEG analysis, p=infantile spasms clinical trials must better define criteria for hypsarrhythmia. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

  13. Brain source localization: A new method based on MUltiple SIgnal Classification algorithm and spatial sparsity of the field signal for electroencephalogram measurements

    Science.gov (United States)

    Vergallo, P.; Lay-Ekuakille, A.

    2013-08-01

    Brain activity can be recorded by means of EEG (Electroencephalogram) electrodes placed on the scalp of the patient. The EEG reflects the activity of groups of neurons located in the head, and the fundamental problem in neurophysiology is the identification of the sources responsible of brain activity, especially if a seizure occurs and in this case it is important to identify it. The studies conducted in order to formalize the relationship between the electromagnetic activity in the head and the recording of the generated external field allow to know pattern of brain activity. The inverse problem, that is given the sampling field at different electrodes the underlying asset must be determined, is more difficult because the problem may not have a unique solution, or the search for the solution is made difficult by a low spatial resolution which may not allow to distinguish between activities involving sources close to each other. Thus, sources of interest may be obscured or not detected and known method in source localization problem as MUSIC (MUltiple SIgnal Classification) could fail. Many advanced source localization techniques achieve a best resolution by exploiting sparsity: if the number of sources is small as a result, the neural power vs. location is sparse. In this work a solution based on the spatial sparsity of the field signal is presented and analyzed to improve MUSIC method. For this purpose, it is necessary to set a priori information of the sparsity in the signal. The problem is formulated and solved using a regularization method as Tikhonov, which calculates a solution that is the better compromise between two cost functions to minimize, one related to the fitting of the data, and another concerning the maintenance of the sparsity of the signal. At the first, the method is tested on simulated EEG signals obtained by the solution of the forward problem. Relatively to the model considered for the head and brain sources, the result obtained allows to

  14. Nucleated red blood cells and early EEG: predicting Sarnat stage and two year outcome.

    LENUS (Irish Health Repository)

    Walsh, B H

    2012-01-31

    AIMS: Hypoxic Ischaemic Encephalopathy (HIE) causes characteristic changes of the electroencephalogram (EEG), and a raised Nucleated Red Blood Cell (NRBC) count compared to controls. We wished to examine whether combining these markers could improve their ability to predict HIE severity in the first 24h. METHODS: Term infants with HIE were recruited. NRBC count and continuous multi-channel EEG were recorded within the first 24h. Neurological assessment was carried out at 24 months. A control population with NRBC counts in the first 24h was recruited. RESULTS: 44 infants with HIE and 43 control infants were recruited. Of the HIE population 39 completed a 2 year follow-up. The median NRBC count differed significantly between the controls and those with HIE (3\\/100 WBC [range of 0-11] vs 12.3\\/100 WBC [0-240]) (p<0.001). Within the HIE population the median NRBC count was significantly greater in infants with moderate\\/severe HIE than mild (16\\/100 WBC [range of 0-240] vs 8\\/100 WBC [1-23]) (p=0.016), and among infants with abnormal outcome compared to normal (21.3\\/100 WBC [1-239.8] vs 8.3\\/100 WBC [0-50])(p=0.03). The predictive ability of EEG changed with time post-delivery, therefore results are given at both 12 and 24h of age. At both time points the combined marker had a stronger correlation than EEG alone; with HIE severity (12h: r=0.661 vs r=0.622), (24h: r=0.645 vs r=0.598), and with outcome at 2 years (12h: r=0.756 vs r=0.652), (24h: r=0.802 vs r=0.746). CONCLUSION: Combining early EEG and NRBC count to predict HIE severity and neurological outcome, improved the predictive ability of either in isolation.

  15. Effects of nootropics on the EEG in conscious rats and their modification by glutamatergic inhibitors.

    Science.gov (United States)

    Vorobyov, Vasily; Kaptsov, Vladimir; Kovalev, Georgy; Sengpiel, Frank

    2011-05-30

    To study the effects of acute and repeated injections of nootropics and to learn how glutamate receptors might be involved in their mediation, the frequency spectra of cortical and hippocampal electroencephalogram (EEG) were analyzed in non-narcotized rats subcutaneously injected repeatedly with Piracetam (400mg/kg) or its analogue, Noopept (0.2mg/kg), after intracerebroventricular infusions of saline (5 μl) or the antagonists of NMDA and quisqualate/AMPA receptors: CPP (0.1 nmol) and GDEE (1 μmol), respectively. Piracetam increased alpha/beta1 EEG activity in the left frontal cortex, and alpha activity in both the right cortex and hippocampus, with a 10-min latency and 40-min duration. Noopept increased alpha/beta1 activity, with 30-min latency and 40-min duration in all brain areas. CPP pretreatment eliminated Piracetam EEG effects; reduced Noopept effects in the cortex and completely suppressed them in the hippocampus. After four injections of Piracetam, EEG effects were very small in the cortex, and completely lacking in the hippocampus, while GDEE pretreatment partially recovered them. The effect of Noopept in the alpha/beta1 ranges was replaced by increased beta2 activity after the eighth injection, while no effects were observed after the ninth one. GDEE pretreatment restored the effect of Noopept in the beta2 frequency range. These results demonstrate similarities in EEG effects and their mediatory mechanisms for Piracetam and its much more effective analogue, Noopept. Activation of NMDA receptors is involved in the effects of a single injection of the nootropics, whereas activation of quisqualate/AMPA receptors is associated with the decrease in their efficacy after repeated use. Copyright © 2011 Elsevier Inc. All rights reserved.

  16. Similarities between explicit and implicit motor imagery in mental rotation of hands: an EEG study.

    Science.gov (United States)

    Osuagwu, Bethel A; Vuckovic, Aleksandra

    2014-12-01

    Chronometric and imaging studies have shown that motor imagery is used implicitly during mental rotation tasks in which subjects for example judge the laterality of human hand pictures at various orientations. Since explicit motor imagery is known to activate the sensorimotor areas of the cortex, mental rotation is expected to do similar if it involves a form of motor imagery. So far, functional magnetic resonance imaging and positron emission tomography have been used to study mental rotation and less attention has been paid to electroencephalogram (EEG) which offers a high time-frequency resolution. The time-frequency analysis is an established method for studying explicit motor imagery. Although hand mental rotation is claimed to involve motor imagery, the time-frequency characteristics of mental rotation have never been compared with those of explicit motor imagery. In this study, time-frequency responses of EEG recorded during explicit motor imagery and during a mental rotation task, inducing implicit motor imagery, were compared. Fifteen right-handed healthy volunteers performed motor imagery of hands in one condition and hand laterality judgement tasks in another while EEG of the whole head was recorded. The hand laterality judgement was the mental rotation task used to induce implicit motor imagery. The time-frequency analysis and sLORETA localisation of the EEG showed that the activities in the sensorimotor areas had similar spatial and time-frequency characteristics in explicit motor imagery and implicit motor imagery conditions. Furthermore this sensorimotor activity was different for the left and for the right hand in both explicit and implicit motor imagery. This result supports that motor imagery is used during mental rotation and that it can be detected and studied with EEG technology. This result should encourage the use of mental rotation of body parts in rehabilitation programmes in a similar manner as motor imagery. Copyright © 2014. Published

  17. Prediction of the outcome in cardiac arrest patients undergoing hypothermia using EEG wavelet entropy.

    Science.gov (United States)

    Moshirvaziri, Hana; Ramezan-Arab, Nima; Asgari, Shadnaz

    2016-08-01

    Cardiac arrest (CA) is the leading cause of death in the United States. Induction of hypothermia has been found to improve the functional recovery of CA patients after resuscitation. However, there is no clear guideline for the clinicians yet to determine the prognosis of the CA when patients are treated with hypothermia. The present work aimed at the development of a prognostic marker for the CA patients undergoing hypothermia. A quantitative measure of the complexity of Electroencephalogram (EEG) signals, called wavelet sub-band entropy, was employed to predict the patients' outcomes. We hypothesized that the EEG signals of the patients who survived would demonstrate more complexity and consequently higher values of wavelet sub-band entropies. A dataset of 16-channel EEG signals collected from CA patients undergoing hypothermia at Long Beach Memorial Medical Center was used to test the hypothesis. Following preprocessing of the signals and implementation of the wavelet transform, the wavelet sub-band entropies were calculated for different frequency bands and EEG channels. Then the values of wavelet sub-band entropies were compared among two groups of patients: survived vs. non-survived. Our results revealed that the brain high frequency oscillations (between 64100 Hz) captured from the inferior frontal lobes are significantly more complex in the CA patients who survived (p-value <; 0.02). Given that the non-invasive measurement of EEG is part of the standard clinical assessment for CA patients, the results of this study can enhance the management of the CA patients treated with hypothermia.

  18. Automatic epileptic seizure detection in EEGs using MF-DFA, SVM based on cloud computing.

    Science.gov (United States)

    Zhang, Zhongnan; Wen, Tingxi; Huang, Wei; Wang, Meihong; Li, Chunfeng

    2017-01-01

    Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and support vector machine (SVM). New scheme first extracts features from EEG by MF-DFA during the first stage. Then, the scheme applies a genetic algorithm (GA) to calculate parameters used in SVM and classify the training data according to the selected features using SVM. Finally, the trained SVM classifier is exploited to detect neurological diseases. The algorithm utilizes MLlib from library of SPARK and runs on cloud platform. Applying to a public dataset for experiment, the study results show that the new feature extraction method and scheme can detect signals with less features and the accuracy of the classification reached up to 99%. MF-DFA is a promising approach to extract features for analyzing EEG, because of its simple algorithm procedure and less parameters. The features obtained by MF-DFA can represent samples as well as traditional wavelet transform and Lyapunov exponents. GA can always find useful parameters for SVM with enough execution time. The results illustrate that the classification model can achieve comparable accuracy, which means that it is effective in epileptic seizure detection.

  19. Design of EEG Signal Acquisition System Using Arduino MEGA1280 and EEGAnalyzer

    Directory of Open Access Journals (Sweden)

    Saptono Debyo

    2016-01-01

    Full Text Available This study integrates the hardware circuit design and software development to achieve a 16 channels Electroencephalogram (EEG system for Brain Computer Interface (BCI applications. Signals obtained should be strong enough amplitude that is usually expressed in units of millivolts and reasonably clean of noise that appears when the data acquisition process. The process of data acquisition consists of two stages are the acquisition of the original EEG signal can be done by the active electrode with an instrumentation amplifier or a preamplifier and processing the signal to get better signals with improved signal quality by removing noise using filters with IC OPAMP. The design of a preamplifier with high common-mode rejection ratio and high signal-to-noise ratio is very important. Moreover, the friction between the electrode pads and the skin as well as the design of dual power supply. Designs used single-power AC-coupled circuit, which effectively reduces the DC bias and improves the error caused by the effects of part errors. At the same time, the digital way is applied to design the adjustable amplification and filter function, which can design for different EEG frequency bands. The next step, those EEG signals received by the microcontroller through a port Analog to Digital Converter (ADC that integrated and converted into digital signals and stored in the RAM of microcontroller which simultaneously at 16 ports in accordance with the minimal number of points of data collection on the human scalp. Implementation results have shown the series of acquisitions to work properly so that it can be displayed EEG signals via software EEGAnalyzer.

  20. Changes in EEG indices and serotonin concentrations in depression and anxiety disorders

    Directory of Open Access Journals (Sweden)

    I. V. Kichuk

    2016-01-01

    Full Text Available Electroencephalogram (EEG is an important tool to study brain function. EEG can evaluate the current functional state of the brain with high temporal resolution and identify metabolic and ion disorders that cannot be detected by magnetic resonance imaging.Objective: to analyze the relationship between some neurophysiological and biochemical parameters with a Neuro-KM hardware-software complex for the topographic mapping of brain electrical activity.Patients and methods. 75 patients with depression, 101 with anxiety disorders (AD, and 86 control individuals were examined. EEG spectrum and coherence changes were estimated in the depression and AD groups versus the control group. Correlation analysis of EEG indices and blood serotonin concentrations was carried out.Results and discussion. The patients with depression and those with AD as compared to the controls were observed to have similar EEG spectral changes in the beta band. Coherence analysis in the beta-band showed that both disease groups versus the control group had oppositely directed changes: a reduction in intra- and interhemispheric coherence for depression and its increase for AD (p < 0.001. That in the theta and alpha bands revealed that both disease groups had unidirectional interhemispheric coherence changes: a decrease in integration in the alpha band and its increase in the theta and delta bands in the depression and AD groups (p < 0.05 and multidirectional changes in intrahemispheric coherence: its reduction in the depression group and an increase in the AD group (p < 0.05. Correlation analysis of EEG parameters and platelet serotonin concentrations showed opposite correlations of serotonin concentrations and EEG percentage power in the theta and beta bands. When there were higher serotonin concentrations in the patients with depression, EEG demonstrated a preponderance of a synchronization pattern; when these were in the patients with AD, there was a predominance

  1. Entropy of the electroencephalogram as applied in the M-Entropy S ...

    African Journals Online (AJOL)

    Background: It has been suggested that spectral entropy of the electroencephalogram as applied in the M-Entropy S/5TM Module (GE Healthcare) does not detect the effects of nitrous oxide (N2O). The aim of this study was to investigate the effect on entropy by graded increases in N2O concentrations in the presence of a ...

  2. Electroencephalogram Power Density and Slow Wave Sleep as a Function of Prior Waking and Circadian Phase

    NARCIS (Netherlands)

    Dijk, Derk-Jan; Brunner, Daniel P.; Beersma, Domien G.M.; Borbély, Alexander A.

    1990-01-01

    Human sleep electroencephalograms, recorded in four experiments, were subjected to spectral analysis. Waking prior to sleep varied from 12 to 36 h and sleep was initiated at different circadian phases. Power density of delta and theta frequencies in rapid-eye-movement (REM) sleep and non-REM (NREM)

  3. An efficient detection of epileptic seizure by differentiation and spectral analysis of electroencephalograms.

    Science.gov (United States)

    Kang, Jae-Hwan; Chung, Yoon Gi; Kim, Sung-Phil

    2015-11-01

    Epilepsy is a critical neurological disorder resulting from abnormal hyper-excitability of neurons in the brain. Studies have shown that epilepsy can be detected in electroencephalography (EEG) recordings of patients suffering from seizures. The performance of EEG-based epileptic seizure detection relies largely on how well one can extract features from an EEG that characterize seizure activity. Conventional feature extraction methods using time-series analysis, spectral analysis and nonlinear dynamic analysis have advanced in recent years to improve detection. The computational complexity has also increased to obtain a higher detection rate. This study aimed to develop an efficient feature extraction method based on Hjorth's mobility to reduce computational complexity while maintaining high detection accuracy. A new feature extraction method was proposed by computing the spectral power of Hjorth's mobility components, which were effectively estimated by differentiating EEG signals in real-time. Using EEG data in five epileptic patients, this method resulted in a detection rate of 99.46% between interictal and epileptic EEG signals and 99.78% between normal and epileptic EEG signals, which is comparable to most advanced nonlinear methods. These results suggest that the spectral features of Hjorth's mobility components in EEG signals can represent seizure activity and may pave the way for developing a fast and reliable epileptic seizure detection method. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. EEG frequency PCA in EEG-ERP dynamics.

    Science.gov (United States)

    Barry, Robert J; De Blasio, Frances M

    2018-05-01

    Principal components analysis (PCA) has long been used to decompose the ERP into components, and these mathematical entities are increasingly accepted as meaningful and useful representatives of the electrophysiological components constituting the ERP. A similar expansion appears to be beginning in regard to decomposition of the EEG amplitude spectrum into frequency components via frequency PCA. However, to date, there has been no exploration of the brain's dynamic EEG-ERP linkages using PCA decomposition to assess components in each measure. Here, we recorded intrinsic EEG in both eyes-closed and eyes-open resting conditions, followed by an equiprobable go/no-go task. Frequency PCA of the EEG, including the nontask resting and within-task prestimulus periods, found seven frequency components within the delta to beta range. These differentially predicted PCA-derived go and no-go N1 and P3 ERP components. This demonstration suggests that it may be beneficial in future brain dynamics studies to implement PCA for the derivation of data-driven components from both the ERP and EEG. © 2017 Society for Psychophysiological Research.

  5. Wireless physical layer security

    OpenAIRE

    Poor, H. Vincent; Schaefer, Rafael F.

    2016-01-01

    Security is a very important issue in the design and use of wireless networks. Traditional methods of providing security in such networks are impractical for some emerging types of wireless networks due to the light computational abilities of some wireless devices [such as radio-frequency identification (RFID) tags, certain sensors, etc.] or to the very large scale or loose organizational structure of some networks. Physical layer security has the potential to address these concerns by taking...

  6. A comparison of the electroencephalogram between institutionalized and community children in Romania.

    Science.gov (United States)

    Marshall, Peter J; Fox, Nathan A

    2004-10-01

    Electroencephalographic (EEG) data were collected from a sample of institutionalized infants and young children in Bucharest, Romania, and were compared with EEG data from age-matched children from the local community who had never been institutionalized and who were living with their families in the Bucharest area. Compared with the never-institutionalized group, the institutionalized group showed a pattern of increased low-frequency (theta) power in posterior scalp regions and decreased high-frequency (alpha and beta) power, particularly at frontal and temporal electrode sites. This finding is consistent with EEG studies of children facing environmental adversity and children with learning disorders. The institutionalized group also showed less marked hemispheric EEG asymmetries than the never-institutionalized group, particularly in the temporal region. The results are discussed in the context of two models: that the pattern of EEG in the institutionalized children reflects a maturational lag in nervous system development, or that it reflects tonic cortical hypoactivation.

  7. Flexible Method for the Automated Offline-Detection of Artifacts in Multi-Channel Electroencephalogram Recordings

    DEFF Research Database (Denmark)

    Waser, Markus; Garn, Heinrich; Benke, Thomas

    2017-01-01

    . However, these preprocessing steps do not allow for complete artifact correction. We propose a method for the automated offline-detection of remaining artifacts after preprocessing in multi-channel EEG recordings. In contrast to existing methods it requires neither adaptive parameters varying between...... recordings nor a topography template. It is suited for short EEG segments and is flexible with regard to target applications. The algorithm was developed and tested on 60 clinical EEG samples of 20 seconds each that were recorded both in resting state and during cognitive activation to gain a realistic...... in conjunction with receiver operating characteristics (ROC curves). We observed high sensitivity of 95.5%±4.8 and specificity of 88.8%±2.1. The method has thus shown great potential and is promising as a possible tool for both EEG-based clinical applications and EEG-related research....

  8. A neural mass model of interconnected regions simulates rhythm propagation observed via TMS-EEG.

    Science.gov (United States)

    Cona, F; Zavaglia, M; Massimini, M; Rosanova, M; Ursino, M

    2011-08-01

    Knowledge of cortical rhythms represents an important aspect of modern neuroscience, to understand how the brain realizes its functions. Recent data suggest that different regions in the brain may exhibit distinct electroencephalogram (EEG) rhythms when perturbed by Transcranial Magnetic Stimulation (TMS) and that these rhythms can change due to the connectivity among regions. In this context, in silico simulations may help the validation of these hypotheses that would be difficult to be verified in vivo. Neural mass models can be very useful to simulate specific aspects of electrical brain activity and, above all, to analyze and identify the overall frequency content of EEG in a cortical region of interest (ROI). In this work we implemented a model of connectivity among cortical regions to fit the impulse responses in three ROIs recorded during a series of TMS/EEG experiments performed in five subjects and using three different impulse intensities. In particular we investigated Brodmann Area (BA) 19 (occipital lobe), BA 7 (parietal lobe) and BA 6 (frontal lobe). Results show that the model can reproduce the natural rhythms of the three regions quite well, acting on a few internal parameters. Moreover, the model can explain most rhythm changes induced by stimulation of another region, and inter-subject variability, by estimating just a few long-range connectivity parameters among ROIs. Copyright © 2011 Elsevier Inc. All rights reserved.

  9. Association of autonomic nervous system and EEG scalp potential during playing 2D Grand Turismo 5.

    Science.gov (United States)

    Subhani, Ahmad Rauf; Likun, Xia; Saeed Malik, Aamir

    2012-01-01

    Cerebral activation and autonomic nervous system have importance in studies such as mental stress. The aim of this study is to analyze variations in EEG scalp potential which may influence autonomic activation of heart while playing video games. Ten healthy participants were recruited in this study. Electroencephalogram (EEG) and electrocardiogram (ECG) signals were measured simultaneously during playing video game and rest conditions. Sympathetic and parasympathetic innervations of heart were evaluated from heart rate variability (HRV), derived from the ECG. Scalp potential was measured by the EEG. The results showed a significant upsurge in the value theta Fz/alpha Pz (p<0.001) while playing game. The results also showed tachycardia while playing video game as compared to rest condition (p<0.005). Normalized low frequency power and ratio of low frequency/high frequency power were significantly increased while playing video game and normalized high frequency power sank during video games. Results showed synchronized activity of cerebellum and sympathetic and parasympathetic innervation of heart.

  10. EEG alpha peak frequency analysis during memorizing of figures in patients with mild cognitive impairment.

    Science.gov (United States)

    Schmidt, Magali Taimo; Anghinah, Renato; Basile, Luis Fernando; Forlenza, Oreste; Gattaz, Wagner Faride

    2009-06-01

    To investigate spectral analysis of electroencephalograms (EEG) for the alpha frequency band during rest and cognitive stimulation in healthy adults and individuals with mild cognitive impairment. We analyzed 56 EEGs from 28 patients, 7 men and 21 women, 12 of whom (40%) were controls, 16 patients with mild cognitive impairment (60%). Ages ranged from 61 to 83 years. All individuals were patients in the Psycho-geriatric Out-patients Clinic of LIM-27, of the Psychiatric Institute of the Clinicas Hospital of the Faculty of Medicine of the University of São Paulo, IPq-HCFMUSP, between 2004 and 2007. Each patient underwent two exams with an interval of at least six months between them. During the exam, performed after a period of wakefulness and rest, the patients memorized series of pictures. Analysis of spectral potential both at rest and during the memorizing task showed no statistical differences between baseline and final recordings. Spectral analysis of EEGs showed coherent results with the clinical stability of the patients evaluated but was unable to distinguish between the control group and patients with MCI. Future studies should include a larger sample and a longer follow up.

  11. Nonlinear Dynamic Complexity and Sources of Resting-state EEG in Abstinent Heroin Addicts.

    Science.gov (United States)

    Zhao, Qinglin; Jiang, Hua; Hu, Bin; Li, Yonghui; Zhong, Ning; Li, Mi; Lin, Wenhua; Liu, Quanying

    2017-07-01

    It has been reported that chronic heroin intake induces both structural and functional changes in human brain; however, few studies have investigated the carry-over adverse effects on brain after heroin withdrawal. In this paper, we examined the neurophysiological differences between the abstinent heroin addicts (AHAs) and healthy controls (HCs) using nonlinear dynamic analysis and source localization analysis in resting-state electroencephalogram (EEG) data; 5 min resting EEG data from 20 AHAs and twenty age-, education-, and gender-matched HCs were recorded using 64 electrodes. The results of nonlinear characteristics (e.g., the correlation dimension, Kolmogorov entropy, and Lempel-Ziv complexity) showed that the EEG signals in alpha band from AHAs were significantly more irregular. Moreover, the source localization results confirmed the neuronal activities in alpha band in AHAs were significantly weaker in parietal lobe (BA3 and BA7), frontal lobe (BA4 and BA6), and limbic lobe (BA24). Together, our analysis at both the sensor level and source level suggested the functional abnormalities in the brain during heroin abstinence, in particular for the neuronal oscillations in alpha band.

  12. Assessing Human Mirror Activity With EEG Mu Rhythm: A Meta-Analysis

    Science.gov (United States)

    Fox, Nathan A.; Bakermans-Kranenburg, Marian J.; Yoo, Kathryn H.; Bowman, Lindsay C.; Cannon, Erin N.; Vanderwert, Ross E.; Ferrari, Pier F.; van IJzendoorn, Marinus H.

    2016-01-01

    A fundamental issue in cognitive neuroscience is how the brain encodes others’ actions and intentions. In recent years, a potential advance in our knowledge on this issue is the discovery of mirror neurons in the motor cortex of the nonhuman primate. These neurons fire to both execution and observation of specific types of actions. Researchers use this evidence to fuel investigations of a human mirror system, suggesting a common neural code for perceptual and motor processes. Among the methods used for inferring mirror system activity in humans are changes in a particular frequency band in the electroencephalogram (EEG) called the mu rhythm. Mu frequency appears to decrease in amplitude (reflecting cortical activity) during both action execution and action observation. The current meta-analysis reviewed 85 studies (1,707 participants) of mu that infer human mirror system activity. Results demonstrated significant effect sizes for mu during execution (Cohen’s d = 0.46, N = 701) as well as observation of action (Cohen’s d = 0.31, N = 1,508), confirming a mirroring property in the EEG. A number of moderators were examined to determine the specificity of these effects. We frame these meta-analytic findings within the current discussion about the development and functions of a human mirror system, and conclude that changes in EEG mu activity provide a valid means for the study of human neural mirroring. Suggestions for improving the experimental and methodological approaches in using mu to study the human mirror system are offered. PMID:26689088

  13. Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals.

    Science.gov (United States)

    Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Mubin, Marizan; Saad, Ismail

    2016-01-01

    In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.

  14. Study of heart-brain interactions through EEG, ECG, and emotions

    Science.gov (United States)

    Ramasamy, Mouli; Varadan, Vijay K.

    2017-04-01

    Neurocardiology is the exploration of neurophysiological, neurological and neuroanatomical facets of neuroscience's influence in cardiology. The paraphernalia of emotions on the heart and brain are premeditated because of the interaction between the central and peripheral nervous system. This is an investigative attempt to study emotion based neurocardiology and the factors that influence this phenomenon. The factors include: interaction between sleep EEG (electroencephalogram) and ECG (electrocardiogram), relationship between emotion and music, psychophysiological coherence between the heart and brain, emotion recognition techniques, and biofeedback mechanisms. Emotions contribute vitally to the mundane life and are quintessential to a numerous biological and everyday-functional modality of a human being. Emotions are best represented through EEG signals, and to a certain extent, can be observed through ECG and body temperature. Confluence of medical and engineering science has enabled the monitoring and discrimination of emotions influenced by happiness, anxiety, distress, excitement and several other factors that influence the thinking patterns and the electrical activity of the brain. Similarly, HRV (Heart Rate Variability) widely investigated for its provision and discerning characteristics towards EEG and the perception in neurocardiology.

  15. A Comparison Study on Multidomain EEG Features for Sleep Stage Classification

    Directory of Open Access Journals (Sweden)

    Yu Zhang

    2017-01-01

    Full Text Available Feature extraction from physiological signals of EEG (electroencephalogram is an essential part for sleep staging. In this study, multidomain feature extraction was investigated based on time domain analysis, nonlinear analysis, and frequency domain analysis. Unlike the traditional feature calculation in time domain, a sequence merging method was developed as a preprocessing procedure. The objective is to eliminate the clutter waveform and highlight the characteristic waveform for further analysis. The numbers of the characteristic activities were extracted as the features from time domain. The contributions of features from different domains to the sleep stages were compared. The effectiveness was further analyzed by automatic sleep stage classification and compared with the visual inspection. The overnight clinical sleep EEG recordings of 3 patients after the treatment of Continuous Positive Airway Pressure (CPAP were tested. The obtained results showed that the developed method can highlight the characteristic activity which is useful for both automatic sleep staging and visual inspection. Furthermore, it can be a training tool for better understanding the appearance of characteristic waveforms from raw sleep EEG which is mixed and complex in time domain.

  16. A new near-lossless EEG compression method using ANN-based reconstruction technique.

    Science.gov (United States)

    Hejrati, Behzad; Fathi, Abdolhossein; Abdali-Mohammadi, Fardin

    2017-08-01

    Compression algorithm is an essential part of Telemedicine systems, to store and transmit large amount of medical signals. Most of existing compression methods utilize fixed transforms such as discrete cosine transform (DCT) and wavelet and usually cannot efficiently extract signal redundancy especially for non-stationary signals such as electroencephalogram (EEG). In this paper, we first propose learning-based adaptive transform using combination of DCT and artificial neural network (ANN) reconstruction technique. This adaptive ANN-based transform is applied to the DCT coefficients of EEG data to reduce its dimensionality and also to estimate the original DCT coefficients of EEG in the reconstruction phase. To develop a new near lossless compression method, the difference between the original DCT coefficients and estimated ones are also quantized. The quantized error is coded using Arithmetic coding and sent along with the estimated DCT coefficients as compressed data. The proposed method was applied to various datasets and the results show higher compression rate compared to the state-of-the-art methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. High levels of incidental physical activity are positively associated with cognition and EEG activity in aging.

    Science.gov (United States)

    Sanchez-Lopez, Javier; Silva-Pereyra, Juan; Fernández, Thalía; Alatorre-Cruz, Graciela C; Castro-Chavira, Susana A; González-López, Mauricio; Sánchez-Moguel, Sergio M

    2018-01-01

    High levels of physical activity seem to positively influence health and cognition across the lifespan. Several studies have found that aerobic exercise enhances cognition and likely prevents cognitive decline in the elderly. Nevertheless, the association of incidental physical activity (IPA) with health and cognition during aging has not been studied. Thus, the aim of this study was to evaluate the association of IPA level with cognitive functions and resting electroencephalogram (EEG) in healthy old participants. Participants (n = 97) with normal scores on psychometric and neuropsychological tests and normal values in blood analyses were included. A cluster analysis based on the scores of the Yale Physical Activity Scale (YPAS) allowed the formation of two groups: active, with high levels of IPA, and passive, with low levels of IPA. Eyes-closed resting EEG was recorded from the participants; the fast Fourier transform was used offline to calculate absolute power (AP), relative power (RP), and mean frequency (MF) measures. There were no differences in socioeconomic status, cognitive reserve, general cognitive status, or lipid and TSH profiles between the groups. The results of cognitive tests revealed significant differences in the performance variables of the WAIS scores (p = .015), with advantages for the active group. The resting EEG exhibited significantly slower activity involving the frontal, central, and temporal regions in the passive group (p aging.

  18. Assessing human mirror activity with EEG mu rhythm: A meta-analysis.

    Science.gov (United States)

    Fox, Nathan A; Bakermans-Kranenburg, Marian J; Yoo, Kathryn H; Bowman, Lindsay C; Cannon, Erin N; Vanderwert, Ross E; Ferrari, Pier F; van IJzendoorn, Marinus H

    2016-03-01

    A fundamental issue in cognitive neuroscience is how the brain encodes others' actions and intentions. In recent years, a potential advance in our knowledge on this issue is the discovery of mirror neurons in the motor cortex of the nonhuman primate. These neurons fire to both execution and observation of specific types of actions. Researchers use this evidence to fuel investigations of a human mirror system, suggesting a common neural code for perceptual and motor processes. Among the methods used for inferring mirror system activity in humans are changes in a particular frequency band in the electroencephalogram (EEG) called the mu rhythm. Mu frequency appears to decrease in amplitude (reflecting cortical activity) during both action execution and action observation. The current meta-analysis reviewed 85 studies (1,707 participants) of mu that infer human mirror system activity. Results demonstrated significant effect sizes for mu during execution (Cohen's d = 0.46, N = 701) as well as observation of action (Cohen's d = 0.31, N = 1,508), confirming a mirroring property in the EEG. A number of moderators were examined to determine the specificity of these effects. We frame these meta-analytic findings within the current discussion about the development and functions of a human mirror system, and conclude that changes in EEG mu activity provide a valid means for the study of human neural mirroring. Suggestions for improving the experimental and methodological approaches in using mu to study the human mirror system are offered. (c) 2016 APA, all rights reserved).

  19. P2-15: EEG Analysis on Story Change in TV Drama

    Directory of Open Access Journals (Sweden)

    Chung-Yeon Lee

    2012-10-01

    Full Text Available The human brain naturally recognizes a change of environment or atmosphere without great effort, and this is essential for interactive communication in social life and a specific reaction in an emergency situation. Most studies have investigated change detection of the brain with conditional experimental paradigms rather than the performance of everyday tasks. However, naturally occurring sensory stimuli are multimodal and dynamic. In an effort to study the relationship between users' induced physiological responses and changes of environment and atmosphere under more naturalistic and ecological conditions, we performed a basic experiment using audio-visual movies and electroencephalogram (EEG measurement. 8 healthy subjects were asked to watch a television sitcom without any responses, and their EEG signals were recorded simultaneously with 126 electrodes mounted in an elastic electrode cap. Time-frequency analysis of EEG revealed distinctive neural oscillations at the point of story change in the movie. This result could be used for applications in brain-computer interfaces, and provides a reference to cognitive impairment studies such as Attention Deficit Disorder (ADD or Attention-Deficit/Hyperactivity Disorder (ADHD.

  20. Wireless security in mobile health.

    Science.gov (United States)

    Osunmuyiwa, Olufolabi; Ulusoy, Ali Hakan

    2012-12-01

    Mobile health (m-health) is an extremely broad term that embraces mobile communication in the health sector and data packaging. The four broad categories of wireless networks are wireless personal area network, wireless metropolitan area network, wireless wide area network, and wireless local area network. Wireless local area network is the most notable of the wireless networking tools obtainable in the health sector. Transfer of delicate and critical information on radio frequencies should be secure, and the right to use must be meticulous. This article covers the business opportunities in m-health, threats faced by wireless networks in hospitals, and methods of mitigating these threats.

  1. EEG PHASE RESET OF THE DEFAULT MODE NETWORK

    Directory of Open Access Journals (Sweden)

    Robert W. Thatcher

    2014-07-01

    Full Text Available Objectives: The purpose of this study was to explore phase reset of 3-dimensional current sources located in Brodmann areas located in the human default mode network (DMN using Low Resolution Electromagnetic Tomography (LORETA of the human electroencephalogram (EEG. Methods: The EEG was recorded from 19 scalp locations from 70 healthy normal subjects ranging in age from 13 to 20 years. A time point by time point computation of LORETA current sources were computed for 14 Brodman areas comprising the DMN in the delta frequency band. The Hilbert transform of the LORETA time series was used to compute the instantaneous phase differences between all pairs of Brodmann areas. Phase shift and lock durations were calculated based on the 1st & 2nd derivatives of the time series of phase differences. Results: Phase shift duration exhibited three discrete modes at approximately: 1- 30 msec,, 2- 55 msec and, 3- 65 msec. Phase lock duration present primarily at: 1- 300 to 350 msec and, 2- 350 msec to 450 msec. Phase shift and lock durations were inversely related and exhibited an exponential change with distance between Brodmann areas. Conclusions: The results are explained by local neural packing density of network hubs and an exponential decrease in connections with distance from a hub. The results are consistent with a discrete temporal model of brain function where anatomical hubs behave like a ‘shutter’ that opens and closes at specific durations as nodes of a network giving rise to temporarily phase locked clusters of neurons for specific durations.

  2. Wireless Emulation Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — The Wireless Emulation Laboratory (WEL) is a researchtest bed used to investigate fundamental issues in networkscience. It is a research infrastructure that emulates...

  3. Adaptive Wireless Transceiver Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Wireless technologies are an increasingly attractive means for spatial data, input, manipulation, and distribution. Mobitrum is proposing an innovative Adaptive...

  4. Fusion of Electroencephalogram dynamics and musical contents for estimating emotional responses in music listening

    Directory of Open Access Journals (Sweden)

    Yuan-Pin eLin

    2014-05-01

    Full Text Available Electroencephalography (EEG-based emotion classification during music listening has gained increasing attention nowadays due to its promise of potential applications such as musical affective brain-computer interface (ABCI, neuromarketing, music therapy, and implicit multimedia tagging and triggering. However, music is an ecologically valid and complex stimulus that conveys certain emotions to listeners through compositions of musical elements. Using solely EEG signals to distinguish emotions remained challenging. This study aimed to assess the applicability of a multimodal approach by leveraging the EEG dynamics and acoustic characteristics of musical contents for the classification of emotional valence and arousal. To this end, this study adopted machine-learning methods to systematically elucidate the roles of the EEG and music modalities in the emotion modeling. The empirical results suggested that when whole-head EEG signals were available, the inclusion of musical contents did not improve the classification performance. The obtained performance of 74~76% using solely EEG modality was statistically comparable to that using the multimodality approach. However, if EEG dynamics were only available from a small set of electrodes (likely the case in real-life applications, the music modality would play a complementary role and augment the EEG results from around 61% to 67% in valence classification and from around 58% to 67% in arousal classification. The musical timbre appeared to replace less-discriminative EEG features and led to improvements in both valence and arousal classification, whereas musical loudness was contributed specifically to the arousal classification. The present study not only provided principles for constructing an EEG-based multimodal approach, but also revealed the fundamental insights into the interplay of the brain activity and musical contents in emotion modeling.

  5. Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

    Science.gov (United States)

    Bulea, Thomas C.; Kilicarslan, Atilla; Ozdemir, Recep; Paloski, William H.; Contreras-Vidal, Jose L.

    2013-01-01

    Recent studies support the involvement of supraspinal networks in control of bipedal human walking. Part of this evidence encompasses studies, including our previous work, demonstrating that gait kinematics and limb coordination during treadmill walking can be inferred from the scalp electroencephalogram (EEG) with reasonably high decoding accuracies. These results provide impetus for development of non-invasive brain-machine-interface (BMI) systems for use in restoration and/or augmentation of gait- a primary goal of rehabilitation research. To date, studies examining EEG decoding of activity during gait have been limited to treadmill walking in a controlled environment. However, to be practically viable a BMI system must be applicable for use in everyday locomotor tasks such as over ground walking and turning. Here, we present a novel protocol for non-invasive collection of brain activity (EEG), muscle activity (electromyography (EMG)), and whole-body kinematic data (head, torso, and limb trajectories) during both treadmill and over ground walking tasks. By collecting these data in the uncontrolled environment insight can be gained regarding the feasibility of decoding unconstrained gait and surface EMG from scalp EEG. PMID:23912203

  6. Motor Imagery Classification Using Mu and Beta Rhythms of EEG with Strong Uncorrelating Transform Based Complex Common Spatial Patterns.

    Science.gov (United States)

    Kim, Youngjoo; Ryu, Jiwoo; Kim, Ko Keun; Took, Clive C; Mandic, Danilo P; Park, Cheolsoo

    2016-01-01

    Recent studies have demonstrated the disassociation between the mu and beta rhythms of electroencephalogram (EEG) during motor imagery tasks. The proposed algorithm in this paper uses a fully data-driven multivariate empirical mode decomposition (MEMD) in order to obtain the mu and beta rhythms from the nonlinear EEG signals. Then, the strong uncorrelating transform complex common spatial patterns (SUTCCSP) algorithm is applied to the rhythms so that the complex data, constructed with the mu and beta rhythms, becomes uncorrelated and its pseudocovariance provides supplementary power difference information between the two rhythms. The extracted features using SUTCCSP that maximize the interclass variances are classified using various classification algorithms for the separation of the left- and right-hand motor imagery EEG acquired from the Physionet database. This paper shows that the supplementary information of the power difference between mu and beta rhythms obtained using SUTCCSP provides an important feature for the classification of the left- and right-hand motor imagery tasks. In addition, MEMD is proved to be a preferred preprocessing method for the nonlinear and nonstationary EEG signals compared to the conventional IIR filtering. Finally, the random forest classifier yielded a high performance for the classification of the motor imagery tasks.

  7. Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients’ Consciousness Level Based on Anesthesiologists Experience

    Directory of Open Access Journals (Sweden)

    George J. A. Jiang

    2015-01-01

    Full Text Available Electroencephalogram (EEG signals, as it can express the human brain’s activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA. Bispectral (BIS index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD method and analyzed using sample entropy (SampEn analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN model through using expert assessment of consciousness level (EACL which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.

  8. Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience.

    Science.gov (United States)

    Jiang, George J A; Fan, Shou-Zen; Abbod, Maysam F; Huang, Hui-Hsun; Lan, Jheng-Yan; Tsai, Feng-Fang; Chang, Hung-Chi; Yang, Yea-Wen; Chuang, Fu-Lan; Chiu, Yi-Fang; Jen, Kuo-Kuang; Wu, Jeng-Fu; Shieh, Jiann-Shing

    2015-01-01

    Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.

  9. Detection of Paroxysms in Long-Term, Single-Channel EEG-Monitoring of Patients with Typical Absence Seizures

    Science.gov (United States)

    Kjaer, Troels W.; Sorensen, Helge B. D.; Groenborg, Sabine; Pedersen, Charlotte R.

    2017-01-01

    Absence seizures are associated with generalized 2.5–5 Hz spike-wave discharges in the electroencephalogram (EEG). Rarely are patients, parents, or physicians aware of the duration or incidence of seizures. Six patients were monitored with a portable EEG-device over four times 24 h to evaluate how easily outpatients are monitored and how well an automatic seizure detection algorithm can identify the absences. Based on patient-specific modeling, we achieved a sensitivity of 98.4% with only 0.23 false detections per hour. This yields a clinically satisfying performance with a positive predictive value of 87.1%. Portable EEG-recorders identifying paroxystic events in epilepsy outpatients are a promising tool for patients and physicians dealing with absence epilepsy. Albeit the small size of the EEG-device, some children still complained about the obtrusive nature of the device. We aim at developing less obtrusive though still very efficient devices, e.g., hidden in the ear canal or below the skin. PMID:29018634

  10. Motor Imagery Classification Using Mu and Beta Rhythms of EEG with Strong Uncorrelating Transform Based Complex Common Spatial Patterns

    Directory of Open Access Journals (Sweden)

    Youngjoo Kim

    2016-01-01

    Full Text Available Recent studies have demonstrated the disassociation between the mu and beta rhythms of electroencephalogram (EEG during motor imagery tasks. The proposed algorithm in this paper uses a fully data-driven multivariate empirical mode decomposition (MEMD in order to obtain the mu and beta rhythms from the nonlinear EEG signals. Then, the strong uncorrelating transform complex common spatial patterns (SUTCCSP algorithm is applied to the rhythms so that the complex data, constructed with the mu and beta rhythms, becomes uncorrelated and its pseudocovariance provides supplementary power difference information between the two rhythms. The extracted features using SUTCCSP that maximize the interclass variances are classified using various classification algorithms for the separation of the left- and right-hand motor imagery EEG acquired from the Physionet database. This paper shows that the supplementary information of the power difference between mu and beta rhythms obtained using SUTCCSP provides an important feature for the classification of the left- and right-hand motor imagery tasks. In addition, MEMD is proved to be a preferred preprocessing method for the nonlinear and nonstationary EEG signals compared to the conventional IIR filtering. Finally, the random forest classifier yielded a high performance for the classification of the motor imagery tasks.

  11. Motor Imagery Classification Using Mu and Beta Rhythms of EEG with Strong Uncorrelating Transform Based Complex Common Spatial Patterns

    Science.gov (United States)

    Ryu, Jiwoo; Kim, Ko Keun; Mandic, Danilo P.

    2016-01-01

    Recent studies have demonstrated the disassociation between the mu and beta rhythms of electroencephalogram (EEG) during motor imagery tasks. The proposed algorithm in this paper uses a fully data-driven multivariate empirical mode decomposition (MEMD) in order to obtain the mu and beta rhythms from the nonlinear EEG signals. Then, the strong uncorrelating transform complex common spatial patterns (SUTCCSP) algorithm is applied to the rhythms so that the complex data, constructed with the mu and beta rhythms, becomes uncorrelated and its pseudocovariance provides supplementary power difference information between the two rhythms. The extracted features using SUTCCSP that maximize the interclass variances are classified using various classification algorithms for the separation of the left- and right-hand motor imagery EEG acquired from the Physionet database. This paper shows that the supplementary information of the power difference between mu and beta rhythms obtained using SUTCCSP provides an important feature for the classification of the left- and right-hand motor imagery tasks. In addition, MEMD is proved to be a preferred preprocessing method for the nonlinear and nonstationary EEG signals compared to the conventional IIR filtering. Finally, the random forest classifier yielded a high performance for the classification of the motor imagery tasks. PMID:27795702

  12. myBrain: a novel EEG embedded system for epilepsy monitoring.

    Science.gov (United States)

    Pinho, Francisco; Cerqueira, João; Correia, José; Sousa, Nuno; Dias, Nuno

    2017-10-01

    The World Health Organisation has pointed that a successful health care delivery, requires effective medical devices as tools for prevention, diagnosis, treatment and rehabilitation. Several studies have concluded that longer monitoring periods and outpatient settings might increase diagnosis accuracy and success rate of treatment selection. The long-term monitoring of epileptic patients through electroencephalography (EEG) has been considered a powerful tool to improve the diagnosis, disease classification, and treatment of patients with such condition. This work presents the development of a wireless and wearable EEG acquisition platform suitable for both long-term and short-term monitoring in inpatient and outpatient settings. The developed platform features 32 passive dry electrodes, analogue-to-digital signal conversion with 24-bit resolution and a variable sampling frequency from 250 Hz to 1000 Hz per channel, embedded in a stand-alone module. A computer-on-module embedded system runs a Linux ® operating system that rules the interface between two software frameworks, which interact to satisfy the real-time constraints of signal acquisition as well as parallel recording, processing and wireless data transmission. A textile structure was developed to accommodate all components. Platform performance was evaluated in terms of hardware, software and signal quality. The electrodes were characterised through electrochemical impedance spectroscopy and the operating system performance running an epileptic discrimination algorithm was evaluated. Signal quality was thoroughly assessed in two different approaches: playback of EEG reference signals and benchmarking with a clinical-grade EEG system in alpha-wave replacement and steady-state visual evoked potential paradigms. The proposed platform seems to efficiently monitor epileptic patients in both inpatient and outpatient settings and paves the way to new ambulatory clinical regimens as well as non-clinical EEG

  13. Wireless mobile Internet security

    CERN Document Server

    Rhee, Man Young

    2013-01-01

      The mobile industry for wireless cellular services has grown at a rapid pace over the past decade. Similarly, Internet service technology has also made dramatic growth through the World Wide Web with a wire line infrastructure. Realization for complete wired/wireless mobile Internet technologies will become the future objectives for convergence of these technologies thr

  14. Objective Audiometry using Ear-EEG

    DEFF Research Database (Denmark)

    Christensen, Christian Bech; Kidmose, Preben

    Recently, a novel electroencephalographic (EEG) method called ear-EEG [1], that enable recording of auditory evoked potentials (AEPs) from a personalized earpiece was introduced. Initial investigations show that well established AEPs, such as ASSR and P1-N1-P2 complex can be observed from ear-EEG...

  15. Acquiring research-grade ERPs on a shoestring budget: A comparison of a modified Emotiv and commercial SynAmps EEG system.

    Science.gov (United States)

    Barham, Michael P; Clark, Gillian M; Hayden, Melissa J; Enticott, Peter G; Conduit, Russell; Lum, Jarrad A G

    2017-09-01

    This study compared the performance of a low-cost wireless EEG system to a research-grade EEG system on an auditory oddball task designed to elicit N200 and P300 ERP components. Participants were 15 healthy adults (6 female) aged between 19 and 40 (M = 28.56; SD = 6.38). An auditory oddball task was presented comprising 1,200 presentations of a standard tone interspersed by 300 trials comprising a deviant tone. EEG was simultaneously recorded from a modified Emotiv EPOC and a NeuroScan SynAmps RT EEG system. The modifications made to the Emotiv system included attaching research grade electrodes to the Bluetooth transmitter. Additional modifications enabled the Emotiv system to connect to a portable impedance meter. The cost of these modifications and portable impedance meter approached the purchase value of the Emotiv system. Preliminary analyses revealed significantly more trials were rejected from data acquired by the modified Emotiv compared to the SynAmps system. However, the ERP waveforms captured by the Emotiv system were found to be highly similar to the corresponding waveform from the SynAmps system. The latency and peak amplitude of N200 and P300 components were also found to be similar between systems. Overall, the results indicate that, in the context of an oddball task, the ERP acquired by a low-cost wireless EEG system can be of comparable quality to research-grade EEG acquisition equipment. © 2017 Society for Psychophysiological Research.

  16. Incidence of epileptiform EEG activity in children during mask induction of anaesthesia with brief administration of 8% sevoflurane.

    Directory of Open Access Journals (Sweden)

    Barbara Schultz

    Full Text Available BACKGROUND: A high incidence of epileptiform activity in the electroencephalogram (EEG was reported in children undergoing mask induction of anaesthesia with administration of high doses of sevoflurane for 5 minutes and longer. This study was performed to investigate whether reducing the time of exposure to a high inhaled sevoflurane concentration would affect the incidence of epileptiform EEG activity. It was hypothesized that no epileptiform activity would occur, when the inhaled sevoflurane concentration would be reduced from 8% to 4% immediately after the loss of consciousness. METHODOLOGY/PRINCIPAL FINDINGS: 70 children (age 7-96 months, ASA I-II, premedication with midazolam were anaesthetized with 8% sevoflurane in 100% oxygen via face mask. Immediately after loss of consciousness, the sevoflurane concentration was reduced to 4%. EEGs were recorded continuously and were later analyzed visually with regard to epileptiform EEG patterns. Sevoflurane at a concentration of 8% was given for 1.2 ± 0.4 min (mean ± SD. In 14 children (20% epileptiform EEG patterns without motor manifestations were observed (delta with spikes (DSP, rhythmic polyspikes (PSR, epileptiform discharges (PED in 10, 10, 4 children (14%, 14%, 6%. 38 children (54% had slow, rhythmic delta waves with high amplitudes (DS appearing on average before DSP. CONCLUSIONS/SIGNIFICANCE: The hypothesis that no epileptiform potentials would occur during induction of anaesthesia with a reduction of the inspired sevoflurane concentration from 8% to 4% directly after LOC was not proved. Even if 8% sevoflurane is administered only briefly for induction of anaesthesia, epileptiform EEG activity may be observed in children despite premedication with midazolam.

  17. Effects of amphetamine, diazepam and caffeine on polysomnography (EEG, EMG, EOG)-derived variables measured using telemetry in Cynomolgus monkeys.

    Science.gov (United States)

    Authier, Simon; Bassett, Leanne; Pouliot, Mylene; Rachalski, Adeline; Troncy, Eric; Paquette, Dominique; Mongrain, Valérie

    2014-01-01

    Medication-induced sleep disturbances are a major concern in drug development as a multitude of prescription drugs alter sleep patterns, often negatively. Polysomnography is used in clinical diagnostics but is also applicable to animal models. Rodent sleep architecture (nocturnal) differs from larger diurnal mammals, including humans, increasing the translational potential of non-rodent species to the clinic. This study aimed to characterize the response to pharmacological agents known to affect sleep structure and EEG activity in a non-human primate (Macaca fascicularis) using telemetry-based polysomnography. Animals were instrumented with telemetry transmitters for continuous electroencephalogram (EEG), electro-oculogram (EOG) and electromyogram (EMG) monitoring combined with video. EEG, EMG and EOG were monitored for 12 to 24h to establish baseline values, followed by administration of pharmacological agents (saline, d-amphetamine, diazepam or caffeine). Amphetamine (0.3 and 1mg/kg, by oral administration (PO)) significantly reduced total sleep time, including the duration of both non-rapid eye movement [NREM] sleep and REM sleep. It also decreased EEG activity in low frequencies (i.e., 4-6Hz) during wakefulness. Diazepam (2mg/kg, PO) did not significantly alter sleep duration, but importantly reduced EEG activity in low frequencies (approximately 2-12Hz) during wakefulness, NREM and REM sleep. Finally, caffeine (10 and 30mg/kg, PO) decreased both NREM and REM sleep duration. In addition, spectral analysis revealed important decreases in low frequency activity (i.e., 1-8Hz) during wakefulness with a parallel increase in high frequency activity (i.e., 20-50Hz) during NREM sleep. As these observations are similar to previously reported pharmacological effects in humans, results support that EEG, EOG and EMG monitoring by telemetry in Cynomolgus monkeys represents a useful non-clinical model to investigate and quantify drug-induced sleep disturbances. Copyright

  18. [Neurological and neuropsychological comparison between subjects with learning disorder and those suffering from learning difficulties when eeg abnormalities are detected at pediatric age].

    Science.gov (United States)

    Borsetti, L; Viberti, B; Ariano, C; Isocrono, A

    2015-12-01

    The objective of the study is to compare data and investigate the points of overlap between the two clinical conditions. The hypothesis is to observe a similar cognitive and neuropsychological profile in LD children and subjects with electroencephalogram (EEG) abnormalities. The present study consists of a descriptive analysis of 35 children who have been tested for suspected learning disorder (LD). The diagnostic protocol includes a detailed cognitive and neuropsychological evaluation, as well as logopedic and neuropsychomotor assessment. Children carried neurological visit, EEG in waking and encephalic nuclear magnetic resonance (NMR). In this study, anamnestic data and the results of some of the neuropsychological tests were administrated to children and subsequently were analyzed. Depending on EEG report (positive or negative), subjects were split in two subsample: subjects with "pure" LD and subjects who showed significant paroxysmal abnormalities at the EEG. This comparison shows that the profile of the two subsamples matches for many aspects. The only statistically significant differences are the increased impairment of meta-phonological skills and reading speed in children with EEG abnormalities. On the other hand, children with "pure" LD are inclined to manifest more frequently difficulties in highly-modularized processes, such as counting. In conclusion, the substantial overlap of the two profiles causes a reflection about the difficulty in making differential diagnosis in children who show a suspected LD, in absence of an accurate neurophysiological and neuroradiological investigation. The study did not find out useful markers to select subjects who should carry EEG and encephalic NMR. Our team established to keep EEG in waking in the diagnostic protocol, for all children with LD diagnosis. Only in case of abnormalities at the track, we prescribed EEG in sleeping and encephalic NMR.

  19. Joint Time-Frequency-Space Classification of EEG in a Brain-Computer Interface Application

    Directory of Open Access Journals (Sweden)

    Molina Gary N Garcia

    2003-01-01

    Full Text Available Brain-computer interface is a growing field of interest in human-computer interaction with diverse applications ranging from medicine to entertainment. In this paper, we present a system which allows for classification of mental tasks based on a joint time-frequency-space decorrelation, in which mental tasks are measured via electroencephalogram (EEG signals. The efficiency of this approach was evaluated by means of real-time experimentations on two subjects performing three different mental tasks. To do so, a number of protocols for visualization, as well as training with and without feedback, were also developed. Obtained results show that it is possible to obtain good classification of simple mental tasks, in view of command and control, after a relatively small amount of training, with accuracies around 80%, and in real time.

  20. Classification of multi-class motor imagery EEG using four band common spatial pattern.

    Science.gov (United States)

    Mahmood, Amama; Zainab, Rida; Ahmad, Rushda Basir; Saeed, Maryam; Kamboh, Awais Mehmood

    2017-07-01

    Brain Computer Interfaces (BCIs) serve as an integration tool between acquired brain signals and external devices. Precise classification of the acquired brain signals with the least misclassification error is an arduous task. Existing techniques for classification of multi-class motor imagery electroencephalogram (EEG) have low accuracy and are computationally inefficient. This paper introduces a classification algorithm, which uses two frequency ranges, mu and beta rythms, for feature extraction using common spatial pattern (CSP) along with support vector machine (SVM) for classification. The technique uses only four frequency bands with no feature reduction and consequently less computational cost. The implementation of this algorithm on BCI competition III dataset IIIa, resulted in the highest classification accuracy in comparison to existing algorithms. A mean accuracy of 85.5 for offline classification has been achieved using this technique.

  1. Fast L1-based sparse representation of EEG for motor imagery signal classification.

    Science.gov (United States)

    Younghak Shin; Heung-No Lee; Balasingham, Ilangko

    2016-08-01

    Improvement of classification performance is one of the key challenges in electroencephalogram (EEG) based motor imagery brain-computer interface (BCI). Recently, sparse representation based classification (SRC) method has been shown to provide satisfactory classification accuracy in motor imagery classification. In this paper, we aim to evaluate the performance of the SRC method in terms of not only its classification accuracy but also of its computation time. For this purpose, we investigate the performance of recently developed fast L1 minimization methods for their use in SRC, such as homotopy and fast iterative soft-thresholding algorithm (FISTA). From experimental analysis, we note that the SRC method with the fast L1 minimization algorithms is shown to provide robust classification performance, compared to support vector machine (SVM), both in time and accuracy.

  2. Parameterized entropy analysis of EEG following hypoxic-ischemic brain injury

    International Nuclear Information System (INIS)

    Tong Shanbao; Bezerianos, Anastasios; Malhotra, Amit; Zhu Yisheng; Thakor, Nitish

    2003-01-01

    In the present study Tsallis and Renyi entropy methods were used to study the electric activity of brain following hypoxic-ischemic (HI) injury. We investigated the performances of these parameterized information measures in describing the electroencephalogram (EEG) signal of controlled experimental animal HI injury. The results show that (a): compared with Shannon and Renyi entropy, the parameterized Tsallis entropy acts like a spatial filter and the information rate can either tune to long range rhythms or to short abrupt changes, such as bursts or spikes during the beginning of recovery, by the entropic index q; (b): Renyi entropy is a compact and predictive indicator for monitoring the physiological changes during the recovery of brain injury. There is a reduction in the Renyi entropy after brain injury followed by a gradual recovery upon resuscitation

  3. Electroencephalogram γ-band activity during the perception of music key levels.

    Science.gov (United States)

    Ma, Weiyi; Lai, Yongxiu; Zheng, Xiaojing; Yang, Hua; Yao, Dezhong

    2013-03-06

    An interdisciplinary research field, music perception involves various disciplines, such as psychology, neuroscience, and even physics. Research on music perception offers us a window into the mechanism of the brain. In music perception, the same distance of key shift in different directions tends to be perceived as different degrees of change. It, however, still remains unclear whether directional asymmetry is specific to key shift perception or a general phenomenon of key perception. Using both behavioral and electroencephalogram methods, this study examined Chinese nonmusicians' subjective ratings and electroencephalogram γ-band activity related to a piece of music performed at three different key levels and presented in three separate performances, none of which contained a key shift. This study showed that directional asymmetry is a general phenomenon of key level perception rather than specific to key shift perception. Furthermore, a counterclockwisely modulated key is related to stronger γ-band spectral power than a clockwisely modulated key.

  4. [A unified autocorrelated method of electroencephalogram analysis and some results of its use].

    Science.gov (United States)

    Khizhun, A F; Saĭko, Iu V

    1993-01-01

    Unified autocorrelation method of EEG analysis (UNIFAC-EEG) represents some optimized algorithms of EEG-signals processing according to the sign of maximum stability to the broadband noise. These algorithms are united by the common aim to identify generation models of these signals narrowband components on the basis of concrete empirical data and estimation of their source dynamic parameters according to the identification results. On the basis of this method it was proved that alpha-rhythm sources belonged to the type of structural stable dynamic systems, and its decrement value could serve as one of the parameters of the functional state of the central nervous system. The effect was found of the interaction between the heart electric fields and human brain in EEG frequent alpha-range.

  5. Valor do EEG na caracterização e prognóstico de patologias neurológicas em recém-nascidos prematuros Relationship of EEG, neurological diseases and follow-up in preterm newborns

    Directory of Open Access Journals (Sweden)

    Magoa Lahorgue Nunes

    1995-09-01

    neurological and electroencephalogram (EEG or polysomnography (PS evaluation. EEG/PS alterations, pathologies and prognosis were reported. The EEG were classified according to basal activity alterations, presence of paroxysmal activity and sleep stages organization and maturity. The most frequent pathology was perinatal asphyxia (40% followed by intraventricular hemorrhage (HIV, 16%. The most frequent complaint for exam indication was apnea (71% followed by convulsion (19%. Fifty-five percent of the exams exclusively required because apnea complaint were considered normal and out of all exams required because seizures only 31 % were normal. The EEG alteration most frequently related to perinatal asphyxia, HIV and intrauterine growth delay was immaturity and in the NB with seizures immature EEG and abnormal paroxysms. Many different alterations were registered in the NB with nervous system infection. The EEG findings more correlated with unfavorable prognosis were isoelectricity and abnormal paroxysmal activity including positive sharp waves (100%.

  6. Security for multihop wireless networks

    CERN Document Server

    Khan, Shafiullah

    2014-01-01

    Security for Multihop Wireless Networks provides broad coverage of the security issues facing multihop wireless networks. Presenting the work of a different group of expert contributors in each chapter, it explores security in mobile ad hoc networks, wireless sensor networks, wireless mesh networks, and personal area networks.Detailing technologies and processes that can help you secure your wireless networks, the book covers cryptographic coprocessors, encryption, authentication, key management, attacks and countermeasures, secure routing, secure medium access control, intrusion detection, ep

  7. Comparative animal experimental studies of the effects of different radiopharmaceuticals on the electroencephalogram

    International Nuclear Information System (INIS)

    Bretschneider, T.; Gundlach, H.J.; Krueger, M.; Reincke, R.; Schwebke, R.

    1979-01-01

    Some of the radiopharmaceuticals recently recommended for isotope cisternography were compared as to their effects on the bioelectric activity. Relaxed cats did not reveal any effects of 198 Au or 169 Yb-Ca-DTPA on the bioelectric activity. Following suboccipital administration, 169 Yb-DTPA and 131 I-HSA caused changes of the electroencephalogram in one of 7 and 6 cases, respectively. (author)

  8. Comparison of Bispectral Index and Entropy values with electroencephalogram during surgical anaesthesia with sevoflurane.

    Science.gov (United States)

    Aho, A J; Kamata, K; Jäntti, V; Kulkas, A; Hagihira, S; Huhtala, H; Yli-Hankala, A

    2015-08-01

    Concomitantly recorded Bispectral Index® (BIS) and Entropy™ values sometimes show discordant trends during general anaesthesia. Previously, no attempt had been made to discover which EEG characteristics cause discrepancies between BIS and Entropy. We compared BIS and Entropy values, and analysed the changes in the raw EEG signal during surgical anaesthesia with sevoflurane. In this prospective, open-label study, 65 patients receiving general anaesthesia with sevoflurane were enrolled. BIS, Entropy and multichannel digital EEG were recorded. Concurrent BIS and State Entropy (SE) values were selected. Whenever BIS and SE values showed ≥10-unit disagreement for ≥60 s, the raw EEG signal was analysed both in time and frequency domain. A ≥10-unit disagreement ≥60 s was detected 428 times in 51 patients. These 428 episodes accounted for 5158 (11%) out of 45 918 analysed index pairs. During EEG burst suppression, SE was higher than BIS in 35 out of 49 episodes. During delta-theta dominance, BIS was higher than SE in 141 out of 157 episodes. During alpha or beta activity, SE was higher than BIS in all 49 episodes. During electrocautery, both BIS and SE changed, sometimes in the opposite direction, but returned to baseline values after electrocautery. Electromyography caused index disagreement four times (BIS > SE). Certain specific EEG patterns, and artifacts, are associated with discrepancies between BIS and SE. Time and frequency domain analyses of the original EEG improve the interpretation of studies involving BIS, Entropy and other EEG-based indices. NCT01077674. © The Author 2015. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  9. Altered Frequency Distribution in the Electroencephalogram is Correlated to the Analgesic Effect of Remifentanil

    DEFF Research Database (Denmark)

    Graversen, Carina; Malver, Lasse P; Kurita, Geana P

    2015-01-01

    Opioids alter resting state brain oscillations by multiple and complex factors, which are still to be elucidated. To increase our knowledge, multi-channel electroencephalography (EEG) was subjected to multivariate pattern analysis (MVPA), to identify the most descriptive frequency bands and scalp...... remifentanil treatment. As the EEG alterations were correlated to the analgesic effect, the approach may prove to be a novel methodology for monitoring individual efficacy to opioids....

  10. Odds ratio product of sleep EEG as a continuous measure of sleep state.

    Science.gov (United States)

    Younes, Magdy; Ostrowski, Michele; Soiferman, Marc; Younes, Henry; Younes, Mark; Raneri, Jill; Hanly, Patrick

    2015-04-01

    To develop and validate an algorithm that provides a continuous estimate of sleep depth from the electroencephalogram (EEG). Retrospective analysis of polysomnograms. Research laboratory. 114 patients who underwent clinical polysomnography in sleep centers at the University of Manitoba (n = 58) and the University of Calgary (n = 56). None. Power spectrum of EEG was determined in 3-second epochs and divided into delta, theta, alpha-sigma, and beta frequency bands. The range of powers in each band was divided into 10 aliquots. EEG patterns were assigned a 4-digit number that reflects the relative power in the 4 frequency ranges (10,000 possible patterns). Probability of each pattern occurring in 30-s epochs staged awake was determined, resulting in a continuous probability value from 0% to 100%. This was divided by 40 (% of epochs staged awake) producing the odds ratio product (ORP), with a range of 0-2.5. In validation testing, average ORP decreased progressively as EEG progressed from wakefulness (2.19 ± 0.29) to stage N3 (0.13 ± 0.05). ORP sleep and ORP > 2.0 predicted wakefulness in > 95% of 30-s epochs. Epochs with intermediate ORP occurred in unstable sleep with a high arousal index (> 70/h) and were subject to much interrater scoring variability. There was an excellent correlation (r(2) = 0.98) between ORP in current 30-s epochs and the likelihood of arousal or awakening occurring in the next 30-s epoch. Our results support the use of the odds ratio product (ORP) as a continuous measure of sleep depth. © 2015 Associated Professional Sleep Societies, LLC.

  11. Changes in the EEG and SEP of patients with cerebrovascular major strokes

    International Nuclear Information System (INIS)

    Iwayama, Kaoru; Tanaka, Keisei; Yamashiro, Katsumi; Mori, Kazuo

    1980-01-01

    Changes in the electroencephalogram (EEG) and somatosensory evoked potential (SEP) were studied in 27 cases with unilateral cerebrovascular disorders. From the CT findings (EMI-1010), the locations of the diseases were divided into two types: superficial (within the cortex and subcortical white matter) or deep (at the level of the basal ganglia and thalamus). Each of these was then further subdivided according to the nature of the disease, whether hemorrhage or infarction. The EEG and SEP were recorded from the scalp area corresponding to the post-Rolandic arm area (Shagass' point) throughout the investigation. The stimulus intensities applied to the median nerve were fixed at 5 - 10 V. higher than the motor threshold. EEG power spectrum analysis was done with an ATAC 2300 computer, while slow-wave indices (SWI), the ratio of power in the slow-wave range (2 - 6 Hz) to the total power (2 - 25 Hz), were calculated manually. In deep lesions, the deterioration of the SEP was prominent. Especially in the 5 cases with bleeding, all 5 showed a ''flat SEP.'' These patients also revealed motor and sensory disturbance clinically. In the 6 cases with infarction, although no one showed sensory disturbances, the changes in the SEP were also remarkable. In deep lesions, a decrease in the amplitude of the early components of SEP (N 1 , P 1 ) was the most conspicuous feature. On the contrary, in superficial lesions, changes in the SEP were less prominent, and if they occurred, the late components (P 2 , N 3 ) were more easily affected. The amount of the slow wave increased remarkably and bilaterally in these superficial cases, and the slower the frequency of the EEG, the more attenuation of the amplitude of SEP was noted on the lesion side. On the opposite side, however, no such correlations could be noted between an increased slow-wave power and a decreased amplitude of SEP. Further studies to clarify these differences will be necessary. (author)

  12. Directed Motor-Auditory EEG Connectivity Is Modulated by Music Tempo.

    Science.gov (United States)

    Nicolaou, Nicoletta; Malik, Asad; Daly, Ian; Weaver, James; Hwang, Faustina; Kirke, Alexis; Roesch, Etienne B; Williams, Duncan; Miranda, Eduardo R; Nasuto, Slawomir J

    2017-01-01

    Beat perception is fundamental to how we experience music, and yet the mechanism behind this spontaneous building of the internal beat representation is largely unknown. Existing findings support links between the tempo (speed) of the beat and enhancement of electroencephalogram (EEG) activity at tempo-related frequencies, but there are no studies looking at how tempo may affect the underlying long-range interactions between EEG activity at different electrodes. The present study investigates these long-range interactions using EEG activity recorded from 21 volunteers listening to music stimuli played at 4 different tempi (50, 100, 150 and 200 beats per minute). The music stimuli consisted of piano excerpts designed to convey the emotion of "peacefulness". Noise stimuli with an identical acoustic content to the music excerpts were also presented for comparison purposes. The brain activity interactions were characterized with the imaginary part of coherence (iCOH) in the frequency range 1.5-18 Hz (δ, θ, α and lower β) between all pairs of EEG electrodes for the four tempi and the music/noise conditions, as well as a baseline resting state (RS) condition obtained at the start of the experimental task. Our findings can be summarized as follows: (a) there was an ongoing long-range interaction in the RS engaging fronto-posterior areas; (b) this interaction was maintained in both music and noise, but its strength and directionality were modulated as a result of acoustic stimulation; (c) the topological patterns of iCOH were similar for music, noise and RS, however statistically significant differences in strength and direction of iCOH were identified; and (d) tempo had an effect on the direction and strength of motor-auditory interactions. Our findings are in line with existing literature and illustrate a part of the mechanism by which musical stimuli with different tempi can entrain changes in cortical activity.

  13. Directed Motor-Auditory EEG Connectivity Is Modulated by Music Tempo

    Science.gov (United States)

    Nicolaou, Nicoletta; Malik, Asad; Daly, Ian; Weaver, James; Hwang, Faustina; Kirke, Alexis; Roesch, Etienne B.; Williams, Duncan; Miranda, Eduardo R.; Nasuto, Slawomir J.

    2017-01-01

    Beat perception is fundamental to how we experience music, and yet the mechanism behind this spontaneous building of the internal beat representation is largely unknown. Existing findings support links between the tempo (speed) of the beat and enhancement of electroencephalogram (EEG) activity at tempo-related frequencies, but there are no studies looking at how tempo may affect the underlying long-range interactions between EEG activity at different electrodes. The present study investigates these long-range interactions using EEG activity recorded from 21 volunteers listening to music stimuli played at 4 different tempi (50, 100, 150 and 200 beats per minute). The music stimuli consisted of piano excerpts designed to convey the emotion of “peacefulness”. Noise stimuli with an identical acoustic content to the music excerpts were also presented for comparison purposes. The brain activity interactions were characterized with the imaginary part of coherence (iCOH) in the frequency range 1.5–18 Hz (δ, θ, α and lower β) between all pairs of EEG electrodes for the four tempi and the music/noise conditions, as well as a baseline resting state (RS) condition obtained at the start of the experimental task. Our findings can be summarized as follows: (a) there was an ongoing long-range interaction in the RS engaging fronto-posterior areas; (b) this interaction was maintained in both music and noise, but its strength and directionality were modulated as a result of acoustic stimulation; (c) the topological patterns of iCOH were similar for music, noise and RS, however statistically significant differences in strength and direction of iCOH were identified; and (d) tempo had an effect on the direction and strength of motor-auditory interactions. Our findings are in line with existing literature and illustrate a part of the mechanism by which musical stimuli with different tempi can entrain changes in cortical activity. PMID:29093672

  14. Automatic Change Detection for Real-Time Monitoring of EEG Signals

    Directory of Open Access Journals (Sweden)

    Zhen Gao

    2018-04-01

    Full Text Available In recent years, automatic change detection for real-time monitoring of electroencephalogram (EEG signals has attracted widespread interest with a large number of clinical applications. However, it is still a challenging problem. This paper presents a novel framework for this task where joint time-domain features are firstly computed to extract temporal fluctuations of a given EEG data stream; and then, an auto-regressive (AR linear model is adopted to model the data and temporal anomalies are subsequently calculated from that model to reflect the possibilities that a change occurs; a non-parametric statistical test based on Randomized Power Martingale (RPM is last performed for making change decision from the resulting anomaly scores. We conducted experiments on the publicly-available Bern-Barcelona EEG database where promising results for terms of detection precision (96.97%, detection recall (97.66% as well as computational efficiency have been achieved. Meanwhile, we also evaluated the proposed method for real detection of seizures occurrence for a monitoring epilepsy patient. The results of experiments by using both the testing database and real application demonstrated the effectiveness and feasibility of the method for the purpose of change detection in EEG signals. The proposed framework has two additional properties: (1 it uses a pre-defined AR model for modeling of the past observed data so that it can be operated in an unsupervised manner, and (2 it uses an adjustable threshold to achieve a scalable decision making so that a coarse-to-fine detection strategy can be developed for quick detection or further analysis purposes.

  15. Directed Motor-Auditory EEG Connectivity Is Modulated by Music Tempo

    Directory of Open Access Journals (Sweden)

    Nicoletta Nicolaou

    2017-10-01

    Full Text Available Beat perception is fundamental to how we experience music, and yet the mechanism behind this spontaneous building of the internal beat representation is largely unknown. Existing findings support links between the tempo (speed of the beat and enhancement of electroencephalogram (EEG activity at tempo-related frequencies, but there are no studies looking at how tempo may affect the underlying long-range interactions between EEG activity at different electrodes. The present study investigates these long-range interactions using EEG activity recorded from 21 volunteers listening to music stimuli played at 4 different tempi (50, 100, 150 and 200 beats per minute. The music stimuli consisted of piano excerpts designed to convey the emotion of “peacefulness”. Noise stimuli with an identical acoustic content to the music excerpts were also presented for comparison purposes. The brain activity interactions were characterized with the imaginary part of coherence (iCOH in the frequency range 1.5–18 Hz (δ, θ, α and lower β between all pairs of EEG electrodes for the four tempi and the music/noise conditions, as well as a baseline resting state (RS condition obtained at the start of the experimental task. Our findings can be summarized as follows: (a there was an ongoing long-range interaction in the RS engaging fronto-posterior areas; (b this interaction was maintained in both music and noise, but its strength and directionality were modulated as a result of acoustic stimulation; (c the topological patterns of iCOH were similar for music, noise and RS, however statistically significant differences in strength and direction of iCOH were identified; and (d tempo had an effect on the direction and strength of motor-auditory interactions. Our findings are in line with existing literature and illustrate a part of the mechanism by which musical stimuli with different tempi can entrain changes in cortical activity.

  16. The wireless internet explained

    CERN Document Server

    Rhoton, John

    2001-01-01

    The Wireless Internet Explained covers the full spectrum of wireless technologies from a wide range of vendors, including initiatives by Microsoft and Compaq. The Wireless Internet Explained takes a practical look at wireless technology. Rhoton explains the concepts behind the physics, and provides an overview that clarifies the convoluted set of standards heaped together under the umbrella of wireless. It then expands on these technical foundations to give a panorama of the increasingly crowded landscape of wireless product offerings. When it comes to actual implementation the book gives abundant down-to-earth advice on topics ranging from the selection and deployment of mobile devices to the extremely sensitive subject of security.Written by an expert on Internet messaging, the author of Digital Press''s successful Programmer''s Guide to Internet Mail and X.400 and SMTP: Battle of the E-mail Protocols, The Wireless Internet Explained describes and evaluates the current state of the fast-growing and crucial...

  17. Wireless rechargeable sensor networks

    CERN Document Server

    Yang, Yuanyuan

    2015-01-01

    This SpringerBrief provides a concise guide to applying wireless energy transfer techniques in traditional battery-powered sensor networks. It examines the benefits and challenges of wireless power including efficiency and reliability. The authors build a wireless rechargeable sensor networks from scratch and aim to provide perpetual network operation. Chapters cover a wide range of topics from the collection of energy information and recharge scheduling to joint design with typical sensing applications such as data gathering. Problems are approached using a natural combination of probability

  18. Wireless network pricing

    CERN Document Server

    Huang, Jianwei

    2013-01-01

    Today's wireless communications and networking practices are tightly coupled with economic considerations, to the extent that it is almost impossible to make a sound technology choice without understanding the corresponding economic implications. This book aims at providing a foundational introduction on how microeconomics, and pricing theory in particular, can help us to understand and build better wireless networks. The book can be used as lecture notes for a course in the field of network economics, or a reference book for wireless engineers and applied economists to understand how pricing

  19. Wireless mesh networks

    CERN Document Server

    Held, Gilbert

    2005-01-01

    Wireless mesh networking is a new technology that has the potential to revolutionize how we access the Internet and communicate with co-workers and friends. Wireless Mesh Networks examines the concept and explores its advantages over existing technologies. This book explores existing and future applications, and examines how some of the networking protocols operate.The text offers a detailed analysis of the significant problems affecting wireless mesh networking, including network scale issues, security, and radio frequency interference, and suggests actual and potential solutions for each pro

  20. Optical and wireless technologies

    CERN Document Server

    Tiwari, Manish; Singh, Ghanshyam; Minzioni, Paolo

    2018-01-01

    This book presents selected papers from 1st International Conference on Optical and Wireless Technologies, providing insights into the analytical, experimental, and developmental aspects of systems, techniques, and devices in these spheres. It explores the combined use of various optical and wireless technologies in next-generation networking applications, and discusses the latest developments in applications such as photonics, high-speed communication systems and networks, visible light communication, nanophotonics, and wireless and multiple-input-multiple-output (MIMO) systems. The book will serve as a valuable reference resource for academics and researchers across the globe.

  1. PyEEG: an open source Python module for EEG/MEG feature extraction.

    Science.gov (United States)

    Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

    2011-01-01

    Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction.

  2. Design and validation of a wearable "DRL-less" EEG using a novel fully-reconfigurable architecture.

    Science.gov (United States)

    Mahajan, Ruhi; Morshed, Bashir I; Bidelman, Gavin M

    2016-08-01

    The conventional EEG system consists of a driven-right-leg (DRL) circuit, which prohibits modularization of the system. We propose a Lego-like connectable fully reconfigurable architecture of wearable EEG that can be easily customized and deployed at naturalistic settings for collecting neurological data. We have designed a novel Analog Front End (AFE) that eliminates the need for DRL while maintaining a comparable signal quality of EEG. We have prototyped this AFE for a single channel EEG, referred to as Smart Sensing Node (SSN), that senses brain signals and sends it to a Command Control Node (CCN) via an I2C bus. The AFE of each SSN (referential-montage) consists of an off-the-shelf instrumentation amplifier (gain=26), an active notch filter fc = 60Hz), 2nd-order active Butterworth low-pass filter followed by a passive low pass filter (fc = 47.5 Hz, gain = 1.61) and a passive high pass filter fc = 0.16 Hz, gain = 0.83). The filtered signals are digitized using a low-power microcontroller (MSP430F5528) with a 12-bit ADC at 512 sps, and transmitted to the CCN every 1 s at a bus rate of 100 kbps. The CCN can further transmit this data wirelessly using Bluetooth to the paired computer at a baud rate of 115.2 kbps. We have compared temporal and frequency-domain EEG signals of our system with a research-grade EEG. Results show that the proposed reconfigurable EEG captures comparable signals, and is thus promising for practical routine neurological monitoring in non-clinical settings where a flexible number of EEG channels are needed.

  3. Wireless capsule endoscopy

    Science.gov (United States)

    Iddan, Gavriel; Meron, Gavriel; Glukhovsky, Arkady; Swain, Paul

    2000-05-01

    We have developed a new type of endoscopy, which for the first time allows painless endoscopic imaging of the whole of the small bowel. This procedure involves a wireless capsule endoscope and we describe here its successful testing in humans.

  4. Green heterogeneous wireless networks

    CERN Document Server

    Ismail, Muhammad; Nee, Hans-Peter; Qaraqe, Khalid A; Serpedin, Erchin

    2016-01-01

    This book focuses on the emerging research topic "green (energy efficient) wireless networks" which has drawn huge attention recently from both academia and industry. This topic is highly motivated due to important environmental, financial, and quality-of-experience (QoE) considerations. Specifically, the high energy consumption of the wireless networks manifests in approximately 2% of all CO2 emissions worldwide. This book presents the authors’ visions and solutions for deployment of energy efficient (green) heterogeneous wireless communication networks. The book consists of three major parts. The first part provides an introduction to the "green networks" concept, the second part targets the green multi-homing resource allocation problem, and the third chapter presents a novel deployment of device-to-device (D2D) communications and its successful integration in Heterogeneous Networks (HetNets). The book is novel in that it specifically targets green networking in a heterogeneous wireless medium, which re...

  5. Wireless radio a history

    CERN Document Server

    Coe, Lewis

    2006-01-01

    ""Informative...recommended""--Choice; ""interesting...a good read...well worth reading""--Contact Magazine. This history first looks at Marconi's wireless communications system and then explores its many applications, including marine radio, cellular telephones, police and military uses, television and radar. Radio collecting is also discussed, and brief biographies are provided for the major figures in the development and use of the wireless.

  6. Wireless Networks Security

    OpenAIRE

    Jenko, Marko

    2016-01-01

    In this thesis we deal with security, penetration testing and different types of attacks on wireless networks. We theoretically familiarize with the field of wireless networks, security mechanisms and the most used security standards. Then we systematically overview the field of penetration testing. We divide the penetration testing on types and different methodologies. We explain the tasks of the provider and the subscriber in penetration testing. Description of the process of conducting the...

  7. Reduction in time-to-sleep through EEG based brain state detection and audio stimulation.

    Science.gov (United States)

    Zhuo Zhang; Cuntai Guan; Ti Eu Chan; Juanhong Yu; Aung Aung Phyo Wai; Chuanchu Wang; Haihong Zhang

    2015-08-01

    We developed an EEG- and audio-based sleep sensing and enhancing system, called iSleep (interactive Sleep enhancement apparatus). The system adopts a closed-loop approach which optimizes the audio recording selection based on user's sleep status detected through our online EEG computing algorithm. The iSleep prototype comprises two major parts: 1) a sleeping mask integrated with a single channel EEG electrode and amplifier, a pair of stereo earphones and a microcontroller with wireless circuit for control and data streaming; 2) a mobile app to receive EEG signals for online sleep monitoring and audio playback control. In this study we attempt to validate our hypothesis that appropriate audio stimulation in relation to brain state can induce faster onset of sleep and improve the quality of a nap. We conduct experiments on 28 healthy subjects, each undergoing two nap sessions - one with a quiet background and one with our audio-stimulation. We compare the time-to-sleep in both sessions between two groups of subjects, e.g., fast and slow sleep onset groups. The p-value obtained from Wilcoxon Signed Rank Test is 1.22e-04 for slow onset group, which demonstrates that iSleep can significantly reduce the time-to-sleep for people with difficulty in falling sleep.

  8. Resting state EEG power, intra-hemisphere and inter-hemisphere coherence in bipolar disorder

    Science.gov (United States)

    Handayani, Nita; Khotimah, S. N.; Haryanto, F.; Arif, I.; Taruno, Warsito P.

    2017-02-01

    This paper examines the differences of EEG power and coherence between bipolar disorder patients and healthy subjects in the resting state. Observations are focused on the prefrontal cortex area by calculating intra-hemisphere and inter-hemisphere coherence. EEG data acquisition are conducted by using wireless Emotiv Epoc on AF3, AF4, FC5, FC6, F7 and F8 channels. The power spectral analysis shows that in bipolar disoder there is an increase of power in the delta, theta and beta frequencies, and power decrease in the alpha frequency. The coherence test results show that both intra-hemisphere and inter-hemisphere coherence in bipolar disorder patients are lower than healthy subjects. This shows the lack of brain synchronization in bipolar disorder patients.

  9. A Synchronous Multi-Body Sensor Platform in a Wireless Body Sensor Network: Design and Implementation

    OpenAIRE

    Gil, Yeongjoon; Wu, Wanqing; Lee, Jungtae

    2012-01-01

    Background: Human life can be further improved if diseases and disorders can be predicted before they become dangerous, by correctly recognizing signals from the human body, so in order to make disease detection more precise, various body-signals need to be measured simultaneously in a synchronized manner. Object: This research aims at developing an integrated system for measuring four signals (EEG, ECG, respiration, and PPG) and simultaneously producing synchronous signals on a Wireless Body...

  10. Energy Efficient Cross-Layer Design for Wireless Body Area Monitoring Networks in Healthcare Applications

    OpenAIRE

    Awad, Alaa; Mohamed, Amr; El-Sherif, Amr A.

    2013-01-01

    Growing number of patients with chronic diseases requiring constant monitoring has created a major impetus to developing scalable Body Area Sensor Networks (BASNs) for remote health applications. In this paper, to anatomize, control, and optimize the behavior of the wireless EEG monitoring system under the energy constraint, we develop an Energy-Rate-Distortion (E-R-D) analysis framework. This framework extends the traditional distortion analysis by including the energy consumption dimension....

  11. Roles of adrenergic α1 and dopamine D1 and D2 receptors in the mediation of the desynchronization effects of modafinil in a mouse EEG synchronization model.

    Directory of Open Access Journals (Sweden)

    Chang-Rui Chen

    Full Text Available BACKGROUND: Synchronized electroencephalogram (EEG activity is observed in pathological stages of cognitive impairment and epilepsy. Modafinil, known to increase the release of catecholamines, is a potent wake-promoting agent, and has shown some abilities to desynchronize EEG,but its receptor mechanisms by which modafinil induces desynchoronization remain to be elucidated. Here we used a pharmacological EEG synchronization model to investigate the involvement of adrenergic α1 receptors (R, α1R and dopamine (DA D1 and D2 receptors (D1Rs and D2Rs on modafinil-induced desynchronization in mice. METHODOLOGY/PRINCIPAL FINDINGS: Mice were treated with cholinergic receptor antagonist scopolamine and monoamine depletor reserpine to produce experimental EEG synchronization characterized by continuous large-amplitude synchronized activity, with prominent increased delta and decreased theta, alpha, and beta power density. The results showed that modafinil produced an EEG desynchronization in the model. This was characterized by a general decrease in amplitude of all the frequency bands between 0 and 20 Hz, a prominent reduction in delta power density, and an increase in theta power density. Adrenergic α1R antagonist terazosin (1 mg/kg, i.p. completely antagonized the EEG desynchronization effects of modafinil at 90 mg/kg. However, DA D1R and D2R blockers partially attenuated the effects of modafinil. The modafinil-induced decrease in the amplitudes of the delta, theta, alpha, and beta waves and in delta power density were completely abolished by pretreatment with a combination of the D1R antagonist SCH 23390 (30 µg/kg and the D2R antagonist raclopride (2 mg/kg, i.p.. CONCLUSIONS/SIGNIFICANCE: These results suggest that modafinil-mediated desynchronization may be attributed to the activation of adrenergic α1R, and dopaminergic D1R and D2R in a model of EEG synchronization.

  12. Roles of Adrenergic α1 and Dopamine D1 and D2 Receptors in the Mediation of the Desynchronization Effects of Modafinil in a Mouse EEG Synchronization Model

    Science.gov (United States)

    Chen, Chang-Rui; Yang, Su-Rong; Liu, Yuan-Yuan; Qu, Wei-Min; Urade, Yoshihiro; Huang, Zhi-Li

    2013-01-01

    Background Synchronized electroencephalogram (EEG) activity is observed in pathological stages of cognitive impairment and epilepsy. Modafinil, known to increase the release of catecholamines, is a potent wake-promoting agent, and has shown some abilities to desynchronize EEG,but its receptor mechanisms by which modafinil induces desynchoronization remain to be elucidated. Here we used a pharmacological EEG synchronization model to investigate the involvement of adrenergic α1 receptors (R, α1R) and dopamine (DA) D1 and D2 receptors (D1Rs and D2Rs) on modafinil-induced desynchronization in mice. Methodology/Principal Findings Mice were treated with cholinergic receptor antagonist scopolamine and monoamine depletor reserpine to produce experimental EEG synchronization characterized by continuous large-amplitude synchronized activity, with prominent increased delta and decreased theta, alpha, and beta power density. The results showed that modafinil produced an EEG desynchronization in the model. This was characterized by a general decrease in amplitude of all the frequency bands between 0 and 20 Hz, a prominent reduction in delta power density, and an increase in theta power density. Adrenergic α1R antagonist terazosin (1 mg/kg, i.p.) completely antagonized the EEG desynchronization effects of modafinil at 90 mg/kg. However, DA D1R and D2R blockers partially attenuated the effects of modafinil. The modafinil-induced decrease in the amplitudes of the delta, theta, alpha, and beta waves and in delta power density were completely abolished by pretreatment with a combination of the D1R antagonist SCH 23390 (30 µg/kg) and the D2R antagonist raclopride (2 mg/kg, i.p.). Conclusions/Significance These results suggest that modafinil-mediated desynchronization may be attributed to the activation of adrenergic α1R, and dopaminergic D1R and D2R in a model of EEG synchronization. PMID:24116090

  13. Wireless Networks: New Meaning to Ubiquitous Computing.

    Science.gov (United States)

    Drew, Wilfred, Jr.

    2003-01-01

    Discusses the use of wireless technology in academic libraries. Topics include wireless networks; standards (IEEE 802.11); wired versus wireless; why libraries implement wireless technology; wireless local area networks (WLANs); WLAN security; examples of wireless use at Indiana State University and Morrisville College (New York); and useful…

  14. Outcomes of patients with altered level of consciousness and abnormal electroencephalogram: A retrospective cohort study.

    Science.gov (United States)

    Sanches, Paula Rodrigues; Corrêa, Thiago Domingos; Ferrari-Marinho, Taissa; Naves, Pedro Vicente Ferreira; Ladeia-Frota, Carol; Caboclo, Luís Otávio

    2017-01-01

    Nonconvulsive seizures (NCS) are frequent in hospitalized patients and may further aggravate injury in the already damaged brain, potentially worsening outcomes in encephalopathic patients. Therefore, both early seizure recognition and treatment have been advocated to prevent further neurological damage. Evaluate the main EEG patterns seen in patients with impaired consciousness and address the effect of treatment with antiepileptic drugs (AEDs), continuous intravenous anesthetic drugs (IVADs), or the combination of both, on outcomes. This was a single center retrospective cohort study conducted in a private, tertiary care hospital. Consecutive adult patients with altered consciousness submitted to a routine EEG between January 2008 and February 2011 were included in this study. Based on EEG pattern, patients were assigned to one of three groups: Group Interictal Patterns (IP; EEG showing only interictal epileptiform discharges or triphasic waves), Group Rhythmic and Periodic Patterns (RPP; at least one EEG with rhythmic or periodic patterns), and Group Ictal (Ictal; at least one EEG showing ictal pattern). Groups were compared in terms of administered antiepileptic treatment and frequency of unfavorable outcomes (modified Rankin scale ≥3 and in-hospital mortality). Two hundred and six patients (475 EEGs) were included in this analysis. Interictal pattern was observed in 35.4% (73/206) of patients, RPP in 53.4% (110/206) and ictal in 11.2% (23/206) of patients. Treatment with AEDs, IVADs or a combination of both was administered in half of the patients. While all Ictal group patients received treatment (AEDs or IVADs), only 24/73 (32.9%) IP group patients and 55/108 (50.9%) RPP group patients were treated (p<0.001). Hospital length of stay (LOS) and frequency of unfavorable outcomes did not differ among the groups. In-hospital mortality was higher in IVADs treated RPP patients compared to AEDs treated RPP patients [11/19 (57.9%) vs. 11/36 (30.6%) patients

  15. Multi-channel EEG signal feature extraction and pattern recognition on horizontal mental imagination task of 1-D cursor movement for brain computer interface.

    Science.gov (United States)

    Serdar Bascil, M; Tesneli, Ahmet Y; Temurtas, Feyzullah

    2015-06-01

    Brain computer interfaces (BCIs), based on multi-channel electroencephalogram (EEG) signal processing convert brain signal activities to machine control commands. It provides new communication way with a computer by extracting electroencephalographic activity. This paper, deals with feature extraction and classification of horizontal mental task pattern on 1-D cursor movement from EEG signals. The hemispherical power changes are computed and compared on alpha & beta frequencies and horizontal cursor control extracted with only mental imagination of cursor movements. In the first stage, features are extracted with the well-known average signal power or power difference (alpha and beta) method. Principal component analysis is used for reducing feature dimensions. All features are classified and the mental task patterns are recognized by three neural network classifiers which learning vector quantization, multilayer neural network and probabilistic neural network due to obtaining acceptable good results and using successfully in pattern recognition via k-fold cross validation technique.

  16. Independent EEG sources are dipolar.

    Directory of Open Access Journals (Sweden)

    Arnaud Delorme

    Full Text Available Independent component analysis (ICA and blind source separation (BSS methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR effected by each decomposition, and decomposition 'dipolarity' defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA; best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison.

  17. EEG correlates of virtual reality hypnosis.

    Science.gov (United States)

    White, David; Ciorciari, Joseph; Carbis, Colin; Liley, David

    2009-01-01

    The study investigated hypnosis-related electroencephalographic (EEG) coherence and power spectra changes in high and low hypnotizables (Stanford Hypnotic Clinical Scale) induced by a virtual reality hypnosis (VRH) induction system. In this study, the EEG from 17 participants (Mean age = 21.35, SD = 1.58) were compared based on their hypnotizability score. The EEG recording associated with a 2-minute, eyes-closed baseline state was compared to the EEG during a hypnosis-related state. This novel induction system was able to produce EEG findings consistent with previous hypnosis literature. Interactions of significance were found with EEG beta coherence. The high susceptibility group (n = 7) showed decreased coherence, while the low susceptibility group (n = 10) demonstrated an increase in coherence between medial frontal and lateral left prefrontal sites. Methodological and efficacy issues are discussed.

  18. Electromagnetic fields, such as those from mobile phones, alter regional cerebral blood flow and sleep and waking EEG.

    Science.gov (United States)

    Huber, R; Treyer, V; Borbély, A A; Schuderer, J; Gottselig, J M; Landolt, H-P; Werth, E; Berthold, T; Kuster, N; Buck, A; Achermann, P

    2002-12-01

    Usage of mobile phones is rapidly increasing, but there is limited data on the possible effects of electromagnetic field (EMF) exposure on brain physiology. We investigated the effect of EMF vs. sham control exposure on waking regional cerebral blood flow (rCBF) and on waking and sleep electroencephalogram (EEG) in humans. In Experiment 1, positron emission tomography (PET) scans were taken after unilateral head exposure to 30-min pulse-modulated 900 MHz electromagnetic field (pm-EMF). In Experiment 2, night-time sleep was polysomnographically recorded after EMF exposure. Pulse-modulated EMF exposure increased relative rCBF in the dorsolateral prefrontal cortex ipsilateral to exposure. Also, pm-EMF exposure enhanced EEG power in the alpha frequency range prior to sleep onset and in the spindle frequency range during stage 2 sleep. Exposure to EMF without pulse modulation did not enhance power in the waking or sleep EEG. We previously observed EMF effects on the sleep EEG (A. A. Borbély, R. Huber, T. Graf, B. Fuchs, E. Gallmann and P. Achermann. Neurosci. Lett., 1999, 275: 207-210; R. Huber, T. Graf, K. A. Cote, L. Wittmann, E. Gallmann, D. Matter, J. Schuderer, N. Kuster, A. A. Borbély, and P. Achermann. Neuroreport, 2000, 11: 3321-3325), but the basis for these effects was unknown. The present results show for the first time that (1) pm-EMF alters waking rCBF and (2) pulse modulation of EMF is necessary to induce waking and sleep EEG changes. Pulse-modulated EMF exposure may provide a new, non-invasive method for modifying brain function for experimental, diagnostic and therapeutic purposes.

  19. The utility of amplitude-integrated EEG and NIRS measurements as indices of hypoxic ischaemia in the newborn pig.

    Science.gov (United States)

    Zhang, Dandan; Hou, Xinlin; Liu, Yunfeng; Zhou, Congle; Luo, Yuejia; Ding, Haiyan

    2012-08-01

    The early detection and stratification of potential hypoxic ischaemia (HI) injury in neonates are crucial for reducing the risk of neural disability. This study investigates early changes in brain function caused by acute HI of varying severities in the neonatal pig. Two non-invasive techniques, amplitude-integrated electroencephalogram (aEEG) and near-infrared spectroscopy (NIRS), were used to monitor electrocortical and cerebral haemodynamic function, respectively. The fraction of inspired oxygen (FiO(2)) was varied to produce different HI severities. The sensitivity and HI correlation of these methods were systematically analysed to assess their abilities to both detect injury early and assess HI severity accurately. The tissue oxygen index measured via NIRS detected acute changes in cerebral oxygenation and was highly sensitive to HI (sensitivity=0.97), whereas aEEG was comparatively insensitive to HI. On the other hand, aEEG measurements correlated well with FiO(2) during the entire HI event as well as the 3-h recovery period (R=0.43-0.61). NIRS measurements did not correlate well with FiO(2). Parameters measured via aEEG and NIRS displayed different time profiles during and following the HI event. These results highlight the potential advantage of using aEEG and NIRS in conjunction to monitor neonatal brain function, and provide an objective and rigorous method for the characterisation of cerebral function both during and following HI insults. Copyright © 2011 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  20. The effects of cardiorespiratory fitness and acute aerobic exercise on executive functioning and EEG entropy in adolescents

    Directory of Open Access Journals (Sweden)

    Michael John Hogan

    2015-10-01

    Full Text Available The current study examined the effects of cardiorespiratory fitness, identified with a continuous graded cycle ergometry, and aerobic exercise on cognitive functioning and entropy of the electroencephalogram (EEG in 30 adolescents between the ages of 13 and 14 years. Higher and lower fit participants performed an executive function task after a bout of acute exercise and after rest while watching a film. EEG entropy, using the sample entropy measure, was repeatedly measured during the 1500ms post-stimulus interval to evaluate changes in entropy over time. Analysis of the behavioural data for lower and higher fit groups revealed an interaction between fitness levels and acute physical exercise. Notably, lower fit, but not higher fit, participants had higher error rates for NoGo relative to Go trials in the rest condition, whereas in the acute exercise condition there were no differences in error rates between groups; higher fit participants also had significantly faster reaction times in the exercise condition in comparison with the rest condition. Analysis of EEG data revealed that higher fit participants demonstrated lower entropy post-stimulus than lower fit participants in the left frontal hemisphere, possibly indicating increased efficiency of early stage stimulus processing and more efficient allocation of cognitive resources to the task demands. The results suggest that EEG entropy is sensitive to stimulus processing demands and varies as a function of physical fitness levels, but not acute exercise. Physical fitness, in turn, may enhance cognition in adolescence by facilitating higher functionality of the attentional system in the context of lower levels of frontal EEG entropy.

  1. Stage-independent, single lead EEG sleep spindle detection using the continuous wavelet transform and local weighted smoothing

    Directory of Open Access Journals (Sweden)

    Athanasios eTsanas

    2015-04-01

    Full Text Available Sleep spindles are critical in characterizing sleep and have been associated with cognitive function and pathophysiological assessment. Typically, their detection relies on the subjective and time-consuming visual examination of electroencephalogram (EEG signal(s by experts, and has led to large inter-rater variability as a result of poor definition of sleep spindle characteristics. Hitherto, many algorithmic spindle detectors inherently make signal stationarity assumptions (e.g. Fourier transform-based approaches which are inappropriate for EEG signals, and frequently rely on additional information which may not be readily available in many practical settings (e.g. more than one EEG channels, or prior hypnogram assessment. This study proposes a novel signal processing methodology relying solely on a single EEG channel, and provides objective, accurate means towards probabilistically assessing the presence of sleep spindles in EEG signals. We use the intuitively appealing continuous wavelet transform (CWT with a Morlet basis function, identifying regions of interest where the power of the CWT coefficients corresponding to the frequencies of spindles (11-16 Hz is large. The potential for assessing the signal segment as a spindle is refined using local weighted smoothing techniques. We evaluate our findings on two databases: the MASS database comprising 19 healthy controls and the DREAMS sleep spindle database comprising eight participants diagnosed with various sleep pathologies. We demonstrate that we can replicate the experts’ sleep spindles assessment accurately in both databases (MASS database: sensitivity: 84%, specificity: 90%, false discovery rate 83%, DREAMS database: sensitivity: 76%, specificity: 92%, false discovery rate: 67%, outperforming six competing automatic sleep spindle detection algorithms in terms of correctly replicating the experts’ assessment of detected spindles.

  2. Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA–WT during Working Memory Tasks

    Science.gov (United States)

    Al-Qazzaz, Noor Kamal; Hamid Bin Mohd Ali, Sawal; Ahmad, Siti Anom; Islam, Mohd Shabiul; Escudero, Javier

    2017-01-01

    Characterizing dementia is a global challenge in supporting personalized health care. The electroencephalogram (EEG) is a promising tool to support the diagnosis and evaluation of abnormalities in the human brain. The EEG sensors record the brain activity directly with excellent time resolution. In this study, EEG sensor with 19 electrodes were used to test the background activities of the brains of five vascular dementia (VaD), 15 stroke-related patients with mild cognitive impairment (MCI), and 15 healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the recorded EEG signals using a novel technique that combines automatic independent component analysis (AICA) and wavelet transform (WT), that is, the AICA–WT technique; second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. The proposed AICA–WT technique is a four-stage approach. In the first stage, the independent components (ICs) were estimated. In the second stage, three-step artifact identification metrics were applied to detect the artifactual components. The components identified as artifacts were marked as critical and denoised through DWT in the third stage. In the fourth stage, the corrected ICs were reconstructed to obtain artifact-free EEG signals. The performance of the proposed AICA–WT technique was compared with those of two other techniques based on AICA and WT denoising methods using cross-correlation XCorr and peak signal to noise ratio (PSNR) (ANOVA, p ˂ 0.05). The AICA–WT technique exhibited the best artifact removal performance. The assumption that there would be a deceleration of EEG dominant frequencies in VaD and MCI patients compared with control subjects was assessed with AICA–WT (ANOVA, p ˂ 0.05). Therefore, this study may provide information on post-stroke dementia particularly VaD and stroke-related MCI patients

  3. Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA-WT during Working Memory Tasks.

    Science.gov (United States)

    Al-Qazzaz, Noor Kamal; Hamid Bin Mohd Ali, Sawal; Ahmad, Siti Anom; Islam, Mohd Shabiul; Escudero, Javier

    2017-06-08

    Characterizing dementia is a global challenge in supporting personalized health care. The electroencephalogram (EEG) is a promising tool to support the diagnosis and evaluation of abnormalities in the human brain. The EEG sensors record the brain activity directly with excellent time resolution. In this study, EEG sensor with 19 electrodes were used to test the background activities of the brains of five vascular dementia (VaD), 15 stroke-related patients with mild cognitive impairment (MCI), and 15 healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the recorded EEG signals using a novel technique that combines automatic independent component analysis (AICA) and wavelet transform (WT), that is, the AICA-WT technique; second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. The proposed AICA-WT technique is a four-stage approach. In the first stage, the independent components (ICs) were estimated. In the second stage, three-step artifact identification metrics were applied to detect the artifactual components. The components identified as artifacts were marked as critical and denoised through DWT in the third stage. In the fourth stage, the corrected ICs were reconstructed to obtain artifact-free EEG signals. The performance of the proposed AICA-WT technique was compared with those of two other techniques based on AICA and WT denoising methods using cross-correlation X C o r r and peak signal to noise ratio ( P S N R ) (ANOVA, p ˂ 0.05). The AICA-WT technique exhibited the best artifact removal performance. The assumption that there would be a deceleration of EEG dominant frequencies in VaD and MCI patients compared with control subjects was assessed with AICA-WT (ANOVA, p ˂ 0.05). Therefore, this study may provide information on post-stroke dementia particularly VaD and stroke-related MCI patients through

  4. Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA–WT during Working Memory Tasks

    Directory of Open Access Journals (Sweden)

    Noor Kamal Al-Qazzaz

    2017-06-01

    Full Text Available Characterizing dementia is a global challenge in supporting personalized health care. The electroencephalogram (EEG is a promising tool to support the diagnosis and evaluation of abnormalities in the human brain. The EEG sensors record the brain activity directly with excellent time resolution. In this study, EEG sensor with 19 electrodes were used to test the background activities of the brains of five vascular dementia (VaD, 15 stroke-related patients with mild cognitive impairment (MCI, and 15 healthy subjects during a working memory (WM task. The objective of this study is twofold. First, it aims to enhance the recorded EEG signals using a novel technique that combines automatic independent component analysis (AICA and wavelet transform (WT, that is, the AICA–WT technique; second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. The proposed AICA–WT technique is a four-stage approach. In the first stage, the independent components (ICs were estimated. In the second stage, three-step artifact identification metrics were applied to detect the artifactual components. The components identified as artifacts were marked as critical and denoised through DWT in the third stage. In the fourth stage, the corrected ICs were reconstructed to obtain artifact-free EEG signals. The performance of the proposed AICA–WT technique was compared with those of two other techniques based on AICA and WT denoising methods using cross-correlation X C o r r and peak signal to noise ratio ( P S N R (ANOVA, p ˂ 0.05. The AICA–WT technique exhibited the best artifact removal performance. The assumption that there would be a deceleration of EEG dominant frequencies in VaD and MCI patients compared with control subjects was assessed with AICA–WT (ANOVA, p ˂ 0.05. Therefore, this study may provide information on post-stroke dementia particularly VaD and stroke

  5. Juvenile myoclonic epilepsy: clinical and EEG features

    DEFF Research Database (Denmark)

    Pedersen, S B; Petersen, K A

    1998-01-01

    We aimed to characterize the clinical profile and EEG features of 43 patients with juvenile myoclonic epilepsy. In a retrospective design we studied the records of, and re-interviewed, 43 patients diagnosed with JME from the epilepsy clinic data base. Furthermore, available EEGs were re-evaluated...... were sleep deprivation (84%), stress (70%), and alcohol consumption (51%). EEG findings included rapid spike-wave and polyspike-wave....

  6. Tele-transmission of EEG recordings.

    Science.gov (United States)

    Lemesle, M; Kubis, N; Sauleau, P; N'Guyen The Tich, S; Touzery-de Villepin, A

    2015-03-01

    EEG recordings can be sent for remote interpretation. This article aims to define the tele-EEG procedures and technical guidelines. Tele-EEG is a complete medical act that needs to be carried out with the same quality requirements as a local one in terms of indications, formulation of the medical request and medical interpretation. It adheres to the same quality requirements for its human resources and materials. It must be part of a medical organization (technical and medical network) and follow all rules and guidelines of good medical practices. The financial model of this organization must include costs related to performing the EEG recording, operating and maintenance of the tele-EEG network and medical fees of the physician interpreting the EEG recording. Implementing this organization must be detailed in a convention between all parties involved: physicians, management of the healthcare structure, and the company providing the tele-EEG service. This convention will set rules for network operation and finance, and also the continuous training of all staff members. The tele-EEG system must respect all rules for safety and confidentiality, and ensure the traceability and storing of all requests and reports. Under these conditions, tele-EEG can optimize the use of human resources and competencies in its zone of utilization and enhance the organization of care management. Copyright © 2015. Published by Elsevier SAS.

  7. Test-retest reliability of cognitive EEG

    Science.gov (United States)

    McEvoy, L. K.; Smith, M. E.; Gevins, A.

    2000-01-01

    OBJECTIVE: Task-related EEG is sensitive to changes in cognitive state produced by increased task difficulty and by transient impairment. If task-related EEG has high test-retest reliability, it could be used as part of a clinical test to assess changes in cognitive function. The aim of this study was to determine the reliability of the EEG recorded during the performance of a working memory (WM) task and a psychomotor vigilance task (PVT). METHODS: EEG was recorded while subjects rested quietly and while they performed the tasks. Within session (test-retest interval of approximately 1 h) and between session (test-retest interval of approximately 7 days) reliability was calculated for four EEG components: frontal midline theta at Fz, posterior theta at Pz, and slow and fast alpha at Pz. RESULTS: Task-related EEG was highly reliable within and between sessions (r0.9 for all components in WM task, and r0.8 for all components in the PVT). Resting EEG also showed high reliability, although the magnitude of the correlation was somewhat smaller than that of the task-related EEG (r0.7 for all 4 components). CONCLUSIONS: These results suggest that under appropriate conditions, task-related EEG has sufficient retest reliability for use in assessing clinical changes in cognitive status.

  8. Combining Different Tools for EEG Analysis to Study the Distributed Character of Language Processing

    Directory of Open Access Journals (Sweden)

    Armando Freitas da Rocha

    2015-01-01

    Full Text Available Recent studies on language processing indicate that language cognition is better understood if assumed to be supported by a distributed intelligent processing system enrolling neurons located all over the cortex, in contrast to reductionism that proposes to localize cognitive functions to specific cortical structures. Here, brain activity was recorded using electroencephalogram while volunteers were listening or reading small texts and had to select pictures that translate meaning of these texts. Several techniques for EEG analysis were used to show this distributed character of neuronal enrollment associated with the comprehension of oral and written descriptive texts. Low Resolution Tomography identified the many different sets (si of neurons activated in several distinct cortical areas by text understanding. Linear correlation was used to calculate the information H(ei provided by each electrode of the 10/20 system about the identified si. H(ei Principal Component Analysis (PCA was used to study the temporal and spatial activation of these sources si. This analysis evidenced 4 different patterns of H(ei covariation that are generated by neurons located at different cortical locations. These results clearly show that the distributed character of language processing is clearly evidenced by combining available EEG technologies.

  9. Predictable Internal Brain Dynamics in EEG and Its Relation to Conscious States

    Directory of Open Access Journals (Sweden)

    Jaewook eYoo

    2014-06-01

    Full Text Available Consciousness is a complex and multi-faceted phenomenon defying scientific explanation. Part of the reason why this is the case is due to its subjective nature. In our previous computational experiments, to avoid such a subjective trap, we took a strategy to investigate objective necessary conditions of consciousness. Our basic hypothesis was that predictive internal dynamics serves as such a condition. This is in line with theories of consciousness that treat retention (memory, protention (anticipation, and primary impression as the tripartite temporal structure of consciousness. To test our hypothesis, we analyzed publicly available sleep and awake electroencephalogram (EEG data. Our results show that EEG signals from awake or rapid eye movement (REM sleep states have more predictable dynamics compared to those from slow-wave sleep (SWS. Since awakeness and REM sleep are associated with conscious states and SWS with unconscious or less consciousness states, these results support our hypothesis. The results suggest an intricate relationship among prediction, consciousness, and time, with potential applications to time perception and neurorobotics.

  10. Combining Different Tools for EEG Analysis to Study the Distributed Character of Language Processing.

    Science.gov (United States)

    Rocha, Armando Freitas da; Foz, Flávia Benevides; Pereira, Alfredo

    2015-01-01

    Recent studies on language processing indicate that language cognition is better understood if assumed to be supported by a distributed intelligent processing system enrolling neurons located all over the cortex, in contrast to reductionism that proposes to localize cognitive functions to specific cortical structures. Here, brain activity was recorded using electroencephalogram while volunteers were listening or reading small texts and had to select pictures that translate meaning of these texts. Several techniques for EEG analysis were used to show this distributed character of neuronal enrollment associated with the comprehension of oral and written descriptive texts. Low Resolution Tomography identified the many different sets (s i ) of neurons activated in several distinct cortical areas by text understanding. Linear correlation was used to calculate the information H(e i ) provided by each electrode of the 10/20 system about the identified s i . H(e i ) Principal Component Analysis (PCA) was used to study the temporal and spatial activation of these sources s i . This analysis evidenced 4 different patterns of H(e i ) covariation that are generated by neurons located at different cortical locations. These results clearly show that the distributed character of language processing is clearly evidenced by combining available EEG technologies.

  11. Development of double density whole brain fNIRS with EEG system for brain machine interface.

    Science.gov (United States)

    Ishikawa, A; Udagawa, H; Masuda, Y; Kohno, S; Amita, T; Inoue, Y

    2011-01-01

    Brain-machine interfaces (BMI) are expected as new man-machine interfaces. Non-invasive BMI have the potential to improve the quality of life of many disabled individuals with safer operation. The non-invasive BMI using the functional functional near-infrared spectroscopy (fNIRS) with the electroencephalogram (EEG) has potential applicability beyond the restoration of lost movement and rehabilitation in paraplegics and would enable normal individuals to have direct brain control of external devices in their daily lives. To shift stage of the non-invasive BMI from laboratory to clinical, the key factor is to develop high-accuracy signal decoding technology and highly restrictive of the measurement area. In this article, we present the development of a high-accuracy brain activity measurement system by combining fNIRS and EEG. The new fNIRS had high performances with high spatial resolution using double density technique and a large number of measurement channels to cover a whole human brain.

  12. The cortical focus in childhood absence epilepsy; evidence from nonlinear analysis of scalp EEG recordings.

    Science.gov (United States)

    Sarrigiannis, Ptolemaios G; Zhao, Yifan; He, Fei; Billings, Stephen A; Baster, Kathleen; Rittey, Chris; Yianni, John; Zis, Panagiotis; Wei, Hualiang; Hadjivassiliou, Marios; Grünewald, Richard

    2018-03-01

    To determine the origin and dynamic characteristics of the generalised hyper-synchronous spike and wave (SW) discharges in childhood absence epilepsy (CAE). We applied nonlinear methods, the error reduction ratio (ERR) causality test and cross-frequency analysis, with a nonlinear autoregressive exogenous (NARX) model, to electroencephalograms (EEGs) from CAE, selected with stringent electro-clinical criteria (17 cases, 42 absences). We analysed the pre-ictal and ictal strength of association between homologous and heterologous EEG derivations and estimated the direction of synchronisation and corresponding time lags. A frontal/fronto-central onset of the absences is detected in 13 of the 17 cases with the highest ictal strength of association between homologous frontal followed by centro-temporal and fronto-central areas. Delays consistently in excess of 4 ms occur at the very onset between these regions, swiftly followed by the emergence of "isochronous" (0-2 ms) synchronisation but dynamic time lag changes occur during SW discharges. In absences an initial cortico-cortical spread leads to dynamic lag changes to include periods of isochronous interhemispheric synchronisation, which we hypothesize is mediated by the thalamus. Absences from CAE show ictal epileptic network dynamics remarkably similar to those observed in WAG/Rij rats which guided the formulation of the cortical focus theory. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  13. Classification of Targets and Distractors Present in Visual Hemifields Using Time-Frequency Domain EEG Features

    Directory of Open Access Journals (Sweden)

    Sweeti

    2018-01-01

    Full Text Available This paper presents a classification system to classify the cognitive load corresponding to targets and distractors present in opposite visual hemifields. The approach includes the study of EEG (electroencephalogram signal features acquired in a spatial attention task. The process comprises of EEG feature selection based on the feature distribution, followed by the stepwise discriminant analysis- (SDA- based channel selection. Repeated measure analysis of variance (rANOVA is applied to test the statistical significance of the selected features. Classifiers are developed and compared using the selected features to classify the target and distractor present in visual hemifields. The results provide a maximum classification accuracy of 87.2% and 86.1% and an average classification accuracy of 76.5 ± 4% and 76.2 ± 5.3% over the thirteen subjects corresponding to the two task conditions. These correlates present a step towards building a feature-based neurofeedback system for visual attention.

  14. New KF-PP-SVM classification method for EEG in brain-computer interfaces.

    Science.gov (United States)

    Yang, Banghua; Han, Zhijun; Zan, Peng; Wang, Qian

    2014-01-01

    Classification methods are a crucial direction in the current study of brain-computer interfaces (BCIs). To improve the classification accuracy for electroencephalogram (EEG) signals, a novel KF-PP-SVM (kernel fisher, posterior probability, and support vector machine) classification method is developed. Its detailed process entails the use of common spatial patterns to obtain features, based on which the within-class scatter is calculated. Then the scatter is added into the kernel function of a radial basis function to construct a new kernel function. This new kernel is integrated into the SVM to obtain a new classification model. Finally, the output of SVM is calculated based on posterior probability and the final recognition result is obtained. To evaluate the effectiveness of the proposed KF-PP-SVM method, EEG data collected from laboratory are processed with four different classification schemes (KF-PP-SVM, KF-SVM, PP-SVM, and SVM). The results showed that the overall average improvements arising from the use of the KF-PP-SVM scheme as opposed to KF-SVM, PP-SVM and SVM schemes are 2.49%, 5.83 % and 6.49 % respectively.

  15. An SSVEP based BCI to control a humanoid robot by using portable EEG device.

    Science.gov (United States)

    Güneysu, Arzu; Akin, H Levent

    2013-01-01

    Brain Computer Interfaces (BCIs) are systems that allow human subjects to interact with the environment by interpreting brain signals into machine commands. This work provides a design for a BCI to control a humanoid robot by using signals obtained from the Emotiv EPOC, a portable electroencephalogram (EEG) device with 14 electrodes and sampling rate of 128 Hz. The main objective is to process the neuroelectric responses to an externally driven stimulus and generate control signals for the humanoid robot Nao accordingly. We analyze steady-state visually evoked potential (SSVEP) induced by one of four groups of light emitting diodes (LED) by using two distinct signals obtained from the two channels of the EEG device which reside on top of the occipital lobe. An embedded system is designed for generating pulse width modulated square wave signals in order to flicker each group of LEDs with different frequencies. The subject chooses the direction by looking at one of these groups of LEDs that represent four directions. Fast Fourier Transform and a Gaussian model are used to detect the dominant frequency component by utilizing harmonics and neighbor frequencies. Then, a control signal is sent to the robot in order to draw a fixed sized line in that selected direction by BCI. Experimental results display satisfactory performance where the correct target is detected 75% of the time on the average across all test subjects without any training.

  16. Cortico-muscular coherence on artifact corrected EEG-EMG data recorded with a MRI scanner.

    Science.gov (United States)

    Muthuraman, M; Galka, A; Hong, V N; Heute, U; Deuschl, G; Raethjen, J

    2013-01-01

    Simultaneous recording of electroencephalogram (EEG) and electromyogram (EMG) with magnetic resonance imaging (MRI) provides great potential for studying human brain activity with high temporal and spatial resolution. But, due to the MRI, the recorded signals are contaminated with artifacts. The correction of these artifacts is important to use these signals for further spectral analysis. The coherence can reveal the cortical representation of peripheral muscle signal in particular motor tasks, e.g. finger movements. The artifact correction of these signals was done by two different algorithms the Brain vision analyzer (BVA) and the Matlab FMRIB plug-in for EEGLAB. The Welch periodogram method was used for estimating the cortico-muscular coherence. Our analysis revealed coherence with a frequency of 5Hz in the contralateral side of the brain. The entropy is estimated for the calculated coherence to get the distribution of coherence in the scalp. The significance of the paper is to identify the optimal algorithm to rectify the MR artifacts and as a first step to use both these signals EEG and EMG in conjunction with MRI for further studies.

  17. Combination of EEG Complexity and Spectral Analysis for Epilepsy Diagnosis and Seizure Detection

    Directory of Open Access Journals (Sweden)

    Wan-Lin Chang

    2010-01-01

    Full Text Available Approximately 1% of the world's population has epilepsy, and 25% of epilepsy patients cannot be treated sufficiently by any available therapy. If an automatic seizure-detection system was available, it could reduce the time required by a neurologist to perform an off-line diagnosis by reviewing electroencephalogram (EEG data. It could produce an on-line warning signal to alert healthcare professionals or to drive a treatment device such as an electrical stimulator to enhance the patient's safety and quality of life. This paper describes a systematic evaluation of current approaches to seizure detection in the literature. This evaluation was then used to suggest a reliable, practical epilepsy detection method. The combination of complexity analysis and spectrum analysis on an EEG can perform robust evaluations on the collected data. Principle component analysis (PCA and genetic algorithms (GAs were applied to various linear and nonlinear methods. The best linear models resulted from using all of the features without other processing. For the nonlinear models, applying PCA for feature reduction provided better results than applying GAs. The feasibility of executing the proposed methods on a personal computer for on-line processing was also demonstrated.

  18. A Novel Visualization Method for Sleep Spindles Based on Source Localization of High Density EEG.

    Science.gov (United States)

    Lee, Soohyun; Kim, Seunghwan; Choi, Jee Hyun

    2017-12-01

    Equivalent dipole source localization is a well-established approach to localizing the electrical activity in electroencephalogram (EEG). So far, source localization has been used primarily in localizing the epileptic source in human epileptic patients. Currently, source localization techniques have been applied to account for localizing epileptic source among the epileptic patients. Here, we present the first application of source localization in the field of sleep spindle in mouse brain. The spatial distribution of cortical potential was obtained by high density EEG and then the anterior and posterior sleep spindles were classified based on the K-mean clustering algorithm. To solve the forward problem, a realistic geometry brain model was produced based on boundary element method (BEM) using mouse MRI. Then, we applied four different source estimation algorithms (minimum norm, eLORETA, sLORETA, and LORETA) to estimate the spatial location of equivalent dipole source of sleep spindles. The estimated sources of anterior and posterior spindles were plotted in a cine-mode that revealed different topographic patterns of spindle propagation. The characterization of sleep spindles may be better be distinguished by our novel visualization method.

  19. Evaluation of different EEG acquisition systems concerning their suitability for building a brain-computer interface

    Directory of Open Access Journals (Sweden)

    Andreas Pinegger

    2016-09-01

    Full Text Available One important aspect in non-invasive brain-computer interface (BCI research is to acquire the electroencephalogram (EEG in a proper way. From an end-user perspective this means with maximum comfort and without any extra inconveniences (e.g., washing the hair. Whereas from a technical perspective, the signal quality has to be optimal to make the BCI work effectively and efficiently.In this work we evaluated three different commercially available EEG acquisition systems that differ in the type of electrode (gel-, water-, and dry-based, the amplifier technique, and the data transmission method. Every system was tested regarding three different aspects, namely, technical, BCI effectiveness and efficiency (P300 communication and control, and user satisfaction (comfort.We found that the water-based system had the lowest short circuit noise level, the hydrogel-based system had the highest P300 spelling accuracies, and the dry electrode system caused the least inconveniences.Therefore, building a reliable BCI is possible with all evaluated systems and it is on the user to decide which system meets the given requirements best.

  20. Modeling state entropy of the EEG and auditory evoked potentials: hypnotic and analgesic interactions.

    Science.gov (United States)

    Castro, Ana; Amorim, Pedro; Nunes, Catarina S

    2007-01-01

    Because of the complexity of raw electroencephalogram (EEG), for the anesthesiologist it is very difficult to evaluate the patient's hypnosis state. Because of this, several depth of anesthesia monitors have been developed, and are in current use at the operating room (OR). These monitors convert the information supplied by the EEG or derived signals into a simple, easy to understand index. Nowadays, general anesthesia is controlled only by the clinician, which decides what is the best drug combination for the patient, regarding all information given by monitors and sensors in the OR. In this work, we collected data from two study groups with auditory evoked potentials (AEP) monitoring, and Entropy (SE) monitoring. A model was fitted to the signals and the Hill equation parameters adjusted, in both study groups. The objective was to predict hypnosis indices, regarding only the drugs administered to a patient, and capture the initial individual patient characteristics that might influence the drugs interaction in the human body. Hypnotic and analgesic drugs interact in different ways throughout the anaesthesia stages. The models obtained captured the different dynamic interaction of drugs, during the induction and maintenance phases, demonstrating that the model must have incorporated all this information in order to perform satisfactorily. Other information like haemodynamic variables might be included in the search for the optimum model.

  1. Interictal Epileptiform Discharges (IEDs) classification in EEG data of epilepsy patients

    Science.gov (United States)

    Puspita, J. W.; Soemarno, G.; Jaya, A. I.; Soewono, E.

    2017-12-01

    Interictal Epileptiform Dischargers (IEDs), which consists of spike waves and sharp waves, in human electroencephalogram (EEG) are characteristic signatures of epilepsy. Spike waves are characterized by a pointed peak with a duration of 20-70 ms, while sharp waves has a duration of 70-200 ms. The purpose of the study was to classify spike wave and sharp wave of EEG data of epilepsy patients using Backpropagation Neural Network. The proposed method consists of two main stages: feature extraction stage and classification stage. In the feature extraction stage, we use frequency, amplitude and statistical feature, such as mean, standard deviation, and median, of each wave. The frequency values of the IEDs are very sensitive to the selection of the wave baseline. The selected baseline must contain all data of rising and falling slopes of the IEDs. Thus, we have a feature that is able to represent the type of IEDs, appropriately. The results show that the proposed method achieves the best classification results with the recognition rate of 93.75 % for binary sigmoid activation function and learning rate of 0.1.

  2. Knowledge-based Identification of Sleep Stages based on Two Forehead Electroencephalogram Channels

    Directory of Open Access Journals (Sweden)

    Chih-Sheng eHuang

    2014-09-01

    Full Text Available Sleep quality is important, especially given the considerable number of sleep-related pathologies. The distribution of sleep stages is a highly effective and objective way of quantifying sleep quality. As a standard multi-channel recording used in the study of sleep, polysomnography (PSG is a widely used diagnostic scheme in sleep medicine. However, the standard process of sleep clinical test, including PSG recording and manual scoring, is complex, uncomfortable, and time-consuming. This process is difficult to implement when taking the whole PSG measurements at home for general healthcare purposes. This work presents a novel sleep stage classification system, based on features from the two forehead EEG channels FP1 and FP2. By recording EEG from forehead, where there is no hair, the proposed system can monitor physiological changes during sleep in a more practical way than previous systems. Through a headband or self-adhesive technology, the necessary sensors can be applied easily by users at home. Analysis results demonstrate that classification performance of the proposed system overcomes the individual differences between different participants in terms of automatically classifying sleep stages. Additionally, the proposed sleep stage classification system can identify kernel sleep features extracted from forehead EEG, which are closely related with sleep clinician’s expert knowledge. Moreover, forehead EEG features are classified into five sleep stages by using the relevance vector machine. In a leave-one-subject-out cross validation analysis, we found our system to correctly classify five sleep stages at an average accuracy of 76.7 ± 4.0 (SD % (average kappa 0.68 ± 0.06 (SD. Importantly, the proposed sleep stage classification system using forehead EEG features is a viable alternative for measuring EEG signals at home easily and conveniently to evaluate sleep quality reliably, ultimately improving public healthcare.

  3. Denoising of Ictal EEG Data Using Semi-Blind Source Separation Methods Based on Time-Frequency Priors.

    Science.gov (United States)

    Hajipour Sardouie, Sepideh; Bagher Shamsollahi, Mohammad; Albera, Laurent; Merlet, Isabelle

    2015-05-01

    Removing muscle activity from ictal ElectroEncephaloGram (EEG) data is an essential preprocessing step in diagnosis and study of epileptic disorders. Indeed, at the very beginning of seizures, ictal EEG has a low amplitude and its morphology in the time domain is quite similar to muscular activity. Contrary to the time domain, ictal signals have specific characteristics in the time-frequency domain. In this paper, we use the time-frequency signature of ictal discharges as a priori information on the sources of interest. To extract the time-frequency signature of ictal sources, we use the Canonical Correlation Analysis (CCA) method. Then, we propose two time-frequency based semi-blind source separation approaches, namely the Time-Frequency-Generalized EigenValue Decomposition (TF-GEVD) and the Time-Frequency-Denoising Source Separation (TF-DSS), for the denoising of ictal signals based on these time-frequency signatures. The performance of the proposed methods is compared with that of CCA and Independent Component Analysis (ICA) approaches for the denoising of simulated ictal EEGs and of real ictal data. The results show the superiority of the proposed methods in comparison with CCA and ICA.

  4. Paradoxical dopaminergic drug effects in extraversion: Dose- and time-dependent effects of Sulpiride on EEG theta activity

    Directory of Open Access Journals (Sweden)

    Mira-Lynn eChavanon

    2013-04-01

    Full Text Available Dopaminergic drugs frequently produce paradoxical effects depending on baseline performance levels, genotype or personality traits. The present study for the first time aimed to specify the mechanisms underlying such opposite effects using the following recently reported scenario as an example: Depending on the personality trait agentic extraversion (aE; i.e. assertiveness, dominance, ambition, positive emotionality the selective dopamine D2 receptor antagonist sulpiride (200 mg had opposite effects on resting posterior versus anterior theta activity in the electroencephalogram (EEG. In order to better describe these opposite pharmaco-EEG effects and to generate hypotheses regarding the underlying mechanisms, we measured the EEG intermittently over five hours in 80 healthy male volunteers extremely high or low in aE who had received either placebo or one of three doses of sulpiride (50 mg, 200 mg, or 400 mg. The findings suggest a model postulating stronger pre- versus postsynaptic subreceptor effects in high aE individuals compared to low aE individuals. Future studies may now systematically apply the model to other examples of paradoxical dopaminergic drug effects and examine the molecular basis of individual differences in pre- versus postsynaptic dopamine D2 subreceptor sensitivities and densities.

  5. Theta-alpha EEG phase distributions in the frontal area for dissociation of visual and auditory working memory.

    Science.gov (United States)

    Akiyama, Masakazu; Tero, Atsushi; Kawasaki, Masahiro; Nishiura, Yasumasa; Yamaguchi, Yoko

    2017-03-07

    Working memory (WM) is known to be associated with synchronization of the theta and alpha bands observed in electroencephalograms (EEGs). Although frontal-posterior global theta synchronization appears in modality-specific WM, local theta synchronization in frontal regions has been found in modality-independent WM. How frontal theta oscillations separately synchronize with task-relevant sensory brain areas remains an open question. Here, we focused on theta-alpha phase relationships in frontal areas using EEG, and then verified their functional roles with mathematical models. EEG data showed that the relationship between theta (6 Hz) and alpha (12 Hz) phases in the frontal areas was about 1:2 during both auditory and visual WM, and that the phase distributions between auditory and visual WM were different. Next, we used the differences in phase distributions to construct FitzHugh-Nagumo type mathematical models. The results replicated the modality-specific branching by orthogonally of the trigonometric functions for theta and alpha oscillations. Furthermore, mathematical and experimental results were consistent with regards to the phase relationships and amplitudes observed in frontal and sensory areas. These results indicate the important role that different phase distributions of theta and alpha oscillations have in modality-specific dissociation in the brain.

  6. Reflection in Learning through a Self-monitoring Device: Design Research on EEG Self-Monitoring during a Study Session

    Directory of Open Access Journals (Sweden)

    Eva Durall

    2017-04-01

    Full Text Available The increasing availability of self-monitoring technologies has created opportunities for gaining awareness about one’s own behavior and reflecting on it. In teaching and learning, there is interest in using self-monitoring technologies, but very few studies have explored the possibilities. In this paper, we present a design study that investigates a technology (called Feeler that guides students to follow a specific learning script, monitors changes in their electroencephalogram (EEG while studying, and later provides visualization of the EEG data. The results are two-fold: (1 the hardware/software prototype and (2 the conclusions from the proof-of-concept research conducted with the prototype and six participants. In the research, we collected qualitative data from interviews to identify whether the prototype supported students to develop their reflective skills. The thematic analysis of the interviews showed that the Feeler’s learning script and visualization of the EEG data supported greater levels of reflection by fostering students’ curiosity, puzzlement, and personal inquiry. The proof-of-concept research also provided insights into several factors, such as the value of personal experience, the challenge of assumptions, and the contextualization of the data that trigger reflective thinking. The results validate the design concept and the role of the prototype in supporting awareness of and reflection about students’ mental states when they perform academic tasks.

  7. Multi-modal causality analysis of eyes-open and eyes-closed data from simultaneously recorded EEG and MEG.

    Science.gov (United States)

    Anwar, Abdul Rauf; Mideska, Kidist Gebremariam; Hellriegel, Helge; Hoogenboom, Nienke; Krause, Holger; Schnitzler, Alfons; Deuschl, Günther; Raethjen, Jan; Heute, Ulrich; Muthuraman, Muthuraman

    2014-01-01

    Owing to the recent advances in multi-modal data analysis, the aim of the present study was to analyze the functional network of the brain which remained the same during the eyes-open (EO) and eyes-closed (EC) resting task. The simultaneously recorded electroencephalogram (EEG) and magnetoencephalogram (MEG) were used for this study, recorded from five distinct cortical regions of the brain. We focused on the 'alpha' functional network, corresponding to the individual peak frequency in the alpha band. The total data set of 120 seconds was divided into three segments of 18 seconds each, taken from start, middle, and end of the recording. This segmentation allowed us to analyze the evolution of the underlying functional network. The method of time-resolved partial directed coherence (tPDC) was used to assess the causality. This method allowed us to focus on the individual peak frequency in the 'alpha' band (7-13 Hz). Because of the significantly higher power in the recorded EEG in comparison to MEG, at the individual peak frequency of the alpha band, results rely only on EEG. The MEG was used only for comparison. Our results show that different regions of the brain start to `disconnect' from one another over the course of time. The driving signals, along with the feedback signals between different cortical regions start to recede over time. This shows that, with the course of rest, brain regions reduce communication with each another.

  8. Long-range correlations of different EEG derivations in rats: sleep stage-dependent generators may play a key role

    International Nuclear Information System (INIS)

    Fang, Guangzhan; Xia, Yang; Lai, Yongxiu; You, Zili; Yao, Dezhong

    2010-01-01

    For the electroencephalogram (EEG), topographic differences in the long-range temporal correlations would imply that these signals might be affected by specific mechanisms related to the generation of a given neuronal process. So the properties of the generators of various EEG oscillations might be investigated by their spatial differences of the long-range temporal correlations. In the present study, these correlations were characterized with respect to their topography during different vigilance states by detrended fluctuation analysis (DFA). The results indicated that (1) most of the scaling exponents acquired from different EEG derivations for various oscillations were significantly different in each vigilance state; these differences might be resulted from the different quantities and different locations of sleep stage-dependent generators of various neuronal processes; (2) there might be multiple generators of delta and theta over the brain and many of them were sleep stage-dependent; (3) the best site of the frontal electrode in a fronto-parietal bipolar electrode for sleep staging might be above the anterior midline cortex. We suggest that DFA analysis can be used to explore the properties of the generators of a given neuronal oscillation, and the localizations of these generators if more electrodes are involved

  9. Wavelet-based study of valence-arousal model of emotions on EEG signals with LabVIEW.

    Science.gov (United States)

    Guzel Aydin, Seda; Kaya, Turgay; Guler, Hasan

    2016-06-01

    This paper illustrates the wavelet-based feature extraction for emotion assessment using electroencephalogram (EEG) signal through graphical coding design. Two-dimensional (valence-arousal) emotion model was studied. Different emotions (happy, joy, melancholy, and disgust) were studied for assessment. These emotions were stimulated by video clips. EEG signals obtained from four subjects were decomposed into five frequency bands (gamma, beta, alpha, theta, and delta) using "db5" wavelet function. Relative features were calculated to obtain further information. Impact of the emotions according to valence value was observed to be optimal on power spectral density of gamma band. The main objective of this work is not only to investigate the influence of the emotions on different frequency bands but also to overcome the difficulties in the text-based program. This work offers an alternative approach for emotion evaluation through EEG processing. There are a number of methods for emotion recognition such as wavelet transform-based, Fourier transform-based, and Hilbert-Huang transform-based methods. However, the majority of these methods have been applied with the text-based programming languages. In this study, we proposed and implemented an experimental feature extraction with graphics-based language, which provides great convenience in bioelectrical signal processing.

  10. Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification.

    Science.gov (United States)

    Wen, Tingxi; Zhang, Zhongnan

    2017-05-01

    In this paper, genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of interclass distance and intraclass distance. Moreover, the proposed feature search method can search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable; thus, GAFDS exhibits good extensibility. Multiple classical classifiers (i.e., k-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Naïve Bayes) achieve satisfactory classification accuracies by using the features generated by the GAFDS method and the optimized feature selection. The accuracies for 2-classification and 3-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in the extraction of effective features for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy.

  11. A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation.

    Science.gov (United States)

    Zhang, Zutao; Luo, Dianyuan; Rasim, Yagubov; Li, Yanjun; Meng, Guanjun; Xu, Jian; Wang, Chunbai

    2016-02-19

    In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver's EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver's vigilance level. Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model.

  12. OPTICAL WIRELESS COMMUNICATION SYSTEM

    Directory of Open Access Journals (Sweden)

    JOSHUA L.Y. CHIENG

    2016-02-01

    Full Text Available The growing demand of bandwidth in this modern internet age has been testing the existing telecommunication infrastructures around the world. With broadband speeds moving towards the region of Gbps and Tbps, many researches have begun on the development of using optical wireless technology as feasible and future methods to the current wireless technology. Unlike the existing radio frequency wireless applications, optical wireless uses electromagnetic spectrums that are unlicensed and free. With that, this project aim to understand and gain better understanding of optical wireless communication system by building an experimental and simulated model. The quality of service and system performance will be investigated and reviewed. This project employs laser diode as the propagation medium and successfully transferred audio signals as far as 15 meters. On its quality of service, results of the project model reveal that the bit error rate increases, signal-to-noise ratio and quality factor decreases as the link distance between the transmitter and receiver increases. OptiSystem was used to build the simulated model and MATLAB was used to assist signal-to-noise ratio calculations. By comparing the simulated and experimental receiver’s power output, the experimental model’s efficiency is at 66.3%. Other than the system’s performance, challenges and factors affecting the system have been investigated and discussed. Such challenges include beam divergence, misalignment and particle absorption.

  13. EXPERIMENTAL PERFORMANCE ANALYSIS OF WIRELESS ...

    African Journals Online (AJOL)

    ABSTRACT. Wireless networking is currently being deployed for various applications. However, the application of wireless networking in healthcare remains a challenge mainly because of security and reliability concerns. This paper presents experimental results of performance analysis of a wireless network for healthcare ...

  14. High density scalp EEG in frontal lobe epilepsy.

    Science.gov (United States)

    Feyissa, Anteneh M; Britton, Jeffrey W; Van Gompel, Jamie; Lagerlund, Terrance L; So, Elson; Wong-Kisiel, Lilly C; Cascino, Gregory C; Brinkman, Benjamin H; Nelson, Cindy L; Watson, Robert; Worrell, Gregory A

    2017-01-01

    Localization of seizures in frontal lobe epilepsy using the 10-20 system scalp EEG is often challenging because neocortical seizure can spread rapidly, significant muscle artifact, and the suboptimal spatial resolution for seizure generators involving mesial frontal lobe cortex. Our aim in this study was to determine the value of visual interpretation of 76 channel high density EEG (hdEEG) monitoring (10-10 system) in patients with suspected frontal lobe epilepsy, and to evaluate concordance with MRI, subtraction ictal SPECT co-registered to MRI (SISCOM), conventional EEG, and intracranial EEG (iEEG). We performed a retrospective cohort study of 14 consecutive patients who underwent hdEEG monitoring for suspected frontal lobe seizures. The gold standard for localization was considered to be iEEG. Concordance of hdEEG findings with MRI, subtraction ictal SPECT co-registered to MRI (SISCOM), conventional 10-20 EEG, and iEEG as well as correlation of hdEEG localization with surgical outcome were examined. hdEEG localization was concordant with iEEG in 12/14 and was superior to conventional EEG 3/14 (pfrontal epilepsy requiring localization of epileptogenic brain. hdEEG may assist in developing a hypothesis for iEEG monitoring and could potentially augment EEG source localization. Published by Elsevier B.V.

  15. Use of electroencephalography (EEG) to assess CNS changes produced by pesticides with different modes of action: Effects of permethrin, deltamethrin, fipronil, imidacloprid, carbaryl, and triadimefon

    Energy Technology Data Exchange (ETDEWEB)

    Freeborn, Danielle L., E-mail: Freeborn.danielle@epa.gov; McDaniel, Katherine L., E-mail: McDaniel.kathy@epa.gov; Moser, Virginia C., E-mail: Moser.ginger@epa.gov; Herr, David W., E-mail: Herr.david@epa.gov

    2015-01-15

    The electroencephalogram (EEG) is an apical measure, capable of detecting changes in brain neuronal activity produced by internal or external stimuli. We assessed whether pesticides with different modes of action produced different changes in the EEG of adult male Long–Evans rats. The EEG was recorded using two montages (visual cortex referenced to the cerebellum and to the frontal cortex) in unrestrained rats at the time of peak behavioral effects. Pesticides included: permethrin and deltamethrin (Type I and Type II pyrethroids; 2 h), fipronil (single and repeated doses; phenylpyrazole; 6 h), imidacloprid (neonicotinoid; 2 h), carbaryl (carbamate; 0.5 h), and triadimefon (triazole; 1 h), using dosages that produced approximately an ED{sub 30} or an ED{sub 50}–ED{sub 80} change in motor activity. Permethrin (43, 100 mg/kg) increased amplitudes or areas (delta, alpha, or gamma bands) in the EEG. Deltamethrin (2.5, 5.5 mg/kg) reduced the amplitudes or areas of the delta, theta, alpha, beta, and gamma bands, but the changes were not dose-related. A single treatment with fipronil (25, 50 mg/kg, but not 5, 10 mg/kg) decreased gamma band area. Additional changes in the delta, theta, and gamma bands were observed when fipronil (5, 10 mg/kg) was administered for 14 days. Imidacloprid (50, 100 mg/kg) did not alter the EEG. Carbaryl (10, 50 mg/kg) decreased theta area, and decreased delta and increased beta frequency. Triadimefon (75, 150 mg/kg) produced minimal changes in the EEG. The results show that the EEG is affected differently by approximately equipotent doses of pesticides with different modes of action. - Highlights: • Pesticides with different modes of action have different effects on in vivo rodent EEG. • The EEG was also changed differently after single vs. repeated treatment with fipronil. • The data suggest that EEG may be used as an apical measure for detecting chemical effects on the central nervous system.

  16. Fiber wireless networks

    Science.gov (United States)

    Nirmalathas, A.; Bakaul, M.; Lim, C.; Novak, D.; Waterhouse, R.

    2005-11-01

    Broadband wireless networks based on a number of new frequency windows at higher microwave and millimeter-wave frequencies have been actively pursued to provide ultra-high bandwidth services over a wireless networks. These networks will have a large number of antenna base-stations with high throughput. Significant reductions in antenna base-station complexity can be achieved if most of the signal routing and switching functions centralized at a central office in the network. In such a network, fiber feed networks can be effectively deployed to provide high bandwidth interconnections between multiple antenna base-stations and the central office. With wavelength division multiplexing, efficient optical fiber feed network architectures could be realised to provide interconnection to a large number of antenna base-stations. In this paper, we present an over view of our recent research into system technologies for fiber wireless networks.

  17. Wireless physical layer security

    Science.gov (United States)

    Poor, H. Vincent; Schaefer, Rafael F.

    2017-01-01

    Security in wireless networks has traditionally been considered to be an issue to be addressed separately from the physical radio transmission aspects of wireless systems. However, with the emergence of new networking architectures that are not amenable to traditional methods of secure communication such as data encryption, there has been an increase in interest in the potential of the physical properties of the radio channel itself to provide communications security. Information theory provides a natural framework for the study of this issue, and there has been considerable recent research devoted to using this framework to develop a greater understanding of the fundamental ability of the so-called physical layer to provide security in wireless networks. Moreover, this approach is also suggestive in many cases of coding techniques that can approach fundamental limits in practice and of techniques for other security tasks such as authentication. This paper provides an overview of these developments.

  18. Design and evaluation of a motor imagery electroencephalogram-controlled robot system

    Directory of Open Access Journals (Sweden)

    Baoguo Xu

    2015-03-01

    Full Text Available Brain–computer interface provides a new communication channel to control external device by directly translating the brain activity into commands. In this article, as the foundation of electroencephalogram-based robot-assisted upper limb rehabilitation therapy, we report on designing a brain–computer interface–based online robot control system which is made up of electroencephalogram amplifier, acquisition and experimental platform, feature extraction algorithm based on discrete wavelet transform and autoregressive model, linear discriminant analysis classifier, robot control board, and Rhino XR-1 robot. The performance of the system has been tested by 30 participants, and satisfactory results are achieved with an average error rate of 8.5%. Moreover, the advantage of the feature extraction method was further validated by the Graz data set for brain–computer interface competition 2003, and an error rate of 10.0% was obtained. This method provides a useful way for the research of brain–computer interface system and lays a foundation for brain–computer interface–based robotic upper extremity rehabilitation therapy.

  19. Nonlinear analysis of electroencephalogram at rest and during cognitive tasks in patients with schizophrenia

    Science.gov (United States)

    Carlino, Elisa; Sigaudo, Monica; Pollo, Antonella; Benedetti, Fabrizio; Mongini, Tullia; Castagna, Filomena; Vighetti, Sergio; Rocca, Paola

    2012-01-01

    Background In spite of the large number of studies on schizophrenia, a full understanding of its core pathology still eludes us. The application of the nonlinear theory of electroencephalography (EEG) analysis provides an interesting tool to differentiate between physiologic conditions (e.g., resting state and mathematical task) and normal and pathologic brain activities. The aim of the present study was to investigate nonlinear EEG activity in patients with schizophrenia. Methods We recorded 19-lead EEGs in patients with stable schizophrenia and healthy controls under 4 different conditions: eyes closed, eyes open, forward counting and backward counting. A nonlinear measure of complexity was calculated by means of correlation dimension (D2). Results We included 17 patients and 17 controls in our analysis. Comparing the 2 populations, we observed greater D2 values in the patient group. In controls, increased D2 values were observed during active states (eyes open and the 2 cognitive tasks) compared with baseline conditions. This increase of brain complexity, which can be interpreted as an increase of information processing and integration, was not preserved in the patient population. Limitations Patients with schizophrenia were taking antipsychotic medications, so the presence of medication effects cannot be excluded. Conclusion Our results suggest that patients with schizophrenia present changes in brain activity compared with healthy controls, and this pathologic alteration can be successfully studied with nonlinear EEG analysis. PMID:22353633

  20. Terabit Wireless Communication Challenges

    Science.gov (United States)

    Hwu, Shian U.

    2012-01-01

    This presentation briefly discusses a research effort on Terabit Wireless communication systems for possible space applications. Recently, terahertz (THz) technology (300-3000 GHz frequency) has attracted a great deal of interest from academia and industry. This is due to a number of interesting features of THz waves, including the nearly unlimited bandwidths available, and the non-ionizing radiation nature which does not damage human tissues and DNA with minimum health threat. Also, as millimeter-wave communication systems mature, the focus of research is, naturally, moving to the THz range. Many scientists regard THz as the last great frontier of the electromagnetic spectrum, but finding new applications outside the traditional niches of radio astronomy, Earth and planetary remote sensing, and molecular spectroscopy particularly in biomedical imaging and wireless communications has been relatively slow. Radiologists find this area of study so attractive because t-rays are non-ionizing, which suggests no harm is done to tissue or DNA. They also offer the possibility of performing spectroscopic measurements over a very wide frequency range, and can even capture signatures from liquids and solids. According to Shannon theory, the broad bandwidth of the THz frequency bands can be used for terabit-per-second (Tb/s) wireless communication systems. This enables several new applications, such as cell phones with 360 degrees autostereoscopic displays, optic-fiber replacement, and wireless Tb/s file transferring. Although THz technology could satisfy the demand for an extremely high data rate, a number of technical challenges need to be overcome before its development. This presentation provides an overview the state-of-the- art in THz wireless communication and the technical challenges for an emerging application in Terabit wireless systems. The main issue for THz wave propagation is the high atmospheric attenuation, which is dominated by water vapor absorption in the THz