[Detection of quadratic phase coupling between EEG signal components by nonparamatric and parametric methods of bispectral analysis].

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Schmidt, K; Witte, H

1999-11-01

Recently the assumption of the independence of individual frequency components in a signal has been rejected, for example, for the EEG during defined physiological states such as sleep or sedation [9, 10]. Thus, the use of higher-order spectral analysis capable of detecting interrelations between individual signal components has proved useful. The aim of the present study was to investigate the quality of various non-parametric and parametric estimation algorithms using simulated as well as true physiological data. We employed standard algorithms available for the MATLAB. The results clearly show that parametric bispectral estimation is superior to non-parametric estimation in terms of the quality of peak localisation and the discrimination from other peaks.

Single-trial log transformation is optimal in frequency analysis of resting EEG alpha.

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Smulders, Fren T Y; Ten Oever, Sanne; Donkers, Franc C L; Quaedflieg, Conny W E M; van de Ven, Vincent

2018-02-01

The appropriate definition and scaling of the magnitude of electroencephalogram (EEG) oscillations is an underdeveloped area. The aim of this study was to optimize the analysis of resting EEG alpha magnitude, focusing on alpha peak frequency and nonlinear transformation of alpha power. A family of nonlinear transforms, Box-Cox transforms, were applied to find the transform that (a) maximized a non-disputed effect: the increase in alpha magnitude when the eyes are closed (Berger effect), and (b) made the distribution of alpha magnitude closest to normal across epochs within each participant, or across participants. The transformations were performed either at the single epoch level or at the epoch-average level. Alpha peak frequency showed large individual differences, yet good correspondence between various ways to estimate it in 2 min of eyes-closed and 2 min of eyes-open resting EEG data. Both alpha magnitude and the Berger effect were larger for individual alpha than for a generic (8-12 Hz) alpha band. The log-transform on single epochs (a) maximized the t-value of the contrast between the eyes-open and eyes-closed conditions when tested within each participant, and (b) rendered near-normally distributed alpha power across epochs and participants, thereby making further transformation of epoch averages superfluous. The results suggest that the log-normal distribution is a fundamental property of variations in alpha power across time in the order of seconds. Moreover, effects on alpha power appear to be multiplicative rather than additive. These findings support the use of the log-transform on single epochs to achieve appropriate scaling of alpha magnitude. © 2018 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Correlates of a single cortical action potential in the epidural EEG

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Teleńczuk, Bartosz; Baker, Stuart N; Kempter, Richard; Curio, Gabriel

2015-01-01

To identify the correlates of a single cortical action potential in surface EEG, we recorded simultaneously epidural EEG and single-unit activity in the primary somatosensory cortex of awake macaque monkeys. By averaging over EEG segments coincident with more than hundred thousand single spikes, we found short-lived (≈ 0.5 ms) triphasic EEG deflections dominated by high-frequency components > 800 Hz. The peak-to-peak amplitude of the grand-averaged spike correlate was 80 nV, which matched theoretical predictions, while single-neuron amplitudes ranged from 12 to 966 nV. Combining these estimates with post-stimulus-time histograms of single-unit responses to median-nerve stimulation allowed us to predict the shape of the evoked epidural EEG response and to estimate the number of contributing neurons. These findings establish spiking activity of cortical neurons as a primary building block of high-frequency epidural EEG, which thus can serve as a quantitative macroscopic marker of neuronal spikes. PMID:25554430

A preliminary study of muscular artifact cancellation in single-channel EEG.

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Chen, Xun; Liu, Aiping; Peng, Hu; Ward, Rabab K

2014-10-01

Electroencephalogram (EEG) recordings are often contaminated with muscular artifacts that strongly obscure the EEG signals and complicates their analysis. For the conventional case, where the EEG recordings are obtained simultaneously over many EEG channels, there exists a considerable range of methods for removing muscular artifacts. In recent years, there has been an increasing trend to use EEG information in ambulatory healthcare and related physiological signal monitoring systems. For practical reasons, a single EEG channel system must be used in these situations. Unfortunately, there exist few studies for muscular artifact cancellation in single-channel EEG recordings. To address this issue, in this preliminary study, we propose a simple, yet effective, method to achieve the muscular artifact cancellation for the single-channel EEG case. This method is a combination of the ensemble empirical mode decomposition (EEMD) and the joint blind source separation (JBSS) techniques. We also conduct a study that compares and investigates all possible single-channel solutions and demonstrate the performance of these methods using numerical simulations and real-life applications. The proposed method is shown to significantly outperform all other methods. It can successfully remove muscular artifacts without altering the underlying EEG activity. It is thus a promising tool for use in ambulatory healthcare systems.

Automatic detection and classification of artifacts in single-channel EEG

DEFF Research Database (Denmark)

Olund, Thomas; Duun-Henriksen, Jonas; Kjaer, Troels W.

2014-01-01

Ambulatory EEG monitoring can provide medical doctors important diagnostic information, without hospitalizing the patient. These recordings are however more exposed to noise and artifacts compared to clinically recorded EEG. An automatic artifact detection and classification algorithm for single......-channel EEG is proposed to help identifying these artifacts. Features are extracted from the EEG signal and wavelet subbands. Subsequently a selection algorithm is applied in order to identify the best discriminating features. A non-linear support vector machine is used to discriminate among different...... artifact classes using the selected features. Single-channel (Fp1-F7) EEG recordings are obtained from experiments with 12 healthy subjects performing artifact inducing movements. The dataset was used to construct and validate the model. Both subject-specific and generic implementation, are investigated...

Kernel PLS Estimation of Single-trial Event-related Potentials

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Rosipal, Roman; Trejo, Leonard J.

2004-01-01

Nonlinear kernel partial least squaes (KPLS) regressior, is a novel smoothing approach to nonparametric regression curve fitting. We have developed a KPLS approach to the estimation of single-trial event related potentials (ERPs). For improved accuracy of estimation, we also developed a local KPLS method for situations in which there exists prior knowledge about the approximate latency of individual ERP components. To assess the utility of the KPLS approach, we compared non-local KPLS and local KPLS smoothing with other nonparametric signal processing and smoothing methods. In particular, we examined wavelet denoising, smoothing splines, and localized smoothing splines. We applied these methods to the estimation of simulated mixtures of human ERPs and ongoing electroencephalogram (EEG) activity using a dipole simulator (BESA). In this scenario we considered ongoing EEG to represent spatially and temporally correlated noise added to the ERPs. This simulation provided a reasonable but simplified model of real-world ERP measurements. For estimation of the simulated single-trial ERPs, local KPLS provided a level of accuracy that was comparable with or better than the other methods. We also applied the local KPLS method to the estimation of human ERPs recorded in an experiment on co,onitive fatigue. For these data, the local KPLS method provided a clear improvement in visualization of single-trial ERPs as well as their averages. The local KPLS method may serve as a new alternative to the estimation of single-trial ERPs and improvement of ERP averages.

Ongoing EEG phase as a trial-by-trial predictor of perceptual and attentional variability

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Rufin eVanRullen

2011-04-01

Full Text Available Even in well-controlled laboratory environments, apparently identical repetitions of an experimental trial can give rise to highly variable perceptual outcomes and behavioral responses. This variability is generally discarded as a reflection of intrinsic noise in neuronal systems. However, part of this variability may be accounted for by trial-by-trial fluctuations of the phase of ongoing oscillations at the moment of stimulus presentation. For example, the phase of an EEG oscillation reflecting the rapid waxing and waning of sustained attention can predict the perception of a subsequent visual stimulus at threshold. Similar ongoing periodicities account for a portion of the trial-by-trial variability of visual reaction times. We review the available experimental evidence linking ongoing EEG phase to perceptual and attentional variability, and the corresponding methodology. We propose future tests of this relation, and discuss the theoretical implications for understanding the neuronal dynamics of sensory perception.

Working memory load-dependent spatio-temporal activity of single-trial P3 response detected with an adaptive wavelet denoiser.

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Zhang, Qiushi; Yang, Xueqian; Yao, Li; Zhao, Xiaojie

2017-03-27

Working memory (WM) refers to the holding and manipulation of information during cognitive tasks. Its underlying neural mechanisms have been explored through both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). Trial-by-trial coupling of simultaneously collected EEG and fMRI signals has become an important and promising approach to study the spatio-temporal dynamics of such cognitive processes. Previous studies have demonstrated a modulation effect of the WM load on both the BOLD response in certain brain areas and the amplitude of P3. However, much remains to be explored regarding the WM load-dependent relationship between the amplitude of ERP components and cortical activities, and the low signal-to-noise ratio (SNR) of the EEG signal still poses a challenge to performing single-trial analyses. In this paper, we investigated the spatio-temporal activities of P3 during an n-back verbal WM task by introducing an adaptive wavelet denoiser into the extraction of single-trial P3 features and using general linear model (GLM) to integrate simultaneously collected EEG and fMRI data. Our results replicated the modulation effect of the WM load on the P3 amplitude. Additionally, the activation of single-trial P3 amplitudes was detected in multiple brain regions, including the insula, the cuneus, the lingual gyrus (LG), and the middle occipital gyrus (MOG). Moreover, we found significant correlations between P3 features and behavioral performance. These findings suggest that the single-trial integration of simultaneous EEG and fMRI signals may provide new insights into classical cognitive functions. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Separability of motor imagery of the self from interpretation of motor intentions of others at the single trial level: an EEG study.

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Andrade, João; Cecílio, José; Simões, Marco; Sales, Francisco; Castelo-Branco, Miguel

2017-06-26

We aimed to investigate the separability of the neural correlates of 2 types of motor imagery, self and third person (actions owned by the participant himself vs. another individual). If possible this would allow for the development of BCI interfaces to train disorders of action and intention understanding beyond simple imitation, such as autism. We used EEG recordings from 20 healthy participants, as well as electrocorticography (ECoG) in one, based on a virtual reality setup. To test feasibility of discrimination between each type of imagery at the single trial level, time-frequency and source analysis were performed and further assessed by data-driven statistical classification using Support Vector Machines. The main observed differences between self-other imagery conditions in topographic maps were found in Frontal and Parieto-Occipital regions, in agreement with the presence of 2 independent non μ related contributions in the low alpha frequency range. ECOG corroborated such separability. Source analysis also showed differences near the temporo-parietal junction and single-trial average classification accuracy between both types of motor imagery was 67 ± 1%, and raised above 70% when 3 trials were used. The single-trial classification accuracy was significantly above chance level for all the participants of this study (p Person MI use distinct electrophysiological mechanisms detectable at the scalp (and ECOG) at the single trial level, with separable levels of involvement of the mirror neuron system in different regions. These observations provide a promising step to develop new BCI training/rehabilitation paradigms for patients with neurodevelopmental disorders of action understanding beyond simple imitation, such as autism, who would benefit from training and anticipation of the perceived intention of others as opposed to own intentions in social contexts.

A Parametric Empirical Bayesian framework for the EEG/MEG inverse problem: generative models for multisubject and multimodal integration

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Richard N Henson

2011-08-01

Full Text Available We review recent methodological developments within a Parametric Empirical Bayesian (PEB framework for reconstructing intracranial sources of extracranial electroencephalographic (EEG and magnetoencephalographic (MEG data under linear Gaussian assumptions. The PEB framework offers a natural way to integrate multiple constraints (spatial priors on this inverse problem, such as those derived from different modalities (e.g., from functional magnetic resonance imaging, fMRI or from multiple replications (e.g., subjects. Using variations of the same basic generative model, we illustrate the application of PEB to three cases: 1 symmetric integration (fusion of MEG and EEG; 2 asymmetric integration of MEG or EEG with fMRI, and 3 group-optimisation of spatial priors across subjects. We evaluate these applications on multimodal data acquired from 18 subjects, focusing on energy induced by face perception within a time-frequency window of 100-220ms, 8-18Hz. We show the benefits of multi-modal, multi-subject integration in terms of the model evidence and the reproducibility (over subjects of cortical responses to faces.

A three domain covariance framework for EEG/MEG data.

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Roś, Beata P; Bijma, Fetsje; de Gunst, Mathisca C M; de Munck, Jan C

2015-10-01

In this paper we introduce a covariance framework for the analysis of single subject EEG and MEG data that takes into account observed temporal stationarity on small time scales and trial-to-trial variations. We formulate a model for the covariance matrix, which is a Kronecker product of three components that correspond to space, time and epochs/trials, and consider maximum likelihood estimation of the unknown parameter values. An iterative algorithm that finds approximations of the maximum likelihood estimates is proposed. Our covariance model is applicable in a variety of cases where spontaneous EEG or MEG acts as source of noise and realistic noise covariance estimates are needed, such as in evoked activity studies, or where the properties of spontaneous EEG or MEG are themselves the topic of interest, like in combined EEG-fMRI experiments in which the correlation between EEG and fMRI signals is investigated. We use a simulation study to assess the performance of the estimator and investigate the influence of different assumptions about the covariance factors on the estimated covariance matrix and on its components. We apply our method to real EEG and MEG data sets. Copyright © 2015 Elsevier Inc. All rights reserved.

Highly Efficient Compression Algorithms for Multichannel EEG.

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Shaw, Laxmi; Rahman, Daleef; Routray, Aurobinda

2018-05-01

The difficulty associated with processing and understanding the high dimensionality of electroencephalogram (EEG) data requires developing efficient and robust compression algorithms. In this paper, different lossless compression techniques of single and multichannel EEG data, including Huffman coding, arithmetic coding, Markov predictor, linear predictor, context-based error modeling, multivariate autoregression (MVAR), and a low complexity bivariate model have been examined and their performances have been compared. Furthermore, a high compression algorithm named general MVAR and a modified context-based error modeling for multichannel EEG have been proposed. The resulting compression algorithm produces a higher relative compression ratio of 70.64% on average compared with the existing methods, and in some cases, it goes up to 83.06%. The proposed methods are designed to compress a large amount of multichannel EEG data efficiently so that the data storage and transmission bandwidth can be effectively used. These methods have been validated using several experimental multichannel EEG recordings of different subjects and publicly available standard databases. The satisfactory parametric measures of these methods, namely percent-root-mean square distortion, peak signal-to-noise ratio, root-mean-square error, and cross correlation, show their superiority over the state-of-the-art compression methods.

Data-driven forward model inference for EEG brain imaging

DEFF Research Database (Denmark)

Hansen, Sofie Therese; Hauberg, Søren; Hansen, Lars Kai

2016-01-01

Electroencephalography (EEG) is a flexible and accessible tool with excellent temporal resolution but with a spatial resolution hampered by volume conduction. Reconstruction of the cortical sources of measured EEG activity partly alleviates this problem and effectively turns EEG into a brain......-of-concept study, we show that, even when anatomical knowledge is unavailable, a suitable forward model can be estimated directly from the EEG. We propose a data-driven approach that provides a low-dimensional parametrization of head geometry and compartment conductivities, built using a corpus of forward models....... Combined with only a recorded EEG signal, we are able to estimate both the brain sources and a person-specific forward model by optimizing this parametrization. We thus not only solve an inverse problem, but also optimize over its specification. Our work demonstrates that personalized EEG brain imaging...

Trial latencies estimation of event-related potentials in EEG by means of genetic algorithms

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Da Pelo, P.; De Tommaso, M.; Monaco, A.; Stramaglia, S.; Bellotti, R.; Tangaro, S.

2018-04-01

Objective. Event-related potentials (ERPs) are usually obtained by averaging thus neglecting the trial-to-trial latency variability in cognitive electroencephalography (EEG) responses. As a consequence the shape and the peak amplitude of the averaged ERP are smeared and reduced, respectively, when the single-trial latencies show a relevant variability. To date, the majority of the methodologies for single-trial latencies inference are iterative schemes providing suboptimal solutions, the most commonly used being the Woody’s algorithm. Approach. In this study, a global approach is developed by introducing a fitness function whose global maximum corresponds to the set of latencies which renders the trial signals most aligned as possible. A suitable genetic algorithm has been implemented to solve the optimization problem, characterized by new genetic operators tailored to the present problem. Main results. The results, on simulated trials, showed that the proposed algorithm performs better than Woody’s algorithm in all conditions, at the cost of an increased computational complexity (justified by the improved quality of the solution). Application of the proposed approach on real data trials, resulted in an increased correlation between latencies and reaction times w.r.t. the output from RIDE method. Significance. The above mentioned results on simulated and real data indicate that the proposed method, providing a better estimate of single-trial latencies, will open the way to more accurate study of neural responses as well as to the issue of relating the variability of latencies to the proper cognitive and behavioural correlates.

Performance Analysis Of Single-Pumped And Dual-Pumped Parametric Optical Amplifier

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Sandar Myint

2015-06-01

Full Text Available Abstract In this study we present a performance analysis of single-pumped and dual- pumped parametric optical amplifier and present the analysis of gain flatness in dual- pumped Fiber Optical Parametric Amplifier FOPA based on four-wave mixing FWM. Result shows that changing the signal power and pump power give the various gains in FOPA. It is also found out that the parametric gain increase with increase in pump power and decrease in signal power. .Moreover in this paper the phase matching condition in FWM plays a vital role in predicting the gain profile of the FOPAbecause the parametric gain is maximum when the total phase mismatch is zero.In this paper single-pumped parametric amplification over a 50nm gain bandwidth is demonstrated using 500 nm highly nonlinear fiber HNLF and signal achieves about 31dB gain. For dual-pumped parametric amplification signal achieves 26.5dB gains over a 50nm gain bandwidth. Therefore dual-pumped parametric amplifier can provide relatively flat gain over a much wider bandwidth than the single-pumped FOPA.

Recording human cortical population spikes non-invasively--An EEG tutorial.

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Waterstraat, Gunnar; Fedele, Tommaso; Burghoff, Martin; Scheer, Hans-Jürgen; Curio, Gabriel

2015-07-30

Non-invasively recorded somatosensory high-frequency oscillations (sHFOs) evoked by electric nerve stimulation are markers of human cortical population spikes. Previously, their analysis was based on massive averaging of EEG responses. Advanced neurotechnology and optimized off-line analysis can enhance the signal-to-noise ratio of sHFOs, eventually enabling single-trial analysis. The rationale for developing dedicated low-noise EEG technology for sHFOs is unfolded. Detailed recording procedures and tailored analysis principles are explained step-by-step. Source codes in Matlab and Python are provided as supplementary material online. Combining synergistic hardware and analysis improvements, evoked sHFOs at around 600 Hz ('σ-bursts') can be studied in single-trials. Additionally, optimized spatial filters increase the signal-to-noise ratio of components at about 1 kHz ('κ-bursts') enabling their detection in non-invasive surface EEG. sHFOs offer a unique possibility to record evoked human cortical population spikes non-invasively. The experimental approaches and algorithms presented here enable also non-specialized EEG laboratories to combine measurements of conventional low-frequency EEG with the analysis of concomitant cortical population spike responses. Copyright © 2014 Elsevier B.V. All rights reserved.

Single-channel in-ear-EEG detects the focus of auditory attention to concurrent tone streams and mixed speech

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Fiedler, Lorenz; Wöstmann, Malte; Graversen, Carina; Brandmeyer, Alex; Lunner, Thomas; Obleser, Jonas

2017-06-01

Objective. Conventional, multi-channel scalp electroencephalography (EEG) allows the identification of the attended speaker in concurrent-listening (‘cocktail party’) scenarios. This implies that EEG might provide valuable information to complement hearing aids with some form of EEG and to install a level of neuro-feedback. Approach. To investigate whether a listener’s attentional focus can be detected from single-channel hearing-aid-compatible EEG configurations, we recorded EEG from three electrodes inside the ear canal (‘in-Ear-EEG’) and additionally from 64 electrodes on the scalp. In two different, concurrent listening tasks, participants (n  =  7) were fitted with individualized in-Ear-EEG pieces and were either asked to attend to one of two dichotically-presented, concurrent tone streams or to one of two diotically-presented, concurrent audiobooks. A forward encoding model was trained to predict the EEG response at single EEG channels. Main results. Each individual participants’ attentional focus could be detected from single-channel EEG response recorded from short-distance configurations consisting only of a single in-Ear-EEG electrode and an adjacent scalp-EEG electrode. The differences in neural responses to attended and ignored stimuli were consistent in morphology (i.e. polarity and latency of components) across subjects. Significance. In sum, our findings show that the EEG response from a single-channel, hearing-aid-compatible configuration provides valuable information to identify a listener’s focus of attention.

Single Trial Classification of Evoked EEG Signals Due to RGB Colors

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Eman Alharbi

2016-03-01

Full Text Available Recently, the impact of colors on the brain signals has become one of the leading researches in BCI systems. These researches are based on studying the brain behavior after color stimulus, and finding a way to classify its signals offline without considering the real time. Moving to the next step, we present a real time classification model (online for EEG signals evoked by RGB colors stimuli, which is not presented in previous studies. In this research, EEG signals were recorded from 7 subjects through BCI2000 toolbox. The Empirical Mode Decomposition (EMD technique was used at the signal analysis stage. Various feature extraction methods were investigated to find the best and reliable set, including Event-related spectral perturbations (ERSP, Target mean with Feast Fourier Transform (FFT, Wavelet Packet Decomposition (WPD, Auto Regressive model (AR and EMD residual. A new feature selection method was created based on the peak's time of EEG signal when red and blue colors stimuli are presented. The ERP image was used to find out the peak's time, which was around 300 ms for the red color and around 450 ms for the blue color. The classification was performed using the Support Vector Machine (SVM classifier, LIBSVM toolbox being used for that purpose. The EMD residual was found to be the most reliable method that gives the highest classification accuracy with an average of 88.5% and with an execution time of only 14 seconds.

Single-trial EEG-informed fMRI analysis of emotional decision problems in hot executive function.

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Guo, Qian; Zhou, Tiantong; Li, Wenjie; Dong, Li; Wang, Suhong; Zou, Ling

2017-07-01

Executive function refers to conscious control in psychological process which relates to thinking and action. Emotional decision is a part of hot executive function and contains emotion and logic elements. As a kind of important social adaptation ability, more and more attention has been paid in recent years. Gambling task can be well performed in the study of emotional decision. As fMRI researches focused on gambling task show not completely consistent brain activation regions, this study adopted EEG-fMRI fusion technology to reveal brain neural activity related with feedback stimuli. In this study, an EEG-informed fMRI analysis was applied to process simultaneous EEG-fMRI data. First, relative power-spectrum analysis and K-means clustering method were performed separately to extract EEG-fMRI features. Then, Generalized linear models were structured using fMRI data and using different EEG features as regressors. The results showed that in the win versus loss stimuli, the activated regions almost covered the caudate, the ventral striatum (VS), the orbital frontal cortex (OFC), and the cingulate. Wide activation areas associated with reward and punishment were revealed by the EEG-fMRI integration analysis than the conventional fMRI results, such as the posterior cingulate and the OFC. The VS and the medial prefrontal cortex (mPFC) were found when EEG power features were performed as regressors of GLM compared with results entering the amplitudes of feedback-related negativity (FRN) as regressors. Furthermore, the brain region activation intensity was the strongest when theta-band power was used as a regressor compared with the other two fusion results. The EEG-based fMRI analysis can more accurately depict the whole-brain activation map and analyze emotional decision problems.

A three domain covariance framework for EEG/MEG data

NARCIS (Netherlands)

Ros, B.P.; Bijma, F.; de Gunst, M.C.M.; de Munck, J.C.

2015-01-01

In this paper we introduce a covariance framework for the analysis of single subject EEG and MEG data that takes into account observed temporal stationarity on small time scales and trial-to-trial variations. We formulate a model for the covariance matrix, which is a Kronecker product of three

Identification of Auditory Object-Specific Attention from Single-Trial Electroencephalogram Signals via Entropy Measures and Machine Learning

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Yun Lu

2018-05-01

Full Text Available Existing research has revealed that auditory attention can be tracked from ongoing electroencephalography (EEG signals. The aim of this novel study was to investigate the identification of peoples’ attention to a specific auditory object from single-trial EEG signals via entropy measures and machine learning. Approximate entropy (ApEn, sample entropy (SampEn, composite multiscale entropy (CmpMSE and fuzzy entropy (FuzzyEn were used to extract the informative features of EEG signals under three kinds of auditory object-specific attention (Rest, Auditory Object1 Attention (AOA1 and Auditory Object2 Attention (AOA2. The linear discriminant analysis and support vector machine (SVM, were used to construct two auditory attention classifiers. The statistical results of entropy measures indicated that there were significant differences in the values of ApEn, SampEn, CmpMSE and FuzzyEn between Rest, AOA1 and AOA2. For the SVM-based auditory attention classifier, the auditory object-specific attention of Rest, AOA1 and AOA2 could be identified from EEG signals using ApEn, SampEn, CmpMSE and FuzzyEn as features and the identification rates were significantly different from chance level. The optimal identification was achieved by the SVM-based auditory attention classifier using CmpMSE with the scale factor τ = 10. This study demonstrated a novel solution to identify the auditory object-specific attention from single-trial EEG signals without the need to access the auditory stimulus.

Multi-feature classifiers for burst detection in single EEG channels from preterm infants

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Navarro, X.; Porée, F.; Kuchenbuch, M.; Chavez, M.; Beuchée, Alain; Carrault, G.

2017-08-01

Objective. The study of electroencephalographic (EEG) bursts in preterm infants provides valuable information about maturation or prognostication after perinatal asphyxia. Over the last two decades, a number of works proposed algorithms to automatically detect EEG bursts in preterm infants, but they were designed for populations under 35 weeks of post menstrual age (PMA). However, as the brain activity evolves rapidly during postnatal life, these solutions might be under-performing with increasing PMA. In this work we focused on preterm infants reaching term ages (PMA  ⩾36 weeks) using multi-feature classification on a single EEG channel. Approach. Five EEG burst detectors relying on different machine learning approaches were compared: logistic regression (LR), linear discriminant analysis (LDA), k-nearest neighbors (kNN), support vector machines (SVM) and thresholding (Th). Classifiers were trained by visually labeled EEG recordings from 14 very preterm infants (born after 28 weeks of gestation) with 36-41 weeks PMA. Main results. The most performing classifiers reached about 95% accuracy (kNN, SVM and LR) whereas Th obtained 84%. Compared to human-automatic agreements, LR provided the highest scores (Cohen’s kappa  =  0.71) using only three EEG features. Applying this classifier in an unlabeled database of 21 infants  ⩾36 weeks PMA, we found that long EEG bursts and short inter-burst periods are characteristic of infants with the highest PMA and weights. Significance. In view of these results, LR-based burst detection could be a suitable tool to study maturation in monitoring or portable devices using a single EEG channel.

Classification of Hand Grasp Kinetics and Types Using Movement-Related Cortical Potentials and EEG Rhythms

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Mads Jochumsen

2017-01-01

Full Text Available Detection of single-trial movement intentions from EEG is paramount for brain-computer interfacing in neurorehabilitation. These movement intentions contain task-related information and if this is decoded, the neurorehabilitation could potentially be optimized. The aim of this study was to classify single-trial movement intentions associated with two levels of force and speed and three different grasp types using EEG rhythms and components of the movement-related cortical potential (MRCP as features. The feature importance was used to estimate encoding of discriminative information. Two data sets were used. 29 healthy subjects executed and imagined different hand movements, while EEG was recorded over the contralateral sensorimotor cortex. The following features were extracted: delta, theta, mu/alpha, beta, and gamma rhythms, readiness potential, negative slope, and motor potential of the MRCP. Sequential forward selection was performed, and classification was performed using linear discriminant analysis and support vector machines. Limited classification accuracies were obtained from the EEG rhythms and MRCP-components: 0.48±0.05 (grasp types, 0.41±0.07 (kinetic profiles, motor execution, and 0.39±0.08 (kinetic profiles, motor imagination. Delta activity contributed the most but all features provided discriminative information. These findings suggest that information from the entire EEG spectrum is needed to discriminate between task-related parameters from single-trial movement intentions.

Comparison of different Kalman filter approaches in deriving time varying connectivity from EEG data.

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Ghumare, Eshwar; Schrooten, Maarten; Vandenberghe, Rik; Dupont, Patrick

2015-08-01

Kalman filter approaches are widely applied to derive time varying effective connectivity from electroencephalographic (EEG) data. For multi-trial data, a classical Kalman filter (CKF) designed for the estimation of single trial data, can be implemented by trial-averaging the data or by averaging single trial estimates. A general linear Kalman filter (GLKF) provides an extension for multi-trial data. In this work, we studied the performance of the different Kalman filtering approaches for different values of signal-to-noise ratio (SNR), number of trials and number of EEG channels. We used a simulated model from which we calculated scalp recordings. From these recordings, we estimated cortical sources. Multivariate autoregressive model parameters and partial directed coherence was calculated for these estimated sources and compared with the ground-truth. The results showed an overall superior performance of GLKF except for low levels of SNR and number of trials.

A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking

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Jiuqi Han

2018-04-01

Full Text Available Superior feature extraction, channel selection and classification methods are essential for designing electroencephalography (EEG classification frameworks. However, the performance of most frameworks is limited by their improper channel selection methods and too specifical design, leading to high computational complexity, non-convergent procedure and narrow expansibility. In this paper, to remedy these drawbacks, we propose a fast, open EEG classification framework centralized by EEG feature compression, low-dimensional representation, and convergent iterative channel ranking. First, to reduce the complexity, we use data clustering to compress the EEG features channel-wise, packing the high-dimensional EEG signal, and endowing them with numerical signatures. Second, to provide easy access to alternative superior methods, we structurally represent each EEG trial in a feature vector with its corresponding numerical signature. Thus, the recorded signals of many trials shrink to a low-dimensional structural matrix compatible with most pattern recognition methods. Third, a series of effective iterative feature selection approaches with theoretical convergence is introduced to rank the EEG channels and remove redundant ones, further accelerating the EEG classification process and ensuring its stability. Finally, a classical linear discriminant analysis (LDA model is employed to classify a single EEG trial with selected channels. Experimental results on two real world brain-computer interface (BCI competition datasets demonstrate the promising performance of the proposed framework over state-of-the-art methods.

Single Trial Decoding of Belief Decision Making from EEG and fMRI Data Using ICA Features

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Pamela eDouglas

2013-07-01

Full Text Available The complex task of assessing the veracity of a statement is thought to activate uniquely distributed brain regions based on whether a subject believes or disbelieves a given assertion. In the current work, we present parallel machine learning methods for predicting a subject’s decision response to a given propositional statement based on independent component (IC features derived from EEG and fMRI data. Our results demonstrate that IC features outperformed features derived from event related spectral perturbations derived from any single spectral band, yet were similar to accuracy across all spectral bands combined. We compared our diagnostic IC spatial maps with our conventional general linear model (GLM results, and found that informative ICs had significant spatial overlap with our GLM results, yet also revealed unique regions like amygdala that were not statistically significant in GLM analyses. Overall, these results suggest that ICs may yield a parsimonious feature set that can be used along with a decision tree structure for interpretation of features used in classifying complex cognitive processes such as belief and disbelief across both fMRI and EEG neuroimaging modalities.

Inter-trial alignment of EEG data and phase-locking

Science.gov (United States)

Testorf, M. E.; Horak, P.; Connolly, A.; Holmes, G. L.; Jobst, B. C.

2015-09-01

Neuro-scientific studies are often aimed at imaging brain activity, which is time-locked to external stimuli. This provides the possibility to use statistical methods to extract even weak signal components, which occur with each stimulus. For electroencephalographic recordings this concept is limited by inevitable time jitter, which cannot be controlled in all cases. Our study is based on a cross-correlation analysis of trials to alignment trials based on the recorded data. This is demonstrated both with simulated signals and with clinical EEG data, which were recorded intracranially. Special attention is given to the evaluation of the time-frequency resolved phase-locking across multiple trails.

On seeing the trees and the forest: single-signal and multisignal analysis of periictal intracranial EEG.

Science.gov (United States)

Schindler, Kaspar; Gast, Heidemarie; Goodfellow, Marc; Rummel, Christian

2012-09-01

Epileptic seizures are associated with a dysregulation of electrical brain activity on many different spatial scales. To better understand the dynamics of epileptic seizures, that is, how the seizures initiate, propagate, and terminate, it is important to consider changes of electrical brain activity on different spatial scales. Herein we set out to analyze periictal electrical brain activity on comparatively small and large spatial scales by assessing changes in single intracranial electroencephalography (EEG) signals and of averaged interdependences of pairs of EEG signals. Single and multiple EEG signals are analyzed by combining methods from symbolic dynamics and information theory. This computationally efficient approach is chosen because at its core it consists of analyzing the occurrence of patterns and bears analogy to classical visual EEG reading. Symbolization is achieved by first mapping the EEG signals into bit strings, that is, long sequences of zeros and ones, depending solely on whether their amplitudes increase or decrease. Bit strings reflect relational aspects between consecutive values of the original EEG signals, but not the values themselves. For each bit string the relative frequencies of the different constituent short bit patterns are then determined and used to compute two information theoretical measures: (1) redundancy (R) of single bit strings characterizes electrical brain activity on a comparatively small spatial scale represented by a single EEG signal and (2) averaged pair-wise mutual information with all other bit strings (M), which allows tracking of larger-scale EEG dynamics. We analyzed 20 periictal intracranial EEG recordings from five patients with pharmacoresistant temporal lobe epilepsy. At seizure onset, R first strongly increased and then decreased toward seizure termination, whereas M gradually increased throughout the seizure. Bit strings with maximal R were always derived from EEG signals recorded from the visually

Single-Trial Evoked Potential Estimating Based on Sparse Coding under Impulsive Noise Environment

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Nannan Yu

2018-01-01

Full Text Available Estimating single-trial evoked potentials (EPs corrupted by the spontaneous electroencephalogram (EEG can be regarded as signal denoising problem. Sparse coding has significant success in signal denoising and EPs have been proven to have strong sparsity over an appropriate dictionary. In sparse coding, the noise generally is considered to be a Gaussian random process. However, some studies have shown that the background noise in EPs may present an impulsive characteristic which is far from Gaussian but suitable to be modeled by the α-stable distribution 1<α≤2. Consequently, the performances of general sparse coding will degrade or even fail. In view of this, we present a new sparse coding algorithm using p-norm optimization in single-trial EPs estimating. The algorithm can track the underlying EPs corrupted by α-stable distribution noise, trial-by-trial, without the need to estimate the α value. Simulations and experiments on human visual evoked potentials and event-related potentials are carried out to examine the performance of the proposed approach. Experimental results show that the proposed method is effective in estimating single-trial EPs under impulsive noise environment.

Joint time-frequency analysis of EEG signals based on a phase-space interpretation of the recording process

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Testorf, M. E.; Jobst, B. C.; Kleen, J. K.; Titiz, A.; Guillory, S.; Scott, R.; Bujarski, K. A.; Roberts, D. W.; Holmes, G. L.; Lenck-Santini, P.-P.

2012-10-01

Time-frequency transforms are used to identify events in clinical EEG data. Data are recorded as part of a study for correlating the performance of human subjects during a memory task with pathological events in the EEG, called spikes. The spectrogram and the scalogram are reviewed as tools for evaluating spike activity. A statistical evaluation of the continuous wavelet transform across trials is used to quantify phase-locking events. For simultaneously improving the time and frequency resolution, and for representing the EEG of several channels or trials in a single time-frequency plane, a multichannel matching pursuit algorithm is used. Fundamental properties of the algorithm are discussed as well as preliminary results, which were obtained with clinical EEG data.

Drowsiness detection for single channel EEG by DWT best m-term approximation

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Tiago da Silveira

Full Text Available Introduction In this paper we propose a promising new technique for drowsiness detection. It consists of applying the best m-term approximation on a single-channel electroencephalography (EEG signal preprocessed through a discrete wavelet transform. Methods In order to classify EEG epochs as awake or drowsy states, the most significant m terms from the wavelet expansion of an EEG signal are selected according to the magnitude of their coefficients related to the alpha and beta rhythms. Results By using a simple thresholding strategy it provides hit rates comparable to those using more complex techniques. It was tested on a set of 6 hours and 50 minutes EEG drowsiness signals from PhysioNet Sleep Database yielding an overall sensitivity (TPR of 84.98% and 98.65% of precision (PPV. Conclusion The method has proved itself efficient at separating data from different brain rhythms, thus alleviating the requirement for complex post-processing classification algorithms.

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...

Preterm EEG: a multimodal neurophysiological protocol.

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Stjerna, Susanna; Voipio, Juha; Metsäranta, Marjo; Kaila, Kai; Vanhatalo, Sampsa

2012-02-18

Since its introduction in early 1950s, electroencephalography (EEG) has been widely used in the neonatal intensive care units (NICU) for assessment and monitoring of brain function in preterm and term babies. Most common indications are the diagnosis of epileptic seizures, assessment of brain maturity, and recovery from hypoxic-ischemic events. EEG recording techniques and the understanding of neonatal EEG signals have dramatically improved, but these advances have been slow to penetrate through the clinical traditions. The aim of this presentation is to bring theory and practice of advanced EEG recording available for neonatal units. In the theoretical part, we will present animations to illustrate how a preterm brain gives rise to spontaneous and evoked EEG activities, both of which are unique to this developmental phase, as well as crucial for a proper brain maturation. Recent animal work has shown that the structural brain development is clearly reflected in early EEG activity. Most important structures in this regard are the growing long range connections and the transient cortical structure, subplate. Sensory stimuli in a preterm baby will generate responses that are seen at a single trial level, and they have underpinnings in the subplate-cortex interaction. This brings neonatal EEG readily into a multimodal study, where EEG is not only recording cortical function, but it also tests subplate function via different sensory modalities. Finally, introduction of clinically suitable dense array EEG caps, as well as amplifiers capable of recording low frequencies, have disclosed multitude of brain activities that have as yet been overlooked. In the practical part of this video, we show how a multimodal, dense array EEG study is performed in neonatal intensive care unit from a preterm baby in the incubator. The video demonstrates preparation of the baby and incubator, application of the EEG cap, and performance of the sensory stimulations.

All-in-one interictal presurgical imaging in patients with epilepsy: single-session EEG/PET/(f)MRI

Energy Technology Data Exchange (ETDEWEB)

Grouiller, Frederic; Delattre, Benedicte M.A.; Lazeyras, Francois; Ratib, Osman; Vargas, Maria I.; Garibotto, Valentina [Geneva University Hospital, Department of Radiology and Medical Informatics, Geneva 14 (Switzerland); Pittau, Francesca; Spinelli, Laurent; Seeck, Margitta; Vulliemoz, Serge [Geneva University Hospital, EEG and Epilepsy Unit, Department of Neurology, Geneva 14 (Switzerland); Heinzer, Susanne [Philips AG Healthcare, Zuerich (Switzerland); Iannotti, Giannina R. [Geneva University Hospital, Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, Geneva 14 (Switzerland)

2015-04-17

In patients with pharmacoresistant focal epilepsy, resection of the epileptic focus can lead to freedom from seizures or significant improvement in well-selected candidates. Localization of the epileptic focus with multimodal concordance is crucial for a good postoperative outcome. Beyond the detection of epileptogenic lesions on structural MRI and focal hypometabolism on FDG PET, EEG-based Electric Source Imaging (ESI) and simultaneous EEG and functional MRI (EEG-fMRI) are increasingly applied for mapping epileptic activity. We here report presurgical multimodal interictal imaging using a hybrid PET/MR scanner for single-session FDG PET, MRI, EEG-fMRI and ESI. This quadrimodal imaging procedure was performed in a single session in 12 patients using a high-density (256 electrodes) MR-compatible EEG system and a hybrid PET/MR scanner. EEG was used to exclude subclinical seizures during uptake of the PET tracer, to compute ESI on interictal epileptiform discharges and to guide fMRI analysis for mapping haemodynamic changes correlated with interictal epileptiform activity. The whole multimodal recording was performed in less than 2 hours with good patient comfort and data quality. Clinically contributory examinations with at least two modalities were obtained in nine patients and with all modalities in five patients. This single-session quadrimodal imaging procedure provided reliable and contributory interictal clinical data. This procedure avoids multiple scanning sessions and is associated with less radiation exposure than PET-CT. Moreover, it guarantees the same medication level and medical condition for all modalities. The procedure improves workflow and could reduce the duration and cost of presurgical epilepsy evaluations. (orig.)

All-in-one interictal presurgical imaging in patients with epilepsy: single-session EEG/PET/(f)MRI

International Nuclear Information System (INIS)

Grouiller, Frederic; Delattre, Benedicte M.A.; Lazeyras, Francois; Ratib, Osman; Vargas, Maria I.; Garibotto, Valentina; Pittau, Francesca; Spinelli, Laurent; Seeck, Margitta; Vulliemoz, Serge; Heinzer, Susanne; Iannotti, Giannina R.

2015-01-01

In patients with pharmacoresistant focal epilepsy, resection of the epileptic focus can lead to freedom from seizures or significant improvement in well-selected candidates. Localization of the epileptic focus with multimodal concordance is crucial for a good postoperative outcome. Beyond the detection of epileptogenic lesions on structural MRI and focal hypometabolism on FDG PET, EEG-based Electric Source Imaging (ESI) and simultaneous EEG and functional MRI (EEG-fMRI) are increasingly applied for mapping epileptic activity. We here report presurgical multimodal interictal imaging using a hybrid PET/MR scanner for single-session FDG PET, MRI, EEG-fMRI and ESI. This quadrimodal imaging procedure was performed in a single session in 12 patients using a high-density (256 electrodes) MR-compatible EEG system and a hybrid PET/MR scanner. EEG was used to exclude subclinical seizures during uptake of the PET tracer, to compute ESI on interictal epileptiform discharges and to guide fMRI analysis for mapping haemodynamic changes correlated with interictal epileptiform activity. The whole multimodal recording was performed in less than 2 hours with good patient comfort and data quality. Clinically contributory examinations with at least two modalities were obtained in nine patients and with all modalities in five patients. This single-session quadrimodal imaging procedure provided reliable and contributory interictal clinical data. This procedure avoids multiple scanning sessions and is associated with less radiation exposure than PET-CT. Moreover, it guarantees the same medication level and medical condition for all modalities. The procedure improves workflow and could reduce the duration and cost of presurgical epilepsy evaluations. (orig.)

Single-trial detection of visual evoked potentials by common spatial patterns and wavelet filtering for brain-computer interface.

Science.gov (United States)

Tu, Yiheng; Huang, Gan; Hung, Yeung Sam; Hu, Li; Hu, Yong; Zhang, Zhiguo

2013-01-01

Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems as input signals conveying a subject's intention. A fast and reliable single-trial ERP detection method can be used to develop a BCI system with both high speed and high accuracy. However, most of single-trial ERP detection methods are developed for offline EEG analysis and thus have a high computational complexity and need manual operations. Therefore, they are not applicable to practical BCI systems, which require a low-complexity and automatic ERP detection method. This work presents a joint spatial-time-frequency filter that combines common spatial patterns (CSP) and wavelet filtering (WF) for improving the signal-to-noise (SNR) of visual evoked potentials (VEP), which can lead to a single-trial ERP-based BCI.

Correlation between single-trial visual evoked potentials and the blood oxygenation level dependent response in simultaneously recorded electroencephalography-functional magnetic resonance imaging

DEFF Research Database (Denmark)

Fuglø, Dan; Pedersen, Henrik; Rostrup, Egill

2012-01-01

in different occipital and extraoccipital cortical areas not explained by the boxcar regressor. The results suggest that the P1-N2 regressor is the best EEG-based regressor to model the visual paradigm, but when looking for additional effects like habituation or attention modulation that cannot be modeled......To compare different electroencephalography (EEG)-based regressors and their ability to predict the simultaneously recorded blood oxygenation level dependent response during blocked visual stimulation, simultaneous EEG-functional magnetic resonance imaging in 10 healthy volunteers was performed....... The performance of different single-trial EEG regressors was compared in terms of predicting the measured blood oxygenation level dependent response. The EEG-based regressors were the amplitude and latency of the primary positive (P1) and negative (N2) peaks of the visual evoked potential, the combined P1-N2...

Supramodal parametric working memory processing in humans.

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Spitzer, Bernhard; Blankenburg, Felix

2012-03-07

Previous studies of delayed-match-to-sample (DMTS) frequency discrimination in animals and humans have succeeded in delineating the neural signature of frequency processing in somatosensory working memory (WM). During retention of vibrotactile frequencies, stimulus-dependent single-cell and population activity in prefrontal cortex was found to reflect the task-relevant memory content, whereas increases in occipital alpha activity signaled the disengagement of areas not relevant for the tactile task. Here, we recorded EEG from human participants to determine the extent to which these mechanisms can be generalized to frequency retention in the visual and auditory domains. Subjects performed analogous variants of a DMTS frequency discrimination task, with the frequency information presented either visually, auditorily, or by vibrotactile stimulation. Examining oscillatory EEG activity during frequency retention, we found characteristic topographical distributions of alpha power over visual, auditory, and somatosensory cortices, indicating systematic patterns of inhibition and engagement of early sensory areas, depending on stimulus modality. The task-relevant frequency information, in contrast, was found to be represented in right prefrontal cortex, independent of presentation mode. In each of the three modality conditions, parametric modulations of prefrontal upper beta activity (20-30 Hz) emerged, in a very similar manner as recently found in vibrotactile tasks. Together, the findings corroborate a view of parametric WM as supramodal internal scaling of abstract quantity information and suggest strong relevance of previous evidence from vibrotactile work for a more general framework of quantity processing in human working memory.

Assessing a novel polymer-wick based electrode for EEG neurophysiological research.

Science.gov (United States)

Pasion, Rita; Paiva, Tiago O; Pedrosa, Paulo; Gaspar, Hugo; Vasconcelos, Beatriz; Martins, Ana C; Amaral, Maria H; Nóbrega, João M; Páscoa, Ricardo; Fonseca, Carlos; Barbosa, Fernando

2016-07-15

The EEG technique has decades of valid applications in clinical and experimental neurophysiology. EEG equipment and data analysis methods have been characterized by remarkable developments, but the skin-to-electrode signal transfer remains a challenge for EEG recording. A novel quasi-dry system - the polymer wick-based electrode - was developed to overcome the limitations of conventional dry and wet silver/silver-chloride (Ag/AgCl) electrodes for EEG recording. Nine participants completed an auditory oddball protocol with simultaneous EEG acquisition using both the conventional Ag/AgCl and the wick electrodes. Wick system successfully recorded the expected P300 modulation. Standard ERP analysis, residual random noise analysis, and single-trial analysis of the P300 wave were performed in order to compare signal acquired by both electrodes. It was found that the novel wick electrode performed similarly to the conventional Ag/AgCl electrodes. The developed wick electrode appears to be a reliable alternative for EEG research, representing a promising halfway alternative between wet and dry electrodes. Copyright © 2016 Elsevier B.V. All rights reserved.

Donepezil impairs memory in healthy older subjects: behavioural, EEG and simultaneous EEG/fMRI biomarkers.

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Joshua H Balsters

Full Text Available Rising life expectancies coupled with an increasing awareness of age-related cognitive decline have led to the unwarranted use of psychopharmaceuticals, including acetylcholinesterase inhibitors (AChEIs, by significant numbers of healthy older individuals. This trend has developed despite very limited data regarding the effectiveness of such drugs on non-clinical groups and recent work indicates that AChEIs can have negative cognitive effects in healthy populations. For the first time, we use a combination of EEG and simultaneous EEG/fMRI to examine the effects of a commonly prescribed AChEI (donepezil on cognition in healthy older participants. The short- and long-term impact of donepezil was assessed using two double-blind, placebo-controlled trials. In both cases, we utilised cognitive (paired associates learning (CPAL and electrophysiological measures (resting EEG power that have demonstrated high-sensitivity to age-related cognitive decline. Experiment 1 tested the effects of 5 mg/per day dosage on cognitive and EEG markers at 6-hour, 2-week and 4-week follow-ups. In experiment 2, the same markers were further scrutinised using simultaneous EEG/fMRI after a single 5 mg dose. Experiment 1 found significant negative effects of donepezil on CPAL and resting Alpha and Beta band power. Experiment 2 replicated these results and found additional drug-related increases in the Delta band. EEG/fMRI analyses revealed that these oscillatory differences were associated with activity differences in the left hippocampus (Delta, right frontal-parietal network (Alpha, and default-mode network (Beta. We demonstrate the utility of simple cognitive and EEG measures in evaluating drug responses after acute and chronic donepezil administration. The presentation of previously established markers of age-related cognitive decline indicates that AChEIs can impair cognitive function in healthy older individuals. To our knowledge this is the first study to identify

Decoding speech perception by native and non-native speakers using single-trial electrophysiological data.

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Alex Brandmeyer

Full Text Available Brain-computer interfaces (BCIs are systems that use real-time analysis of neuroimaging data to determine the mental state of their user for purposes such as providing neurofeedback. Here, we investigate the feasibility of a BCI based on speech perception. Multivariate pattern classification methods were applied to single-trial EEG data collected during speech perception by native and non-native speakers. Two principal questions were asked: 1 Can differences in the perceived categories of pairs of phonemes be decoded at the single-trial level? 2 Can these same categorical differences be decoded across participants, within or between native-language groups? Results indicated that classification performance progressively increased with respect to the categorical status (within, boundary or across of the stimulus contrast, and was also influenced by the native language of individual participants. Classifier performance showed strong relationships with traditional event-related potential measures and behavioral responses. The results of the cross-participant analysis indicated an overall increase in average classifier performance when trained on data from all participants (native and non-native. A second cross-participant classifier trained only on data from native speakers led to an overall improvement in performance for native speakers, but a reduction in performance for non-native speakers. We also found that the native language of a given participant could be decoded on the basis of EEG data with accuracy above 80%. These results indicate that electrophysiological responses underlying speech perception can be decoded at the single-trial level, and that decoding performance systematically reflects graded changes in the responses related to the phonological status of the stimuli. This approach could be used in extensions of the BCI paradigm to support perceptual learning during second language acquisition.

EEG Neurofeedback treatments in children with ADHD: An updated meta-analysis of Randomized Controlled Trials

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Jean-Arthur eMicoulaud Franchi

2014-11-01

Full Text Available Objective We undertook a meta-analysis of published Randomized Controlled Trials (RCT with semi-active control and sham-NF groups to determine whether EEG-NF significantly improves the overall symptoms, inattention and hyperactivity/impulsivity dimensions for probably unblinded assessment (parent assessment and probably blinded assessment (teacher assessment in children with Attention Deficit Hyperactivity Disorder (ADHD.Data Sources A systematic review identified independent studies that were eligible for inclusion in a random effects meta-analysis.Data Extraction Effect sizes for ADHD symptoms were expressed as standardized mean differences (SMD with 95% confidence intervals.ResultsFive identified studies met eligibility criteria, 263 patients with ADHD were included, 146 patients were trained with EEG-NF. On parent assessment (probably unblinded assessment, the overall ADHD score (SMD=-0.49 [-0.74, -0.24], the inattention score (SMD=-0.46 [-0.76, -0.15] and the hyperactivity/impulsivity score (SMD=-0.34 [-0.59, -0.09] were significantly improved in patients receiving EEG-NF compared to controls. On teacher assessment (probably blinded assessment, only the inattention score was significantly improved in patients receiving EEG-NF compared to controls (SMD=-0.30 [-0.58, -0.03]. ConclusionsThis meta-analysis of EEG-NF in children with ADHD highlights improvement in the inattention dimension of ADHD symptoms. Future investigations should pay greater attention to adequately blinded studies and EEG-NF protocols that carefully control the implementation and embedding of training.

Continuous parametric feedback cooling of a single atom in an optical cavity

Science.gov (United States)

Sames, C.; Hamsen, C.; Chibani, H.; Altin, P. A.; Wilk, T.; Rempe, G.

2018-05-01

We demonstrate a feedback algorithm to cool a single neutral atom trapped inside a standing-wave optical cavity. The algorithm is based on parametric modulation of the confining potential at twice the natural oscillation frequency of the atom, in combination with fast and repetitive atomic position measurements. The latter serve to continuously adjust the modulation phase to a value for which parametric excitation of the atomic motion is avoided. Cooling is limited by the measurement backaction which decoheres the atomic motion after only a few oscillations. Nonetheless, applying this feedback scheme to an ˜5 -kHz oscillation mode increases the average storage time of a single atom in the cavity by a factor of 60 to more than 2 s. In contrast to previous feedback schemes, our algorithm is also capable of cooling a much faster ˜500 -kHz oscillation mode within just microseconds. This demonstrates that parametric cooling is a powerful technique that can be applied in all experiments where optical access is limited.

DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG.

Science.gov (United States)

Supratak, Akara; Dong, Hao; Wu, Chao; Guo, Yike

2017-11-01

This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-engineered features, which require prior knowledge of sleep analysis. Only a few of them encode the temporal information, such as transition rules, which is important for identifying the next sleep stages, into the extracted features. In the proposed model, we utilize convolutional neural networks to extract time-invariant features, and bidirectional-long short-term memory to learn transition rules among sleep stages automatically from EEG epochs. We implement a two-step training algorithm to train our model efficiently. We evaluated our model using different single-channel EEGs (F4-EOG (left), Fpz-Cz, and Pz-Oz) from two public sleep data sets, that have different properties (e.g., sampling rate) and scoring standards (AASM and R&K). The results showed that our model achieved similar overall accuracy and macro F1-score (MASS: 86.2%-81.7, Sleep-EDF: 82.0%-76.9) compared with the state-of-the-art methods (MASS: 85.9%-80.5, Sleep-EDF: 78.9%-73.7) on both data sets. This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different data sets without utilizing any hand-engineered features.

Level-statistics in Disordered Systems: A single parametric scaling and Connection to Brownian Ensembles

OpenAIRE

Shukla, Pragya

2004-01-01

We find that the statistics of levels undergoing metal-insulator transition in systems with multi-parametric Gaussian disorders and non-interacting electrons behaves in a way similar to that of the single parametric Brownian ensembles \\cite{dy}. The latter appear during a Poisson $\\to$ Wigner-Dyson transition, driven by a random perturbation. The analogy provides the analytical evidence for the single parameter scaling of the level-correlations in disordered systems as well as a tool to obtai...

Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM☆

Science.gov (United States)

López, J.D.; Litvak, V.; Espinosa, J.J.; Friston, K.; Barnes, G.R.

2014-01-01

The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy—an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithm. PMID:24041874

A MISO-ARX-Based Method for Single-Trial Evoked Potential Extraction

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Nannan Yu

2017-01-01

Full Text Available In this paper, we propose a novel method for solving the single-trial evoked potential (EP estimation problem. In this method, the single-trial EP is considered as a complex containing many components, which may originate from different functional brain sites; these components can be distinguished according to their respective latencies and amplitudes and are extracted simultaneously by multiple-input single-output autoregressive modeling with exogenous input (MISO-ARX. The extraction process is performed in three stages: first, we use a reference EP as a template and decompose it into a set of components, which serve as subtemplates for the remaining steps. Then, a dictionary is constructed with these subtemplates, and EPs are preliminarily extracted by sparse coding in order to roughly estimate the latency of each component. Finally, the single-trial measurement is parametrically modeled by MISO-ARX while characterizing spontaneous electroencephalographic activity as an autoregression model driven by white noise and with each component of the EP modeled by autoregressive-moving-average filtering of the subtemplates. Once optimized, all components of the EP can be extracted. Compared with ARX, our method has greater tracking capabilities of specific components of the EP complex as each component is modeled individually in MISO-ARX. We provide exhaustive experimental results to show the effectiveness and feasibility of our method.

Reliable single chip genotyping with semi-parametric log-concave mixtures.

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Ralph C A Rippe

Full Text Available The common approach to SNP genotyping is to use (model-based clustering per individual SNP, on a set of arrays. Genotyping all SNPs on a single array is much more attractive, in terms of flexibility, stability and applicability, when developing new chips. A new semi-parametric method, named SCALA, is proposed. It is based on a mixture model using semi-parametric log-concave densities. Instead of using the raw data, the mixture is fitted on a two-dimensional histogram, thereby making computation time almost independent of the number of SNPs. Furthermore, the algorithm is effective in low-MAF situations.Comparisons between SCALA and CRLMM on HapMap genotypes show very reliable calling of single arrays. Some heterozygous genotypes from HapMap are called homozygous by SCALA and to lesser extent by CRLMM too. Furthermore, HapMap's NoCalls (NN could be genotyped by SCALA, mostly with high probability. The software is available as R scripts from the website www.math.leidenuniv.nl/~rrippe.

Could the beta rebound in the EEG be suitable to realize a "brain switch"?

Science.gov (United States)

Pfurtscheller, G; Solis-Escalante, T

2009-01-01

Performing foot motor imagery is accompanied by a peri-imagery ERD and a post-imagery beta ERS (beta rebound). Our aim was to study whether the post-imagery beta rebound is a suitable feature for a simple "brain switch". Such a brain switch is a specifically designed brain-computer interface (BCI) with the aim to detect only one predefined brain state (e.g. EEG pattern) in ongoing brain activity. One EEG (Laplacian) recorded at the vertex during cue-based brisk foot motor imagery was analysed in 5 healthy subjects. The peri-imagery ERD and the post-imagery beta rebound (ERS) were analysed in detail between 6 and 40Hz and classified with two support vector machines. The ERD was detected in ongoing EEG (simulation of asynchronous BCI) with a true positive rate (TPR) of 28.4%+/-13.5 and the beta rebound with a TPR of 59.2%+/-20.3. In single runs with 30 cues each, the TPR for beta rebound detection was 78.6%+/-12.8. The false positive rate was always kept below 10%. The findings suggest that the beta rebound at Cz during foot motor imagery is a relatively stable and reproducible phenomenon detectable in single EEG trials. Our results indicate that the beta rebound is a suitable feature to realize a "brain switch" with one single EEG (Laplacian) channel only.

Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns

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Shih-Cheng Liao

2017-06-01

Full Text Available Major depressive disorder (MDD has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP. The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total. Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be

Review on solving the inverse problem in EEG source analysis

Directory of Open Access Journals (Sweden)

Fabri Simon G

2008-11-01

Full Text Available Abstract In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided. This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF, SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF

Acute single channel EEG predictors of cognitive function after stroke.

Directory of Open Access Journals (Sweden)

Anna Aminov

Full Text Available Early and accurate identification of factors that predict post-stroke cognitive outcome is important to set realistic targets for rehabilitation and to guide patients and their families accordingly. However, behavioral measures of cognition are difficult to obtain in the acute phase of recovery due to clinical factors (e.g. fatigue and functional barriers (e.g. language deficits. The aim of the current study was to test whether single channel wireless EEG data obtained acutely following stroke could predict longer-term cognitive function.Resting state Relative Power (RP of delta, theta, alpha, beta, delta/alpha ratio (DAR, and delta/theta ratio (DTR were obtained from a single electrode over FP1 in 24 participants within 72 hours of a first-ever stroke. The Montreal Cognitive Assessment (MoCA was administered at 90-days post-stroke. Correlation and regression analyses were completed to identify relationships between 90-day cognitive function and electrophysiological data, neurological status, and demographic characteristics at admission.Four acute qEEG indices demonstrated moderate to high correlations with 90-day MoCA scores: DTR (r = -0.57, p = 0.01, RP theta (r = 0.50, p = 0.01, RP delta (r = -0.47, p = 0.02, and DAR (r = -0.45, p = 0.03. Acute DTR (b = -0.36, p < 0.05 and stroke severity on admission (b = -0.63, p < 0.01 were the best linear combination of predictors of MoCA scores 90-days post-stroke, accounting for 75% of variance.Data generated by a single pre-frontal electrode support the prognostic value of acute DAR, and identify DTR as a potential marker of post-stroke cognitive outcome. Use of single channel recording in an acute clinical setting may provide an efficient and valid predictor of cognitive function after stroke.

The application of particle filters in single trial event-related potential estimation

International Nuclear Information System (INIS)

Mohseni, Hamid R; Nazarpour, Kianoush; Sanei, Saeid; Wilding, Edward L

2009-01-01

In this paper, an approach for the estimation of single trial event-related potentials (ST-ERPs) using particle filters (PFs) is presented. The method is based on recursive Bayesian mean square estimation of ERP wavelet coefficients using their previous estimates as prior information. To enable a performance evaluation of the approach in the Gaussian and non-Gaussian distributed noise conditions, we added Gaussian white noise (GWN) and real electroencephalogram (EEG) signals recorded during rest to the simulated ERPs. The results were compared to that of the Kalman filtering (KF) approach demonstrating the robustness of the PF over the KF to the added GWN noise. The proposed method also outperforms the KF when the assumption about the Gaussianity of the noise is violated. We also applied this technique to real EEG potentials recorded in an odd-ball paradigm and investigated the correlation between the amplitude and the latency of the estimated ERP components. Unlike the KF method, for the PF there was a statistically significant negative correlation between amplitude and latency of the estimated ERPs, matching previous neurophysiological findings

Reliable Single Chip Genotyping with Semi-Parametric Log-Concave Mixtures

NARCIS (Netherlands)

R.C.A. Rippe (Ralph); J.J. Meulman (Jacqueline); P.H.C. Eilers (Paul)

2012-01-01

textabstractThe common approach to SNP genotyping is to use (model-based) clustering per individual SNP, on a set of arrays. Genotyping all SNPs on a single array is much more attractive, in terms of flexibility, stability and applicability, when developing new chips. A new semi-parametric method,

Automatic characterization of sleep need dissipation dynamics using a single EEG signal.

Science.gov (United States)

Garcia-Molina, Gary; Bellesi, Michele; Riedner, Brady; Pastoor, Sander; Pfundtner, Stefan; Tononi, Giulio

2015-01-01

In the two-process model of sleep regulation, slow-wave activity (SWA, i.e. the EEG power in the 0.5-4 Hz frequency band) is considered a direct indicator of sleep need. SWA builds up during non-rapid eye movement (NREM) sleep, declines before the onset of rapid-eye-movement (REM) sleep, remains low during REM and the level of increase in successive NREM episodes gets progressively lower. Sleep need dissipates with a speed that is proportional to SWA and can be characterized in terms of the initial sleep need, and the decay rate. The goal in this paper is to automatically characterize sleep need from a single EEG signal acquired at a frontal location. To achieve this, a highly specific and reasonably sensitive NREM detection algorithm is proposed that leverages the concept of a single-class Kernel-based classifier. Using automatic NREM detection, we propose a method to estimate the decay rate and the initial sleep need. This method was tested on experimental data from 8 subjects who recorded EEG during three nights at home. We found that on average the estimates of the decay rate and the initial sleep need have higher values when automatic NREM detection was used as compared to manual NREM annotation. However, the average variability of these estimates across multiple nights of the same subject was lower when the automatic NREM detection classifier was used. While this method slightly over estimates the sleep need parameters, the reduced variability across subjects makes it more effective for within subject statistical comparisons of a given sleep intervention.

A Preliminary Study of Muscular Artifact Cancellation in Single-Channel EEG

OpenAIRE

Chen, Xun; Liu, Aiping; Peng, Hu; Ward, Rabab K.

2014-01-01

Electroencephalogram (EEG) recordings are often contaminated with muscular artifacts that strongly obscure the EEG signals and complicates their analysis. For the conventional case, where the EEG recordings are obtained simultaneously over many EEG channels, there exists a considerable range of methods for removing muscular artifacts. In recent years, there has been an increasing trend to use EEG information in ambulatory healthcare and related physiological signal monitoring systems. For pra...

Fiber Laser Pumped Continuous-wave Singly-resonant Optical Parametric Oscillator

NARCIS (Netherlands)

Klein, M.E.; Gross, P.; Walde, T.; Boller, Klaus J.; Auerbach, M.; Wessels, P.; Fallnich, C.; Fejer, Martin M.

2002-01-01

We report on the first fiber-pumped CW LiNbO/sub 3/ optical parametric oscillator (OPO). The OPO is singly resonant (SRO) and generates idler wavelengths in the range of 3.0 /spl mu/m to 3.7 /spl mu/m with a maximum output power of 1.9 watt.

High-resolution time-frequency representation of EEG data using multi-scale wavelets

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Li, Yang; Cui, Wei-Gang; Luo, Mei-Lin; Li, Ke; Wang, Lina

2017-09-01

An efficient time-varying autoregressive (TVAR) modelling scheme that expands the time-varying parameters onto the multi-scale wavelet basis functions is presented for modelling nonstationary signals and with applications to time-frequency analysis (TFA) of electroencephalogram (EEG) signals. In the new parametric modelling framework, the time-dependent parameters of the TVAR model are locally represented by using a novel multi-scale wavelet decomposition scheme, which can allow the capability to capture the smooth trends as well as track the abrupt changes of time-varying parameters simultaneously. A forward orthogonal least square (FOLS) algorithm aided by mutual information criteria are then applied for sparse model term selection and parameter estimation. Two simulation examples illustrate that the performance of the proposed multi-scale wavelet basis functions outperforms the only single-scale wavelet basis functions or Kalman filter algorithm for many nonstationary processes. Furthermore, an application of the proposed method to a real EEG signal demonstrates the new approach can provide highly time-dependent spectral resolution capability.

Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification

DEFF Research Database (Denmark)

Treder, Matthias S.; Purwins, Hendrik; Miklody, Daniel

2014-01-01

. Here, we explore polyphonic music as a novel stimulation approach for future use in a brain-computer interface. In a musical oddball experiment, we had participants shift selective attention to one out of three different instruments in music audio clips, with each instrument occasionally playing one...... 11 participants. This is a proof of concept that attention paid to a particular instrument in polyphonic music can be inferred from ongoing EEG, a finding that is potentially relevant for both brain-computer interface and music research....

Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns.

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Liao, Shih-Cheng; Wu, Chien-Te; Huang, Hao-Chuan; Cheng, Wei-Teng; Liu, Yi-Hung

2017-06-14

Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP

Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM.

Science.gov (United States)

López, J D; Litvak, V; Espinosa, J J; Friston, K; Barnes, G R

2014-01-01

The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy-an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithm. © 2013. Published by Elsevier Inc. All rights reserved.

Dynamic range enhancement and amplitude regeneration in single pump fibre optic parametric amplifiers using DPSK modulation

DEFF Research Database (Denmark)

Peucheret, Christophe; Lorenzen, Michael Rodas; Seoane, Jorge

2008-01-01

Input power dynamic range enhancement and amplitude regeneration of highly distorted signals are demonstrated experimentally for 40 Gbit/s RZ-DPSK in a single-pump fibre parametric amplifier with 22 dB smallsignal gain.......Input power dynamic range enhancement and amplitude regeneration of highly distorted signals are demonstrated experimentally for 40 Gbit/s RZ-DPSK in a single-pump fibre parametric amplifier with 22 dB smallsignal gain....

Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients

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Lahmiri, Salim

2018-01-01

The aim of our current study is to check whether multifractal patterns of the electroencephalographic (EEG) signals of normal and epileptic patients are statistically similar or different. In this regard, the generalized Hurst exponent (GHE) method is used for robust estimation of the multifractals in each type of EEG signals, and three powerful statistical tests are performed to check existence of differences between estimated GHEs from healthy control subjects and epileptic patients. The obtained results show that multifractals exist in both types of EEG signals. Particularly, it was found that the degree of fractal is more pronounced in short variations of normal EEG signals than in short variations of EEG signals with seizure free intervals. In contrary, it is more pronounced in long variations of EEG signals with seizure free intervals than in normal EEG signals. Importantly, both parametric and nonparametric statistical tests show strong evidence that estimated GHEs of normal EEG signals are statistically and significantly different from those with seizure free intervals. Therefore, GHEs can be efficiently used to distinguish between healthy and patients suffering from epilepsy.

Sequential inhibitory control processes assessed through simultaneous EEG-fMRI.

Science.gov (United States)

Baumeister, Sarah; Hohmann, Sarah; Wolf, Isabella; Plichta, Michael M; Rechtsteiner, Stefanie; Zangl, Maria; Ruf, Matthias; Holz, Nathalie; Boecker, Regina; Meyer-Lindenberg, Andreas; Holtmann, Martin; Laucht, Manfred; Banaschewski, Tobias; Brandeis, Daniel

2014-07-01

Inhibitory response control has been extensively investigated in both electrophysiological (ERP) and hemodynamic (fMRI) studies. However, very few multimodal results address the coupling of these inhibition markers. In fMRI, response inhibition has been most consistently linked to activation of the anterior insula and inferior frontal cortex (IFC), often also the anterior cingulate cortex (ACC). ERP work has established increased N2 and P3 amplitudes during NoGo compared to Go conditions in most studies. Previous simultaneous EEG-fMRI imaging reported association of the N2/P3 complex with activation of areas like the anterior midcingulate cortex (aMCC) and anterior insula. In this study we investigated inhibitory control in 23 healthy young adults (mean age=24.7, n=17 for EEG during fMRI) using a combined Flanker/NoGo task during simultaneous EEG and fMRI recording. Separate fMRI and ERP analysis yielded higher activation in the anterior insula, IFG and ACC as well as increased N2 and P3 amplitudes during NoGo trials in accordance with the literature. Combined analysis modelling sequential N2 and P3 effects through joint parametric modulation revealed correlation of higher N2 amplitude with deactivation in parts of the default mode network (DMN) and the cingulate motor area (CMA) as well as correlation of higher central P3 amplitude with activation of the left anterior insula, IFG and posterior cingulate. The EEG-fMRI results resolve the localizations of these sequential activations. They suggest a general role for allocation of attentional resources and motor inhibition for N2 and link memory recollection and internal reflection to P3 amplitude, in addition to previously described response inhibition as reflected by the anterior insula. Copyright © 2014 Elsevier Inc. All rights reserved.

Source-Modeling Auditory Processes of EEG Data Using EEGLAB and Brainstorm

Directory of Open Access Journals (Sweden)

Maren Stropahl

2018-05-01

Full Text Available Electroencephalography (EEG source localization approaches are often used to disentangle the spatial patterns mixed up in scalp EEG recordings. However, approaches differ substantially between experiments, may be strongly parameter-dependent, and results are not necessarily meaningful. In this paper we provide a pipeline for EEG source estimation, from raw EEG data pre-processing using EEGLAB functions up to source-level analysis as implemented in Brainstorm. The pipeline is tested using a data set of 10 individuals performing an auditory attention task. The analysis approach estimates sources of 64-channel EEG data without the prerequisite of individual anatomies or individually digitized sensor positions. First, we show advanced EEG pre-processing using EEGLAB, which includes artifact attenuation using independent component analysis (ICA. ICA is a linear decomposition technique that aims to reveal the underlying statistical sources of mixed signals and is further a powerful tool to attenuate stereotypical artifacts (e.g., eye movements or heartbeat. Data submitted to ICA are pre-processed to facilitate good-quality decompositions. Aiming toward an objective approach on component identification, the semi-automatic CORRMAP algorithm is applied for the identification of components representing prominent and stereotypic artifacts. Second, we present a step-wise approach to estimate active sources of auditory cortex event-related processing, on a single subject level. The presented approach assumes that no individual anatomy is available and therefore the default anatomy ICBM152, as implemented in Brainstorm, is used for all individuals. Individual noise modeling in this dataset is based on the pre-stimulus baseline period. For EEG source modeling we use the OpenMEEG algorithm as the underlying forward model based on the symmetric Boundary Element Method (BEM. We then apply the method of dynamical statistical parametric mapping (dSPM to obtain

Relationship between speed and EEG activity during imagined and executed hand movements

Science.gov (United States)

Yuan, Han; Perdoni, Christopher; He, Bin

2010-04-01

The relationship between primary motor cortex and movement kinematics has been shown in nonhuman primate studies of hand reaching or drawing tasks. Studies have demonstrated that the neural activities accompanying or immediately preceding the movement encode the direction, speed and other information. Here we investigated the relationship between the kinematics of imagined and actual hand movement, i.e. the clenching speed, and the EEG activity in ten human subjects. Study participants were asked to perform and imagine clenching of the left hand and right hand at various speeds. The EEG activity in the alpha (8-12 Hz) and beta (18-28 Hz) frequency bands were found to be linearly correlated with the speed of imagery clenching. Similar parametric modulation was also found during the execution of hand movements. A single equation relating the EEG activity to the speed and the hand (left versus right) was developed. This equation, which contained a linear independent combination of the two parameters, described the time-varying neural activity during the tasks. Based on the model, a regression approach was developed to decode the two parameters from the multiple-channel EEG signals. We demonstrated the continuous decoding of dynamic hand and speed information of the imagined clenching. In particular, the time-varying clenching speed was reconstructed in a bell-shaped profile. Our findings suggest an application to providing continuous and complex control of noninvasive brain-computer interface for movement-impaired paralytics.

Effects of Inaccurate Identification of Interictal Epileptiform Discharges in Concurrent EEG-fMRI

Science.gov (United States)

Gkiatis, K.; Bromis, K.; Kakkos, I.; Karanasiou, I. S.; Matsopoulos, G. K.; Garganis, K.

2017-11-01

Concurrent continuous EEG-fMRI is a novel multimodal technique that is finding its way into clinical practice in epilepsy. EEG timeseries are used to identify the timing of interictal epileptiform discharges (IEDs) which is then included in a GLM analysis in fMRI to localize the epileptic onset zone. Nevertheless, there are still some concerns about its reliability concerning BOLD changes correlated with IEDs. Even though IEDs are identified by an experienced neurologist-epiliptologist, the reliability and concordance of the mark-ups is depending on many factors including the level of fatigue, the amount of time that he spent or, in some cases, even the screen that is being used for the display of timeseries. This investigation is aiming to unravel the effect of misidentification or inaccuracy in the mark-ups of IEDs in the fMRI statistical parametric maps. Concurrent EEG-fMRI was conducted in six subjects with various types of epilepsy. IEDs were identified by an experienced neurologist-epiliptologist. Analysis of EEG was performed with EEGLAB and analysis of fMRI was conducted in FSL. Preliminary results revealed lower statistical significance for missing events or larger period of IEDs than the actual ones and the introduction of false positives and false negatives in statistical parametric maps when random events were included in the GLM on top of the IEDs. Our results suggest that mark-ups in EEG for simultaneous EEG-fMRI should be done with caution from an experienced and restful neurologist as it affects the fMRI results in various and unpredicted ways.

A single camera photogrammetry system for multi-angle fast localization of EEG electrodes.

Science.gov (United States)

Qian, Shuo; Sheng, Yang

2011-11-01

Photogrammetry has become an effective method for the determination of electroencephalography (EEG) electrode positions in three dimensions (3D). Capturing multi-angle images of the electrodes on the head is a fundamental objective in the design of photogrammetry system for EEG localization. Methods in previous studies are all based on the use of either a rotating camera or multiple cameras, which are time-consuming or not cost-effective. This study aims to present a novel photogrammetry system that can realize simultaneous acquisition of multi-angle head images in a single camera position. Aligning two planar mirrors with the angle of 51.4°, seven views of the head with 25 electrodes are captured simultaneously by the digital camera placed in front of them. A complete set of algorithms for electrode recognition, matching, and 3D reconstruction is developed. It is found that the elapsed time of the whole localization procedure is about 3 min, and camera calibration computation takes about 1 min, after the measurement of calibration points. The positioning accuracy with the maximum error of 1.19 mm is acceptable. Experimental results demonstrate that the proposed system provides a fast and cost-effective method for the EEG positioning.

Use of statistical parametric mapping of 18F-FDG-PET in frontal lobe epilepsy

International Nuclear Information System (INIS)

Plotkin, M.; Amthauer, H.; Luedemann, L.; Hartkop, E.; Ruf, J.; Gutberlet, M.; Bertram, H.; Felix, R.; Venz, St.; Merschhemke, M.; Meencke, H.-J.

2003-01-01

Aim: Evaluation of the use of statistical parametrical mapping (SPM) of FDG-PET for seizure lateralization in frontal lobe epilepsy. Patients: 38 patients with suspected frontal lobe epilepsy supported by clinical findings and video-EEG monitoring. Method: Statistical parametrical maps were generated by subtraction of individual scans from a control group, formed by 16 patients with negative neurological/psychiatric history and no abnormalities in the MR scan. The scans were also analyzed visually as well as semiquantitatively by manually drawn ROIs. Results: SPM showed a better accordance to the results of surface EEG monitoring compared with visual scan analysis and ROI quantification. In comparison with intracranial EEG recordings, the best performance was achieved by combining the ROI based quantification with SPM analysis. Conclusion: These findings suggest that SPM analysis of FDG-PET data could be a useful as complementary tool in the evaluation of seizure focus lateralization in patients with supposed frontal lobe epilepsy. (orig.)

A Single Session of rTMS Enhances Small-Worldness in Writer’s Cramp: Evidence from Simultaneous EEG-fMRI Multi-Modal Brain Graph

Directory of Open Access Journals (Sweden)

Rose D. Bharath

2017-09-01

Full Text Available Background and Purpose: Repetitive transcranial magnetic stimulation (rTMS induces widespread changes in brain connectivity. As the network topology differences induced by a single session of rTMS are less known we undertook this study to ascertain whether the network alterations had a small-world morphology using multi-modal graph theory analysis of simultaneous EEG-fMRI.Method: Simultaneous EEG-fMRI was acquired in duplicate before (R1 and after (R2 a single session of rTMS in 14 patients with Writer’s Cramp (WC. Whole brain neuronal and hemodynamic network connectivity were explored using the graph theory measures and clustering coefficient, path length and small-world index were calculated for EEG and resting state fMRI (rsfMRI. Multi-modal graph theory analysis was used to evaluate the correlation of EEG and fMRI clustering coefficients.Result: A single session of rTMS was found to increase the clustering coefficient and small-worldness significantly in both EEG and fMRI (p < 0.05. Multi-modal graph theory analysis revealed significant modulations in the fronto-parietal regions immediately after rTMS. The rsfMRI revealed additional modulations in several deep brain regions including cerebellum, insula and medial frontal lobe.Conclusion: Multi-modal graph theory analysis of simultaneous EEG-fMRI can supplement motor physiology methods in understanding the neurobiology of rTMS in vivo. Coinciding evidence from EEG and rsfMRI reports small-world morphology for the acute phase network hyper-connectivity indicating changes ensuing low-frequency rTMS is probably not “noise”.

Bayesian learning for spatial filtering in an EEG-based brain-computer interface.

Science.gov (United States)

Zhang, Haihong; Yang, Huijuan; Guan, Cuntai

2013-07-01

Spatial filtering for EEG feature extraction and classification is an important tool in brain-computer interface. However, there is generally no established theory that links spatial filtering directly to Bayes classification error. To address this issue, this paper proposes and studies a Bayesian analysis theory for spatial filtering in relation to Bayes error. Following the maximum entropy principle, we introduce a gamma probability model for describing single-trial EEG power features. We then formulate and analyze the theoretical relationship between Bayes classification error and the so-called Rayleigh quotient, which is a function of spatial filters and basically measures the ratio in power features between two classes. This paper also reports our extensive study that examines the theory and its use in classification, using three publicly available EEG data sets and state-of-the-art spatial filtering techniques and various classifiers. Specifically, we validate the positive relationship between Bayes error and Rayleigh quotient in real EEG power features. Finally, we demonstrate that the Bayes error can be practically reduced by applying a new spatial filter with lower Rayleigh quotient.

Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior

Science.gov (United States)

Bridwell, David A.; Cavanagh, James F.; Collins, Anne G. E.; Nunez, Michael D.; Srinivasan, Ramesh; Stober, Sebastian; Calhoun, Vince D.

2018-01-01

Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or “components” derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function. PMID:29632480

Electroencephalography (EEG) for neurological prognostication after cardiac arrest and targeted temperature management; rationale and study design.

Science.gov (United States)

Westhall, Erik; Rosén, Ingmar; Rossetti, Andrea O; van Rootselaar, Anne-Fleur; Kjaer, Troels Wesenberg; Horn, Janneke; Ullén, Susann; Friberg, Hans; Nielsen, Niklas; Cronberg, Tobias

2014-08-16

Electroencephalography (EEG) is widely used to assess neurological prognosis in patients who are comatose after cardiac arrest, but its value is limited by varying definitions of pathological patterns and by inter-rater variability. The American Clinical Neurophysiology Society (ACNS) has recently proposed a standardized EEG-terminology for critical care to address these limitations. In the TTM-trial, 399 post cardiac arrest patients who remained comatose after rewarming underwent a routine EEG. The presence of clinical seizures, use of sedatives and antiepileptic drugs during the EEG-registration were prospectively documented. A well-defined terminology for interpreting post cardiac arrest EEGs is critical for the use of EEG as a prognostic tool. The TTM-trial is registered at ClinicalTrials.gov (NCT01020916).

Neural network classifications and correlation analysis of EEG and MEG activity accompanying spontaneous reversals of the Necker cube.

Science.gov (United States)

Gaetz, M; Weinberg, H; Rzempoluck, E; Jantzen, K J

1998-04-01

It has recently been suggested that reentrant connections are essential in systems that process complex information [A. Damasio, H. Damasio, Cortical systems for the retrieval of concrete knowledge: the convergence zone framework, in: C. Koch, J.L. Davis (Eds.), Large Scale Neuronal Theories of the Brain, The MIT Press, Cambridge, 1995, pp. 61-74; G. Edelman, The Remembered Present, Basic Books, New York, 1989; M.I. Posner, M. Rothbart, Constructing neuronal theories of mind, in: C. Koch, J.L. Davis (Eds.), Large Scale Neuronal Theories of the Brain, The MIT Press, Cambridge, 1995, pp. 183-199; C. von der Malsburg, W. Schneider, A neuronal cocktail party processor, Biol. Cybem., 54 (1986) 29-40]. Reentry is not feedback, but parallel signalling in the time domain between spatially distributed maps, similar to a process of correlation between distributed systems. Accordingly, it was expected that during spontaneous reversals of the Necker cube, complex patterns of correlations between distributed systems would be present in the cortex. The present study included EEG (n=4) and MEG recordings (n=5). Two experimental questions were posed: (1) Can distributed cortical patterns present during perceptual reversals be classified differently using a generalised regression neural network (GRNN) compared to processing of a two-dimensional figure? (2) Does correlated cortical activity increase significantly during perception of a Necker cube reversal? One-second duration single trials of EEG and MEG data were analysed using the GRNN. Electrode/sensor pairings based on cortico-cortical connections were selected to assess correlated activity in each condition. The GRNN significantly classified single trials recorded during Necker cube reversals as different from single trials recorded during perception of a two-dimensional figure for both EEG and MEG. In addition, correlated cortical activity increased significantly in the Necker cube reversal condition for EEG and MEG compared

EEG datasets for motor imagery brain-computer interface.

Science.gov (United States)

Cho, Hohyun; Ahn, Minkyu; Ahn, Sangtae; Kwon, Moonyoung; Jun, Sung Chan

2017-07-01

Most investigators of brain-computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)-based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states. We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information. Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states. © The Authors 2017. Published by Oxford University Press.

SCoT: a Python toolbox for EEG source connectivity.

Science.gov (United States)

Billinger, Martin; Brunner, Clemens; Müller-Putz, Gernot R

2014-01-01

Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG). Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs) require single-trial estimation methods. In this paper, we present SCoT-a source connectivity toolbox for Python. This toolbox implements routines for blind source decomposition and connectivity estimation with the MVARICA approach. Additionally, a novel extension called CSPVARICA is available for labeled data. SCoT estimates connectivity from various spectral measures relying on vector autoregressive (VAR) models. Optionally, these VAR models can be regularized to facilitate ill posed applications such as single-trial fitting. We demonstrate basic usage of SCoT on motor imagery (MI) data. Furthermore, we show simulation results of utilizing SCoT for feature extraction in a BCI application. These results indicate that CSPVARICA and correct regularization can significantly improve MI classification. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (1) brings combined source decomposition and connectivtiy estimation to the open Python platform, and (2) offers tools for single-trial connectivity estimation. The source code is released under the MIT license and is available online at github.com/SCoT-dev/SCoT.

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...

Comparison of feature and classifier algorithms for online automatic sleep staging based on a single EEG signal

NARCIS (Netherlands)

Radha, M.; Garcia Molina, G.; Poel, M.; Tononi, G.

2014-01-01

Automatic sleep staging on an online basis has recently emerged as a research topic motivated by fundamental sleep research. The aim of this paper is to find optimal signal processing methods and machine learning algorithms to achieve online sleep staging on the basis of a single EEG signal. The

SCoT: A Python Toolbox for EEG Source Connectivity

Directory of Open Access Journals (Sweden)

Martin eBillinger

2014-03-01

Full Text Available Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG. Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs require single-trial estimation methods.In this paper, we present SCoT – a source connectivity toolbox for Python. This toolbox implements routines for blind source decomposition and connectivity estimation with theMVARICA approach. Additionally, a novel extension called CSPVARICA is available for labeled data. SCoT estimates connectivity from various spectral measures relying on vector autoregressive (VAR models. Optionally, these VAR models can be regularized to facilitate ill posed applications such as single-trial fitting.We demonstrate basic usage of SCoT on motor imagery (MI data. Furthermore, we show simulation results of utilizing SCoT for feature extraction in a BCI application. These results indicate that CSPVARICA and correct regularization can significantly improve MI classification. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (1 brings combined source decomposition and connectivtiy estimation to the open Python platform, and (2 offers tools for single-trial connectivity estimation. The source code is released under the MIT license and is available online at github.com/SCoT-dev/SCoT.

Combined wide pump tuning and high power of a continuous-wave, singly resonant optical parametric oscillator

NARCIS (Netherlands)

Herpen, M.M.J.W. van; Bisson, S.E.; Ngai, A.K.Y.; Harren, F.J.M.

2004-01-01

A new singly resonant, single-frequency optical parametric oscillator (OPO) has been developed for the 2.6-4.7 mum infrared wavelength region, using a high power (>20 W), widely tunable (1024-1034 nm) Yb:YAG pump source. With the OPO frequency stabilized with an intracavity etalon, the OPO achieved

Non-parametric early seizure detection in an animal model of temporal lobe epilepsy

Science.gov (United States)

Talathi, Sachin S.; Hwang, Dong-Uk; Spano, Mark L.; Simonotto, Jennifer; Furman, Michael D.; Myers, Stephen M.; Winters, Jason T.; Ditto, William L.; Carney, Paul R.

2008-03-01

The performance of five non-parametric, univariate seizure detection schemes (embedding delay, Hurst scale, wavelet scale, nonlinear autocorrelation and variance energy) were evaluated as a function of the sampling rate of EEG recordings, the electrode types used for EEG acquisition, and the spatial location of the EEG electrodes in order to determine the applicability of the measures in real-time closed-loop seizure intervention. The criteria chosen for evaluating the performance were high statistical robustness (as determined through the sensitivity and the specificity of a given measure in detecting a seizure) and the lag in seizure detection with respect to the seizure onset time (as determined by visual inspection of the EEG signal by a trained epileptologist). An optimality index was designed to evaluate the overall performance of each measure. For the EEG data recorded with microwire electrode array at a sampling rate of 12 kHz, the wavelet scale measure exhibited better overall performance in terms of its ability to detect a seizure with high optimality index value and high statistics in terms of sensitivity and specificity.

Automatic identification and removal of ocular artifacts in EEG--improved adaptive predictor filtering for portable applications.

Science.gov (United States)

Zhao, Qinglin; Hu, Bin; Shi, Yujun; Li, Yang; Moore, Philip; Sun, Minghou; Peng, Hong

2014-06-01

Electroencephalogram (EEG) signals have a long history of use as a noninvasive approach to measure brain function. An essential component in EEG-based applications is the removal of Ocular Artifacts (OA) from the EEG signals. In this paper we propose a hybrid de-noising method combining Discrete Wavelet Transformation (DWT) and an Adaptive Predictor Filter (APF). A particularly novel feature of the proposed method is the use of the APF based on an adaptive autoregressive model for prediction of the waveform of signals in the ocular artifact zones. In our test, based on simulated data, the accuracy of noise removal in the proposed model was significantly increased when compared to existing methods including: Wavelet Packet Transform (WPT) and Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT) and Adaptive Noise Cancellation (ANC). The results demonstrate that the proposed method achieved a lower mean square error and higher correlation between the original and corrected EEG. The proposed method has also been evaluated using data from calibration trials for the Online Predictive Tools for Intervention in Mental Illness (OPTIMI) project. The results of this evaluation indicate an improvement in performance in terms of the recovery of true EEG signals with EEG tracking and computational speed in the analysis. The proposed method is well suited to applications in portable environments where the constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices.

Stage-independent, single lead EEG sleep spindle detection using the continuous wavelet transform and local weighted smoothing.

Science.gov (United States)

Tsanas, Athanasios; Clifford, Gari D

2015-01-01

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 toward 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.

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.

Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel

Directory of Open Access Journals (Sweden)

Jianfeng Hu

2017-01-01

Full Text Available Driver fatigue has become an important factor to traffic accidents worldwide, and effective detection of driver fatigue has major significance for public health. The purpose method employs entropy measures for feature extraction from a single electroencephalogram (EEG channel. Four types of entropies measures, sample entropy (SE, fuzzy entropy (FE, approximate entropy (AE, and spectral entropy (PE, were deployed for the analysis of original EEG signal and compared by ten state-of-the-art classifiers. Results indicate that optimal performance of single channel is achieved using a combination of channel CP4, feature FE, and classifier Random Forest (RF. The highest accuracy can be up to 96.6%, which has been able to meet the needs of real applications. The best combination of channel + features + classifier is subject-specific. In this work, the accuracy of FE as the feature is far greater than the Acc of other features. The accuracy using classifier RF is the best, while that of classifier SVM with linear kernel is the worst. The impact of channel selection on the Acc is larger. The performance of various channels is very different.

Overt foot movement detection in one single Laplacian EEG derivation.

Science.gov (United States)

Solis-Escalante, Teodoro; Müller-Putz, Gernot; Pfurtscheller, Gert

2008-10-30

In this work one single Laplacian derivation and a full description of band power values in a broad frequency band are used to detect brisk foot movement execution in the ongoing EEG. Two support vector machines (SVM) are trained to detect the event-related desynchronization (ERD) during motor execution and the following beta rebound (event-related synchronization, ERS) independently. Their performance is measured through the simulation of an asynchronous brain switch. ERS (true positive rate=0.74+/-0.21) after motor execution is shown to be more stable than ERD (true positive rate=0.21+/-0.12). A novel combination of ERD and post-movement ERS is introduced. The SVM outputs are combined with a product rule to merge ERD and ERS detection. For this novel approach the average information transfer rate obtained was 11.19+/-3.61bits/min.

Using EEG/MEG Data of Cognitive Processes in Brain-Computer Interfaces

International Nuclear Information System (INIS)

Gutierrez, David

2008-01-01

Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using electroencephalographic (EEG) and, more recently, magnetoencephalographic (MEG) measurements of the brain function. Most of the current implementations of BCIs rely on EEG/MEG data of motor activities as such neural processes are well characterized, while the use of data related to cognitive activities has been neglected due to its intrinsic complexity. However, cognitive data usually has larger amplitude, lasts longer and, in some cases, cognitive brain signals are easier to control at will than motor signals. This paper briefy reviews the use of EEG/MEG data of cognitive processes in the implementation of BCIs. Specifically, this paper reviews some of the neuromechanisms, signal features, and processing methods involved. This paper also refers to some of the author's work in the area of detection and classifcation of cognitive signals for BCIs using variability enhancement, parametric modeling, and spatial fltering, as well as recent developments in BCI performance evaluation

EEG biofeedback

OpenAIRE

Dvořáček, Michael

2010-01-01

Vznik EEG aktivity v mozku, rozdělení EEG vln podle frekvence, způsob měření EEG, přístroje pro měření EEG. Dále popis biofeedback metody, její možnosti a návrh biofeedback her. Popis zpracování naměřených EEG signálů. EEG generation, brain rhythms, methods of recording EEG, EEG recorder. Description of biofeedback, potentialities of biofeedback, proposal of biofeedback games. Description of processing measured EEG signals. B

Single-center trials in neonatology: Issues to consider.

Science.gov (United States)

Sinha, Ian P; Sinha, Sunil K

2015-12-01

Single-center randomized controlled trials confer certain advantages over multi-center trials, in that they are cheaper and easier to design and conduct. However, recent research suggests that single-center trials are likely to overestimate treatment effects. There are notable examples in neonatology where results from multi-center trials have contradicted results of single-center studies. In this paper we discuss issues around external generalizability of single-center studies, and methodological issues that may cause bias. Copyright © 2015 Elsevier Ltd. All rights reserved.

Single-Trial Inference on Visual Attention

DEFF Research Database (Denmark)

Dyrholm, Mads; Kyllingsbæk, Søren; Vangkilde, Signe Allerup

2011-01-01

In this paper we take a step towards single-trial behavioral modeling within a Theory of Visual Attention (TVA). In selective attention tasks, such as the Partial Report paradigm, the subject is asked to ignore distractors and only report stimuli that belong to the target class. Nothing about...... Report trial. This result retrodicts a latent attentional state of the subject using the observed response from that particular trial and thus differs from other predictions made with TVA which are based on expected values of observed variables. We show an example of the result in single-trial analysis...

Demonstration of Cascaded In-Line Single-Pump Fiber Optical Parametric Amplifiers in Recirculating Loop Transmission

DEFF Research Database (Denmark)

Lali-Dastjerdi, Zohreh; Ozolins, Oskars; An, Yi

2012-01-01

The performance of cascaded single-pump fiber optical parametric amplifiers (FOPAs) is experimentally studied for the first time using recirculating loop transmission with 80-km dispersion managed spans. Error-free performance has been achieved over 320 km for 40-Gbit/s CSRZ-OOK and CSRZ...

Is EEG-biofeedback an effective treatment in autism spectrum disorders? A randomized controlled trial

NARCIS (Netherlands)

Kouijzer, M.E.J.; Schie, H.T. van; Gerrits, B.J.L.; Buitelaar, J.K.; Moor, J.M.H. de

2013-01-01

EEG-biofeedback has been reported to reduce symptoms of autism spectrum disorders (ASD) in several studies. However, these studies did not control for nonspecific effects of EEG-biofeedback and did not distinguish between participants who succeeded in influencing their own EEG activity and

Inflammatory and vascular placental lesions are associated with neonatal amplitude integrated EEG recording in early premature neonates.

Directory of Open Access Journals (Sweden)

Dorit Paz-Levy

Full Text Available Placental histologic examination can assist in revealing the mechanism leading to preterm birth. Accumulating evidence suggests an association between intrauterine pathological processes, morbidity and mortality of premature infants, and their long term outcome. Neonatal brain activity is increasingly monitored in neonatal intensive care units by amplitude integrated EEG (aEEG and indices of background activity and sleep cycling patterns were correlated with long term outcome. We hypothesized an association between types of placental lesions and abnormal neonatal aEEG patterns.To determine the association between the placental lesions observed in extreme preterm deliveries, and their neonatal aEEG patterns and survival.This prospective cohort study included extreme premature infants, who were born ≤ 28 weeks of gestation, their placentas were available for histologic examination, and had a continues aEEG, soon after birthn = 34. Infants and maternal clinical data were collected. aEEG data was assessed for percentage of depressed daily activity in the first 3 days of life and for sleep cycling. Associations of placental histology with clinical findings and aEEG activity were explored using parametric and non-parametric statistics.Twenty two out of the 34 newborns survived to discharge. Preterm prelabor rupture of membranes (PPROM or chorioamnionitis were associated with placental lesions consistent with fetal amniotic fluid infection (AFI or maternal under perfusion (MUP (P < 0.05. Lesions consistent with fetal response to AFI were associated with absence of SWC pattern during the 1st day of life. Fetal-vascular-thrombo-occlusive lesions of inflammatory type were negatively associated with depressed cerebral activity during the 1st day of life, and with aEEG cycling during the 2nd day of life (P<0.05. Placental lesions associated with MUP were associated with depressed neonatal cerebral activity during the first 3 days of life (P = 0

Low-noise parametric amplification at 35 GHz in a single Josephson tunnel junction

DEFF Research Database (Denmark)

Mygind, Jesper; Pedersen, Niels Falsig; Sørensen, O. H.

1979-01-01

Parametric amplification at 35 GHz has been obtained using a single Josephson tunnel junction as the active element. The amplifier was operated in the singly quasidegenerate mode with a pump frequency at 70 GHz. The noise temperature was measured and found correlated with the gain. At the highest...... gain achieved, 11.6 dB, the noise temperature was 400 K. The noise temperature was reduced considerably by decreasing the gain. At 8 and 4 dB we found 165±25 K and 50±30 K, respectively. Applied Physics Letters is copyrighted by The American Institute of Physics....

Prediction of subjective ratings of emotional pictures by EEG features

Science.gov (United States)

McFarland, Dennis J.; Parvaz, Muhammad A.; Sarnacki, William A.; Goldstein, Rita Z.; Wolpaw, Jonathan R.

2017-02-01

Objective. Emotion dysregulation is an important aspect of many psychiatric disorders. Brain-computer interface (BCI) technology could be a powerful new approach to facilitating therapeutic self-regulation of emotions. One possible BCI method would be to provide stimulus-specific feedback based on subject-specific electroencephalographic (EEG) responses to emotion-eliciting stimuli. Approach. To assess the feasibility of this approach, we studied the relationships between emotional valence/arousal and three EEG features: amplitude of alpha activity over frontal cortex; amplitude of theta activity over frontal midline cortex; and the late positive potential over central and posterior mid-line areas. For each feature, we evaluated its ability to predict emotional valence/arousal on both an individual and a group basis. Twenty healthy participants (9 men, 11 women; ages 22-68) rated each of 192 pictures from the IAPS collection in terms of valence and arousal twice (96 pictures on each of 4 d over 2 weeks). EEG was collected simultaneously and used to develop models based on canonical correlation to predict subject-specific single-trial ratings. Separate models were evaluated for the three EEG features: frontal alpha activity; frontal midline theta; and the late positive potential. In each case, these features were used to simultaneously predict both the normed ratings and the subject-specific ratings. Main results. Models using each of the three EEG features with data from individual subjects were generally successful at predicting subjective ratings on training data, but generalization to test data was less successful. Sparse models performed better than models without regularization. Significance. The results suggest that the frontal midline theta is a better candidate than frontal alpha activity or the late positive potential for use in a BCI-based paradigm designed to modify emotional reactions.

A comparative study between a simplified Kalman filter and Sliding Window Averaging for single trial dynamical estimation of event-related potentials

DEFF Research Database (Denmark)

Vedel-Larsen, Esben; Fuglø, Jacob; Channir, Fouad

2010-01-01

, are variable and depend on cognitive function. This study compares the performance of a simplified Kalman filter with Sliding Window Averaging in tracking dynamical changes in single trial P300. The comparison is performed on simulated P300 data with added background noise consisting of both simulated and real...... background EEG in various input signal to noise ratios. While both methods can be applied to track dynamical changes, the simplified Kalman filter has an advantage over the Sliding Window Averaging, most notable in a better noise suppression when both are optimized for faster changing latency and amplitude...

Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task

Directory of Open Access Journals (Sweden)

Lorraine Perronnet

2017-04-01

Full Text Available Neurofeedback is a promising tool for brain rehabilitation and peak performance training. Neurofeedback approaches usually rely on a single brain imaging modality such as EEG or fMRI. Combining these modalities for neurofeedback training could allow to provide richer information to the subject and could thus enable him/her to achieve faster and more specific self-regulation. Yet unimodal and multimodal neurofeedback have never been compared before. In the present work, we introduce a simultaneous EEG-fMRI experimental protocol in which participants performed a motor-imagery task in unimodal and bimodal NF conditions. With this protocol we were able to compare for the first time the effects of unimodal EEG-neurofeedback and fMRI-neurofeedback versus bimodal EEG-fMRI-neurofeedback by looking both at EEG and fMRI activations. We also propose a new feedback metaphor for bimodal EEG-fMRI-neurofeedback that integrates both EEG and fMRI signal in a single bi-dimensional feedback (a ball moving in 2D. Such a feedback is intended to relieve the cognitive load of the subject by presenting the bimodal neurofeedback task as a single regulation task instead of two. Additionally, this integrated feedback metaphor gives flexibility on defining a bimodal neurofeedback target. Participants were able to regulate activity in their motor regions in all NF conditions. Moreover, motor activations as revealed by offline fMRI analysis were stronger during EEG-fMRI-neurofeedback than during EEG-neurofeedback. This result suggests that EEG-fMRI-neurofeedback could be more specific or more engaging than EEG-neurofeedback. Our results also suggest that during EEG-fMRI-neurofeedback, participants tended to regulate more the modality that was harder to control. Taken together our results shed first light on the specific mechanisms of bimodal EEG-fMRI-neurofeedback and on its added-value as compared to unimodal EEG-neurofeedback and fMRI-neurofeedback.

Singular spectrum analysis of sleep EEG in insomnia.

Science.gov (United States)

Aydın, Serap; Saraoǧlu, Hamdi Melih; Kara, Sadık

2011-08-01

In the present study, the Singular Spectrum Analysis (SSA) is applied to sleep EEG segments collected from healthy volunteers and patients diagnosed by either psycho physiological insomnia or paradoxical insomnia. Then, the resulting singular spectra computed for both C3 and C4 recordings are assigned as the features to the Artificial Neural Network (ANN) architectures for EEG classification in diagnose. In tests, singular spectrum of particular sleep stages such as awake, REM, stage1 and stage2, are considered. Three clinical groups are successfully classified by using one hidden layer ANN architecture with respect to their singular spectra. The results show that the SSA can be applied to sleep EEG series to support the clinical findings in insomnia if ten trials are available for the specific sleep stages. In conclusion, the SSA can detect the oscillatory variations on sleep EEG. Therefore, different sleep stages meet different singular spectra. In addition, different healthy conditions generate different singular spectra for each sleep stage. In summary, the SSA can be proposed for EEG discrimination to support the clinical findings for psycho-psychological disorders.

Standardized EEG interpretation accurately predicts prognosis after cardiac arrest

NARCIS (Netherlands)

Westhall, Erik; Rossetti, Andrea O.; van Rootselaar, Anne-Fleur; Wesenberg Kjaer, Troels; Horn, Janneke; Ullén, Susann; Friberg, Hans; Nielsen, Niklas; Rosén, Ingmar; Åneman, Anders; Erlinge, David; Gasche, Yvan; Hassager, Christian; Hovdenes, Jan; Kjaergaard, Jesper; Kuiper, Michael; Pellis, Tommaso; Stammet, Pascal; Wanscher, Michael; Wetterslev, Jørn; Wise, Matt P.; Cronberg, Tobias; Saxena, Manoj; Miller, Jennene; Inskip, Deborah; Macken, Lewis; Finfer, Simon; Eatough, Noel; Hammond, Naomi; Bass, Frances; Yarad, Elizabeth; O'Connor, Anne; Bird, Simon; Jewell, Timothy; Davies, Gareth; Ng, Karl; Coward, Sharon; Stewart, Antony; Micallef, Sharon; Parker, Sharyn; Cortado, Dennis; Gould, Ann; Harward, Meg; Thompson, Kelly; Glass, Parisa; Myburgh, John; Smid, Ondrej; Belholavek, Jan; Juffermans, Nicole P.; Boerma, EC

2016-01-01

To identify reliable predictors of outcome in comatose patients after cardiac arrest using a single routine EEG and standardized interpretation according to the terminology proposed by the American Clinical Neurophysiology Society. In this cohort study, 4 EEG specialists, blinded to outcome,

A novel approach for computer assisted EEG monitoring in the adult ICU

NARCIS (Netherlands)

Cloostermans, M.C.; de Vos, Cecilia Cecilia Clementine; van Putten, Michel Johannes Antonius Maria

2011-01-01

Objective The implementation of a computer assisted system for real-time classification of the electroencephalogram (EEG) in critically ill patients. Methods Eight quantitative features were extracted from the raw EEG and combined into a single classifier. The system was trained with 41 EEG

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.

Diagnostic Accuracy of microEEG: A Miniature, Wireless EEG Device

OpenAIRE

Grant, Arthur C.; Abdel-Baki, Samah G.; Omurtag, Ahmet; Sinert, Richard; Chari, Geetha; Malhotra, Schweta; Weedon, Jeremy; Fenton, Andre A.; Zehtabchi, Shahriar

2014-01-01

Measuring the diagnostic accuracy (DA) of an EEG device is unconventional and complicated by imperfect interrater reliability. We sought to compare the DA of a miniature, wireless, battery-powered EEG device (“microEEG”) to a reference EEG machine in emergency department (ED) patients with altered mental status (AMS). 225 ED patients with AMS underwent 3 EEGs. EEG1 (Nicolet Monitor, “reference”) and EEG2 (microEEG) were recorded simultaneously with EEG cup electrodes using a signal splitter. ...

EEG activity in Muslim prayer: A pilot study

Directory of Open Access Journals (Sweden)

Haider H. Alwasiti

2010-12-01

Full Text Available Almost all religions incorporate some form of meditation. Muslim prayer is the meditation of Islam. It is an obligatory prayer for all Muslims that is performed five times a day. Although a large body of literature exists on EEG changes in meditation, to date there has been no research published in a peer-reviewed journal on EEG changes during Muslim prayer. The purpose of this pilot study is to encourage further investigation on this type of meditation. Results of EEG analysis in twenty-five trials of Muslim prayer are reported. Some of the findings are consistent with the majority of the previous meditation studies (alpha rhythm slowing, increased alpha rhythm coherence. However, Muslim prayer does not show an increase in alpha and/or theta power like most of the results of other meditation studies. The possible cause of this discrepancy in meditation-related studies is highlighted and a systematic and standardised roadmap for future Muslim prayer EEG research is proposed.

Investigation of mental fatigue through EEG signal processing based on nonlinear analysis: Symbolic dynamics

International Nuclear Information System (INIS)

Azarnoosh, Mahdi; Motie Nasrabadi, Ali; Mohammadi, Mohammad Reza; Firoozabadi, Mohammad

2011-01-01

Highlights: Mental fatigue indices’ variation discussed during simple long-term attentive task. Symbolic dynamics of reaction time and EEG signal determine mental state variation. Nonlinear quantifiers such as entropy can display chaotic behaviors of the brain. Frontal and central lobes of the brain are effective in attention investigations. Mental fatigue causes a reduction in the complexity of the brain’s activity. Abstract: To investigate nonlinear analysis of attention physiological indices this study used a simple repetitive attentive task in four consecutive trials that resulted in mental fatigue. Traditional performance indices, such as reaction time, error responses, and EEG signals, were simultaneously recorded to evaluate differences between the trials. Performance indices analysis demonstrated that a selected task leads to mental fatigue. In addition, the study aimed to find a method to determine mental fatigue based on nonlinear analysis of EEG signals. Symbolic dynamics was selected as a qualitative method used to extract some quantitative qualifiers such as entropy. This method was executed on the reaction time of responses, and EEG signals to distinguish mental states. The results revealed that nonlinear analysis of reaction time, and EEG signals of the frontal and central lobes of the brain could differentiate between attention, and occurrence of mental fatigue in trials. In addition, the trend of entropy variation displayed a reduction in the complexity of mental activity as fatigue occurred.

Comparative analysis of MR imaging, Ictal SPECT and EEG in temporal lobe epilepsy: a prospective IAEA multi-center study

Energy Technology Data Exchange (ETDEWEB)

Zaknun, John J. [University Hospital of Innsbruck, Department of Nuclear Medicine, Innsbruck (Austria); International Atomic Energy Agency (IAEA), Nuclear Medicine Section, Division of Human Health, Vienna (Austria); IAEA, Nuclear Medicine Section, Division of Human Health, Wagramer Strasse 5, P.O. Box 100, Wien (Austria); Bal, Chandrasekhar [All India Institute of Medical Sciences, Department of Nuclear Medicine, New Delhi (India); Maes, Alex [Katholieke Universiteit Leuven, Leuven (Belgium); AZ Groeninge, Department of Nuclear Medicine, Kortrijk (Belgium); Tepmongkol, Supatporn [Chulalongkorn University, Nuclear Medicine Division, Department of Radiology, Bangkok (Thailand); Vazquez, Silvia [Instituto de Investigaciones Neurologicas, FLENI, Department of Radiology, Buenos Aires (Argentina); Dupont, Patrick [Katholieke Universiteit Leuven, Leuven (Belgium); Dondi, Maurizio [Ospedale Maggiore, Department of Nuclear Medicine, Bologna (Italy); International Atomic Energy Agency (IAEA), Nuclear Medicine Section, Division of Human Health, Vienna (Austria)

2008-01-15

MR imaging, ictal single-photon emission CT (SPECT) and ictal EEG play important roles in the presurgical localization of epileptic foci. This multi-center study was established to investigate whether the complementary role of perfusion SPECT, MRI and EEG for presurgical localization of temporal lobe epilepsy could be confirmed in a prospective setting involving centers from India, Thailand, Italy and Argentina. We studied 74 patients who underwent interictal and ictal EEG, interictal and ictal SPECT and MRI before surgery of the temporal lobe. In all but three patients, histology was reported. The clinical outcome was assessed using Engel's classification. Sensitivity values of all imaging modalities were calculated, and the add-on value of SPECT was assessed. Outcome (Engel's classification) in 74 patients was class I, 89%; class II, 7%; class III, 3%; and IV, 1%. Regarding the localization of seizure origin, sensitivity was 84% for ictal SPECT, 70% for ictal EEG, 86% for MRI, 55% for interictal SPECT and 40% for interictal EEG. Add-on value of ictal SPECT was shown by its ability to correctly localize 17/22 (77%) of the seizure foci missed by ictal EEG and 8/10 (80%) of the seizure foci not detected by MRI. This prospective multi-center trial, involving centers from different parts of the world, confirms that ictal perfusion SPECT is an effective diagnostic modality for correctly identifying seizure origin in temporal lobe epilepsy, providing complementary information to ictal EEG and MRI. (orig.)

Comparative analysis of MR imaging, Ictal SPECT and EEG in temporal lobe epilepsy: a prospective IAEA multi-center study

International Nuclear Information System (INIS)

Zaknun, John J.; Bal, Chandrasekhar; Maes, Alex; Tepmongkol, Supatporn; Vazquez, Silvia; Dupont, Patrick; Dondi, Maurizio

2008-01-01

MR imaging, ictal single-photon emission CT (SPECT) and ictal EEG play important roles in the presurgical localization of epileptic foci. This multi-center study was established to investigate whether the complementary role of perfusion SPECT, MRI and EEG for presurgical localization of temporal lobe epilepsy could be confirmed in a prospective setting involving centers from India, Thailand, Italy and Argentina. We studied 74 patients who underwent interictal and ictal EEG, interictal and ictal SPECT and MRI before surgery of the temporal lobe. In all but three patients, histology was reported. The clinical outcome was assessed using Engel's classification. Sensitivity values of all imaging modalities were calculated, and the add-on value of SPECT was assessed. Outcome (Engel's classification) in 74 patients was class I, 89%; class II, 7%; class III, 3%; and IV, 1%. Regarding the localization of seizure origin, sensitivity was 84% for ictal SPECT, 70% for ictal EEG, 86% for MRI, 55% for interictal SPECT and 40% for interictal EEG. Add-on value of ictal SPECT was shown by its ability to correctly localize 17/22 (77%) of the seizure foci missed by ictal EEG and 8/10 (80%) of the seizure foci not detected by MRI. This prospective multi-center trial, involving centers from different parts of the world, confirms that ictal perfusion SPECT is an effective diagnostic modality for correctly identifying seizure origin in temporal lobe epilepsy, providing complementary information to ictal EEG and MRI. (orig.)

Physiological responses at short distances from a parametric speaker

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Lee Soomin

2012-06-01

Full Text Available Abstract In recent years, parametric speakers have been used in various circumstances. In our previous studies, we verified that the physiological burden of the sound of parametric speaker set at 2.6 m from the subjects was lower than that of the general speaker. However, nothing has yet been demonstrated about the effects of the sound of a parametric speaker at the shorter distance between parametric speakers the human body. Therefore, we studied this effect on physiological functions and task performance. Nine male subjects participated in this study. They completed three consecutive sessions: a 20-minute quiet period as a baseline, a 30-minute mental task period with general speakers or parametric speakers, and a 20-minute recovery period. We measured electrocardiogram (ECG photoplethysmogram (PTG, electroencephalogram (EEG, systolic and diastolic blood pressure. Four experiments, one with a speaker condition (general speaker and parametric speaker, the other with a distance condition (0.3 m and 1.0 m, were conducted respectively at the same time of day on separate days. To examine the effects of the speaker and distance, three-way repeated measures ANOVA (speaker factor x distance factor x time factor were conducted. In conclusion, we found that the physiological responses were not significantly different between the speaker condition and the distance condition. Meanwhile, it was shown that the physiological burdens increased with progress in time independently of speaker condition and distance condition. In summary, the effects of the parametric speaker at the 2.6 m distance were not obtained at the distance of 1 m or less.

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

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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.

EEG Frequency Changes Prior to Making Errors in an Easy Stroop Task

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Rachel Atchley

2017-10-01

Full Text Available Background: Mind-wandering is a form of off-task attention that has been associated with negative affect and rumination. The goal of this study was to assess potential electroencephalographic markers of task-unrelated thought, or mind-wandering state, as related to error rates during a specialized cognitive task. We used EEG to record frontal frequency band activity while participants completed a Stroop task that was modified to induce boredom, task-unrelated thought, and therefore mind-wandering.Methods: A convenience sample of 27 older adults (50–80 years completed a computerized Stroop matching task. Half of the Stroop trials were congruent (word/color match, and the other half were incongruent (mismatched. Behavioral data and EEG recordings were assessed. EEG analysis focused on the 1-s epochs prior to stimulus presentation in order to compare trials followed by correct versus incorrect responses.Results: Participants made errors on 9% of incongruent trials. There were no errors on congruent trials. There was a decrease in alpha and theta band activity during the epochs followed by error responses.Conclusion: Although replication of these results is necessary, these findings suggest that potential mind-wandering, as evidenced by errors, can be characterized by a decrease in alpha and theta activity compared to on-task, accurate performance periods.

Rapid computation of single PET scan rest-stress myocardial blood flow parametric images by table look up.

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Guehl, Nicolas J; Normandin, Marc D; Wooten, Dustin W; Rozen, Guy; Ruskin, Jeremy N; Shoup, Timothy M; Woo, Jonghye; Ptaszek, Leon M; Fakhri, Georges El; Alpert, Nathaniel M

2017-09-01

We have recently reported a method for measuring rest-stress myocardial blood flow (MBF) using a single, relatively short, PET scan session. The method requires two IV tracer injections, one to initiate rest imaging and one at peak stress. We previously validated absolute flow quantitation in ml/min/cc for standard bull's eye, segmental analysis. In this work, we extend the method for fast computation of rest-stress MBF parametric images. We provide an analytic solution to the single-scan rest-stress flow model which is then solved using a two-dimensional table lookup method (LM). Simulations were performed to compare the accuracy and precision of the lookup method with the original nonlinear method (NLM). Then the method was applied to 16 single scan rest/stress measurements made in 12 pigs: seven studied after infarction of the left anterior descending artery (LAD) territory, and nine imaged in the native state. Parametric maps of rest and stress MBF as well as maps of left (f LV ) and right (f RV ) ventricular spill-over fractions were generated. Regions of interest (ROIs) for 17 myocardial segments were defined in bull's eye fashion on the parametric maps. The mean of each ROI was then compared to the rest (K 1r ) and stress (K 1s ) MBF estimates obtained from fitting the 17 regional TACs with the NLM. In simulation, the LM performed as well as the NLM in terms of precision and accuracy. The simulation did not show that bias was introduced by the use of a predefined two-dimensional lookup table. In experimental data, parametric maps demonstrated good statistical quality and the LM was computationally much more efficient than the original NLM. Very good agreement was obtained between the mean MBF calculated on the parametric maps for each of the 17 ROIs and the regional MBF values estimated by the NLM (K 1map LM  = 1.019 × K 1 ROI NLM  + 0.019, R 2  = 0.986; mean difference = 0.034 ± 0.036 mL/min/cc). We developed a table lookup method for fast

EEG source imaging during two Qigong meditations.

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Faber, Pascal L; Lehmann, Dietrich; Tei, Shisei; Tsujiuchi, Takuya; Kumano, Hiroaki; Pascual-Marqui, Roberto D; Kochi, Kieko

2012-08-01

Experienced Qigong meditators who regularly perform the exercises "Thinking of Nothing" and "Qigong" were studied with multichannel EEG source imaging during their meditations. The intracerebral localization of brain electric activity during the two meditation conditions was compared using sLORETA functional EEG tomography. Differences between conditions were assessed using t statistics (corrected for multiple testing) on the normalized and log-transformed current density values of the sLORETA images. In the EEG alpha-2 frequency, 125 voxels differed significantly; all were more active during "Qigong" than "Thinking of Nothing," forming a single cluster in parietal Brodmann areas 5, 7, 31, and 40, all in the right hemisphere. In the EEG beta-1 frequency, 37 voxels differed significantly; all were more active during "Thinking of Nothing" than "Qigong," forming a single cluster in prefrontal Brodmann areas 6, 8, and 9, all in the left hemisphere. Compared to combined initial-final no-task resting, "Qigong" showed activation in posterior areas whereas "Thinking of Nothing" showed activation in anterior areas. The stronger activity of posterior (right) parietal areas during "Qigong" and anterior (left) prefrontal areas during "Thinking of Nothing" may reflect a predominance of self-reference, attention and input-centered processing in the "Qigong" meditation, and of control-centered processing in the "Thinking of Nothing" meditation.

A generalized Jaynes-Cummings model: The relativistic parametric amplifier and a single trapped ion

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Ojeda-Guillén, D., E-mail: dojedag@ipn.mx [Escuela Superior de Cómputo, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz esq. Av. Miguel Othón de Mendizábal, Col. Lindavista, Delegación Gustavo A. Madero, C.P. 07738 Ciudad de México (Mexico); Mota, R. D. [Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Culhuacán, Instituto Politécnico Nacional, Av. Santa Ana No. 1000, Col. San Francisco Culhuacán, Delegación Coyoacán, C.P. 04430 Ciudad de México (Mexico); Granados, V. D. [Escuela Superior de Física y Matemáticas, Instituto Politécnico Nacional, Ed. 9, Unidad Profesional Adolfo López Mateos, Delegación Gustavo A. Madero, C.P. 07738 Ciudad de México (Mexico)

2016-06-15

We introduce a generalization of the Jaynes-Cummings model and study some of its properties. We obtain the energy spectrum and eigenfunctions of this model by using the tilting transformation and the squeezed number states of the one-dimensional harmonic oscillator. As physical applications, we connect this new model to two important and novelty problems: the relativistic parametric amplifier and the quantum simulation of a single trapped ion.

One-Class FMRI-Inspired EEG Model for Self-Regulation Training.

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Yehudit Meir-Hasson

Full Text Available Recent evidence suggests that learned self-regulation of localized brain activity in deep limbic areas such as the amygdala, may alleviate symptoms of affective disturbances. Thus far self-regulation of amygdala activity could be obtained only via fMRI guided neurofeedback, an expensive and immobile procedure. EEG on the other hand is relatively inexpensive and can be easily implemented in any location. However the clinical utility of EEG neurofeedback for affective disturbances remains limited due to low spatial resolution, which hampers the targeting of deep limbic areas such as the amygdala. We introduce an EEG prediction model of amygdala activity from a single electrode. The gold standard used for training is the fMRI-BOLD signal in the amygdala during simultaneous EEG/fMRI recording. The suggested model is based on a time/frequency representation of the EEG data with varying time-delay. Previous work has shown a strong inhomogeneity among subjects as is reflected by the models created to predict the amygdala BOLD response from EEG data. In that work, different models were constructed for different subjects. In this work, we carefully analyzed the inhomogeneity among subjects and were able to construct a single model for the majority of the subjects. We introduce a method for inhomogeneity assessment. This enables us to demonstrate a choice of subjects for which a single model could be derived. We further demonstrate the ability to modulate brain-activity in a neurofeedback setting using feedback generated by the model. We tested the effect of the neurofeedback training by showing that new subjects can learn to down-regulate the signal amplitude compared to a sham group, which received a feedback obtained by a different participant. This EEG based model can overcome substantial limitations of fMRI-NF. It can enable investigation of NF training using multiple sessions and large samples in various locations.

Multivariate matching pursuit in optimal Gabor dictionaries: theory and software with interface for EEG/MEG via Svarog

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2013-01-01

Background Matching pursuit algorithm (MP), especially with recent multivariate extensions, offers unique advantages in analysis of EEG and MEG. Methods We propose a novel construction of an optimal Gabor dictionary, based upon the metrics introduced in this paper. We implement this construction in a freely available software for MP decomposition of multivariate time series, with a user friendly interface via the Svarog package (Signal Viewer, Analyzer and Recorder On GPL, http://braintech.pl/svarog), and provide a hands-on introduction to its application to EEG. Finally, we describe numerical and mathematical optimizations used in this implementation. Results Optimal Gabor dictionaries, based on the metric introduced in this paper, for the first time allowed for a priori assessment of maximum one-step error of the MP algorithm. Variants of multivariate MP, implemented in the accompanying software, are organized according to the mathematical properties of the algorithms, relevant in the light of EEG/MEG analysis. Some of these variants have been successfully applied to both multichannel and multitrial EEG and MEG in previous studies, improving preprocessing for EEG/MEG inverse solutions and parameterization of evoked potentials in single trials; we mention also ongoing work and possible novel applications. Conclusions Mathematical results presented in this paper improve our understanding of the basics of the MP algorithm. Simple introduction of its properties and advantages, together with the accompanying stable and user-friendly Open Source software package, pave the way for a widespread and reproducible analysis of multivariate EEG and MEG time series and novel applications, while retaining a high degree of compatibility with the traditional, visual analysis of EEG. PMID:24059247

Similar or disparate brain patterns? The intra-personal EEG variability of three women with multiple personality disorder.

Science.gov (United States)

Lapointe, A R; Crayton, J W; DeVito, R; Fichtner, C G; Konopka, L M

2006-07-01

Quantitative EEG was used to assess the intra-personal variability of brain electrical activity for 3 women diagnosed with Multiple Personality Disorder (MPD). Two separate control groups (within-subject and between-subject) were used to test the hypothesis that the intra-personal EEG variability between 2 alters would be less than the interpersonal EEG variability between 2 controls, and similar to the intra-personal EEG variability of a single personality. This hypothesis was partially supported. In general, the 2 EEG records of a MPD subject (alter 1 vs. alter 2) were more different from one another than the 2 EEG records of a single control, but less different from one another than the EEG records of 2 separate controls. Most of the EEG variability between alters involved beta activity in the frontal and temporal lobes.

Independent EEG sources are dipolar.

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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.

Multi-Tasking and Choice of Training Data Influencing Parietal ERP Expression and Single-Trial Detection—Relevance for Neuroscience and Clinical Applications

Science.gov (United States)

Kirchner, Elsa A.; Kim, Su Kyoung

2018-01-01

Event-related potentials (ERPs) are often used in brain-computer interfaces (BCIs) for communication or system control for enhancing or regaining control for motor-disabled persons. Especially results from single-trial EEG classification approaches for BCIs support correlations between single-trial ERP detection performance and ERP expression. Hence, BCIs can be considered as a paradigm shift contributing to new methods with strong influence on both neuroscience and clinical applications. Here, we investigate the relevance of the choice of training data and classifier transfer for the interpretability of results from single-trial ERP detection. In our experiments, subjects performed a visual-motor oddball task with motor-task relevant infrequent (targets), motor-task irrelevant infrequent (deviants), and motor-task irrelevant frequent (standards) stimuli. Under dual-task condition, a secondary senso-motor task was performed, compared to the simple-task condition. For evaluation, average ERP analysis and single-trial detection analysis with different numbers of electrodes were performed. Further, classifier transfer was investigated between simple and dual task. Parietal positive ERPs evoked by target stimuli (but not by deviants) were expressed stronger under dual-task condition, which is discussed as an increase of task emphasis and brain processes involved in task coordination and change of task set. Highest classification performance was found for targets irrespective whether all 62, 6 or 2 parietal electrodes were used. Further, higher detection performance of targets compared to standards was achieved under dual-task compared to simple-task condition in case of training on data from 2 parietal electrodes corresponding to results of ERP average analysis. Classifier transfer between tasks improves classification performance in case that training took place on more varying examples (from dual task). In summary, we showed that P300 and overlaying parietal positive

A randomized placebo-controlled trial of electroencephalographic (EEG) neurofeedback in children with attention-deficit/hyperactivity disorder.

Science.gov (United States)

van Dongen-Boomsma, Martine; Vollebregt, Madelon A; Slaats-Willemse, Dorine; Buitelaar, Jan K

2013-08-01

A double-blind, randomized, placebo-controlled study was designed to assess the efficacy and safety of electroencephalographic (EEG) neurofeedback in children with attention-deficit/hyperactivity disorder (ADHD). The study started in August 2008 and ended in July 2012 and was conducted at Karakter Child and Adolescent Psychiatry University Centre in Nijmegen, The Netherlands. Forty-one children (aged 8-15 years) with a DSM-IV-TR diagnosis of ADHD were randomly assigned to treatment with either EEG neurofeedback (n = 22) or placebo neurofeedback (n = 19) for 30 sessions, given as 2 sessions per week. The children were stratified by age, electrophysiologic state of arousal, and medication use. Everyone involved in the study, except the neurofeedback therapist and the principal investigator, was blinded to treatment assignment. The primary outcome was severity of ADHD symptoms on the ADHD Rating Scale IV, scored at baseline, during treatment, and at study end. Clinical improvement as measured by the Clinical Global Impressions-Improvement scale (CGI-I) was a secondary outcome. While total ADHD symptoms improved over time in both groups (F1,39 = 26.56, P neurofeedback was not superior to placebo neurofeedback in improving ADHD symptoms in children with ADHD. ClinicalTrials.gov identifier: NCT00723684. © Copyright 2013 Physicians Postgraduate Press, Inc.

Bayesian model selection of template forward models for EEG source reconstruction.

Science.gov (United States)

Strobbe, Gregor; van Mierlo, Pieter; De Vos, Maarten; Mijović, Bogdan; Hallez, Hans; Van Huffel, Sabine; López, José David; Vandenberghe, Stefaan

2014-06-01

Several EEG source reconstruction techniques have been proposed to identify the generating neuronal sources of electrical activity measured on the scalp. The solution of these techniques depends directly on the accuracy of the forward model that is inverted. Recently, a parametric empirical Bayesian (PEB) framework for distributed source reconstruction in EEG/MEG was introduced and implemented in the Statistical Parametric Mapping (SPM) software. The framework allows us to compare different forward modeling approaches, using real data, instead of using more traditional simulated data from an assumed true forward model. In the absence of a subject specific MR image, a 3-layered boundary element method (BEM) template head model is currently used including a scalp, skull and brain compartment. In this study, we introduced volumetric template head models based on the finite difference method (FDM). We constructed a FDM head model equivalent to the BEM model and an extended FDM model including CSF. These models were compared within the context of three different types of source priors related to the type of inversion used in the PEB framework: independent and identically distributed (IID) sources, equivalent to classical minimum norm approaches, coherence (COH) priors similar to methods such as LORETA, and multiple sparse priors (MSP). The resulting models were compared based on ERP data of 20 subjects using Bayesian model selection for group studies. The reconstructed activity was also compared with the findings of previous studies using functional magnetic resonance imaging. We found very strong evidence in favor of the extended FDM head model with CSF and assuming MSP. These results suggest that the use of realistic volumetric forward models can improve PEB EEG source reconstruction. Copyright © 2014 Elsevier Inc. All rights reserved.

Brain single-photon emission tomography with 99mTc-HMPAO in neuropsychiatric systemic lupus erythematosus: relations with EEG and MRI findings and clinical manifestations

International Nuclear Information System (INIS)

Colamussi, P.; Giganti, M.; Cittanti, C.; Dovigo, L.; Trotta, F.; Tola, M.R.; Tamarozzi, R.; Lucignani, G.; Piffanelli, A.

1995-01-01

In the reported study the role of single-photon emission tomography (SPET) with technetium-99m hexamethylpropylene amine oxime (HMPAO) in the evaluation of CNS involvement in SLE was assessed and the relations between SPET perfusion defects, EEG examination, magnetic resonance imaging (MRI) findings and clinical presentation were examined. Twenty SLE patients with different NP manifestations were studied. Multiple areas of hypoperfusion, especially in the territory of the middle cerebral artery, were demonstrated by SPET analysis in all 20 patients. The number of hypoperfused areas and the degree of hypoperfusion, expressed by an asymmetry index (AI), were more marked in patients with multiple NP manifestations. MRI and EEG evaluations were positive for 14 of 18 and for 12 of 20 patients, respectively. In the patients with positive SPET and MRI, 87 MRI focal lesions and 63 hypoperfused areas were found, and for 51 of these 63 at least one MRI lesion was found in the same anatomical region. SPET examination of patients with a normal EEG showed fewer hypoperfused areas and a lower degree of asymmetry compared to patients with an abnormal EEG. SPET of patients with focal EEG abnormalities showed more hypoperfused areas (difference not statistically significant) and a higher AI than did SPET of the patients with diffuse EEG abnormalities. Seven of 11 anatomical regions with focal EEG abnormalities. Seven of 11 anatomical regions with focal EEG abnormalities had co-localized hypoperfused areas and in two of these seven no detectable MRI lesions were found. The analysis of SPET and NP manifestations showed that 12 of 20 patients had at least one positive correlation, always involving the areas with the highest AI. In total, 51/88 (58%) hypoperfused areas correlated with the MRI findings and 31/88 (35%) with NP manifestations; for seven of the latter no concurrent MRI lesions were detected in the same anatomical region. (orig.)

Photogrammetry-Based Head Digitization for Rapid and Accurate Localization of EEG Electrodes and MEG Fiducial Markers Using a Single Digital SLR Camera.

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Clausner, Tommy; Dalal, Sarang S; Crespo-García, Maité

2017-01-01

The performance of EEG source reconstruction has benefited from the increasing use of advanced head modeling techniques that take advantage of MRI together with the precise positions of the recording electrodes. The prevailing technique for registering EEG electrode coordinates involves electromagnetic digitization. However, the procedure adds several minutes to experiment preparation and typical digitizers may not be accurate enough for optimal source reconstruction performance (Dalal et al., 2014). Here, we present a rapid, accurate, and cost-effective alternative method to register EEG electrode positions, using a single digital SLR camera, photogrammetry software, and computer vision techniques implemented in our open-source toolbox, janus3D . Our approach uses photogrammetry to construct 3D models from multiple photographs of the participant's head wearing the EEG electrode cap. Electrodes are detected automatically or semi-automatically using a template. The rigid facial features from these photo-based models are then surface-matched to MRI-based head reconstructions to facilitate coregistration to MRI space. This method yields a final electrode coregistration error of 0.8 mm, while a standard technique using an electromagnetic digitizer yielded an error of 6.1 mm. The technique furthermore reduces preparation time, and could be extended to a multi-camera array, which would make the procedure virtually instantaneous. In addition to EEG, the technique could likewise capture the position of the fiducial markers used in magnetoencephalography systems to register head position.

Measuring the Plasticity of Social Approach: A Randomized Controlled Trial of the Effects of the PEERS Intervention on EEG Asymmetry in Adolescents with Autism Spectrum Disorders

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Van Hecke, Amy Vaughan; Stevens, Sheryl; Carson, Audrey M.; Karst, Jeffrey S.; Dolan, Bridget; Schohl, Kirsten; McKindles, Ryan J.; Remmel, Rheanna; Brockman, Scott

2015-01-01

This study examined whether the Program for the Education and Enrichment of Relational Skills ("PEERS: Social skills for teenagers with developmental and autism spectrum disorders: The PEERS treatment manual," Routledge, New York, 2010a) affected neural function, via EEG asymmetry, in a randomized controlled trial of adolescents with…

Analytic sensing for multi-layer spherical models with application to EEG source imaging

OpenAIRE

Kandaswamy, Djano; Blu, Thierry; Van De Ville, Dimitri

2013-01-01

Source imaging maps back boundary measurements to underlying generators within the domain; e. g., retrieving the parameters of the generating dipoles from electrical potential measurements on the scalp such as in electroencephalography (EEG). Fitting such a parametric source model is non-linear in the positions of the sources and renewed interest in mathematical imaging has led to several promising approaches. One important step in these methods is the application of a sensing principle that ...

Neural Signatures of Rational and Heuristic Choice Strategies: A Single Trial ERP Analysis

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Szymon Wichary

2017-08-01

Full Text Available In multi-attribute choice, people use heuristics to simplify decision problems. We studied the use of heuristic and rational strategies and their electrophysiological correlates. Since previous work linked the P3 ERP component to attention and decision making, we were interested whether the amplitude of this component is associated with decision strategy use. To this end, we recorded EEG when participants performed a two-alternative choice task, where they could acquire decision cues in a sequential manner and use them to make choices. We classified participants’ choices as consistent with a rational Weighted Additive rule (WADD or a simple heuristic Take The Best (TTB. Participants differed in their preference for WADD and TTB. Using a permutation-based single trial approach, we analyzed EEG responses to consecutive decision cues and their relation to the individual strategy preference. The preference for WADD over TTB was associated with overall higher signal amplitudes to decision cues in the P3 time window. Moreover, the preference for WADD was associated with similar P3 amplitudes to consecutive cues, whereas the preference for TTB was associated with substantial decreases in P3 amplitudes to consecutive cues. We also found that the preference for TTB was associated with enhanced N1 component to cues that discriminated decision alternatives, suggesting very early attention allocation to such cues by TTB users. Our results suggest that preference for either WADD or TTB has an early neural signature reflecting differences in attentional weighting of decision cues. In light of recent findings and hypotheses regarding P3, we interpret these results as indicating the involvement of catecholamine arousal systems in shaping predecisional information processing and strategy selection.

Neural Signatures of Rational and Heuristic Choice Strategies: A Single Trial ERP Analysis.

Science.gov (United States)

Wichary, Szymon; Magnuski, Mikołaj; Oleksy, Tomasz; Brzezicka, Aneta

2017-01-01

In multi-attribute choice, people use heuristics to simplify decision problems. We studied the use of heuristic and rational strategies and their electrophysiological correlates. Since previous work linked the P3 ERP component to attention and decision making, we were interested whether the amplitude of this component is associated with decision strategy use. To this end, we recorded EEG when participants performed a two-alternative choice task, where they could acquire decision cues in a sequential manner and use them to make choices. We classified participants' choices as consistent with a rational Weighted Additive rule (WADD) or a simple heuristic Take The Best (TTB). Participants differed in their preference for WADD and TTB. Using a permutation-based single trial approach, we analyzed EEG responses to consecutive decision cues and their relation to the individual strategy preference. The preference for WADD over TTB was associated with overall higher signal amplitudes to decision cues in the P3 time window. Moreover, the preference for WADD was associated with similar P3 amplitudes to consecutive cues, whereas the preference for TTB was associated with substantial decreases in P3 amplitudes to consecutive cues. We also found that the preference for TTB was associated with enhanced N1 component to cues that discriminated decision alternatives, suggesting very early attention allocation to such cues by TTB users. Our results suggest that preference for either WADD or TTB has an early neural signature reflecting differences in attentional weighting of decision cues. In light of recent findings and hypotheses regarding P3, we interpret these results as indicating the involvement of catecholamine arousal systems in shaping predecisional information processing and strategy selection.

EEG Derived Neuronal Dynamics during Meditation: Progress and Challenges

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Chamandeep Kaur

2015-01-01

Full Text Available Meditation advances positivity but how these behavioral and psychological changes are brought can be explained by understanding neurophysiological effects of meditation. In this paper, a broad spectrum of neural mechanics under a variety of meditation styles has been reviewed. The overall aim of this study is to review existing scientific studies and future challenges on meditation effects based on changing EEG brainwave patterns. Albeit the existing researches evidenced the hold for efficacy of meditation in relieving anxiety and depression and producing psychological well-being, more rigorous studies are required with better design, considering client variables like personality characteristics to avoid negative effects, randomized controlled trials, and large sample sizes. A bigger number of clinical trials that concentrate on the use of meditation are required. Also, the controversial subject of epileptiform EEG changes and other adverse effects during meditation has been raised.

Brain single-photon emission tomography with {sup 99m}Tc-HMPAO in neuropsychiatric systemic lupus erythematosus: relations with EEG and MRI findings and clinical manifestations

Energy Technology Data Exchange (ETDEWEB)

Colamussi, P. [Dept. of Nuclear Medicine, Univ. of Ferrara (Italy); Giganti, M. [Dept. of Nuclear Medicine, Univ. of Ferrara (Italy); Cittanti, C. [Dept. of Nuclear Medicine, Univ. of Ferrara (Italy); Dovigo, L. [Inst. of Neurology, Univ. of Ferrara (Italy); Trotta, F. [Inst. of Neurology, Univ. of Ferrara (Italy); Tola, M.R. [Div. of Rheumatology, S. Anna Hospital, Ferrara (Italy); Tamarozzi, R. [Radiology Dept., S. Anna Hospital, Ferrara (Italy); Lucignani, G. [INB-CNR Dept. of Nuclear Medicine, H.S. Raffaele, Milan (Italy); Piffanelli, A. [Dept. of Nuclear Medicine, Univ. of Ferrara (Italy)

1995-01-01

In the reported study the role of single-photon emission tomography (SPET) with technetium-99m hexamethylpropylene amine oxime (HMPAO) in the evaluation of CNS involvement in SLE was assessed and the relations between SPET perfusion defects, EEG examination, magnetic resonance imaging (MRI) findings and clinical presentation were examined. Twenty SLE patients with different NP manifestations were studied. Multiple areas of hypoperfusion, especially in the territory of the middle cerebral artery, were demonstrated by SPET analysis in all 20 patients. The number of hypoperfused areas and the degree of hypoperfusion, expressed by an asymmetry index (AI), were more marked in patients with multiple NP manifestations. MRI and EEG evaluations were positive for 14 of 18 and for 12 of 20 patients, respectively. In the patients with positive SPET and MRI, 87 MRI focal lesions and 63 hypoperfused areas were found, and for 51 of these 63 at least one MRI lesion was found in the same anatomical region. SPET examination of patients with a normal EEG showed fewer hypoperfused areas and a lower degree of asymmetry compared to patients with an abnormal EEG. SPET of patients with focal EEG abnormalities showed more hypoperfused areas (difference not statistically significant) and a higher AI than did SPET of the patients with diffuse EEG abnormalities. Seven of 11 anatomical regions with focal EEG abnormalities. Seven of 11 anatomical regions with focal EEG abnormalities had co-localized hypoperfused areas and in two of these seven no detectable MRI lesions were found. The analysis of SPET and NP manifestations showed that 12 of 20 patients had at least one positive correlation, always involving the areas with the highest AI. In total, 51/88 (58%) hypoperfused areas correlated with the MRI findings and 31/88 (35%) with NP manifestations; for seven of the latter no concurrent MRI lesions were detected in the same anatomical region. (orig.)

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

Science.gov (United States)

Kim, Kyungsoo; Lim, Sung-Ho; Lee, Jaeseok; Kang, Won-Seok; Moon, Cheil; Choi, Ji-Woong

2016-01-01

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°. PMID:27322267

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°.

Stimulus-dependent spiking relationships with the EEG

Science.gov (United States)

Snyder, Adam C.

2015-01-01

The development and refinement of noninvasive techniques for imaging neural activity is of paramount importance for human neuroscience. Currently, the most accessible and popular technique is electroencephalography (EEG). However, nearly all of what we know about the neural events that underlie EEG signals is based on inference, because of the dearth of studies that have simultaneously paired EEG recordings with direct recordings of single neurons. From the perspective of electrophysiologists there is growing interest in understanding how spiking activity coordinates with large-scale cortical networks. Evidence from recordings at both scales highlights that sensory neurons operate in very distinct states during spontaneous and visually evoked activity, which appear to form extremes in a continuum of coordination in neural networks. We hypothesized that individual neurons have idiosyncratic relationships to large-scale network activity indexed by EEG signals, owing to the neurons' distinct computational roles within the local circuitry. We tested this by recording neuronal populations in visual area V4 of rhesus macaques while we simultaneously recorded EEG. We found substantial heterogeneity in the timing and strength of spike-EEG relationships and that these relationships became more diverse during visual stimulation compared with the spontaneous state. The visual stimulus apparently shifts V4 neurons from a state in which they are relatively uniformly embedded in large-scale network activity to a state in which their distinct roles within the local population are more prominent, suggesting that the specific way in which individual neurons relate to EEG signals may hold clues regarding their computational roles. PMID:26108954

Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG

Science.gov (United States)

Bleichner, Martin G.; Debener, Stefan

2017-01-01

Electroencephalography (EEG) is an important clinical tool and frequently used to study the brain-behavior relationship in humans noninvasively. Traditionally, EEG signals are recorded by positioning electrodes on the scalp and keeping them in place with glue, rubber bands, or elastic caps. This setup provides good coverage of the head, but is impractical for EEG acquisition in natural daily-life situations. Here, we propose the transparent EEG concept. Transparent EEG aims for motion tolerant, highly portable, unobtrusive, and near invisible data acquisition with minimum disturbance of a user's daily activities. In recent years several ear-centered EEG solutions that are compatible with the transparent EEG concept have been presented. We discuss work showing that miniature electrodes placed in and around the human ear are a feasible solution, as they are sensitive enough to pick up electrical signals stemming from various brain and non-brain sources. We also describe the cEEGrid flex-printed sensor array, which enables unobtrusive multi-channel EEG acquisition from around the ear. In a number of validation studies we found that the cEEGrid enables the recording of meaningful continuous EEG, event-related potentials and neural oscillations. Here, we explain the rationale underlying the cEEGrid ear-EEG solution, present possible use cases and identify open issues that need to be solved on the way toward transparent EEG. PMID:28439233

Multirapid Serial Visual Presentation Framework for EEG-Based Target Detection

Directory of Open Access Journals (Sweden)

Zhimin Lin

2017-01-01

Full Text Available Target image detection based on a rapid serial visual presentation (RSVP paradigm is a typical brain-computer interface system with various applications, such as image retrieval. In an RSVP paradigm, a P300 component is detected to determine target images. This strategy requires high-precision single-trial P300 detection methods. However, the performance of single-trial detection methods is relatively lower than that of multitrial P300 detection methods. Image retrieval based on multitrial P300 is a new research direction. In this paper, we propose a triple-RSVP paradigm with three images being presented simultaneously and a target image appearing three times. Thus, multitrial P300 classification methods can be used to improve detection accuracy. In this study, these mechanisms were extended and validated, and the characteristics of the multi-RSVP framework were further explored. Two different P300 detection algorithms were also utilized in multi-RSVP to demonstrate that the scheme is universally applicable. Results revealed that the detection accuracy of the multi-RSVP paradigm was higher than that of the standard RSVP paradigm. The results validate the effectiveness of the proposed method, and this method can provide a whole new idea in the field of EEG-based target detection.

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

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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.

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.

Automatic seizure detection: going from sEEG to iEEG

DEFF Research Database (Denmark)

Henriksen, Jonas; Remvig, Line Sofie; Madsen, Rasmus Elsborg

2010-01-01

Several different algorithms have been proposed for automatic detection of epileptic seizures based on both scalp and intracranial electroencephalography (sEEG and iEEG). Which modality that renders the best result is hard to assess though. From 16 patients with focal epilepsy, at least 24 hours...... of ictal and non-ictal iEEG were obtained. Characteristics of the seizures are represented by use of wavelet transformation (WT) features and classified by a support vector machine. When implementing a method used for sEEG on iEEG data, a great improvement in performance was obtained when the high...... frequency containing lower levels in the WT were included in the analysis. We were able to obtain a sensitivity of 96.4% and a false detection rate (FDR) of 0.20/h. In general, when implementing an automatic seizure detection algorithm made for sEEG on iEEG, great improvement can be obtained if a frequency...

Single camera photogrammetry system for EEG electrode identification and localization.

Science.gov (United States)

Baysal, Uğur; Sengül, Gökhan

2010-04-01

In this study, photogrammetric coordinate measurement and color-based identification of EEG electrode positions on the human head are simultaneously implemented. A rotating, 2MP digital camera about 20 cm above the subject's head is used and the images are acquired at predefined stop points separated azimuthally at equal angular displacements. In order to realize full automation, the electrodes have been labeled by colored circular markers and an electrode recognition algorithm has been developed. The proposed method has been tested by using a plastic head phantom carrying 25 electrode markers. Electrode locations have been determined while incorporating three different methods: (i) the proposed photogrammetric method, (ii) conventional 3D radiofrequency (RF) digitizer, and (iii) coordinate measurement machine having about 6.5 mum accuracy. It is found that the proposed system automatically identifies electrodes and localizes them with a maximum error of 0.77 mm. It is suggested that this method may be used in EEG source localization applications in the human brain.

Hybrid EEG-fNIRS Asynchronous Brain-Computer Interface for Multiple Motor Tasks.

Directory of Open Access Journals (Sweden)

Alessio Paolo Buccino

Full Text Available Non-invasive Brain-Computer Interfaces (BCI have demonstrated great promise for neuroprosthetics and assistive devices. Here we aim to investigate methods to combine Electroencephalography (EEG and functional Near-Infrared Spectroscopy (fNIRS in an asynchronous Sensory Motor rhythm (SMR-based BCI. We attempted to classify 4 different executed movements, namely, Right-Arm-Left-Arm-Right-Hand-Left-Hand tasks. Previous studies demonstrated the benefit of EEG-fNIRS combination. However, since normally fNIRS hemodynamic response shows a long delay, we investigated new features, involving slope indicators, in order to immediately detect changes in the signals. Moreover, Common Spatial Patterns (CSPs have been applied to both EEG and fNIRS signals. 15 healthy subjects took part in the experiments and since 25 trials per class were available, CSPs have been regularized with information from the entire population of participants and optimized using genetic algorithms. The different features have been compared in terms of performance and the dynamic accuracy over trials shows that the introduced methods diminish the fNIRS delay in the detection of changes.

Application of modern tests for stationarity to single-trial MEG data: transferring powerful statistical tools from econometrics to neuroscience.

Science.gov (United States)

Kipiński, Lech; König, Reinhard; Sielużycki, Cezary; Kordecki, Wojciech

2011-10-01

Stationarity is a crucial yet rarely questioned assumption in the analysis of time series of magneto- (MEG) or electroencephalography (EEG). One key drawback of the commonly used tests for stationarity of encephalographic time series is the fact that conclusions on stationarity are only indirectly inferred either from the Gaussianity (e.g. the Shapiro-Wilk test or Kolmogorov-Smirnov test) or the randomness of the time series and the absence of trend using very simple time-series models (e.g. the sign and trend tests by Bendat and Piersol). We present a novel approach to the analysis of the stationarity of MEG and EEG time series by applying modern statistical methods which were specifically developed in econometrics to verify the hypothesis that a time series is stationary. We report our findings of the application of three different tests of stationarity--the Kwiatkowski-Phillips-Schmidt-Schin (KPSS) test for trend or mean stationarity, the Phillips-Perron (PP) test for the presence of a unit root and the White test for homoscedasticity--on an illustrative set of MEG data. For five stimulation sessions, we found already for short epochs of duration of 250 and 500 ms that, although the majority of the studied epochs of single MEG trials were usually mean-stationary (KPSS test and PP test), they were classified as nonstationary due to their heteroscedasticity (White test). We also observed that the presence of external auditory stimulation did not significantly affect the findings regarding the stationarity of the data. We conclude that the combination of these tests allows a refined analysis of the stationarity of MEG and EEG time series.

Spatiotemporal source analysis in scalp EEG vs. intracerebral EEG and SPECT: a case study in a 2-year-old child.

Science.gov (United States)

Aarabi, A; Grebe, R; Berquin, P; Bourel Ponchel, E; Jalin, C; Fohlen, M; Bulteau, C; Delalande, O; Gondry, C; Héberlé, C; Moullart, V; Wallois, F

2012-06-01

This case study aims to demonstrate that spatiotemporal spike discrimination and source analysis are effective to monitor the development of sources of epileptic activity in time and space. Therefore, they can provide clinically useful information allowing a better understanding of the pathophysiology of individual seizures with time- and space-resolved characteristics of successive epileptic states, including interictal, preictal, postictal, and ictal states. High spatial resolution scalp EEGs (HR-EEG) were acquired from a 2-year-old girl with refractory central epilepsy and single-focus seizures as confirmed by intracerebral EEG recordings and ictal single-photon emission computed tomography (SPECT). Evaluation of HR-EEG consists of the following three global steps: (1) creation of the initial head model, (2) automatic spike and seizure detection, and finally (3) source localization. During the source localization phase, epileptic states are determined to allow state-based spike detection and localization of underlying sources for each spike. In a final cluster analysis, localization results are integrated to determine the possible sources of epileptic activity. The results were compared with the cerebral locations identified by intracerebral EEG recordings and SPECT. The results obtained with this approach were concordant with those of MRI, SPECT and distribution of intracerebral potentials. Dipole cluster centres found for spikes in interictal, preictal, ictal and postictal states were situated an average of 6.3mm from the intracerebral contacts with the highest voltage. Both amplitude and shape of spikes change between states. Dispersion of the dipoles was higher in the preictal state than in the postictal state. Two clusters of spikes were identified. The centres of these clusters changed position periodically during the various epileptic states. High-resolution surface EEG evaluated by an advanced algorithmic approach can be used to investigate the

Multivariate spectral-analysis of movement-related EEG data

International Nuclear Information System (INIS)

Andrew, C. M.

1997-01-01

The univariate method of event-related desynchronization (ERD) analysis, which quantifies the temporal evolution of power within specific frequency bands from electroencephalographic (EEG) data recorded during a task or event, is extended to an event related multivariate spectral analysis method. With this method, time courses of cross-spectra, phase spectra, coherence spectra, band-averaged coherence values (event-related coherence, ERCoh), partial power spectra and partial coherence spectra are estimated from an ensemble of multivariate event-related EEG trials. This provides a means of investigating relationships between EEG signals recorded over different scalp areas during the performance of a task or the occurrence of an event. The multivariate spectral analysis method is applied to EEG data recorded during three different movement-related studies involving discrete right index finger movements. The first study investigates the impact of the EEG derivation type on the temporal evolution of interhemispheric coherence between activity recorded at electrodes overlying the left and right sensorimotor hand areas during cued finger movement. The question results whether changes in coherence necessarily reflect changes in functional coupling of the cortical structures underlying the recording electrodes. The method is applied to data recorded during voluntary finger movement and a hypothesis, based on an existing global/local model of neocortical dynamics, is formulated to explain the coherence results. The third study applies partial spectral analysis too, and investigates phase relationships of, movement-related data recorded from a full head montage, thereby providing further results strengthening the global/local hypothesis. (author)

Simultaneous single-shot readout of multi-qubit circuits using a traveling-wave parametric amplifier

Science.gov (United States)

O'Brien, Kevin

Observing and controlling the state of ever larger quantum systems is critical for advancing quantum computation. Utilizing a Josephson traveling wave parametric amplifier (JTWPA), we demonstrate simultaneous multiplexed single shot readout of 10 transmon qubits in a planar architecture. We employ digital image sideband rejection to eliminate noise at the image frequencies. We quantify crosstalk and infidelity due to simultaneous readout and control of multiple qubits. Based on current amplifier technology, this approach can scale to simultaneous readout of at least 20 qubits. This work was supported by the Army Research Office.

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.

EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures

NARCIS (Netherlands)

Wang, Lei; Long, Xi; Arends, J.B.A.M.; Aarts, R.M.

2017-01-01

Background The traditional EEG features in the time and frequency domain show limited seizure detection performance in the epileptic population with intellectual disability (ID). In addition, the influence of EEG seizure patterns on detection performance was less studied. New method A single-channel

Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information

Science.gov (United States)

Mesbah, Mostefa; Balakrishnan, Malarvili; Colditz, Paul B.; Boashash, Boualem

2012-12-01

This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20% sensitivity and 88.60% specificity for the feature fusion case and 95.20% sensitivity and 94.30% specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90%, 86.50%) or HRV only (85.70%, 84.60%).

Screening EEG in Aircrew Selection: Clinical Aerospace Neurology Perspective

Science.gov (United States)

Clark, Jonathan B.; Riley, Terrence

2001-01-01

As clinical aerospace neurologists we do not favor using screening EEG in pilot selection on unselected and otherwise asymptomatic individuals. The role of EEG in aviation screening should be as an adjunct to diagnosis, and the decision to disqualify a pilot should never be based solely on the EEG. Although a policy of using a screening EEG in an unselected population might detect an individual with a potentially increased relative risk, it would needlessly exclude many applicants who would probably never have a seizure. A diagnostic test performed on an asymptomatic individual without clinical indications, in a population with a low prevalence of disease (seizure) may be of limited or possibly detrimental value. We feel that rather than do EEGs on all candidates, a better approach would be to perform an EEG for a specific indication, such as family history of seizure, single convulsion (seizure) , history of unexplained loss of consciousness or head injury. Routine screening EEGs in unselected aviation applications are not done without clinical indication in the U.S. Air Force, Navy, or NASA. The USAF discontinued routine screening EEGs for selection in 1978, the U.S. Navy discontinued it in 1981 , and NASA discontinued it in 1995. EEG as an aeromedical screening tool in the US Navy dates back to 1939. The US Navy routinely used EEGs to screen all aeromedical personnel from 1961 to 1981. The incidence of epileptiform activity on EEG in asymptomatic flight candidates ranges from 0.11 to 2.5%. In 3 studies of asymptomatic flight candidates with epileptiform activity on EEG followed for 2 to 15 years, 1 of 31 (3.2%), 1 of 30 (3.3%), and 0 of 14 (0%) developed a seizure, for a cumulative risk of an individual with an epileptiform EEG developing a seizure of 2.67% (2 in 75). Of 28,658 student naval aviation personnel screened 31 had spikes and/or slow waves on EEG, and only 1 later developed a seizure. Of the 28,627 who had a normal EEG, 4 later developed seizures, or

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.

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.

Localizing Brain Activity from Multiple Distinct Sources via EEG

Directory of Open Access Journals (Sweden)

George Dassios

2014-01-01

Full Text Available An important question arousing in the framework of electroencephalography (EEG is the possibility to recognize, by means of a recorded surface potential, the number of activated areas in the brain. In the present paper, employing a homogeneous spherical conductor serving as an approximation of the brain, we provide a criterion which determines whether the measured surface potential is evoked by a single or multiple localized neuronal excitations. We show that the uniqueness of the inverse problem for a single dipole is closely connected with attaining certain relations connecting the measured data. Further, we present the necessary and sufficient conditions which decide whether the collected data originates from a single dipole or from numerous dipoles. In the case where the EEG data arouses from multiple parallel dipoles, an isolation of the source is, in general, not possible.

EEG II. Annexes and regulations. Comment; EEG II. Anlagen und Verordnungen. Kommentar

Energy Technology Data Exchange (ETDEWEB)

Frenz, Walter (ed.) [Rheinisch-Westfaelische Technische Hochschule Aachen (Germany). Berg-, Umwelt- und Europarecht

2016-11-01

Berlin commentary EEG II: safe through the paraphernalia Like hardly any other law, the Renewable Energies Act (EEG) is subject to constant changes. With the 2014 amendment, the EEG was fundamentally redesigned. This makes the application of the complex rules a challenge even for experts. In addition, the sub-rules contain important statements in the form of numerous annexes and regulations - with the EEG amendment 2014, this has become even more detailed. In it, many calculations are only defined in detail and the legal provisions of the EEG are made more definite and supplemented. The Berlin commentary EEG II accompanies you expertly through this complex matter. Experts explain the widely divergent rules in practice. If necessary for a better understanding, the provisions of the EEG 2014 are also explained. Consistently designed for your practice As a buyer of the work, you also benefit from access to an extensive, regularly updated database. This contains important legal energy regulations of the EU, the federal government and the countries. Even earlier legal positions remain searchable and can be conveniently compared with current versions. So you can see at a glance what has changed. [German] Berliner Kommentar EEG II: sicher durch den Paragrafengeflecht Wie kaum ein anderes Gesetz ist das Erneuerbare-Energien-Gesetz (EEG) staendigen Aenderungen unterworfen. Mit der Novelle 2014 wurde das EEG grundlegend umgestaltet. Dies macht die Anwendung der komplexen Regeln selbst fuer Experten zu einer Herausforderung. Zudem enthaelt auch das untergesetzliche Regelwerk wichtige Aussagen in Form zahlreicher Anlagen und Verordnungen - mit der EEG-Novelle 2014 ist dieses noch ausfuehrlicher geworden. In ihm werden viele Berechnungen erst naeher festgelegt und gesetzliche Bestimmungen des EEG entscheidend konkretisiert und ergaenzt. Der Berliner Kommentar EEG II begleitet Sie fachkundig durch diese komplexe Materie. Experten erlaeutern Ihnen praxisorientiert die

Physiological artifacts in scalp EEG and ear-EEG.

Science.gov (United States)

Kappel, Simon L; Looney, David; Mandic, Danilo P; Kidmose, Preben

2017-08-11

A problem inherent to recording EEG is the interference arising from noise and artifacts. While in a laboratory environment, artifacts and interference can, to a large extent, be avoided or controlled, in real-life scenarios this is a challenge. Ear-EEG is a concept where EEG is acquired from electrodes in the ear. We present a characterization of physiological artifacts generated in a controlled environment for nine subjects. The influence of the artifacts was quantified in terms of the signal-to-noise ratio (SNR) deterioration of the auditory steady-state response. Alpha band modulation was also studied in an open/closed eyes paradigm. Artifacts related to jaw muscle contractions were present all over the scalp and in the ear, with the highest SNR deteriorations in the gamma band. The SNR deterioration for jaw artifacts were in general higher in the ear compared to the scalp. Whereas eye-blinking did not influence the SNR in the ear, it was significant for all groups of scalps electrodes in the delta and theta bands. Eye movements resulted in statistical significant SNR deterioration in both frontal, temporal and ear electrodes. Recordings of alpha band modulation showed increased power and coherence of the EEG for ear and scalp electrodes in the closed-eyes periods. Ear-EEG is a method developed for unobtrusive and discreet recording over long periods of time and in real-life environments. This study investigated the influence of the most important types of physiological artifacts, and demonstrated that spontaneous activity, in terms of alpha band oscillations, could be recorded from the ear-EEG platform. In its present form ear-EEG was more prone to jaw related artifacts and less prone to eye-blinking artifacts compared to state-of-the-art scalp based systems.

Stable Sparse Classifiers Identify qEEG Signatures that Predict Learning Disabilities (NOS) Severity.

Science.gov (United States)

Bosch-Bayard, Jorge; Galán-García, Lídice; Fernandez, Thalia; Lirio, Rolando B; Bringas-Vega, Maria L; Roca-Stappung, Milene; Ricardo-Garcell, Josefina; Harmony, Thalía; Valdes-Sosa, Pedro A

2017-01-01

In this paper, we present a novel methodology to solve the classification problem, based on sparse (data-driven) regressions, combined with techniques for ensuring stability, especially useful for high-dimensional datasets and small samples number. The sensitivity and specificity of the classifiers are assessed by a stable ROC procedure, which uses a non-parametric algorithm for estimating the area under the ROC curve. This method allows assessing the performance of the classification by the ROC technique, when more than two groups are involved in the classification problem, i.e., when the gold standard is not binary. We apply this methodology to the EEG spectral signatures to find biomarkers that allow discriminating between (and predicting pertinence to) different subgroups of children diagnosed as Not Otherwise Specified Learning Disabilities (LD-NOS) disorder. Children with LD-NOS have notable learning difficulties, which affect education but are not able to be put into some specific category as reading (Dyslexia), Mathematics (Dyscalculia), or Writing (Dysgraphia). By using the EEG spectra, we aim to identify EEG patterns that may be related to specific learning disabilities in an individual case. This could be useful to develop subject-based methods of therapy, based on information provided by the EEG. Here we study 85 LD-NOS children, divided in three subgroups previously selected by a clustering technique over the scores of cognitive tests. The classification equation produced stable marginal areas under the ROC of 0.71 for discrimination between Group 1 vs. Group 2; 0.91 for Group 1 vs. Group 3; and 0.75 for Group 2 vs. Group1. A discussion of the EEG characteristics of each group related to the cognitive scores is also presented.

Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach.

Science.gov (United States)

Lie, Octavian V; van Mierlo, Pieter

2017-01-01

The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.

EEG and Coma.

Science.gov (United States)

Ardeshna, Nikesh I

2016-03-01

Coma is defined as a state of extreme unresponsiveness, in which a person exhibits no voluntary movement or behavior even to painful stimuli. The utilization of EEG for patients in coma has increased dramatically over the last few years. In fact, many institutions have set protocols for continuous EEG (cEEG) monitoring for patients in coma due to potential causes such as subarachnoid hemorrhage or cardiac arrest. Consequently, EEG plays an important role in diagnosis, managenent, and in some cases even prognosis of coma patients.

Parametric feedback cooling of a single atom inside on optical cavity

International Nuclear Information System (INIS)

Tatjana Wilk

2014-01-01

An optical cavity can be used as a kind of intensifier to study radiation features of an atom, which are hard to detect in free space, like squeezing. Such experiments make use of strong coupling between atom and cavity mode, which experimentally requires the atom to be well localized in the cavity mode. This can be achieved using feedback on the atomic motion: from intensity variations of a probe beam transmitted through the cavity information about the atomic motion is gained, which is used to synchronously modulate the trapping potential holding the atom, leading to cooling and better localization. Here, we report on efficient parametric feedback cooling of a single atom held in an intra-cavity standing wave dipole trap. In contrast to previous feedback strategies, this scheme cools the fast axial oscillation of the atom as well as the slower radial motion. (author)

Combining SPECT and Quantitative EEG Analysis for the Automated Differential Diagnosis of Disorders with Amnestic Symptoms

Directory of Open Access Journals (Sweden)

Yvonne Höller

2017-09-01

Full Text Available Single photon emission computed tomography (SPECT and Electroencephalography (EEG have become established tools in routine diagnostics of dementia. We aimed to increase the diagnostic power by combining quantitative markers from SPECT and EEG for differential diagnosis of disorders with amnestic symptoms. We hypothesize that the combination of SPECT with measures of interaction (connectivity in the EEG yields higher diagnostic accuracy than the single modalities. We examined 39 patients with Alzheimer's dementia (AD, 69 patients with depressive cognitive impairment (DCI, 71 patients with amnestic mild cognitive impairment (aMCI, and 41 patients with amnestic subjective cognitive complaints (aSCC. We calculated 14 measures of interaction from a standard clinical EEG-recording and derived graph-theoretic network measures. From regional brain perfusion measured by 99mTc-hexamethyl-propylene-aminoxime (HMPAO-SPECT in 46 regions, we calculated relative cerebral perfusion in these patients. Patient groups were classified pairwise with a linear support vector machine. Classification was conducted separately for each biomarker, and then again for each EEG- biomarker combined with SPECT. Combination of SPECT with EEG-biomarkers outperformed single use of SPECT or EEG when classifying aSCC vs. AD (90%, aMCI vs. AD (70%, and AD vs. DCI (100%, while a selection of EEG measures performed best when classifying aSCC vs. aMCI (82% and aMCI vs. DCI (90%. Only the contrast between aSCC and DCI did not result in above-chance classification accuracy (60%. In general, accuracies were higher when measures of interaction (i.e., connectivity measures were applied directly than when graph-theoretical measures were derived. We suggest that quantitative analysis of EEG and machine-learning techniques can support differentiating AD, aMCI, aSCC, and DCC, especially when being combined with imaging methods such as SPECT. Quantitative analysis of EEG connectivity could become

Global optimization of silicon nanowires for efficient parametric processes

DEFF Research Database (Denmark)

Vukovic, Dragana; Xu, Jing; Mørk, Jesper

2013-01-01

We present a global optimization of silicon nanowires for parametric single-pump mixing. For the first time, the effect of surface roughness-induced loss is included in the analysis, significantly influencing the optimum waveguide dimensions.......We present a global optimization of silicon nanowires for parametric single-pump mixing. For the first time, the effect of surface roughness-induced loss is included in the analysis, significantly influencing the optimum waveguide dimensions....

Continuous-wave singly resonant optical parametric oscillator placed inside a ring laser

DEFF Research Database (Denmark)

Abitan, Haim; Buchhave, Preben

2003-01-01

A cw singly resonant optical parametric oscillator (SRO) was built and placed inside the cavity of a ring laser. The system consists of a diode-end-pumped Nd:YVO4 ring laser with intracavity periodically poled lithium niobate as the nonlinear gain medium of the SRO. When the laser was operated...... in a unidirectional mode, we obtained more than 520 mW of signal power in one beam. When the laser was operated in a bidirectional mode, we obtained 600 mW of signal power (300 mW in two separate beams). The power and the spectral features of the laser in the unidirectional and bidirectional modes were measured while...... the laser was coupled with the SRO. The results show that it is preferable to couple a SRO with a unidirectional ring laser....

Cortical and Subcortical Coordination of Visual Spatial Attention Revealed by Simultaneous EEG-fMRI Recording.

Science.gov (United States)

Green, Jessica J; Boehler, Carsten N; Roberts, Kenneth C; Chen, Ling-Chia; Krebs, Ruth M; Song, Allen W; Woldorff, Marty G

2017-08-16

Visual spatial attention has been studied in humans with both electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) individually. However, due to the intrinsic limitations of each of these methods used alone, our understanding of the systems-level mechanisms underlying attentional control remains limited. Here, we examined trial-to-trial covariations of concurrently recorded EEG and fMRI in a cued visual spatial attention task in humans, which allowed delineation of both the generators and modulators of the cue-triggered event-related oscillatory brain activity underlying attentional control function. The fMRI activity in visual cortical regions contralateral to the cued direction of attention covaried positively with occipital gamma-band EEG, consistent with activation of cortical regions representing attended locations in space. In contrast, fMRI activity in ipsilateral visual cortical regions covaried inversely with occipital alpha-band oscillations, consistent with attention-related suppression of the irrelevant hemispace. Moreover, the pulvinar nucleus of the thalamus covaried with both of these spatially specific, attention-related, oscillatory EEG modulations. Because the pulvinar's neuroanatomical geometry makes it unlikely to be a direct generator of the scalp-recorded EEG, these covariational patterns appear to reflect the pulvinar's role as a regulatory control structure, sending spatially specific signals to modulate visual cortex excitability proactively. Together, these combined EEG/fMRI results illuminate the dynamically interacting cortical and subcortical processes underlying spatial attention, providing important insight not realizable using either method alone. SIGNIFICANCE STATEMENT Noninvasive recordings of changes in the brain's blood flow using functional magnetic resonance imaging and electrical activity using electroencephalography in humans have individually shown that shifting attention to a location in space

Actigraphy combined with EEG compared to polysomnography in sleep apnea patients

International Nuclear Information System (INIS)

Fietze, Ingo; Penzel, Thomas; Partinen, Markku; Sauter, Jochen; Küchler, Gert; Suvoro, Alexander; Hein, Holger

2015-01-01

An actigraph extended with electroencephalography (EEG), electroocculography (EOG) and electromyography (EMG) was compared to polysomnography in two studies on patients suffering from sleep disordered breathing. Study A with 30 subjects used a single lead EEG, and study B with 20 subjects used EOG and EMG in addition. Sleep was scored according to Rechtschaffen and Kales rules. Total sleep time (TST), sleep period time (SPT), sleep efficiency (SE), sustained sleep efficiency (SSE), sleep-onset latency (SL), and sleep stages were compared. For study A an epoch-by-epoch comparison of sleep stages revealed an overall agreement of 74.2%. Correlations were high for SE (0.98, p < 0.001), SSE (0.98, p < 0.001), TST (0.99, p < 0.001), SPT (0.99, p < 0.001), and SL (0.98, p < 0.001). Regarding the sleep stages, correlations were high for rapid eye movement (REM) (0.83, p < 0.001), light-sleep (0.78, p < 0.001), and deep sleep (0.73, p < 0.001). For study B, results of an epoch-by-epoch comparison of sleep stages showed an overall agreement of 75.5%. Correlations were high for SE (0.98, p < 0.001), SSE (0.98, p < 0.001), TST (0.87, p < 0.001), SL (0.98, p < 0.001), SPT (0.94, p < 0.001), and for rapid eye movement (REM) (0.91, p < 0.001), light-sleep (0.74, p < 0.001), and deep sleep (0.89, p < 0.001). In summary the study revealed high agreement between polysomnography and single lead EEG in sleep apnea patients. Deviations for REM were slightly higher for the single lead EEG compared to single lead EEG plus EOG/EMG. Both simplified systems proved to be reliable for comfortable out-patient sleep recording. (paper)

Variance stabilization for computing and comparing grand mean waveforms in MEG and EEG.

Science.gov (United States)

Matysiak, Artur; Kordecki, Wojciech; Sielużycki, Cezary; Zacharias, Norman; Heil, Peter; König, Reinhard

2013-07-01

Grand means of time-varying signals (waveforms) across subjects in magnetoencephalography (MEG) and electroencephalography (EEG) are commonly computed as arithmetic averages and compared between conditions, for example, by subtraction. However, the prerequisite for these operations, homogeneity of the variance of the waveforms in time, and for most common parametric statistical tests also between conditions, is rarely met. We suggest that the heteroscedasticity observed instead results because waveforms may differ by factors and additive terms and follow a mixed model. We propose to apply the asinh-transformation to stabilize the variance in such cases. We demonstrate the homogeneous variance and the normal distributions of data achieved by this transformation using simulated waveforms, and we apply it to real MEG data and show its benefits. The asinh-transformation is thus an essential and useful processing step prior to computing and comparing grand mean waveforms in MEG and EEG. Copyright © 2013 Society for Psychophysiological Research.

Modification of EEG functional connectivity and EEG power spectra in overweight and obese patients with food addiction: An eLORETA study.

Science.gov (United States)

Imperatori, Claudio; Fabbricatore, Mariantonietta; Innamorati, Marco; Farina, Benedetto; Quintiliani, Maria Isabella; Lamis, Dorian A; Mazzucchi, Edoardo; Contardi, Anna; Vollono, Catello; Della Marca, Giacomo

2015-12-01

We evaluated the modifications of electroencephalographic (EEG) power spectra and EEG connectivity in overweight and obese patients with elevated food addiction (FA) symptoms. Fourteen overweight and obese patients (3 men and 11 women) with three or more FA symptoms and fourteen overweight and obese patients (3 men and 11 women) with two or less FA symptoms were included in the study. EEG was recorded during three different conditions: 1) five minutes resting state (RS), 2) five minutes resting state after a single taste of a chocolate milkshake (ML-RS), and 3) five minutes resting state after a single taste of control neutral solution (N-RS). EEG analyses were conducted by means of the exact Low Resolution Electric Tomography software (eLORETA). Significant modification was observed only in the ML-RS condition. Compared to controls, patients with three or more FA symptoms showed an increase of delta power in the right middle frontal gyrus (Brodmann Area [BA] 8) and in the right precentral gyrus (BA 9), and theta power in the right insula (BA 13) and in the right inferior frontal gyrus (BA 47). Furthermore, compared to controls, patients with three or more FA symptoms showed an increase of functional connectivity in fronto-parietal areas in both the theta and alpha band. The increase of functional connectivity was also positively associated with the number of FA symptoms. Taken together, our results show that FA has similar neurophysiological correlates of other forms of substance-related and addictive disorders suggesting similar psychopathological mechanisms.

Drug Treated Schizophrenia, Schizoaffective and Bipolar Disorder Patients Evaluated by qEEG Absolute Spectral Power and Mean Frequency Analysis.

Science.gov (United States)

Wix-Ramos, Richard; Moreno, Xiomara; Capote, Eduardo; González, Gilbert; Uribe, Ezequiel; Eblen-Zajjur, Antonio

2014-04-01

Research of electroencephalograph (EEG) power spectrum and mean frequency has shown inconsistent results in patients with schizophrenic, schizoaffective and bipolar disorders during medication when compared to normal subjects thus; the characterization of these parameters is an important task. We applied quantitative EEG (qEEG) to investigate 38 control, 15 schizophrenic, 7 schizoaffective and 11 bipolar disorder subjects which remaine under the administration of psychotropic drugs (except control group). Absolute spectral power (ASP), mean frequency and hemispheric electrical asymmetry were measured by 19 derivation qEEG. Group mean values were compared with non parametrical Mann-Whitney test and spectral EEG maps with z-score method at p Schizoaffective patients received neuroleptic+benzodiazepine (71.4%) and for bipolar disorder patients neuroleptic+antiepileptic (81.8%). Schizophrenic (at all derivations except for Fp1, Fp2, F8 and T6) and schizoaffective (only at C3) show higher values of ASP (+57.7% and +86.1% respectively) compared to control group. ASP of bipolar disorder patients did not show differences against control group. The mean frequency was higher at Fp1 (+14.2%) and Fp2 (+17.4%) in bipolar disorder patients than control group, but no differences were found in frequencies between schizophrenic or schizoaffective patients against the control group. Majority of spectral differences were found at the left hemisphere in schizophrenic and schizoaffective but not in bipolar disorder subjects. The present report contributes to characterize quantitatively the qEEG in drug treated schizophrenic, schizoaffective or bipolar disorder patients.

Whitening of Background Brain Activity via Parametric Modeling

Directory of Open Access Journals (Sweden)

Nidal Kamel

2007-01-01

Full Text Available Several signal subspace techniques have been recently suggested for the extraction of the visual evoked potential signals from brain background colored noise. The majority of these techniques assume the background noise as white, and for colored noise, it is suggested to be whitened, without further elaboration on how this might be done. In this paper, we investigate the whitening capabilities of two parametric techniques: a direct one based on Levinson solution of Yule-Walker equations, called AR Yule-Walker, and an indirect one based on the least-squares solution of forward-backward linear prediction (FBLP equations, called AR-FBLP. The whitening effect of the two algorithms is investigated with real background electroencephalogram (EEG colored noise and compared in time and frequency domains.

Quantitative optical extinction-based parametric method for sizing a single core-shell Ag-Ag2O nanoparticle

International Nuclear Information System (INIS)

Santillan, J M J; Scaffardi, L B; Schinca, D C

2011-01-01

This paper develops a parametric method for determining the core radius and shell thickness in small silver-silver-oxide core-shell nanoparticles (Nps) based on single particle optical extinction spectroscopy. The method is based on the study of the relationship between plasmon peak wavelength, full width at half maximum (FWHM) and contrast of the extinction spectra as a function of core radius and shell thickness. This study reveals that plasmon peak wavelength is strongly dependent on shell thickness, whereas FWHM and contrast depend on both variables. These characteristics may be used for establishing an easy and fast stepwise procedure to size core-shell NPs from single particle absorption spectrum. The importance of the method lies in the possibility of monitoring the growth of the silver-oxide layer around small spherical silver Nps in real time. Using the electrostatic approximation of Mie theory, core-shell single particle extinction spectra were calculated for a silver particle's core size smaller than about 20 nm and different thicknesses of silver oxide around it. Analysis of the obtained curves shows a very particular characteristic of the plasmon peak of small silver-silver-oxide Nps, expressed in the fact that its position is strongly dependent on oxide thickness and weakly dependent on the core radius. Even a very thin oxide layer shifts the plasmon peak noticeably, enabling plasmon tuning with appropriate shell thickness. This characteristic, together with the behaviour of FWHM and contrast of the extinction spectra can be combined into a parametric method for sizing both core and shell of single silver Nps in a medium using only optical information. In turn, shell thickness can be related to oxygen content in the Np's surrounding media. The method proposed is applied to size silver Nps from single particle extinction spectrum. The results are compared with full optical spectrum fitting using the electrostatic approximation in Mie theory. The method

Stable Sparse Classifiers Identify qEEG Signatures that Predict Learning Disabilities (NOS Severity

Directory of Open Access Journals (Sweden)

Jorge Bosch-Bayard

2018-01-01

Full Text Available In this paper, we present a novel methodology to solve the classification problem, based on sparse (data-driven regressions, combined with techniques for ensuring stability, especially useful for high-dimensional datasets and small samples number. The sensitivity and specificity of the classifiers are assessed by a stable ROC procedure, which uses a non-parametric algorithm for estimating the area under the ROC curve. This method allows assessing the performance of the classification by the ROC technique, when more than two groups are involved in the classification problem, i.e., when the gold standard is not binary. We apply this methodology to the EEG spectral signatures to find biomarkers that allow discriminating between (and predicting pertinence to different subgroups of children diagnosed as Not Otherwise Specified Learning Disabilities (LD-NOS disorder. Children with LD-NOS have notable learning difficulties, which affect education but are not able to be put into some specific category as reading (Dyslexia, Mathematics (Dyscalculia, or Writing (Dysgraphia. By using the EEG spectra, we aim to identify EEG patterns that may be related to specific learning disabilities in an individual case. This could be useful to develop subject-based methods of therapy, based on information provided by the EEG. Here we study 85 LD-NOS children, divided in three subgroups previously selected by a clustering technique over the scores of cognitive tests. The classification equation produced stable marginal areas under the ROC of 0.71 for discrimination between Group 1 vs. Group 2; 0.91 for Group 1 vs. Group 3; and 0.75 for Group 2 vs. Group1. A discussion of the EEG characteristics of each group related to the cognitive scores is also presented.

High-accuracy user identification using EEG biometrics.

Science.gov (United States)

Koike-Akino, Toshiaki; Mahajan, Ruhi; Marks, Tim K; Ye Wang; Watanabe, Shinji; Tuzel, Oncel; Orlik, Philip

2016-08-01

We analyze brain waves acquired through a consumer-grade EEG device to investigate its capabilities for user identification and authentication. First, we show the statistical significance of the P300 component in event-related potential (ERP) data from 14-channel EEGs across 25 subjects. We then apply a variety of machine learning techniques, comparing the user identification performance of various different combinations of a dimensionality reduction technique followed by a classification algorithm. Experimental results show that an identification accuracy of 72% can be achieved using only a single 800 ms ERP epoch. In addition, we demonstrate that the user identification accuracy can be significantly improved to more than 96.7% by joint classification of multiple epochs.

Parametric and Non-Parametric System Modelling

DEFF Research Database (Denmark)

Nielsen, Henrik Aalborg

1999-01-01

the focus is on combinations of parametric and non-parametric methods of regression. This combination can be in terms of additive models where e.g. one or more non-parametric term is added to a linear regression model. It can also be in terms of conditional parametric models where the coefficients...... considered. It is shown that adaptive estimation in conditional parametric models can be performed by combining the well known methods of local polynomial regression and recursive least squares with exponential forgetting. The approach used for estimation in conditional parametric models also highlights how...... networks is included. In this paper, neural networks are used for predicting the electricity production of a wind farm. The results are compared with results obtained using an adaptively estimated ARX-model. Finally, two papers on stochastic differential equations are included. In the first paper, among...

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.

Using robust principal component analysis to alleviate day-to-day variability in EEG based emotion classification.

Science.gov (United States)

Ping-Keng Jao; Yuan-Pin Lin; Yi-Hsuan Yang; Tzyy-Ping Jung

2015-08-01

An emerging challenge for emotion classification using electroencephalography (EEG) is how to effectively alleviate day-to-day variability in raw data. This study employed the robust principal component analysis (RPCA) to address the problem with a posed hypothesis that background or emotion-irrelevant EEG perturbations lead to certain variability across days and somehow submerge emotion-related EEG dynamics. The empirical results of this study evidently validated our hypothesis and demonstrated the RPCA's feasibility through the analysis of a five-day dataset of 12 subjects. The RPCA allowed tackling the sparse emotion-relevant EEG dynamics from the accompanied background perturbations across days. Sequentially, leveraging the RPCA-purified EEG trials from more days appeared to improve the emotion-classification performance steadily, which was not found in the case using the raw EEG features. Therefore, incorporating the RPCA with existing emotion-aware machine-learning frameworks on a longitudinal dataset of each individual may shed light on the development of a robust affective brain-computer interface (ABCI) that can alleviate ecological inter-day variability.

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.

EEG, MRI, and SPECT in epilepsy. Relative contributions to preoperative evaluation

International Nuclear Information System (INIS)

Seki, Gaku; Hoshida, Tohru; Goda, Kazuo; Hashimoto, Hiroshi; Nakase, Hiroyuki; Hirabayashi, Hidehiro; Kawaguchi, Shoichiro; Morimoto, Tetsuya; Sakaki, Toshisuke

1998-01-01

We comparatively assessed detection of epileptogenic areas on preoperative evaluation in 33 patients with intractable partial epilepsy using scalp interictal and ictal electroencephalography (EEG), magnetic resonance imaging (MRI), and interictal single photon emission computed tomography (SPECT). There are 22 temporal and 11 extratemporal lobe epilepsies. All or almost of their seizures have stopped after resective surgery for more than 12 months follow-up period, averaged 43 months. MRI studies demonstrated 21 organic lesions, 11 mesial temporal sclerosis and one patient showed normal brain tissue. Scalp EEG could correctly identify the focus in 14 of 33 cases (42%), interictal SPECT in 18 of 26 (69%), MRI in 29 of 33 (88%), interictal scalp EEG-video monitoring in 17 of 24 (71%), and ictal scalp EEG-video monitoring in 15 of 22 (68%). Although neuroimaging studies, especially MRI, are useful to detect not only localization of epileptic lesions but also epileptogenic focus, for example, mesial temporal sclerosis, the exact localization of epileptogenic areas could be done by comprehensive evaluation including ictal scalp EEG-video monitoring. (author)

EEG, MRI, and SPECT in epilepsy. Relative contributions to preoperative evaluation

Energy Technology Data Exchange (ETDEWEB)

Seki, Gaku [Luoyang Medical Coll. Associated Hospital (China); Hoshida, Tohru; Goda, Kazuo; Hashimoto, Hiroshi; Nakase, Hiroyuki; Hirabayashi, Hidehiro; Kawaguchi, Shoichiro; Morimoto, Tetsuya; Sakaki, Toshisuke

1998-07-01

We comparatively assessed detection of epileptogenic areas on preoperative evaluation in 33 patients with intractable partial epilepsy using scalp interictal and ictal electroencephalography (EEG), magnetic resonance imaging (MRI), and interictal single photon emission computed tomography (SPECT). There are 22 temporal and 11 extratemporal lobe epilepsies. All or almost of their seizures have stopped after resective surgery for more than 12 months follow-up period, averaged 43 months. MRI studies demonstrated 21 organic lesions, 11 mesial temporal sclerosis and one patient showed normal brain tissue. Scalp EEG could correctly identify the focus in 14 of 33 cases (42%), interictal SPECT in 18 of 26 (69%), MRI in 29 of 33 (88%), interictal scalp EEG-video monitoring in 17 of 24 (71%), and ictal scalp EEG-video monitoring in 15 of 22 (68%). Although neuroimaging studies, especially MRI, are useful to detect not only localization of epileptic lesions but also epileptogenic focus, for example, mesial temporal sclerosis, the exact localization of epileptogenic areas could be done by comprehensive evaluation including ictal scalp EEG-video monitoring. (author)

A simple system for detection of EEG artifacts in polysomnographic recordings.

Science.gov (United States)

Durka, P J; Klekowicz, H; Blinowska, K J; Szelenberger, W; Niemcewicz, Sz

2003-04-01

We present an efficient parametric system for automatic detection of electroencephalogram (EEG) artifacts in polysomnographic recordings. For each of the selected types of artifacts, a relevant parameter was calculated for a given epoch. If any of these parameters exceeded a threshold, the epoch was marked as an artifact. Performance of the system, evaluated on 18 overnight polysomnographic recordings, revealed concordance with decisions of human experts close to the interexpert agreement and the repeatability of expert's decisions, assessed via a double-blind test. Complete software (Matlab source code) for the presented system is freely available from the Internet at http://brain.fuw.edu.pl/artifacts.

EEG-fMRI Evaluation of Patients with Mesial Temporal Lobe Sclerosis

Science.gov (United States)

Avesani, Mirko; Giacopuzzi, Silvia; Bongiovanni, Luigi Giuseppe; Borelli, Paolo; Cerini, Roberto; Pozzi Mucelli, Roberto; Fiaschi, Antonio

2014-01-01

Summary This preliminary study sought more information on blood oxygen level dependent (BOLD) activation, especially contralateral temporal/extratemporal spread, during continuous EEG-fMRI recordings in four patients with mesial temporal sclerosis (MTS). In two patients, EEG showed unilateral focal activity during the EEG-fMRI session concordant with the interictal focus previously identified with standard and video-poly EEG. In the other two patients EEG demonstrated a contralateral diffusion of the irritative focus. In the third patient (with the most drug-resistant form and also extratemporal clinical signs), there was an extratemporal diffusion over frontal regions, ipsilateral to the irritative focus. fMRI analysis confirmed a single activation in the mesial temporal region in two patients whose EEG showed unilateral focal activity, while it demonstrated a bilateral activation in the mesial temporal regions in the other two patients. In the third patient, fMRI demonstrated an activation in the supplementary motxor area. This study confirms the most significant activation with a high firing rate of the irritative focus, but also suggests the importance of using new techniques (such as EEG-fMRI to examine cerebral blood flow) to identify the controlateral limbic activation, and any other extratemporal activations, possible causes of drug resistance in MTS that may require a more precise pre-surgical evaluation with invasive techniques. PMID:24571833

Human EEG responses to controlled alterations of the Earth's magnetic field.

Science.gov (United States)

Sastre, Antonio; Graham, Charles; Cook, Mary R; Gerkovich, Mary M; Gailey, Paul

2002-09-01

Examine the effects of controlled changes in the Earth's magnetic field on electroencephalogram (EEG) and subjective report. Fifty volunteers were exposed double-blind to changes in field magnitude, angle of inclination, and angle of deviation. Volunteers were also exposed to magnetic field conditions found near the North and South Pole. EEG recorded over temporal and occipital sites was compared across 4s baseline, field exposure, and no-change control trials. No EEG spectral differences as a function of gender or recording site were found. Geomagnetic field alterations had no effect on total energy (0.5-42 Hz), energy within traditional EEG analysis bands, or on the 95% spectral edge. Most volunteers reported no sensations; others reported non-specific symptoms unrelated to type of field change. Three hypothesized field detection mechanisms were not supported: (1) mechanical reception through torque exerted on the ferromagnetic material magnetite; (2) movement-induced induction of an electric field in the body; and (3) enhanced sensitivity due to alterations in the rates of chemical reactions involving electron spin states. Humans have little ability to detect brief alterations in the geomagnetic field, even if these alteration are of a large magnitude.

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...

Rejection Positivity Predicts Trial-to-Trial Reaction Times in an Auditory Selective Attention Task: A Computational Analysis of Inhibitory Control

Directory of Open Access Journals (Sweden)

Sufen eChen

2014-08-01

Full Text Available A series of computer simulations using variants of a formal model of attention (Melara & Algom, 2003 probed the role of rejection positivity (RP, a slow-wave electroencephalographic (EEG component, in the inhibitory control of distraction. Behavioral and EEG data were recorded as participants performed auditory selective attention tasks. Simulations that modulated processes of distractor inhibition accounted well for reaction-time (RT performance, whereas those that modulated target excitation did not. A model that incorporated RP from actual EEG recordings in estimating distractor inhibition was superior in predicting changes in RT as a function of distractor salience across conditions. A model that additionally incorporated momentary fluctuations in EEG as the source of trial-to-trial variation in performance precisely predicted individual RTs within each condition. The results lend support to the linking proposition that RP controls the speed of responding to targets through the inhibitory control of distractors.

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

Prediction of rhythmic and periodic EEG patterns and seizures on continuous EEG with early epileptiform discharges.

Science.gov (United States)

Koren, J; Herta, J; Draschtak, S; Pötzl, G; Pirker, S; Fürbass, F; Hartmann, M; Kluge, T; Baumgartner, C

2015-08-01

Continuous EEG (cEEG) is necessary to document nonconvulsive seizures (NCS), nonconvulsive status epilepticus (NCSE), as well as rhythmic and periodic EEG patterns of 'ictal-interictal uncertainty' (RPPIIU) including periodic discharges, rhythmic delta activity, and spike-and-wave complexes in neurological intensive care patients. However, cEEG is associated with significant recording and analysis efforts. Therefore, predictors from short-term routine EEG with a reasonably high yield are urgently needed in order to select patients for evaluation with cEEG. The aim of this study was to assess the prognostic significance of early epileptiform discharges (i.e., within the first 30 min of EEG recording) on the following: (1) incidence of ictal EEG patterns and RPPIIU on subsequent cEEG, (2) occurrence of acute convulsive seizures during the ICU stay, and (3) functional outcome after 6 months of follow-up. We conducted a separate analysis of the first 30 min and the remaining segments of prospective cEEG recordings according to the ACNS Standardized Critical Care EEG Terminology as well as NCS criteria and review of clinical data of 32 neurological critical care patients. In 17 patients with epileptiform discharges within the first 30 min of EEG (group 1), electrographic seizures were observed in 23.5% (n = 4), rhythmic or periodic EEG patterns of 'ictal-interictal uncertainty' in 64.7% (n = 11), and neither electrographic seizures nor RPPIIU in 11.8% (n = 2). In 15 patients with no epileptiform discharges in the first 30 min of EEG (group 2), no electrographic seizures were recorded on subsequent cEEG, RPPIIU were seen in 26.7% (n = 4), and neither electrographic seizures nor RPPIIU in 73.3% (n = 11). The incidence of EEG patterns on cEEG was significantly different between the two groups (p = 0.008). Patients with early epileptiform discharges developed acute seizures more frequently than patients without early epileptiform discharges (p = 0.009). Finally, functional

EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures.

Science.gov (United States)

Wang, Lei; Long, Xi; Arends, Johan B A M; Aarts, Ronald M

2017-10-01

The traditional EEG features in the time and frequency domain show limited seizure detection performance in the epileptic population with intellectual disability (ID). In addition, the influence of EEG seizure patterns on detection performance was less studied. A single-channel EEG signal can be mapped into visibility graphs (VGS), including basic visibility graph (VG), horizontal VG (HVG), and difference VG (DVG). These graphs were used to characterize different EEG seizure patterns. To demonstrate its effectiveness in identifying EEG seizure patterns and detecting generalized seizures, EEG recordings of 615h on one EEG channel from 29 epileptic patients with ID were analyzed. A novel feature set with discriminative power for seizure detection was obtained by using the VGS method. The degree distributions (DDs) of DVG can clearly distinguish EEG of each seizure pattern. The degree entropy and power-law degree power in DVG were proposed here for the first time, and they show significant difference between seizure and non-seizure EEG. The connecting structure measured by HVG can better distinguish seizure EEG from background than those by VG and DVG. A traditional EEG feature set based on frequency analysis was used here as a benchmark feature set. With a support vector machine (SVM) classifier, the seizure detection performance of the benchmark feature set (sensitivity of 24%, FD t /h of 1.8s) can be improved by combining our proposed VGS features extracted from one EEG channel (sensitivity of 38%, FD t /h of 1.4s). The proposed VGS-based features can help improve seizure detection for ID patients. Copyright © 2017 Elsevier B.V. All rights reserved.

Association between increased EEG signal complexity and cannabis dependence.

Science.gov (United States)

Laprevote, Vincent; Bon, Laura; Krieg, Julien; Schwitzer, Thomas; Bourion-Bedes, Stéphanie; Maillard, Louis; Schwan, Raymund

2017-12-01

Both acute and regular cannabis use affects the functioning of the brain. While several studies have demonstrated that regular cannabis use can impair the capacity to synchronize neural assemblies during specific tasks, less is known about spontaneous brain activity. This can be explored by measuring EEG complexity, which reflects the spontaneous variability of human brain activity. A recent study has shown that acute cannabis use can affect that complexity. Since the characteristics of cannabis use can affect the impact on brain functioning, this study sets out to measure EEG complexity in regular cannabis users with or without dependence, in comparison with healthy controls. We recruited 26 healthy controls, 25 cannabis users without cannabis dependence and 14 cannabis users with cannabis dependence, based on DSM IV TR criteria. The EEG signal was extracted from at least 250 epochs of the 500ms pre-stimulation phase during a visual evoked potential paradigm. Brain complexity was estimated using Lempel-Ziv Complexity (LZC), which was compared across groups by non-parametric Kruskall-Wallis ANOVA. The analysis revealed a significant difference between the groups, with higher LZC in participants with cannabis dependence than in non-dependent cannabis users. There was no specific localization of this effect across electrodes. We showed that cannabis dependence is associated to an increased spontaneous brain complexity in regular users. This result is in line with previous results in acute cannabis users. It may reflect increased randomness of neural activity in cannabis dependence. Future studies should explore whether this effect is permanent or diminishes with cannabis cessation. Copyright © 2017 Elsevier B.V. and ECNP. All rights reserved.

MEMS digital parametric loudspeaker

KAUST Repository

Carreno, Armando Arpys Arevalo

2016-03-23

This paper reports on the design and fabrication of MEMS actuator arrays suitable for Digital Sound reconstruction and Parametric Directional Loudspeakers. Two distinct versions of the device were fabricated: one using the electrostatic principle actuation and the other one, the piezoelectric principle. Both versions used similar membrane dimensions, with a diameter of 500 μm. These devices are the smallest Micro-Machined Ultrasound Transducer (MUT) arrays that can be operated for both modes: Digital Sound Reconstruction and Parametric Loudspeaker. The chips consist of an array with 256 transducers, in a footprint of 12 mm by 12 mm. The total single chip size is: 2.3 cm by 2.3 cm, including the contact pads. © 2016 IEEE.

MEMS digital parametric loudspeaker

KAUST Repository

Carreno, Armando Arpys Arevalo; Castro, David; Conchouso Gonzalez, David; Kosel, Jü rgen; Foulds, Ian G.

2016-01-01

This paper reports on the design and fabrication of MEMS actuator arrays suitable for Digital Sound reconstruction and Parametric Directional Loudspeakers. Two distinct versions of the device were fabricated: one using the electrostatic principle actuation and the other one, the piezoelectric principle. Both versions used similar membrane dimensions, with a diameter of 500 μm. These devices are the smallest Micro-Machined Ultrasound Transducer (MUT) arrays that can be operated for both modes: Digital Sound Reconstruction and Parametric Loudspeaker. The chips consist of an array with 256 transducers, in a footprint of 12 mm by 12 mm. The total single chip size is: 2.3 cm by 2.3 cm, including the contact pads. © 2016 IEEE.

Parametric models to relate spike train and LFP dynamics with neural information processing.

Science.gov (United States)

Banerjee, Arpan; Dean, Heather L; Pesaran, Bijan

2012-01-01

Spike trains and local field potentials (LFPs) resulting from extracellular current flows provide a substrate for neural information processing. Understanding the neural code from simultaneous spike-field recordings and subsequent decoding of information processing events will have widespread applications. One way to demonstrate an understanding of the neural code, with particular advantages for the development of applications, is to formulate a parametric statistical model of neural activity and its covariates. Here, we propose a set of parametric spike-field models (unified models) that can be used with existing decoding algorithms to reveal the timing of task or stimulus specific processing. Our proposed unified modeling framework captures the effects of two important features of information processing: time-varying stimulus-driven inputs and ongoing background activity that occurs even in the absence of environmental inputs. We have applied this framework for decoding neural latencies in simulated and experimentally recorded spike-field sessions obtained from the lateral intraparietal area (LIP) of awake, behaving monkeys performing cued look-and-reach movements to spatial targets. Using both simulated and experimental data, we find that estimates of trial-by-trial parameters are not significantly affected by the presence of ongoing background activity. However, including background activity in the unified model improves goodness of fit for predicting individual spiking events. Uncovering the relationship between the model parameters and the timing of movements offers new ways to test hypotheses about the relationship between neural activity and behavior. We obtained significant spike-field onset time correlations from single trials using a previously published data set where significantly strong correlation was only obtained through trial averaging. We also found that unified models extracted a stronger relationship between neural response latency and trial-by-trial

An Investigation of Stimulant Effects on the EEG of Children With Attention-Deficit/Hyperactivity Disorder.

Science.gov (United States)

Clarke, Adam R; Barry, Robert J; Baker, Iris E; McCarthy, Rory; Selikowitz, Mark

2017-07-01

Stimulant medications are the most commonly prescribed treatment for Attention-Deficit/Hyperactivity Disorder (AD/HD). These medications result in a normalization of the EEG. However, past research has found that complete normalization of the EEG is not always achieved. One reason for this may be that studies have used different medications interchangeably, or groups of subjects on different stimulants. This study investigated whether methylphenidate and dexamphetamine produce different levels of normalization of the EEG in children with AD/HD. Three groups of 20 boys participated in this study. There were 2 groups with a diagnosis of AD/HD; one group, good responders to methylphenidate, and the second, good responders to dexamphetamine. The third group was a normal control group. Baseline EEGs were recorded using an eyes-closed resting condition, and analyzed for total power and relative delta, theta, alpha, and beta. Subjects were placed on a 6-month trial of methylphenidate or dexamphetamine, after which a second EEG was recorded. At baseline, the children with AD/HD had elevated relative theta, less relative alpha and beta compared with controls. Baseline differences were found between the two medication groups, with the dexamphetamine group having greater EEG abnormalities than the methylphenidate group. The results indicate that good responders to methylphenidate and dexamphetamine have different EEG profiles when assessed before medication, and these differences may represent different underlying central nervous system deficits. The 2 medications were found to result in substantial normalization of the EEG, with no significant differences in EEG changes occurring between the 2 medications. This indicates that the degree of pretreatment EEG abnormality was the major factor contributing to the degree of normalization of the EEG. As good responders to the 2 medications appear to have different central nervous system abnormalities, it is recommended that

Quantitative optical extinction-based parametric method for sizing a single core-shell Ag-Ag{sub 2}O nanoparticle

Energy Technology Data Exchange (ETDEWEB)

Santillan, J M J; Scaffardi, L B; Schinca, D C, E-mail: lucias@ciop.unlp.edu.ar [Centro de Investigaciones Opticas (CIOp), (CONICET La Plata-CIC) (Argentina)

2011-03-16

This paper develops a parametric method for determining the core radius and shell thickness in small silver-silver-oxide core-shell nanoparticles (Nps) based on single particle optical extinction spectroscopy. The method is based on the study of the relationship between plasmon peak wavelength, full width at half maximum (FWHM) and contrast of the extinction spectra as a function of core radius and shell thickness. This study reveals that plasmon peak wavelength is strongly dependent on shell thickness, whereas FWHM and contrast depend on both variables. These characteristics may be used for establishing an easy and fast stepwise procedure to size core-shell NPs from single particle absorption spectrum. The importance of the method lies in the possibility of monitoring the growth of the silver-oxide layer around small spherical silver Nps in real time. Using the electrostatic approximation of Mie theory, core-shell single particle extinction spectra were calculated for a silver particle's core size smaller than about 20 nm and different thicknesses of silver oxide around it. Analysis of the obtained curves shows a very particular characteristic of the plasmon peak of small silver-silver-oxide Nps, expressed in the fact that its position is strongly dependent on oxide thickness and weakly dependent on the core radius. Even a very thin oxide layer shifts the plasmon peak noticeably, enabling plasmon tuning with appropriate shell thickness. This characteristic, together with the behaviour of FWHM and contrast of the extinction spectra can be combined into a parametric method for sizing both core and shell of single silver Nps in a medium using only optical information. In turn, shell thickness can be related to oxygen content in the Np's surrounding media. The method proposed is applied to size silver Nps from single particle extinction spectrum. The results are compared with full optical spectrum fitting using the electrostatic approximation in Mie theory

Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

Science.gov (United States)

Wang, Deng; Miao, Duoqian; Blohm, Gunnar

2012-01-01

Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications. PMID:23087607

Effects of Marijuana on Ictal and Interictal EEG Activities in Idiopathic Generalized Epilepsy.

Science.gov (United States)

Sivakumar, Sanjeev; Zutshi, Deepti; Seraji-Bozorgzad, Navid; Shah, Aashit K

2017-01-01

Marijuana-based treatment for refractory epilepsy shows promise in surveys, case series, and clinical trials. However, literature on their EEG effects is sparse. Our objective is to analyze the effect of marijuana on EEG in a 24-year-old patient with idiopathic generalized epilepsy treated with cannabis. We blindly reviewed 3 long-term EEGs-a 24-hour study while only on antiepileptic drugs, a 72-hour EEG with Cannabis indica smoked on days 1 and 3 in addition to antiepileptic drugs, and a 48-hour EEG with combination C indica/sativa smoked on day 1 plus antiepileptic drugs. Generalized spike-wave discharges and diffuse paroxysmal fast activity were categorized as interictal and ictal, based on duration of less than 10 seconds or greater, respectively. Data from three studies concatenated into contiguous time series, with usage of marijuana modeled as time-dependent discrete variable while interictal and ictal events constituted dependent variables. Analysis of variance as initial test for significance followed by time series analysis using Generalized Autoregressive Conditional Heteroscedasticity model was performed. Statistical significance for lower interictal events (analysis of variance P = 0.001) was seen during C indica use, but not for C indica/sativa mixture (P = 0.629) or ictal events (P = 0.087). However, time series analysis revealed a significant inverse correlation between marijuana use, with interictal (P EEG data, we demonstrate a decrease in interictal and ictal electrographic events during marijuana use. Larger samples of patients and EEG, with standardized cannabinoid formulation and dosing, are needed to validate our findings.

Electroencephalography after a single unprovoked seizure.

Science.gov (United States)

Debicki, Derek B

2017-07-01

Electroencephalography (EEG) is an essential diagnostic tool in the evaluation of seizure disorders. In particular, EEG is used as an additional investigation for a single unprovoked seizure. Epileptiform abnormalities are related to seizure disorders and have been shown to predict recurrent unprovoked seizures (i.e., a clinical definition of epilepsy). Thus, the identification of epileptiform abnormalities after a single unprovoked seizure can inform treatment options. The current review addresses the relationship between EEG abnormalities and seizure recurrence. This review also addresses factors that are found to improve the yield of recording epileptiform abnormalities including timing of EEG relative to the new-onset seizure, use of repeat studies, use of sleep deprivation and prolonged recordings. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Comparison of data transformation procedures to enhance topographical accuracy in time-series analysis of the human EEG.

Science.gov (United States)

Hauk, O; Keil, A; Elbert, T; Müller, M M

2002-01-30

We describe a methodology to apply current source density (CSD) and minimum norm (MN) estimation as pre-processing tools for time-series analysis of single trial EEG data. The performance of these methods is compared for the case of wavelet time-frequency analysis of simulated gamma-band activity. A reasonable comparison of CSD and MN on the single trial level requires regularization such that the corresponding transformed data sets have similar signal-to-noise ratios (SNRs). For region-of-interest approaches, it should be possible to optimize the SNR for single estimates rather than for the whole distributed solution. An effective implementation of the MN method is described. Simulated data sets were created by modulating the strengths of a radial and a tangential test dipole with wavelets in the frequency range of the gamma band, superimposed with simulated spatially uncorrelated noise. The MN and CSD transformed data sets as well as the average reference (AR) representation were subjected to wavelet frequency-domain analysis, and power spectra were mapped for relevant frequency bands. For both CSD and MN, the influence of noise can be sufficiently suppressed by regularization to yield meaningful information, but only MN represents both radial and tangential dipole sources appropriately as single peaks. Therefore, when relating wavelet power spectrum topographies to their neuronal generators, MN should be preferred.

CHANGES IN THE RAT EEG SPECTRA AND CORE TEMPERATURE AFTER EXPOSURE TO DIFFERENT DOSES OF CHLORPYRIFOS.

Science.gov (United States)

Our previous study showed that single exposure to 25 mg/kg (p.o.) of organophsphate pesticide chlorpyrifos (CHP) led to significant alterations in all EEG frequency bands within 0.1-50 Hz range, reduction in core temperature (Tc) and motor activity (MA). The alterations in EEG pe...

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.

Parametric analyses of single-zone thorium-fueled molten salt reactor fuel cycle options

International Nuclear Information System (INIS)

Powers, J.J.; Worrall, A.; Gehin, J.C.; Harrison, T.J.; Sunny, E.E.

2013-01-01

Analyses of fuel cycle options based on thorium-fueled Molten Salt Reactors (MSRs) have been performed in support of fuel cycle screening and evaluation activities for the United States Department of Energy. The MSR options considered are based on thermal spectrum MSRs with 3 different separations levels: full recycling, limited recycling, and 'once-through' operation without active separations. A single-fluid, single-zone 2250 MWth (1000 MWe) MSR concept consisting of a fuel-bearing molten salt with graphite moderator and reflectors was used as the basis for this study. Radiation transport and isotopic depletion calculations were performed using SCALE 6.1 with ENDF/B-VII nuclear data. New methodology developed at Oak Ridge National Laboratory (ORNL) enables MSR analysis using SCALE, modeling material feed and removal by taking user-specified parameters and performing multiple SCALE/TRITON simulations to determine the resulting equilibrium operating conditions. Parametric analyses examined the sensitivity of the performance of a thorium MSR to variations in the separations efficiency for protactinium and fission products. Results indicate that self-sustained operation is possible with full or limited recycling but once-through operation would require an external neutron source. (authors)

EEG in connection with coma.

Science.gov (United States)

Wilson, John A; Nordal, Helge J

2013-01-08

Coma is a dynamic condition that may have various causes. Important changes may take place rapidly, often with consequences for treatment. The purpose of this article is to provide a brief overview of EEG patterns in comas with various causes, and indicate how EEG contributes in an assessment of the prognosis for coma patients. The article is based on many years of clinical and research-based experience of EEG used for patients in coma. A self-built reference database was supplemented by searches for relevant articles in PubMed. EEG reveals immediate changes in coma, and can provide early information on cause and prognosis. It is the only diagnostic tool for detecting a non-convulsive epileptic status. Locked-in- syndrome may be overseen without EEG. Repeated EEG scans increase diagnostic certainty and make it possible to monitor the development of coma. EEG reflects brain function continuously and therefore holds a key place in the assessment and treatment of coma.

The use of Matlab for colour fuzzy representation of multichannel EEG short time spectra.

Science.gov (United States)

Bigan, C; Strungaru, R

1998-01-01

During the last years, a lot of EEG research efforts was directed to intelligent methods for automatic analysis of data from multichannel EEG recordings. However, all the applications reported were focused on specific single tasks like detection of one specific "event" in the EEG signal: spikes, sleep spindles, epileptic seizures, K complexes, alpha or other rhythms or even artefacts. The aim of this paper is to present a complex system being able to perform a representation of the dynamic changes in frequency components of each EEG channel. This representation uses colours as a powerful means to show the only one frequency range chosen from the shortest epoch of signal able to be processed with the conventional "Short Time Fast Fourier Transform" (S.T.F.F.T.) method.

Video-EEG epilepsian diagnostiikassa - milloin ja miksi?

OpenAIRE

Mervaala, Esa; Mäkinen, Riikka; Peltola, Jukka; Eriksson, Kai; Jutila, Leena; Immonen, Arto

2009-01-01

Aivosähkötoimintaa mittaava EEG on epilepsian spesifinen tutkimus. Video-EEG:llä (V-EEG) tarkoitetaan EEG:n ja videokuvan samanaikaista tallennusta. Valtaosa epilepsiapotilaista joudutaan diagnosoimaan ilman V-EEG:tä, varsinkin jos kohtauksia on esiintynyt vain muutama. Kohtausten toistuessa tavoite on päästä kohtauksenaikaiseen V-EEG-rekisteröintiin. V-EEG:n käyttöaiheista tärkein on epilepsian diagnostiikka ja erotusdiagnostiikka. V-EEG:llä pystytään erottamaan epileptiset kohtaukset esimer...

Digital spectral analysis parametric, non-parametric and advanced methods

CERN Document Server

Castanié, Francis

2013-01-01

Digital Spectral Analysis provides a single source that offers complete coverage of the spectral analysis domain. This self-contained work includes details on advanced topics that are usually presented in scattered sources throughout the literature.The theoretical principles necessary for the understanding of spectral analysis are discussed in the first four chapters: fundamentals, digital signal processing, estimation in spectral analysis, and time-series models.An entire chapter is devoted to the non-parametric methods most widely used in industry.High resolution methods a

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.

Surface EEG-Transcranial Direct Current Stimulation (tDCS) Closed-Loop System.

Science.gov (United States)

Leite, Jorge; Morales-Quezada, Leon; Carvalho, Sandra; Thibaut, Aurore; Doruk, Deniz; Chen, Chiun-Fan; Schachter, Steven C; Rotenberg, Alexander; Fregni, Felipe

2017-09-01

Conventional transcranial direct current stimulation (tDCS) protocols rely on applying electrical current at a fixed intensity and duration without using surrogate markers to direct the interventions. This has led to some mixed results; especially because tDCS induced effects may vary depending on the ongoing level of brain activity. Therefore, the objective of this preliminary study was to assess the feasibility of an EEG-triggered tDCS system based on EEG online analysis of its frequency bands. Six healthy volunteers were randomized to participate in a double-blind sham-controlled crossover design to receive a single session of 10[Formula: see text]min 2[Formula: see text]mA cathodal and sham tDCS. tDCS trigger controller was based upon an algorithm designed to detect an increase in the relative beta power of more than 200%, accompanied by a decrease of 50% or more in the relative alpha power, based on baseline EEG recordings. EEG-tDCS closed-loop-system was able to detect the predefined EEG magnitude deviation and successfully triggered the stimulation in all participants. This preliminary study represents a proof-of-concept for the development of an EEG-tDCS closed-loop system in humans. We discuss and review here different methods of closed loop system that can be considered and potential clinical applications of such system.

On Parametric (and Non-Parametric Variation

Directory of Open Access Journals (Sweden)

Neil Smith

2009-11-01

Full Text Available This article raises the issue of the correct characterization of ‘Parametric Variation’ in syntax and phonology. After specifying their theoretical commitments, the authors outline the relevant parts of the Principles–and–Parameters framework, and draw a three-way distinction among Universal Principles, Parameters, and Accidents. The core of the contribution then consists of an attempt to provide identity criteria for parametric, as opposed to non-parametric, variation. Parametric choices must be antecedently known, and it is suggested that they must also satisfy seven individually necessary and jointly sufficient criteria. These are that they be cognitively represented, systematic, dependent on the input, deterministic, discrete, mutually exclusive, and irreversible.

EEG and Eye Tracking Signatures of Target Encoding during Structured Visual Search

Directory of Open Access Journals (Sweden)

Anne-Marie Brouwer

2017-05-01

Full Text Available EEG and eye tracking variables are potential sources of information about the underlying processes of target detection and storage during visual search. Fixation duration, pupil size and event related potentials (ERPs locked to the onset of fixation or saccade (saccade-related potentials, SRPs have been reported to differ dependent on whether a target or a non-target is currently fixated. Here we focus on the question of whether these variables also differ between targets that are subsequently reported (hits and targets that are not (misses. Observers were asked to scan 15 locations that were consecutively highlighted for 1 s in pseudo-random order. Highlighted locations displayed either a target or a non-target stimulus with two, three or four targets per trial. After scanning, participants indicated which locations had displayed a target. To induce memory encoding failures, participants concurrently performed an aurally presented math task (high load condition. In a low load condition, participants ignored the math task. As expected, more targets were missed in the high compared with the low load condition. For both conditions, eye tracking features distinguished better between hits and misses than between targets and non-targets (with larger pupil size and shorter fixations for missed compared with correctly encoded targets. In contrast, SRP features distinguished better between targets and non-targets than between hits and misses (with average SRPs showing larger P300 waveforms for targets than for non-targets. Single trial classification results were consistent with these averages. This work suggests complementary contributions of eye and EEG measures in potential applications to support search and detect tasks. SRPs may be useful to monitor what objects are relevant to an observer, and eye variables may indicate whether the observer should be reminded of them later.

Electroencephalogy (EEG) Feedback in Decision-Making

Science.gov (United States)

2015-08-26

Electroencephalogy ( EEG ) Feedback In Decision- Making The goal of this project is to investigate whether Electroencephalogy ( EEG ) can provide useful...feedback when training rapid decision-making. More specifically, EEG will allow us to provide online feedback about the neural decision processes...Electroencephalogy ( EEG ) Feedback In Decision-Making Report Title The goal of this project is to investigate whether Electroencephalogy ( EEG ) can provide useful

Simultaneous ocular and muscle artifact removal from EEG data by exploiting diverse statistics.

Science.gov (United States)

Chen, Xun; Liu, Aiping; Chen, Qiang; Liu, Yu; Zou, Liang; McKeown, Martin J

2017-09-01

Electroencephalography (EEG) recordings are frequently contaminated by both ocular and muscle artifacts. These are normally dealt with separately, by employing blind source separation (BSS) techniques relying on either second-order or higher-order statistics (SOS & HOS respectively). When HOS-based methods are used, it is usually in the setting of assuming artifacts are statistically independent to the EEG. When SOS-based methods are used, it is assumed that artifacts have autocorrelation characteristics distinct from the EEG. In reality, ocular and muscle artifacts do not completely follow the assumptions of strict temporal independence to the EEG nor completely unique autocorrelation characteristics, suggesting that exploiting HOS or SOS alone may be insufficient to remove these artifacts. Here we employ a novel BSS technique, independent vector analysis (IVA), to jointly employ HOS and SOS simultaneously to remove ocular and muscle artifacts. Numerical simulations and application to real EEG recordings were used to explore the utility of the IVA approach. IVA was superior in isolating both ocular and muscle artifacts, especially for raw EEG data with low signal-to-noise ratio, and also integrated usually separate SOS and HOS steps into a single unified step. Copyright © 2017 Elsevier Ltd. All rights reserved.

Current sharing temperature of NbTi SULTAN samples compared to prediction using a single pinning mechanism parametrization for NbTi strand

International Nuclear Information System (INIS)

Pong, Ian; Vostner, Alexander; Devred, Arnaud; Bessette, Denis; Mitchell, Neil; Bordini, Bernardo; Bottura, Luca; Jewell, Matthew; Long Feng; Wu Yu

2012-01-01

NbTi strands to be used in four of the six ITER poloidal field (PF) coils, all the correction coils (CC) and all the superconducting feeder busbars are being produced in China. Short full-size qualification conductor (cabled and jacketed) samples have been developed at ASIPP and tested at CRPP. Single pinning mechanism parametrization for this Chinese strand (type S2) has been obtained using the Bottura scaling law. The determination of the scaling parameters using a Kramer-type regression method will be described. A comparison between the critical temperature at the operating current and field of a single strand as determined by the parametrization and the current sharing temperature (T CS ) of a few conductor samples tested at the SULTAN facility will be made. The validity and limitation of the estimation will be discussed. The estimated T CS dependence on various (superconducting critical as well as geometric and volumetric) parameters will be assessed using the modelled critical surface. Errors propagated from critical current (I c ) measurements of the strands and parameter fitting, and other uncertainties, will be quantified. (paper)

Optimal spatiotemporal representation of multichannel EEG for recognition of brain states associated with distinct visual stimulus

Science.gov (United States)

Hramov, Alexander; Musatov, Vyacheslav Yu.; Runnova, Anastasija E.; Efremova, Tatiana Yu.; Koronovskii, Alexey A.; Pisarchik, Alexander N.

2018-04-01

In the paper we propose an approach based on artificial neural networks for recognition of different human brain states associated with distinct visual stimulus. Based on the developed numerical technique and the analysis of obtained experimental multichannel EEG data, we optimize the spatiotemporal representation of multichannel EEG to provide close to 97% accuracy in recognition of the EEG brain states during visual perception. Different interpretations of an ambiguous image produce different oscillatory patterns in the human EEG with similar features for every interpretation. Since these features are inherent to all subjects, a single artificial network can classify with high quality the associated brain states of other subjects.

Simultaneous head tissue conductivity and EEG source location estimation.

Science.gov (United States)

Akalin Acar, Zeynep; Acar, Can E; Makeig, Scott

2016-01-01

Accurate electroencephalographic (EEG) source localization requires an electrical head model incorporating accurate geometries and conductivity values for the major head tissues. While consistent conductivity values have been reported for scalp, brain, and cerebrospinal fluid, measured brain-to-skull conductivity ratio (BSCR) estimates have varied between 8 and 80, likely reflecting both inter-subject and measurement method differences. In simulations, mis-estimation of skull conductivity can produce source localization errors as large as 3cm. Here, we describe an iterative gradient-based approach to Simultaneous tissue Conductivity And source Location Estimation (SCALE). The scalp projection maps used by SCALE are obtained from near-dipolar effective EEG sources found by adequate independent component analysis (ICA) decomposition of sufficient high-density EEG data. We applied SCALE to simulated scalp projections of 15cm(2)-scale cortical patch sources in an MR image-based electrical head model with simulated BSCR of 30. Initialized either with a BSCR of 80 or 20, SCALE estimated BSCR as 32.6. In Adaptive Mixture ICA (AMICA) decompositions of (45-min, 128-channel) EEG data from two young adults we identified sets of 13 independent components having near-dipolar scalp maps compatible with a single cortical source patch. Again initialized with either BSCR 80 or 25, SCALE gave BSCR estimates of 34 and 54 for the two subjects respectively. The ability to accurately estimate skull conductivity non-invasively from any well-recorded EEG data in combination with a stable and non-invasively acquired MR imaging-derived electrical head model could remove a critical barrier to using EEG as a sub-cm(2)-scale accurate 3-D functional cortical imaging modality. Copyright © 2015 Elsevier Inc. All rights reserved.

Educational simulation of the electroencephalogram (EEG)

NARCIS (Netherlands)

Beer, de N.A.M.; Meurs, van W.L.; Grit, M.B.M.; Good, M.L.; Gravenstein, D.

2001-01-01

We describe a model for simulating a spontaneous electroencephalogram (EEG) and for simulating the effects of anesthesia on the EEG, to allow anesthesiologists and EEG technicians to learn and practice intraoperative EEG monitoring. For this purpose, we developed a linear model to manipulate the

Using recurrence plot for determinism analysis of EEG recordings in genetic absence epilepsy rats.

Science.gov (United States)

Ouyang, Gaoxiang; Li, Xiaoli; Dang, Chuangyin; Richards, Douglas A

2008-08-01

Understanding the transition of brain activity towards an absence seizure is a challenging task. In this paper, we use recurrence quantification analysis to indicate the deterministic dynamics of EEG series at the seizure-free, pre-seizure and seizure states in genetic absence epilepsy rats. The determinism measure, DET, based on recurrence plot, was applied to analyse these three EEG datasets, each dataset containing 300 single-channel EEG epochs of 5-s duration. Then, statistical analysis of the DET values in each dataset was carried out to determine whether their distributions over the three groups were significantly different. Furthermore, a surrogate technique was applied to calculate the significance level of determinism measures in EEG recordings. The mean (+/-SD) DET of EEG was 0.177+/-0.045 in pre-seizure intervals. The DET values of pre-seizure EEG data are significantly higher than those of seizure-free intervals, 0.123+/-0.023, (Pdeterminism in EEG epochs was present in 25 of 300 (8.3%), 181 of 300 (60.3%) and 289 of 300 (96.3%) in seizure-free, pre-seizure and seizure intervals, respectively. Results provide some first indications that EEG epochs during pre-seizure intervals exhibit a higher degree of determinism than seizure-free EEG epochs, but lower than those in seizure EEG epochs in absence epilepsy. The proposed methods have the potential of detecting the transition between normal brain activity and the absence seizure state, thus opening up the possibility of intervention, whether electrical or pharmacological, to prevent the oncoming seizure.

How to Evaluate Phase Differences between Trial Groups in Ongoing Electrophysiological Signals

Science.gov (United States)

VanRullen, Rufin

2016-01-01

A growing number of studies endeavor to reveal periodicities in sensory and cognitive functions, by comparing the distribution of ongoing (pre-stimulus) oscillatory phases between two (or more) trial groups reflecting distinct experimental outcomes. A systematic relation between the phase of spontaneous electrophysiological signals, before a stimulus is even presented, and the eventual result of sensory or cognitive processing for that stimulus, would be indicative of an intrinsic periodicity in the underlying neural process. Prior studies of phase-dependent perception have used a variety of analytical methods to measure and evaluate phase differences, and there is currently no established standard practice in this field. The present report intends to remediate this need, by systematically comparing the statistical power of various measures of “phase opposition” between two trial groups, in a number of real and simulated experimental situations. Seven measures were evaluated: one parametric test (circular Watson-Williams test), and three distinct measures of phase opposition (phase bifurcation index, phase opposition sum, and phase opposition product) combined with two procedures for non-parametric statistical testing (permutation, or a combination of z-score and permutation). While these are obviously not the only existing or conceivable measures, they have all been used in recent studies. All tested methods performed adequately on a previously published dataset (Busch et al., 2009). On a variety of artificially constructed datasets, no single measure was found to surpass all others, but instead the suitability of each measure was contingent on several experimental factors: the time, frequency, and depth of oscillatory phase modulation; the absolute and relative amplitudes of post-stimulus event-related potentials for the two trial groups; the absolute and relative trial numbers for the two groups; and the number of permutations used for non-parametric testing

Correlation of BOLD Signal with Linear and Nonlinear Patterns of EEG in Resting State EEG-Informed fMRI

Directory of Open Access Journals (Sweden)

Galina V. Portnova

2018-01-01

Full Text Available Concurrent EEG and fMRI acquisitions in resting state showed a correlation between EEG power in various bands and spontaneous BOLD fluctuations. However, there is a lack of data on how changes in the complexity of brain dynamics derived from EEG reflect variations in the BOLD signal. The purpose of our study was to correlate both spectral patterns, as linear features of EEG rhythms, and nonlinear EEG dynamic complexity with neuronal activity obtained by fMRI. We examined the relationships between EEG patterns and brain activation obtained by simultaneous EEG-fMRI during the resting state condition in 25 healthy right-handed adult volunteers. Using EEG-derived regressors, we demonstrated a substantial correlation of BOLD signal changes with linear and nonlinear features of EEG. We found the most significant positive correlation of fMRI signal with delta spectral power. Beta and alpha spectral features had no reliable effect on BOLD fluctuation. However, dynamic changes of alpha peak frequency exhibited a significant association with BOLD signal increase in right-hemisphere areas. Additionally, EEG dynamic complexity as measured by the HFD of the 2–20 Hz EEG frequency range significantly correlated with the activation of cortical and subcortical limbic system areas. Our results indicate that both spectral features of EEG frequency bands and nonlinear dynamic properties of spontaneous EEG are strongly associated with fluctuations of the BOLD signal during the resting state condition.

Automatic detection of rhythmic and periodic patterns in critical care EEG based on American Clinical Neurophysiology Society (ACNS) standardized terminology.

Science.gov (United States)

Fürbass, F; Hartmann, M M; Halford, J J; Koren, J; Herta, J; Gruber, A; Baumgartner, C; Kluge, T

2015-09-01

Continuous EEG from critical care patients needs to be evaluated time efficiently to maximize the treatment effect. A computational method will be presented that detects rhythmic and periodic patterns according to the critical care EEG terminology (CCET) of the American Clinical Neurophysiology Society (ACNS). The aim is to show that these detected patterns support EEG experts in writing neurophysiological reports. First of all, three case reports exemplify the evaluation procedure using graphically presented detections. Second, 187 hours of EEG from 10 critical care patients were used in a comparative trial study. For each patient the result of a review session using the EEG and the visualized pattern detections was compared to the original neurophysiology report. In three out of five patients with reported seizures, all seizures were reported correctly. In two patients, several subtle clinical seizures with unclear EEG correlation were missed. Lateralized periodic patterns (LPD) were correctly found in 2/2 patients and EEG slowing was correctly found in 7/9 patients. In 8/10 patients, additional EEG features were found including LPDs, EEG slowing, and seizures. The use of automatic pattern detection will assist in review of EEG and increase efficiency. The implementation of bedside surveillance devices using our detection algorithm appears to be feasible and remains to be confirmed in further multicenter studies. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

OpenAIRE

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 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.

Investigation on phase noise of the signal from a singly resonant optical parametric oscillator

Science.gov (United States)

Jinxia, Feng; Yuanji, Li; Kuanshou, Zhang

2018-04-01

The phase noise of the signal from a singly resonant optical parametric oscillator (SRO) is investigated theoretically and experimentally. An SRO based on periodically poled lithium niobate is built up that generates the signal with a maximum power of 5.2 W at 1.5 µm. The intensity noise of the signal reaches the shot noise level for frequencies above 5 MHz. The phase noise of the signal oscillates depending on the analysis frequency, and there are phase noise peaks above the shot noise level at the peak frequencies. To explain the phase noise feature of the signal, a semi-classical theoretical model of SROs including the guided acoustic wave Brillouin scattering effect within the nonlinear crystal is developed. The theoretical predictions are in good agreement with the experimental results.

Changes in decibel scale wavelength properties of EEG with alertness levels while performing sustained attention tasks.

Science.gov (United States)

Arjunan, Sridhar P; Kumar, Dinesh K; Jung, Tzyy-Ping

2009-01-01

Loss of alertness can have dire consequences for people controlling motorized equipment or for people in professions such as defense. Electroencephalogram (EEG) is known to be related to alertness of the person, but due to high level of noise and low signal strength, the use of EEG for such applications has been considered to be unreliable. This study reports the fractal analysis of EEG and identifies the use of maximum fractal length (MFL) as a feature that is inversely correlated with the alertness of the subject. The results show that MFL (of only single channel of EEG) indicates the loss of alertness of the individual with mean (inverse) correlation coefficient = 0.82.

Universal parametrization for quark and lepton substructure

International Nuclear Information System (INIS)

Akama, Keiichi; Terazawa, Hidezumi.

1994-01-01

A universal parametrization for possible quark and lepton substructure is advocated in terms of quark and lepton form factors. It is emphasized that the lower bounds on compositeness scale, Λ c , to be determined experimentally strongly depend on their definitions in composite models. From the recent HERA data, it is estimated to be Λ c > 50 GeV, 0.4 TeV and 10 TeV, depending on the parametrizations with a single-pole form factor, a contact interaction and a logarithmic form factor, respectively. (author)

Insights on the neural basis of motor plasticity induced by theta burst stimulation from TMS-EEG

Science.gov (United States)

VERNET, Marine; BASHIR, Shahid; YOO, Woo-Kyoung; PEREZ, Jennifer M.; NAJIB, Umer; PASCUAL-LEONE, Alvaro

2014-01-01

Transcranial magnetic stimulation (TMS) is a useful tool to induce and measure plasticity in the human brain. However, the cortical effects are generally indirectly evaluated with motor-evoked potentials (MEPs) reflective of modulation of cortico-spinal excitability. In this study, we aim to provide direct measures of cortical plasticity by combining TMS with electroencephalography (EEG). Continuous theta-burst stimulation (cTBS) was applied over the primary motor cortex (M1) of young healthy adults; and we measured modulation of (i) motor evoked-potentials (MEPs), (ii) TMS-induced EEG evoked potentials (TEPs), (iii) TMS-induced EEG synchronization and (iv) eyes-closed resting EEG. Our results show the expected cTBS-induced decrease in MEPs size, which we found to be paralleled by a modulation of a combination of TEPs. Furthermore, we found that cTBS increased the power in the theta band of eyes-closed resting EEG, whereas it decreased single-pulse TMS-induced power in the theta and alpha bands. In addition, cTBS decreased the power in the beta band of eyes-closed resting EEG, whereas it increased single-pulse TMS-induced power in the beta band. We suggest that cTBS acts by modulating the phase alignment between already active oscillators; it synchronizes low frequency (theta and/or alpha) oscillators and desynchronizes high frequency (beta) oscillators. These results provide novel insights into the cortical effects of cTBS and could be useful for exploring cTBS-induced plasticity outside of the motor cortex. PMID:23190020

Applying Improved Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG

Directory of Open Access Journals (Sweden)

Ming-ai Li

2017-01-01

Full Text Available Electroencephalography (EEG is considered the output of a brain and it is a bioelectrical signal with multiscale and nonlinear properties. Motor Imagery EEG (MI-EEG not only has a close correlation with the human imagination and movement intention but also contains a large amount of physiological or disease information. As a result, it has been fully studied in the field of rehabilitation. To correctly interpret and accurately extract the features of MI-EEG signals, many nonlinear dynamic methods based on entropy, such as Approximate Entropy (ApEn, Sample Entropy (SampEn, Fuzzy Entropy (FE, and Permutation Entropy (PE, have been proposed and exploited continuously in recent years. However, these entropy-based methods can only measure the complexity of MI-EEG based on a single scale and therefore fail to account for the multiscale property inherent in MI-EEG. To solve this problem, Multiscale Sample Entropy (MSE, Multiscale Permutation Entropy (MPE, and Multiscale Fuzzy Entropy (MFE are developed by introducing scale factor. However, MFE has not been widely used in analysis of MI-EEG, and the same parameter values are employed when the MFE method is used to calculate the fuzzy entropy values on multiple scales. Actually, each coarse-grained MI-EEG carries the characteristic information of the original signal on different scale factors. It is necessary to optimize MFE parameters to discover more feature information. In this paper, the parameters of MFE are optimized independently for each scale factor, and the improved MFE (IMFE is applied to the feature extraction of MI-EEG. Based on the event-related desynchronization (ERD/event-related synchronization (ERS phenomenon, IMFE features from multi channels are fused organically to construct the feature vector. Experiments are conducted on a public dataset by using Support Vector Machine (SVM as a classifier. The experiment results of 10-fold cross-validation show that the proposed method yields

A multi-dimensional functional principal components analysis of EEG data.

Science.gov (United States)

Hasenstab, Kyle; Scheffler, Aaron; Telesca, Donatello; Sugar, Catherine A; Jeste, Shafali; DiStefano, Charlotte; Şentürk, Damla

2017-09-01

The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal, and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations. © 2017, The International Biometric Society.

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...

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.

EEG patterns in theta and gamma frequency range and their probable relation to human voluntary movement organization.

Science.gov (United States)

Popivanov, D; Mineva, A; Krekule, I

1999-05-21

In experiments with EEG accompanying continuous slow goal-directed voluntary movements we found abrupt short-term transients (STs) of the coefficients of EEG time-varying autoregressive (TVAR) model. The onset of STs indicated (i) a positive EEG wave related to an increase of 3-7 Hz oscillations in time period before the movement start, (ii) synchronization of 35-40 Hz prior to movement start and during the movement when the target is nearly reached. Both these phenomena are expressed predominantly over supplementary motor area, premotor and parietal cortices. These patterns were detected after averaging of EEG segments synchronized to the abrupt changes of the TVAR coefficients computed in the time course of EEG single records. The results are discussed regarding the cognitive aspect of organization of goal-directed movements.

Parametric instabilities in advanced gravitational wave detectors

International Nuclear Information System (INIS)

Gras, S; Zhao, C; Blair, D G; Ju, L

2010-01-01

As the LIGO interferometric gravitational wave detectors have finished gathering a large observational data set, an intense effort is underway to upgrade these observatories to improve their sensitivity by a factor of ∼10. High circulating power in the arm cavities is required, which leads to the possibility of parametric instability due to three-mode opto-acoustic resonant interactions between the carrier, transverse optical modes and acoustic modes. Here, we present detailed numerical analysis of parametric instability in a configuration that is similar to Advanced LIGO. After examining parametric instability for a single three-mode interaction in detail, we examine instability for the best and worst cases, as determined by the resonance condition of transverse modes in the power and signal recycling cavities. We find that, in the best case, the dual recycling detector is substantially less susceptible to instability than a single cavity, but its susceptibility is dependent on the signal recycling cavity design, and on tuning for narrow band operation. In all cases considered, the interferometer will experience parametric instability at full power operation, but the gain varies from 3 to 1000, and the number of unstable modes varies between 7 and 30 per test mass. The analysis focuses on understanding the detector complexity in relation to opto-acoustic interactions, on providing insights that can enable predictions of the detector response to transient disturbances, and of variations in thermal compensation conditions.

Ear-EEG detects ictal and interictal abnormalities in focal and generalized epilepsy - A comparison with scalp EEG monitoring.

Science.gov (United States)

Zibrandtsen, I C; Kidmose, P; Christensen, C B; Kjaer, T W

2017-12-01

Ear-EEG is recording of electroencephalography from a small device in the ear. This is the first study to compare ictal and interictal abnormalities recorded with ear-EEG and simultaneous scalp-EEG in an epilepsy monitoring unit. We recorded and compared simultaneous ear-EEG and scalp-EEG from 15 patients with suspected temporal lobe epilepsy. EEGs were compared visually by independent neurophysiologists. Correlation and time-frequency analysis was used to quantify the similarity between ear and scalp electrodes. Spike-averages were used to assess similarity of interictal spikes. There were no differences in sensitivity or specificity for seizure detection. Mean correlation coefficient between ear-EEG and nearest scalp electrode was above 0.6 with a statistically significant decreasing trend with increasing distance away from the ear. Ictal morphology and frequency dynamics can be observed from visual inspection and time-frequency analysis. Spike averages derived from ear-EEG electrodes yield a recognizable spike appearance. Our results suggest that ear-EEG can reliably detect electroencephalographic patterns associated with focal temporal lobe seizures. Interictal spike morphology from sufficiently large temporal spike sources can be sampled using ear-EEG. Ear-EEG is likely to become an important tool in clinical epilepsy monitoring and diagnosis. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Classification of single normal and Alzheimer’s disease individuals from cortical sources of resting state EEG rhythms

Directory of Open Access Journals (Sweden)

Claudio eBabiloni

2016-02-01

Full Text Available Previous studies have shown abnormal power and functional connectivity of resting state electroencephalographic (EEG rhythms in groups of Alzheimer’s disease (AD compared to healthy elderly (Nold subjects. Here we tested the best classification rate of 120 AD patients and 100 matched Nold subjects using EEG markers based on cortical sources of power and functional connectivity of these rhythms. EEG data were recorded during resting state eyes-closed condition. Exact low-resolution brain electromagnetic tomography (eLORETA estimated the power and functional connectivity of cortical sources in frontal, central, parietal, occipital, temporal, and limbic regions. Delta (2-4 Hz, theta (4-8 Hz, alpha 1 (8-10.5 Hz, alpha 2 (10.5-13 Hz, beta 1 (13-20 Hz, beta 2 (20-30 Hz, and gamma (30-40 Hz were the frequency bands of interest. The classification rates of interest were those with an area under the receiver operating characteristic curve (AUROC higher than 0.7 as a threshold for a moderate classification rate (i.e. 70%. Results showed that the following EEG markers overcame this threshold: (i central, parietal, occipital, temporal, and limbic delta/alpha 1 current density; (ii central, parietal, occipital temporal, and limbic delta/alpha 2 current density; (iii frontal theta/alpha 1 current density; (iv occipital delta/alpha 1 inter-hemispherical connectivity; (v occipital-temporal theta/alpha 1 right and left intra-hemispherical connectivity; and (vi parietal-limbic alpha 1 right intra-hemispherical connectivity. Occipital delta/alpha 1 current density showed the best classification rate (sensitivity of 73.3%, specificity of 78%, accuracy of 75.5%, and AUROC of 82%. These results suggest that EEG source markers can classify Nold and AD individuals with a moderate classification rate higher than 80%.

Dynamic Principal Component Analysis with Nonoverlapping Moving Window and Its Applications to Epileptic EEG Classification

Directory of Open Access Journals (Sweden)

Shengkun Xie

2014-01-01

Full Text Available Classification of electroencephalography (EEG is the most useful diagnostic and monitoring procedure for epilepsy study. A reliable algorithm that can be easily implemented is the key to this procedure. In this paper a novel signal feature extraction method based on dynamic principal component analysis and nonoverlapping moving window is proposed. Along with this new technique, two detection methods based on extracted sparse features are applied to deal with signal classification. The obtained results demonstrated that our proposed methodologies are able to differentiate EEGs from controls and interictal for epilepsy diagnosis and to separate EEGs from interictal and ictal for seizure detection. Our approach yields high classification accuracy for both single-channel short-term EEGs and multichannel long-term EEGs. The classification performance of the method is also compared with other state-of-the-art techniques on the same datasets and the effect of signal variability on the presented methods is also studied.

Extracting attempted hand movements from EEGs in people with complete hand paralysis following stroke

Directory of Open Access Journals (Sweden)

Abirami eMuralidharan

2011-03-01

Full Text Available This study examines the feasibility of using electroencephalograms (EEGs to rapidly detect the intent to open one’s hand in individuals with complete hand paralysis following a subcortical ischemic stroke. If detectable, this motor planning activity could be used in real time to trigger a motorized hand exoskeleton or an electrical stimulation device that opens/closes the hand. While EEG-triggered movement-assist devices could restore function, they may also promote recovery by reinforcing the use of remaining cortical circuits. EEGs were recorded while participants were cued to either relax or attempt to extend their fingers. Linear discriminant analysis was used to detect onset of finger extension from the EEGs in a leave-one-trial-out cross-validation process. In each testing trial, the classifier was applied in pseudo real time starting from an initial hand-relaxed phase, through movement planning, and into the initial attempted finger extension phase (finger extension phase estimated from typical time-to-movement-onset measured in the unaffected hand. The classifiers detected attempted finger-extension at a significantly higher rate during both motor planning and early attempted execution compared to rest. To reduce inappropriate triggering of a movement-assist device during rest, the classification threshold could be adjusted to require more certainty about one’s intent to move before triggering a device. Additionally, a device could be set to activate only after multiple time samples in a row were classified as finger extension events. These options resulted in some sessions with no false triggers while the person was resting, but moderate-to-high true trigger rates during attempted movements.

Pharmaco-EEG-based assessment of the interaction between ethanol and oxcarbazepine.

Science.gov (United States)

Pietrzak, Bogusława; Czarnecka, Elzbieta

2010-01-01

Oxcarbazepine is a representative molecule for a new class of anticonvulsant drugs that can treat alcohol dependence in addition to other disorders. Interestingly, the central mechanism of action in oxcarbazepine is very similar to ethanol, suggesting that these two agents may interact and cause enhanced effects in the central nervous system. In this study, we used a pharmaco-EEG method to examine the influence of oxcarbazepine on the effect of ethanol on the EEG of rabbits (midbrain reticular formation, hippocampus, frontal cortex). Oxcarbazepine was administered po as a single dose (20 mg/kg or 80 mg/kg) or repeatedly at a dose of 40 mg/kg/day for 14 days. Ethanol was injected iv at a dose of 0.8 g/kg 60 min after the administration of oxcarbazepine. Ethanol caused an increase in the low frequencies (0.5-4 Hz) in the recordings, and it caused a marked decrease in higher frequencies (13-30 Hz and 30-45 Hz). Oxcarbazepine altered the EEG pattern in rabbits; this interaction was dependent on the dose of the drug and whether it was administered as a single dose or as multiple doses. Oxcarbazepine administered at a lower dose had a synergistic effect with ethanol in the frontal cortex and midbrain reticular formation, and a similar effect was observed in the hippocampus at a higher dose. Changes in EEG recordings after the administration of oxcarbazepine alone were more pronounced after multiple administrations. The drug decreased the sensitivity of the hippocampus to ethanol, an observation that may be important for the treatment of alcohol addiction.

Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface

Directory of Open Access Journals (Sweden)

Michael H. Thaut

2005-11-01

Full Text Available Most EEG-based BCI systems make use of well-studied patterns of brain activity. However, those systems involve tasks that indirectly map to simple binary commands such as “yes” or “no” or require many weeks of biofeedback training. We hypothesized that signal processing and machine learning methods can be used to discriminate EEG in a direct “yes”/“no” BCI from a single session. Blind source separation (BSS and spectral transformations of the EEG produced a 180-dimensional feature space. We used a modified genetic algorithm (GA wrapped around a support vector machine (SVM classifier to search the space of feature subsets. The GA-based search found feature subsets that outperform full feature sets and random feature subsets. Also, BSS transformations of the EEG outperformed the original time series, particularly in conjunction with a subset search of both spaces. The results suggest that BSS and feature selection can be used to improve the performance of even a “direct,” single-session BCI.

Hypnagogic imagery and EEG activity.

Science.gov (United States)

Hayashi, M; Katoh, K; Hori, T

1999-04-01

The relationships between hypnagogic imagery and EEG activity were studied. 7 subjects (4 women and 3 men) reported the content of hypnagogic imagery every minute and the hypnagogic EEGs were classified into 5 stages according to Hori's modified criteria. The content of the hypnagogic imagery changed as a function of the hypnagogic EEG stages.

EEG applications for sport and performance.

Science.gov (United States)

Thompson, Trevor; Steffert, Tony; Ros, Tomas; Leach, Joseph; Gruzelier, John

2008-08-01

One approach to understanding processes that underlie skilled performing has been to study electrical brain activity using electroencephalography (EEG). A notorious problem with EEG is that genuine cerebral data is often contaminated by artifacts of non-cerebral origin. Unfortunately, such artifacts tend to be exacerbated when the subject is in motion, meaning that obtaining reliable data during exercise is inherently problematic. These problems may explain the limited number of studies using EEG as a methodological tool in the sports sciences. This paper discusses how empirical studies have generally tackled the problem of movement artifact by adopting alternative paradigms which avoid recording during actual physical exertion. Moreover, the specific challenges that motion presents to obtaining reliable EEG data are discussed along with practical and computational techniques to confront these challenges. Finally, as EEG recording in sports is often underpinned by a desire to optimise performance, a brief review of EEG-biofeedback and peak performance studies is also presented. A knowledge of practical aspects of EEG recording along with the advent of new technology and increasingly sophisticated processing models offer a promising approach to minimising, if perhaps not entirely circumventing, the problem of obtaining reliable EEG data during motion.

Assessment of Event-Related EEG Power After Single-Pulse TMS in Unresponsive Wakefulness Syndrome and Minimally Conscious State Patients.

Science.gov (United States)

Formaggio, Emanuela; Cavinato, Marianna; Storti, Silvia Francesca; Tonin, Paolo; Piccione, Francesco; Manganotti, Paolo

2016-03-01

In patients without a behavioral response, non-invasive techniques and new methods of data analysis can complement existing diagnostic tools by providing a method for detecting covert signs of residual cognitive function and awareness. The aim of this study was to investigate the brain oscillatory activities synchronized by single-pulse transcranial magnetic stimulation (TMS) delivered over the primary motor area in the time-frequency domain in patients with the unresponsive wakefulness syndrome or in a minimally conscious state as compared to healthy controls. A time-frequency analysis based on the wavelet transform was used to characterize rapid modifications of oscillatory EEG rhythms induced by TMS in patients as compared to healthy controls. The pattern of EEG changes in the patients differed from that of healthy controls. In the controls there was an early synchronization of slow waves immediately followed by a desynchronization of alpha and beta frequency bands over the frontal and centro-parietal electrodes, whereas an opposite early synchronization, particularly over motor areas for alpha and beta and over the frontal and parietal electrodes for beta power, was seen in the patients. In addition, no relevant modification in slow rhythms (delta and theta) after TMS was noted in patients. The clinical impact of these findings could be relevant in neurorehabilitation settings for increasing the awareness of these patients and defining new treatment procedures.

A New Generation of Brain-Computer Interfaces Driven by Discovery of Latent EEG-fMRI Linkages Using Tensor Decomposition.

Science.gov (United States)

Deshpande, Gopikrishna; Rangaprakash, D; Oeding, Luke; Cichocki, Andrzej; Hu, Xiaoping P

2017-01-01

A Brain-Computer Interface (BCI) is a setup permitting the control of external devices by decoding brain activity. Electroencephalography (EEG) has been extensively used for decoding brain activity since it is non-invasive, cheap, portable, and has high temporal resolution to allow real-time operation. Due to its poor spatial specificity, BCIs based on EEG can require extensive training and multiple trials to decode brain activity (consequently slowing down the operation of the BCI). On the other hand, BCIs based on functional magnetic resonance imaging (fMRI) are more accurate owing to its superior spatial resolution and sensitivity to underlying neuronal processes which are functionally localized. However, due to its relatively low temporal resolution, high cost, and lack of portability, fMRI is unlikely to be used for routine BCI. We propose a new approach for transferring the capabilities of fMRI to EEG, which includes simultaneous EEG/fMRI sessions for finding a mapping from EEG to fMRI, followed by a BCI run from only EEG data, but driven by fMRI-like features obtained from the mapping identified previously. Our novel data-driven method is likely to discover latent linkages between electrical and hemodynamic signatures of neural activity hitherto unexplored using model-driven methods, and is likely to serve as a template for a novel multi-modal strategy wherein cross-modal EEG-fMRI interactions are exploited for the operation of a unimodal EEG system, leading to a new generation of EEG-based BCIs.

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.

Classification rates: non‐parametric verses parametric models using ...

African Journals Online (AJOL)

This research sought to establish if non parametric modeling achieves a higher correct classification ratio than a parametric model. The local likelihood technique was used to model fit the data sets. The same sets of data were modeled using parametric logit and the abilities of the two models to correctly predict the binary ...

Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF).

Science.gov (United States)

Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R

2018-01-01

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow us to study the active human brain from two perspectives concurrently. Signal processing based artifact reduction techniques are mandatory for this, however, to obtain reasonable EEG quality in simultaneous EEG-fMRI. Current artifact reduction techniques like average artifact subtraction (AAS), typically become less effective when artifact reduction has to be performed on-the-fly. We thus present and evaluate a new technique to improve EEG quality online. This technique adds up with online AAS and combines a prototype EEG-cap for reference recordings of artifacts, with online adaptive filtering and is named reference layer adaptive filtering (RLAF). We found online AAS + RLAF to be highly effective in improving EEG quality. Online AAS + RLAF outperformed online AAS and did so in particular online in terms of the chosen performance metrics, these being specifically alpha rhythm amplitude ratio between closed and opened eyes (3-45% improvement), signal-to-noise-ratio of visual evoked potentials (VEP) (25-63% improvement), and VEPs variability (16-44% improvement). Further, we found that EEG quality after online AAS + RLAF is occasionally even comparable with the offline variant of AAS at a 3T MRI scanner. In conclusion RLAF is a very effective add-on tool to enable high quality EEG in simultaneous EEG-fMRI experiments, even when online artifact reduction is necessary.

A New Generation of Brain-Computer Interfaces Driven by Discovery of Latent EEG-fMRI Linkages Using Tensor Decomposition

OpenAIRE

Gopikrishna Deshpande; Gopikrishna Deshpande; Gopikrishna Deshpande; D. Rangaprakash; D. Rangaprakash; Luke Oeding; Andrzej Cichocki; Andrzej Cichocki; Andrzej Cichocki; Xiaoping P. Hu

2017-01-01

A Brain-Computer Interface (BCI) is a setup permitting the control of external devices by decoding brain activity. Electroencephalography (EEG) has been extensively used for decoding brain activity since it is non-invasive, cheap, portable, and has high temporal resolution to allow real-time operation. Due to its poor spatial specificity, BCIs based on EEG can require extensive training and multiple trials to decode brain activity (consequently slowing down the operation of the BCI). On the o...

Plastic modulation of PTSD resting-state networks by EEG neurofeedback

Science.gov (United States)

Kluetsch, Rosemarie C.; Ros, Tomas; Théberge, Jean; Frewen, Paul A.; Calhoun, Vince D.; Schmahl, Christian; Jetly, Rakesh; Lanius, Ruth A.

2015-01-01

Objective Electroencephalographic (EEG) neurofeedback training has been shown to produce plastic modulations in salience network and default mode network functional connectivity in healthy individuals. In this study, we investigated whether a single session of neurofeedback training aimed at the voluntary reduction of alpha rhythm (8–12 Hz) amplitude would be related to differences in EEG network oscillations, functional MRI (fMRI) connectivity, and subjective measures of state anxiety and arousal in a group of individuals with PTSD. Method 21 individuals with PTSD related to childhood abuse underwent 30 minutes of EEG neurofeedback training preceded and followed by a resting-state fMRI scan. Results Alpha desynchronizing neurofeedback was associated with decreased alpha amplitude during training, followed by a significant increase (‘rebound’) in resting-state alpha synchronization. This rebound was linked to increased calmness, greater salience network connectivity with the right insula, and enhanced default mode network connectivity with bilateral posterior cingulate, right middle frontal gyrus, and left medial prefrontal cortex. Conclusion Our study represents a first step in elucidating the potential neurobehavioral mechanisms mediating the effects of neurofeedback treatment on regulatory systems in PTSD. Moreover, it documents for the first time a spontaneous EEG ‘rebound’ after neurofeedback, pointing to homeostatic/compensatory mechanisms operating in the brain. PMID:24266644

Adaptive noise canceling of electrocardiogram artifacts in single channel electroencephalogram.

Science.gov (United States)

Cho, Sung Pil; Song, Mi Hye; Park, Young Cheol; Choi, Ho Seon; Lee, Kyoung Joung

2007-01-01

A new method for estimating and eliminating electrocardiogram (ECG) artifacts from single channel scalp electroencephalogram (EEG) is proposed. The proposed method consists of emphasis of QRS complex from EEG using least squares acceleration (LSA) filter, generation of synchronized pulse with R-peak and ECG artifacts estimation and elimination using adaptive filter. The performance of the proposed method was evaluated using simulated and real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, which are independent component analysis (ICA) and ensemble average (EA) method. From this we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifacts from single channel EEG and simple to use for ambulatory/portable EEG monitoring system.

Serial EEG findings in anti-NMDA receptor encephalitis: correlation between clinical course and EEG.

Science.gov (United States)

Ueda, Jun; Kawamoto, Michi; Hikiami, Ryota; Ishii, Junko; Yoshimura, Hajime; Matsumoto, Riki; Kohara, Nobuo

2017-12-01

Anti-NMDA receptor encephalitis is a paraneoplastic encephalitis characterised by psychiatric features, involuntary movement, and autonomic instability. Various EEG findings in patients with anti-NMDA receptor encephalitis have been reported, however, the correlation between the EEG findings and clinical course of anti-NMDA receptor encephalitis remains unclear. We describe a patient with anti-NMDA receptor encephalitis with a focus on EEG findings, which included: status epilepticus, generalised rhythmic delta activity, excess beta activity, extreme delta brush, and paroxysmal alpha activity upon arousal from sleep, which we term"arousal alpha pattern". Initially, status epilepticus was observed on the EEG when the patient was comatose with conjugate deviation. The EEG then indicated excess beta activity, followed by the emergence of continuous slow activity, including generalised rhythmic delta activity and extreme delta brush, in the most severe phase. Slow activity gradually faded in parallel with clinical amelioration. Excess beta activity persisted, even after the patient became almost independent in daily activities, and finally disappeared with full recovery. In summary, our patient with anti-NMDA receptor encephalitis demonstrated slow activity on the EEG, including extreme delta brush during the most severe phase, which gradually faded in parallel with clinical amelioration, with excess beta activity persisting into the recovery phase.

Efficient continuous-wave eye-safe region signal output from intra-cavity singly resonant optical parametric oscillator

International Nuclear Information System (INIS)

Li Bin; Ding Xin; Sheng Quan; Yin Su-Jia; Shi Chun-Peng; Li Xue; Wen Wu-Qi; Yao Jian-Quan; Yu Xuan-Yi

2012-01-01

We report an efficient continuous-wave (CW) tunable intra-cavity singly resonant optical parametric oscillator based on the multi-period periodically poled lithium niobate and using a laser diode (LD) end-pumped CW 1064 nm Nd:YVO 4 laser as the pump source. A highly efficiency CW operation is realized through a careful cavity design for mode matching and thermal stability. The signal tuning range is 1401–1500 nm obtained by varying the domain period. The maximum output power of 2.2 W at 1500 nm is obtained with a 17.1 W 808 nm LD power and the corresponding conversion efficiency is 12.9%. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)

EEG Theta Dynamics within Frontal and Parietal Cortices for Error Processing during Reaching Movements in a Prism Adaptation Study Altering Visuo-Motor Predictive Planning.

Directory of Open Access Journals (Sweden)

Pieranna Arrighi

Full Text Available Modulation of frontal midline theta (fmθ is observed during error commission, but little is known about the role of theta oscillations in correcting motor behaviours. We investigate EEG activity of healthy partipants executing a reaching task under variable degrees of prism-induced visuo-motor distortion and visual occlusion of the initial arm trajectory. This task introduces directional errors of different magnitudes. The discrepancy between predicted and actual movement directions (i.e. the error, at the time when visual feedback (hand appearance became available, elicits a signal that triggers on-line movement correction. Analysis were performed on 25 EEG channels. For each participant, the median value of the angular error of all reaching trials was used to partition the EEG epochs into high- and low-error conditions. We computed event-related spectral perturbations (ERSP time-locked either to visual feedback or to the onset of movement correction. ERSP time-locked to the onset of visual feedback showed that fmθ increased in the high- but not in the low-error condition with an approximate time lag of 200 ms. Moreover, when single epochs were sorted by the degree of motor error, fmθ started to increase when a certain level of error was exceeded and, then, scaled with error magnitude. When ERSP were time-locked to the onset of movement correction, the fmθ increase anticipated this event with an approximate time lead of 50 ms. During successive trials, an error reduction was observed which was associated with indices of adaptations (i.e., aftereffects suggesting the need to explore if theta oscillations may facilitate learning. To our knowledge this is the first study where the EEG signal recorded during reaching movements was time-locked to the onset of the error visual feedback. This allowed us to conclude that theta oscillations putatively generated by anterior cingulate cortex activation are implicated in error processing in semi

EEG Theta Dynamics within Frontal and Parietal Cortices for Error Processing during Reaching Movements in a Prism Adaptation Study Altering Visuo-Motor Predictive Planning.

Science.gov (United States)

Arrighi, Pieranna; Bonfiglio, Luca; Minichilli, Fabrizio; Cantore, Nicoletta; Carboncini, Maria Chiara; Piccotti, Emily; Rossi, Bruno; Andre, Paolo

2016-01-01

Modulation of frontal midline theta (fmθ) is observed during error commission, but little is known about the role of theta oscillations in correcting motor behaviours. We investigate EEG activity of healthy partipants executing a reaching task under variable degrees of prism-induced visuo-motor distortion and visual occlusion of the initial arm trajectory. This task introduces directional errors of different magnitudes. The discrepancy between predicted and actual movement directions (i.e. the error), at the time when visual feedback (hand appearance) became available, elicits a signal that triggers on-line movement correction. Analysis were performed on 25 EEG channels. For each participant, the median value of the angular error of all reaching trials was used to partition the EEG epochs into high- and low-error conditions. We computed event-related spectral perturbations (ERSP) time-locked either to visual feedback or to the onset of movement correction. ERSP time-locked to the onset of visual feedback showed that fmθ increased in the high- but not in the low-error condition with an approximate time lag of 200 ms. Moreover, when single epochs were sorted by the degree of motor error, fmθ started to increase when a certain level of error was exceeded and, then, scaled with error magnitude. When ERSP were time-locked to the onset of movement correction, the fmθ increase anticipated this event with an approximate time lead of 50 ms. During successive trials, an error reduction was observed which was associated with indices of adaptations (i.e., aftereffects) suggesting the need to explore if theta oscillations may facilitate learning. To our knowledge this is the first study where the EEG signal recorded during reaching movements was time-locked to the onset of the error visual feedback. This allowed us to conclude that theta oscillations putatively generated by anterior cingulate cortex activation are implicated in error processing in semi-naturalistic motor

Performance evaluation of a motor-imagery-based EEG-Brain computer interface using a combined cue with heterogeneous training data in BCI-Naive subjects

Directory of Open Access Journals (Sweden)

Lee Youngbum

2011-10-01

Full Text Available Abstract Background The subjects in EEG-Brain computer interface (BCI system experience difficulties when attempting to obtain the consistent performance of the actual movement by motor imagery alone. It is necessary to find the optimal conditions and stimuli combinations that affect the performance factors of the EEG-BCI system to guarantee equipment safety and trust through the performance evaluation of using motor imagery characteristics that can be utilized in the EEG-BCI testing environment. Methods The experiment was carried out with 10 experienced subjects and 32 naive subjects on an EEG-BCI system. There were 3 experiments: The experienced homogeneous experiment, the naive homogeneous experiment and the naive heterogeneous experiment. Each experiment was compared in terms of the six audio-visual cue combinations and consisted of 50 trials. The EEG data was classified using the least square linear classifier in case of the naive subjects through the common spatial pattern filter. The accuracy was calculated using the training and test data set. The p-value of the accuracy was obtained through the statistical significance test. Results In the case in which a naive subject was trained by a heterogeneous combined cue and tested by a visual cue, the result was not only the highest accuracy (p Conclusions We propose the use of this measuring methodology of a heterogeneous combined cue for training data and a visual cue for test data by the typical EEG-BCI algorithm on the EEG-BCI system to achieve effectiveness in terms of consistence, stability, cost, time, and resources management without the need for a trial and error process.

Wireless recording systems: from noninvasive EEG-NIRS to invasive EEG devices.

Science.gov (United States)

Sawan, Mohamad; Salam, Muhammad T; Le Lan, Jérôme; Kassab, Amal; Gelinas, Sébastien; Vannasing, Phetsamone; Lesage, Frédéric; Lassonde, Maryse; Nguyen, Dang K

2013-04-01

In this paper, we present the design and implementation of a wireless wearable electronic system dedicated to remote data recording for brain monitoring. The reported wireless recording system is used for a) simultaneous near-infrared spectrometry (NIRS) and scalp electro-encephalography (EEG) for noninvasive monitoring and b) intracerebral EEG (icEEG) for invasive monitoring. Bluetooth and dual radio links were introduced for these recordings. The Bluetooth-based device was embedded in a noninvasive multichannel EEG-NIRS system for easy portability and long-term monitoring. On the other hand, the 32-channel implantable recording device offers 24-bit resolution, tunable features, and a sampling frequency up to 2 kHz per channel. The analog front-end preamplifier presents low input-referred noise of 5 μ VRMS and a signal-to-noise ratio of 112 dB. The communication link is implemented using a dual-band radio frequency transceiver offering a half-duplex 800 kb/s data rate, 16.5 mW power consumption and less than 10(-10) post-correction Bit-Error Rate (BER). The designed system can be accessed and controlled by a computer with a user-friendly graphical interface. The proposed wireless implantable recording device was tested in vitro using real icEEG signals from two patients with refractory epilepsy. The wirelessly recorded signals were compared to the original signals recorded using wired-connection, and measured normalized root-mean square deviation was under 2%.

EEG. Renewables Act. Comment. 4. new rev. and enl. ed.; EEG. Erneuerbare-Energien-Gesetz. Kommentar

Energy Technology Data Exchange (ETDEWEB)

Frenz, Walter [RWTH Aachen Univ. (Germany). Lehr- und Forschungsgebiet Berg-, Umwelt- und Europarecht; Mueggenborg, Hans-Juergen [Technische Hochschule Aachen (Germany); Kassel Univ. (Germany); Cosack, Tilman [Hochschule Trier, Umwelt-Campus Birkenfeld (Germany). IREK - Inst. fuer das Recht der Erneuerbaren Energien, Energieeffizienzrecht und Klimaschutzrecht; Ekardt, Felix (ed.) [Forschungsstelle Nachhaltigkeit und Klimapolitik, Leipzig (Germany)

2015-07-01

Unlike any other Act, the Renewable Energy Sources Act (EEG) changes continuously. Recently it has been fundamentally transformed with the amendment 2014. Comprehensive, readable and practice-oriented. The proven Berliner comment EEG is your reliable companion through the new regulatory regime. All provisions of the EEG 2014 thorough and easy to understand commented by experts of the matter. 2. The EEG Amending Act of 29.6.2015 has already been considered. A detailed introduction and contributions to the relevant European law and the antitrust aspects of the renewable energy sources to guarantee you a broad understanding of the rules. Valuable background information you provide, the digressions of the most important renewable energy technologies, will explain the pictures thanks to numerous the scientific and technical foundations. Moreover you the construction law aspects in the construction of photovoltaic and wind turbines are explained clearly. [German] Wie kaum ein anderes Gesetz veraendert sich das Erneuerbare-Energien-Gesetz (EEG) laufend. Zuletzt wurde es mit der Novelle 2014 grundlegend umgestaltet. Umfassend, verstaendlich und praxisgerecht Der bewaehrte Berliner Kommentar EEG ist Ihr verlaesslicher Begleiter durch das neue Regelungsregime. Alle Vorschriften des EEG 2014 werden gruendlich und leicht verstaendlich von Kennern der Materie kommentiert. Das 2. EEG-Aenderungsgesetz vom 29.06.2015 ist bereits beruecksichtigt. Eine ausfuehrliche Einleitung sowie Beitraege zum einschlaegigen europaeischen Recht und zu den kartellrechtlichen Aspekten der erneuerbaren Energien verhelfen Ihnen zu einem breiten Verstaendnis der Vorschriften. Wertvolles Hintergrundwissen liefern Ihnen auch die Exkurse zu den wichtigsten Erneuerbare-Energien-Technologien, die Ihnen dank zahlreicher Abbildungen die naturwissenschaftlich-technischen Grundlagen erlaeutern. Zudem werden Ihnen die baurechtlichen Aspekte bei der Errichtung von Photovoltaik- und Windenergieanlagen

What future research should bring to help resolving the debate about the efficacy of EEG-neurofeedback in children with ADHD

Directory of Open Access Journals (Sweden)

Madelon A. Vollebregt

2014-05-01

Full Text Available In recent years a rising amount of randomized controlled trials, reviews, and meta-analyses relating to the efficacy of electroencephalographic-neurofeedback (EEG-NF in children with attention-deficit/hyperactivity disorder (ADHD have been published. Although clinical reports and open treatment studies suggest EEG-NF to be effective, double blind placebo-controlled studies as well as a rigorous meta-analysis failed to find support for the efficacy of EEG-NF. Since absence of evidence does not equate with evidence of absence, we will outline how future research might overcome the present methodological limitations. To provide conclusive evidence for the presence or absence of the efficacy of EEG-NF in the treatment of ADHD, there is a need to set up a well-designed study that ensures optimal implementation and embedding of the training, and possibly incorporates different forms of neurofeedback.

Simultaneous recording of EEG and electromyographic polygraphy increases the diagnostic yield of video-EEG monitoring.

Science.gov (United States)

Hill, Aron T; Briggs, Belinda A; Seneviratne, Udaya

2014-06-01

To investigate the usefulness of adjunctive electromyographic (EMG) polygraphy in the diagnosis of clinical events captured during long-term video-EEG monitoring. A total of 40 patients (21 women, 19 men) aged between 19 and 72 years (mean 43) investigated using video-EEG monitoring were studied. Electromyographic activity was simultaneously recorded with EEG in four patients selected on clinical grounds. In these patients, surface EMG electrodes were placed over muscles suspected to be activated during a typical clinical event. Of the 40 patients investigated, 24 (60%) were given a diagnosis, whereas 16 (40%) remained undiagnosed. All four patients receiving adjunctive EMG polygraphy obtained a diagnosis, with three of these diagnoses being exclusively reliant on the EMG recordings. Specifically, one patient was diagnosed with propriospinal myoclonus, another patient was diagnosed with facio-mandibular myoclonus, and a third patient was found to have bruxism and periodic leg movements of sleep. The information obtained from surface EMG recordings aided the diagnosis of clinical events captured during video-EEG monitoring in 7.5% of the total cohort. This study suggests that EEG-EMG polygraphy may be used as a technique of improving the diagnostic yield of video-EEG monitoring in selected cases.

Testing competing hypotheses about single trial fMRI

DEFF Research Database (Denmark)

Hansen, Lars Kai; Purushotham, Archana; Kim, Seong-Ge

2002-01-01

We use a Bayesian framework to compute probabilities of competing hypotheses about functional activation based on single trial fMRI measurements. Within the framework we obtain a complete probabilistic picture of competing hypotheses, hence control of both type I and type II errors....

On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP.

Science.gov (United States)

Winkler, Irene; Debener, Stefan; Müller, Klaus-Robert; Tangermann, Michael

2015-01-01

Standard artifact removal methods for electroencephalographic (EEG) signals are either based on Independent Component Analysis (ICA) or they regress out ocular activity measured at electrooculogram (EOG) channels. Successful ICA-based artifact reduction relies on suitable pre-processing. Here we systematically evaluate the effects of high-pass filtering at different frequencies. Offline analyses were based on event-related potential data from 21 participants performing a standard auditory oddball task and an automatic artifactual component classifier method (MARA). As a pre-processing step for ICA, high-pass filtering between 1-2 Hz consistently produced good results in terms of signal-to-noise ratio (SNR), single-trial classification accuracy and the percentage of `near-dipolar' ICA components. Relative to no artifact reduction, ICA-based artifact removal significantly improved SNR and classification accuracy. This was not the case for a regression-based approach to remove EOG artifacts.

A New Generation of Brain-Computer Interfaces Driven by Discovery of Latent EEG-fMRI Linkages Using Tensor Decomposition

Directory of Open Access Journals (Sweden)

Gopikrishna Deshpande

2017-06-01

Full Text Available A Brain-Computer Interface (BCI is a setup permitting the control of external devices by decoding brain activity. Electroencephalography (EEG has been extensively used for decoding brain activity since it is non-invasive, cheap, portable, and has high temporal resolution to allow real-time operation. Due to its poor spatial specificity, BCIs based on EEG can require extensive training and multiple trials to decode brain activity (consequently slowing down the operation of the BCI. On the other hand, BCIs based on functional magnetic resonance imaging (fMRI are more accurate owing to its superior spatial resolution and sensitivity to underlying neuronal processes which are functionally localized. However, due to its relatively low temporal resolution, high cost, and lack of portability, fMRI is unlikely to be used for routine BCI. We propose a new approach for transferring the capabilities of fMRI to EEG, which includes simultaneous EEG/fMRI sessions for finding a mapping from EEG to fMRI, followed by a BCI run from only EEG data, but driven by fMRI-like features obtained from the mapping identified previously. Our novel data-driven method is likely to discover latent linkages between electrical and hemodynamic signatures of neural activity hitherto unexplored using model-driven methods, and is likely to serve as a template for a novel multi-modal strategy wherein cross-modal EEG-fMRI interactions are exploited for the operation of a unimodal EEG system, leading to a new generation of EEG-based BCIs.

Mild Depression Detection of College Students: an EEG-Based Solution with Free Viewing Tasks.

Science.gov (United States)

Li, Xiaowei; Hu, Bin; Shen, Ji; Xu, Tingting; Retcliffe, Martyn

2015-12-01

Depression is a common mental disorder with growing prevalence; however current diagnoses of depression face the problem of patient denial, clinical experience and subjective biases from self-report. By using a combination of linear and nonlinear EEG features in our research, we aim to develop a more accurate and objective approach to depression detection that supports the process of diagnosis and assists the monitoring of risk factors. By classifying EEG features during free viewing task, an accuracy of 99.1%, which is the highest to our knowledge by far, was achieved using kNN classifier to discriminate depressed and non-depressed subjects. Furthermore, through correlation analysis, comparisons of performance on each electrode were discussed on the availability of single channel EEG recording depression detection system. Combined with wearable EEG collecting devices, our method offers the possibility of cost effective wearable ubiquitous system for doctors to monitor their patients with depression, and for normal people to understand their mental states in time.

Single-trial estimation of stimulus and spike-history effects on time-varying ensemble spiking activity of multiple neurons: a simulation study

International Nuclear Information System (INIS)

Shimazaki, Hideaki

2013-01-01

Neurons in cortical circuits exhibit coordinated spiking activity, and can produce correlated synchronous spikes during behavior and cognition. We recently developed a method for estimating the dynamics of correlated ensemble activity by combining a model of simultaneous neuronal interactions (e.g., a spin-glass model) with a state-space method (Shimazaki et al. 2012 PLoS Comput Biol 8 e1002385). This method allows us to estimate stimulus-evoked dynamics of neuronal interactions which is reproducible in repeated trials under identical experimental conditions. However, the method may not be suitable for detecting stimulus responses if the neuronal dynamics exhibits significant variability across trials. In addition, the previous model does not include effects of past spiking activity of the neurons on the current state of ensemble activity. In this study, we develop a parametric method for simultaneously estimating the stimulus and spike-history effects on the ensemble activity from single-trial data even if the neurons exhibit dynamics that is largely unrelated to these effects. For this goal, we model ensemble neuronal activity as a latent process and include the stimulus and spike-history effects as exogenous inputs to the latent process. We develop an expectation-maximization algorithm that simultaneously achieves estimation of the latent process, stimulus responses, and spike-history effects. The proposed method is useful to analyze an interaction of internal cortical states and sensory evoked activity

Mobile EEG on the bike: disentangling attentional and physical contributions to auditory attention tasks

Science.gov (United States)

Zink, Rob; Hunyadi, Borbála; Van Huffel, Sabine; De Vos, Maarten

2016-08-01

Objective. In the past few years there has been a growing interest in studying brain functioning in natural, real-life situations. Mobile EEG allows to study the brain in real unconstrained environments but it faces the intrinsic challenge that it is impossible to disentangle observed changes in brain activity due to increase in cognitive demands by the complex natural environment or due to the physical involvement. In this work we aim to disentangle the influence of cognitive demands and distractions that arise from such outdoor unconstrained recordings. Approach. We evaluate the ERP and single trial characteristics of a three-class auditory oddball paradigm recorded in outdoor scenario’s while peddling on a fixed bike or biking freely around. In addition we also carefully evaluate the trial specific motion artifacts through independent gyro measurements and control for muscle artifacts. Main results. A decrease in P300 amplitude was observed in the free biking condition as compared to the fixed bike conditions. Above chance P300 single-trial classification in highly dynamic real life environments while biking outdoors was achieved. Certain significant artifact patterns were identified in the free biking condition, but neither these nor the increase in movement (as derived from continuous gyrometer measurements) can explain the differences in classification accuracy and P300 waveform differences with full clarity. The increased cognitive load in real-life scenarios is shown to play a major role in the observed differences. Significance. Our findings suggest that auditory oddball results measured in natural real-life scenarios are influenced mainly by increased cognitive load due to being in an unconstrained environment.

EEG Based Inference of Spatio-Temporal Brain Dynamics

DEFF Research Database (Denmark)

Hansen, Sofie Therese

Electroencephalography (EEG) provides a measure of brain activity and has improved our understanding of the brain immensely. However, there is still much to be learned and the full potential of EEG is yet to be realized. In this thesis we suggest to improve the information gain of EEG using three...... different approaches; 1) by recovery of the EEG sources, 2) by representing and inferring the propagation path of EEG sources, and 3) by combining EEG with functional magnetic resonance imaging (fMRI). The common goal of the methods, and thus of this thesis, is to improve the spatial dimension of EEG...... recovery ability. The forward problem describes the propagation of neuronal activity in the brain to the EEG electrodes on the scalp. The geometry and conductivity of the head layers are normally required to model this path. We propose a framework for inferring forward models which is based on the EEG...

Methods for artifact detection and removal from scalp EEG: A review.

Science.gov (United States)

Islam, Md Kafiul; Rastegarnia, Amir; Yang, Zhi

2016-11-01

Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage. However, this is still an active area of research as no single existing artifact detection/removal method is complete or universal. This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. First, a general overview of the different artifact types that are found in scalp EEG and their effect on particular applications are presented. In addition, the methods are compared based on their ability to remove certain types of artifacts and their suitability in relevant applications (only functional comparison is provided not performance evaluation of methods). Finally, the future direction and expected challenges of current research is discussed. Therefore, this review is expected to be helpful for interested researchers who will develop and/or apply artifact handling algorithm/technique in future for their applications as well as for those willing to improve the existing algorithms or propose a new solution in this particular area of research. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Modification of EEG power spectra and EEG connectivity in autobiographical memory: a sLORETA study.

Science.gov (United States)

Imperatori, Claudio; Brunetti, Riccardo; Farina, Benedetto; Speranza, Anna Maria; Losurdo, Anna; Testani, Elisa; Contardi, Anna; Della Marca, Giacomo

2014-08-01

The aim of the present study was to explore the modifications of scalp EEG power spectra and EEG connectivity during the autobiographical memory test (AM-T) and during the retrieval of an autobiographical event (the high school final examination, Task 2). Seventeen healthy volunteers were enrolled (9 women and 8 men, mean age 23.4 ± 2.8 years, range 19-30). EEG was recorded at baseline and while performing the autobiographical memory (AM) tasks, by means of 19 surface electrodes and a nasopharyngeal electrode. EEG analysis was conducted by means of the standardized LOw Resolution Electric Tomography (sLORETA) software. Power spectra and lagged EEG coherence were compared between EEG acquired during the memory tasks and baseline recording. The frequency bands considered were as follows: delta (0.5-4 Hz); theta (4.5-7.5 Hz); alpha (8-12.5 Hz); beta1 (13-17.5 Hz); beta2 (18-30 Hz); gamma (30.5-60 Hz). During AM-T, we observed a significant delta power increase in left frontal and midline cortices (T = 3.554; p < 0.05) and increased EEG connectivity in delta band in prefrontal, temporal, parietal, and occipital areas, and for gamma bands in the left temporo-parietal regions (T = 4.154; p < 0.05). In Task 2, we measured an increased power in the gamma band located in the left posterior midline areas (T = 3.960; p < 0.05) and a significant increase in delta band connectivity in the prefrontal, temporal, parietal, and occipital areas, and in the gamma band involving right temporo-parietal areas (T = 4.579; p < 0.05). These results indicate that AM retrieval engages in a complex network which is mediated by both low- (delta) and high-frequency (gamma) EEG bands.

Short analysis of the increase of the EEG apportionment 2013; Kurzanalyse des Anstiegs der EEG-Umlage 2013

Energy Technology Data Exchange (ETDEWEB)

Loreck, Charlotte; Matthes, Felix C.; Hermann, Hauke; Jung, Frederieke; Emele, Lukas

2012-10-15

At 15th October, 2012 the transmission system operators had published the EEG apportionment (EEG - Energy Economy Law). For the year 2013. This apportionment amounts 5,277 ct/kWh for non-privileged consumers in comparison to 3,59 ct/kWh for the year 2012. The ongoing enhancement of the renewable energies increases the EEG apportionment by an amount of 0.74 ct/kWh. With 0.26 ct/kWh the photovoltaics has the largest proportion in comparison to photovoltaics. The power generation from biomass as well as from wind energy at onshore sites contribute with 0.21 ct/kWh to the EEG apportionment. The greatest item of 0.48 ct/kWh is the debit balancing of the EEG account. The liquidity reserve for the year 2013 will be enhanced to 10% of the budget deficit. The expansion of the privileged status of the power consumption increases the EEG apportionment by 0.12 ct/kWh.

EEG phase reset due to auditory attention: an inverse time-scale approach

International Nuclear Information System (INIS)

Low, Yin Fen; Strauss, Daniel J

2009-01-01

We propose a novel tool to evaluate the electroencephalograph (EEG) phase reset due to auditory attention by utilizing an inverse analysis of the instantaneous phase for the first time. EEGs were acquired through auditory attention experiments with a maximum entropy stimulation paradigm. We examined single sweeps of auditory late response (ALR) with the complex continuous wavelet transform. The phase in the frequency band that is associated with auditory attention (6–10 Hz, termed as theta–alpha border) was reset to the mean phase of the averaged EEGs. The inverse transform was applied to reconstruct the phase-modified signal. We found significant enhancement of the N100 wave in the reconstructed signal. Analysis of the phase noise shows the effects of phase jittering on the generation of the N100 wave implying that a preferred phase is necessary to generate the event-related potential (ERP). Power spectrum analysis shows a remarkable increase of evoked power but little change of total power after stabilizing the phase of EEGs. Furthermore, by resetting the phase only at the theta border of no attention data to the mean phase of attention data yields a result that resembles attention data. These results show strong connections between EEGs and ERP, in particular, we suggest that the presentation of an auditory stimulus triggers the phase reset process at the theta–alpha border which leads to the emergence of the N100 wave. It is concluded that our study reinforces other studies on the importance of the EEG in ERP genesis

EEG phase reset due to auditory attention: an inverse time-scale approach.

Science.gov (United States)

Low, Yin Fen; Strauss, Daniel J

2009-08-01

We propose a novel tool to evaluate the electroencephalograph (EEG) phase reset due to auditory attention by utilizing an inverse analysis of the instantaneous phase for the first time. EEGs were acquired through auditory attention experiments with a maximum entropy stimulation paradigm. We examined single sweeps of auditory late response (ALR) with the complex continuous wavelet transform. The phase in the frequency band that is associated with auditory attention (6-10 Hz, termed as theta-alpha border) was reset to the mean phase of the averaged EEGs. The inverse transform was applied to reconstruct the phase-modified signal. We found significant enhancement of the N100 wave in the reconstructed signal. Analysis of the phase noise shows the effects of phase jittering on the generation of the N100 wave implying that a preferred phase is necessary to generate the event-related potential (ERP). Power spectrum analysis shows a remarkable increase of evoked power but little change of total power after stabilizing the phase of EEGs. Furthermore, by resetting the phase only at the theta border of no attention data to the mean phase of attention data yields a result that resembles attention data. These results show strong connections between EEGs and ERP, in particular, we suggest that the presentation of an auditory stimulus triggers the phase reset process at the theta-alpha border which leads to the emergence of the N100 wave. It is concluded that our study reinforces other studies on the importance of the EEG in ERP genesis.

Multi-port versus single-port cholecystectomy: results of a multi-centre, randomised controlled trial (MUSIC trial).

Science.gov (United States)

Arezzo, Alberto; Passera, Roberto; Bullano, Alberto; Mintz, Yoav; Kedar, Asaf; Boni, Luigi; Cassinotti, Elisa; Rosati, Riccardo; Fumagalli Romario, Uberto; Sorrentino, Mario; Brizzolari, Marco; Di Lorenzo, Nicola; Gaspari, Achille Lucio; Andreone, Dario; De Stefani, Elena; Navarra, Giuseppe; Lazzara, Salvatore; Degiuli, Maurizio; Shishin, Kirill; Khatkov, Igor; Kazakov, Ivan; Schrittwieser, Rudolf; Carus, Thomas; Corradi, Alessio; Sitzman, Guenther; Lacy, Antonio; Uranues, Selman; Szold, Amir; Morino, Mario

2017-07-01

Single-port laparoscopic surgery as an alternative to conventional laparoscopic cholecystectomy for benign disease has not yet been accepted as a standard procedure. The aim of the multi-port versus single-port cholecystectomy trial was to compare morbidity rates after single-access (SPC) and standard laparoscopy (MPC). This non-inferiority phase 3 trial was conducted at 20 hospital surgical departments in six countries. At each centre, patients were randomly assigned to undergo either SPC or MPC. The primary outcome was overall morbidity within 60 days after surgery. Analysis was by intention to treat. The study was registered with ClinicalTrials.gov (NCT01104727). The study was conducted between April 2011 and May 2015. A total of 600 patients were randomly assigned to receive either SPC (n = 297) or MPC (n = 303) and were eligible for data analysis. Postsurgical complications within 60 days were recorded in 13 patients (4.7 %) in the SPC group and in 16 (6.1 %) in the MPC group (P = 0.468); however, single-access procedures took longer [70 min (range 25-265) vs. 55 min (range 22-185); P risk of incisional hernia following SPC do not appear to be justified. Patient satisfaction with aesthetic results was greater after SPC than after MPC.

INDUCED EEG GAMMA OSCILLATION ALIGNMENT IMPROVES DIFFERENTIATION BETWEEN AUTISM AND ADHD GROUP RESPONSES IN A FACIAL CATEGORIZATION TASK.

Science.gov (United States)

Gross, Eric; El-Baz, Ayman S; Sokhadze, Guela E; Sears, Lonnie; Casanova, Manuel F; Sokhadze, Estate M

2012-01-01

INTRODUCTION: Children diagnosed with an autism spectrum disorder (ASD) often lack the ability to recognize and properly respond to emotional stimuli. Emotional deficits also characterize children with attention deficit/hyperactivity disorder (ADHD), in addition to exhibiting limited attention span. These abnormalities may effect a difference in the induced EEG gamma wave burst (35-45 Hz) peaked approximately 300-400 milliseconds following an emotional stimulus. Because induced gamma oscillations are not fixed at a definite point in time post-stimulus, analysis of averaged EEG data with traditional methods may result in an attenuated gamma burst power. METHODS: We used a data alignment technique to improve the averaged data, making it a better representation of the individual induced EEG gamma oscillations. A study was designed to test the response of a subject to emotional stimuli, presented in the form of emotional facial expression images. In a four part experiment, the subjects were instructed to identify gender in the first two blocks of the test, followed by differentiating between basic emotions in the final two blocks (i.e. anger vs. disgust). EEG data was collected from ASD (n=10), ADHD (n=9), and control (n=11) subjects via a 128 channel EGI system, and processed through a continuous wavelet transform and bandpass filter to isolate the gamma frequencies. A custom MATLAB code was used to align the data from individual trials between 200-600 ms post-stimulus, EEG site, and condition by maximizing the Pearson product-moment correlation coefficient between trials. The gamma power for the 400 ms window of maximum induced gamma burst was then calculated and compared between subject groups. RESULTS AND CONCLUSION: Condition (anger/disgust recognition, gender recognition) × Alignment × Group (ADHD, ASD, Controls) interaction was significant at most of parietal topographies (e.g., P3-P4, P7-P8). These interactions were better manifested in the aligned data set

EEG Clearing Office strengthened by EEG 2012. Alternative dispute resolution in the renewable energies industry; Aufwertung der Clearingstelle EEG durch das EEG 2012. Alternative Dispute Resolution im Bereich der Erneuerbaren Energien

Energy Technology Data Exchange (ETDEWEB)

Chatzinerantzis, Alexandros; Fach, Martin [Linklaters LLP, Frankfurt am Main (Germany). Praxisgruppe Litigation and Arbitration

2012-11-14

The EEG Clearing Office is a special arbitration forum for the purpose of facilitating quick and inexpensive out-of-court dispute resolutions and resolving cases of legal uncertainty in connection with the regulations of the EEG (Renewable Energy Law). The Clearing Office has developed dynamically over the past years, as the numbers of newly registered potential and ongoing procedures impressively show. In the 2012 amendment to the EEG the legislature has fundamentally revised and substantially widened the legal basis for the work of the Clearing Office. This provides the motivation for presenting the Clearing Office and its procedural rules in the following article.

EEG

Science.gov (United States)

... brain dead. EEG cannot be used to measure intelligence. Normal Results Brain electrical activity has a certain ... 2018, A.D.A.M., Inc. Duplication for commercial use must be authorized in writing by ADAM ...

Causality within the Epileptic Network: An EEG-fMRI Study Validated by Intracranial EEG.

Science.gov (United States)

Vaudano, Anna Elisabetta; Avanzini, Pietro; Tassi, Laura; Ruggieri, Andrea; Cantalupo, Gaetano; Benuzzi, Francesca; Nichelli, Paolo; Lemieux, Louis; Meletti, Stefano

2013-01-01

Accurate localization of the Seizure Onset Zone (SOZ) is crucial in patients with drug-resistance focal epilepsy. EEG with fMRI recording (EEG-fMRI) has been proposed as a complementary non-invasive tool, which can give useful additional information in the pre-surgical work-up. However, fMRI maps related to interictal epileptiform activities (IED) often show multiple regions of signal change, or "networks," rather than highly focal ones. Effective connectivity approaches like Dynamic Causal Modeling (DCM) applied to fMRI data potentially offers a framework to address which brain regions drives the generation of seizures and IED within an epileptic network. Here, we present a first attempt to validate DCM on EEG-fMRI data in one patient affected by frontal lobe epilepsy. Pre-surgical EEG-fMRI demonstrated two distinct clusters of blood oxygenation level dependent (BOLD) signal increases linked to IED, one located in the left frontal pole and the other in the ipsilateral dorso-lateral frontal cortex. DCM of the IED-related BOLD signal favored a model corresponding to the left dorso-lateral frontal cortex as driver of changes in the fronto-polar region. The validity of DCM was supported by: (a) the results of two different non-invasive analysis obtained on the same dataset: EEG source imaging (ESI), and "psycho-physiological interaction" analysis; (b) the failure of a first surgical intervention limited to the fronto-polar region; (c) the results of the intracranial EEG monitoring performed after the first surgical intervention confirming a SOZ located over the dorso-lateral frontal cortex. These results add evidence that EEG-fMRI together with advanced methods of BOLD signal analysis is a promising tool that can give relevant information within the epilepsy surgery diagnostic work-up.

Causality within the epileptic network: an EEG-fMRI study validated by intracranial EEG.

Directory of Open Access Journals (Sweden)

Anna Elisabetta eVaudano

2013-11-01

Full Text Available Accurate localization of the Seizure Onset Zone (SOZ is crucial in patients with drug-resistance focal epilepsy. EEG with fMRI recording (EEG-fMRI has been proposed as a complementary non-invasive tool, which can give useful additional information in the pre-surgical workup. However, fMRI maps related to interictal epileptiform activities (IED often show multiple regions of signal change, or networks, rather than highly focal ones. Effective connectivity approaches like Dynamic Causal Modelling (DCM applied to fMRI data potentially offers a framework to address which brain regions drives the generation of seizures and IED within an epileptic network. Here we present a first attempt to validate DCM on EEG-fMRI data in one patient affected by frontal lobe epilepsy. Pre-surgical EEG-fMRI demonstrated two distinct clusters of BOLD signal increases linked to IED, one located in the left frontal pole and the other in the ipsilateral dorso-lateral frontal cortex. DCM of the IED-related BOLD signal favoured a model corresponding to the left dorsolateral frontal cortex as driver of changes in the fronto-polar region. The validity of DCM was supported by: (a the results of two different non-invasive analysis obtained on the same dataset: EEG source imaging (ESI, and psychophysiological interaction analysis (PPI; (b the failure of a first surgical intervention limited to the fronto-polar region; (c the results of the intracranial EEG monitoring performed after the first surgical intervention confirming a SOZ located over the dorso-lateral frontal cortex. These results add evidence that EEG-fMRI together with advanced methods of BOLD signal analysis is a promising tool that can give relevant information within the epilepsy surgery diagnostic work-up.

The EEG 2017 in the overview

International Nuclear Information System (INIS)

Altrock, Martin; Vollprecht, Jens

2016-01-01

On 08.07.2016, the German Bundestag, the German Renewable Energies Act (EEG) in 2017 passed together with the wind-at-sea law. At the same time, the legislature changed 22 other energy legislation, inter alia, also the EnWG. Here, the law de facto a law amending the EEG 2014 is: The EEG is thus not total re-promulgated. Rather essentially part 3 (''Payment of market premium and feed in rate'') of the EEG 2014 renewed, notably Section 3 supplemented by regulations on the newly introduced procurements. But beyond the framework of support is further developed in various details, like the definition of a plant, the promotion of storage facilities and of course, in the again very ambitious and complicated transitional arrangements. Other notable individual changes concern the introduction of regional evidence of directly marketed electricity from renewable sources, the increase of liability for balancing group deviations in paragraph 60 para. 1 EEG 2017 or readjustments in the special equalization scheme, paragraph 64 para. 2 no. 2 EEG. [de

Parametric cost models for space telescopes

Science.gov (United States)

Stahl, H. Philip; Henrichs, Todd; Dollinger, Courtnay

2017-11-01

Multivariable parametric cost models for space telescopes provide several benefits to designers and space system project managers. They identify major architectural cost drivers and allow high-level design trades. They enable cost-benefit analysis for technology development investment. And, they provide a basis for estimating total project cost. A survey of historical models found that there is no definitive space telescope cost model. In fact, published models vary greatly [1]. Thus, there is a need for parametric space telescopes cost models. An effort is underway to develop single variable [2] and multi-variable [3] parametric space telescope cost models based on the latest available data and applying rigorous analytical techniques. Specific cost estimating relationships (CERs) have been developed which show that aperture diameter is the primary cost driver for large space telescopes; technology development as a function of time reduces cost at the rate of 50% per 17 years; it costs less per square meter of collecting aperture to build a large telescope than a small telescope; and increasing mass reduces cost.

Parametric Cost Models for Space Telescopes

Science.gov (United States)

Stahl, H. Philip; Henrichs, Todd; Dollinger, Courtney

2010-01-01

Multivariable parametric cost models for space telescopes provide several benefits to designers and space system project managers. They identify major architectural cost drivers and allow high-level design trades. They enable cost-benefit analysis for technology development investment. And, they provide a basis for estimating total project cost. A survey of historical models found that there is no definitive space telescope cost model. In fact, published models vary greatly [1]. Thus, there is a need for parametric space telescopes cost models. An effort is underway to develop single variable [2] and multi-variable [3] parametric space telescope cost models based on the latest available data and applying rigorous analytical techniques. Specific cost estimating relationships (CERs) have been developed which show that aperture diameter is the primary cost driver for large space telescopes; technology development as a function of time reduces cost at the rate of 50% per 17 years; it costs less per square meter of collecting aperture to build a large telescope than a small telescope; and increasing mass reduces cost.

The 2007 AASM recommendations for EEG electrode placement in polysomnography: impact on sleep and cortical arousal scoring.

Science.gov (United States)

Ruehland, Warren R; O'Donoghue, Fergal J; Pierce, Robert J; Thornton, Andrew T; Singh, Parmjit; Copland, Janet M; Stevens, Bronwyn; Rochford, Peter D

2011-01-01

To examine the impact of using American Academy of Sleep Medicine (AASM) recommended EEG derivations (F4/M1, C4/M1, O2/M1) vs. a single derivation (C4/M1) in polysomnography (PSG) on the measurement of sleep and cortical arousals, including inter- and intra-observer variability. Prospective, non-blinded, randomized comparison. Three Australian tertiary-care hospital clinical sleep laboratories. 30 PSGs from consecutive patients investigated for obstructive sleep apnea (OSA) during December 2007 and January 2008. N/A. To examine the impact of EEG derivations on PSG summary statistics, 3 scorers from different Australian clinical sleep laboratories each scored separate sets of 10 PSGs twice, once using 3 EEG derivations and once using 1 EEG derivation. To examine the impact on inter- and intra-scorer reliability, all 3 scorers scored a subset of 10 PSGs 4 times, twice using each method. All PSGs were de-identified and scored in random order according to the 2007 AASM Manual for the Scoring of Sleep and Associated Events. Using 3 referential EEG derivations during PSG, as recommended in the AASM manual, instead of a single central EEG derivation, as originally suggested by Rechtschaffen and Kales (1968), resulted in a mean ± SE decrease in N1 sleep of 9.6 ± 3.9 min (P = 0.018) and an increase in N3 sleep of 10.6 ± 2.8 min (P = 0.001). No significant differences were observed for any other sleep or arousal scoring summary statistics; nor were any differences observed in inter-scorer or intra-scorer reliability for scoring sleep or cortical arousals. This study provides information for those changing practice to comply with the 2007 AASM recommendations for EEG placement in PSG, for those using portable devices that are unable to comply with the recommendations due to limited channel options, and for the development of future standards for PSG scoring and recording. As the use of multiple EEG derivations only led to small changes in the distribution of derived sleep

Saccadic spike potentials in gamma-band EEG: characterization, detection and suppression.

Science.gov (United States)

Keren, Alon S; Yuval-Greenberg, Shlomit; Deouell, Leon Y

2010-02-01

Analysis of high-frequency (gamma-band) neural activity by means of non-invasive EEG is gaining increasing interest. However, we have recently shown that a saccade-related spike potential (SP) seriously confounds the analysis of EEG induced gamma-band responses (iGBR), as the SP eludes traditional EEG artifact rejection methods. Here we provide a comprehensive profile of the SP and evaluate methods for its detection and suppression, aiming to unveil true cerebral gamma-band activity. The SP appears consistently as a sharp biphasic deflection of about 22 ms starting at the saccade onset, with a frequency band of approximately 20-90 Hz. On the average, larger saccades elicit higher SP amplitudes. The SP amplitude gradually changes from the extra-ocular channels towards posterior sites with the steepest gradients around the eyes, indicating its ocular source. Although the amplitude and the sign of the SP depend on the choice of reference channel, the potential gradients remain the same and non-zero for all references. The scalp topography is modulated almost exclusively by the direction of saccades, with steeper gradients ipsilateral to the saccade target. We discuss how the above characteristics impede attempts to remove these SPs from the EEG by common temporal filtering, choice of different references, or rejection of contaminated trials. We examine the extent to which SPs can be reliably detected without an eye tracker, assess the degree to which scalp current density derivation attenuates the effect of the SP, and propose a tailored ICA procedure for minimizing the effect of the SP. Copyright (c) 2009 Elsevier Inc. All rights reserved.

EEG power and coherence while male adults watch emotional video films.

Science.gov (United States)

Schellberg, D; Besthorn, C; Klos, T; Gasser, T

1990-10-01

Quantitative EEG analysis recorded at F3, F4, T3, T4, P3, P4 was performed for a group of healthy right-handed male adults (n = 9) viewing video films varying in their inductiveness on the affective valence dimension. Digital EOG-correction permitted the inclusion of trials with eye movements. Muscle artifacts were statistically treated by means of analysis of covariance (ANCOVA). The configuration of topographically motivated EEG parameters corresponded to the subjective valence rating of different video films. Low broad band coherences (COHs) ranked films along the subjective ratings within each hemisphere by the fronto-temporal COHs and interhemispherically by the T4-T3 COH, as did, restricted to the right hemisphere, similarity of beta 2 band power topography over time. High frequencies may be involved in the processing and low frequencies in the transmission of differential affective information, which to integrate seemed to utilize resources of both hemispheres. Alpha 2 and beta 1 COHs were sensitive to variations in an integrality/disassociation dimension with regard to the arrangement of verbal-visual affective cues. Power fluctuations at frontal leads pointed to difficulties in interpreting interhemispheric EEG asymmetries in emotion research, if information on time dynamics is discarded.

EEG correlates of verbal and nonverbal working memory

Directory of Open Access Journals (Sweden)

Danker Jared

2005-11-01

Full Text Available Abstract Background Distinct cognitive processes support verbal and nonverbal working memory, with verbal memory depending specifically on the subvocal rehearsal of items. Methods We recorded scalp EEG while subjects performed a Sternberg task. In each trial, subjects judged whether a probe item was one of the three items in a study list. Lists were composed of stimuli from one of five pools whose items either were verbally rehearsable (letters, words, pictures of common objects or resistant to verbal rehearsal (sinusoidal grating patterns, single dot locations. Results We found oscillatory correlates unique to verbal stimuli in the θ (4–8 Hz, α (9–12 Hz, β (14–28 Hz, and γ (30–50 Hz frequency bands. Verbal stimuli generally elicited greater power than did nonverbal stimuli. Enhanced verbal power was found bilaterally in the θ band, over frontal and occipital areas in the α and β bands, and centrally in the γ band. When we looked specifically for cases where oscillatory power in the time interval between item presentations was greater than oscillatory power during item presentation, we found enhanced β activity in the frontal and occipital regions. Conclusion These results implicate stimulus-induced oscillatory activity in verbal working memory and β activity in the process of subvocal rehearsal.

Biogas plants in EEG. 4. new rev. and enl. ed.; Biogasanlagen im EEG

Energy Technology Data Exchange (ETDEWEB)

Loibl, Helmut; Maslaton, Martin; Bredow, Hartwig von; Walter, Rene (eds.)

2016-08-01

With the EEG 2014, the legislature has created a complete revision of all the RES plants. Specifically for biogas plants fundamental changes have been made with the maximum rated power or a new landscape conservation concept. For new biogas plants the legislator arranges not only a much lower remuneration, but also the direct marketing as a rule, which entails fundamental changes in the overall compensation system by itself. The new edition of this highly regarded standard work revives the extensive practical experience to EEG 2009, 2012 and 2014 in detail and in particular and takes into account the large number of newly issued clearinghouses decisions and judgments. All current legal issues and challenges of biogas plants can be found comprehensively presented here. [German] Mit dem EEG 2014 hat der Gesetzgeber eine komplette Neuregelung fuer alle EEG-Anlagen geschaffen. Speziell fuer Biogasanlagen wurden mit der Hoechstbemessungsleistung oder einem neuen Landschaftspflegebegriff grundlegende Aenderungen vorgenommen. Fuer neue Biogasanlagen ordnet der Gesetzgeber nicht nur eine deutlich geringere Verguetung, sondern zudem die Direktvermarktung als Regelfall an, was grundlegende Veraenderungen des gesamten Verguetungssystems nach sich zieht. Die Neuauflage dieses vielbeachteten Standardwerks greift die umfangreichen Praxiserfahrungen zum EEG 2009, 2012 und 2014 detailliert auf und beruecksichtigt insbesondere auch die Vielzahl der neu ergangenen Clearingstellenentscheidungen und Urteile. Alle aktuellen rechtlichen Themen und Herausforderungen bei Biogasanlagen finden Sie hier umfassend dargestellt.

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

Statistical models for brain signals with properties that evolve across trials

KAUST Repository

Ombao, Hernando

2017-12-07

Most neuroscience cognitive experiments involve repeated presentations of various stimuli across several minutes or a few hours. It has been observed that brain responses, even to the same stimulus, evolve over the course of the experiment. These changes in brain activation and connectivity are believed to be associated with learning and/or habituation. In this paper, we present two general approaches to modeling dynamic brain connectivity using electroencephalograms (EEGs) recorded across replicated trials in an experiment. The first approach is the Markovian regime-switching vector autoregressive model (MS-VAR) which treats EEGs as realizations of an underlying brain process that switches between different states both within a trial and across trials in the entire experiment. The second is the slowly evolutionary locally stationary process (SEv-LSP) which characterizes the observed EEGs as a mixture of oscillatory activities at various frequency bands. The SEv-LSP model captures the dynamic nature of the amplitudes of the band-oscillations and cross-correlations between them. The MS-VAR model is able to capture abrupt changes in the dynamics while the SEv-LSP directly gives interpretable results. Moreover, it is nonparametric and hence does not suffer from model misspecification. For both of these models, time-evolving connectivity metrics in the frequency domain are derived from the model parameters for both functional and effective connectivity. We illustrate these two models for estimating cross-trial connectivity in selective attention using EEG data from an oddball paradigm auditory experiment where the goal is to characterize the evolution of brain responses to target stimuli and to standard tones presented randomly throughout the entire experiment. The results suggest dynamic changes in connectivity patterns over trials with inter-subject variability.

Analysis of EEG Related Saccadic Eye Movement

Science.gov (United States)

Funase, Arao; Kuno, Yoshiaki; Okuma, Shigeru; Yagi, Tohru

Our final goal is to establish the model for saccadic eye movement that connects the saccade and the electroencephalogram(EEG). As the first step toward this goal, we recorded and analyzed the saccade-related EEG. In the study recorded in this paper, we tried detecting a certain EEG that is peculiar to the eye movement. In these experiments, each subject was instructed to point their eyes toward visual targets (LEDs) or the direction of the sound sources (buzzers). In the control cases, the EEG was recorded in the case of no eye movemens. As results, in the visual experiments, we found that the potential of EEG changed sharply on the occipital lobe just before eye movement. Furthermore, in the case of the auditory experiments, similar results were observed. In the case of the visual experiments and auditory experiments without eye movement, we could not observed the EEG changed sharply. Moreover, when the subject moved his/her eyes toward a right-side target, a change in EEG potential was found on the right occipital lobe. On the contrary, when the subject moved his/her eyes toward a left-side target, a sharp change in EEG potential was found on the left occipital lobe.

Standardized computer-based organized reporting of EEG

DEFF Research Database (Denmark)

Beniczky, Sándor; Aurlien, Harald; Brøgger, Jan C.

2017-01-01

Standardized terminology for computer-based assessment and reporting of EEG has been previously developed in Europe. The International Federation of Clinical Neurophysiology established a taskforce in 2013 to develop this further, and to reach international consensus. This work resulted in the se......Standardized terminology for computer-based assessment and reporting of EEG has been previously developed in Europe. The International Federation of Clinical Neurophysiology established a taskforce in 2013 to develop this further, and to reach international consensus. This work resulted...... in the second, revised version of SCORE (Standardized Computer-based Organized Reporting of EEG), which is presented in this paper. The revised terminology was implemented in a software package (SCORE EEG), which was tested in clinical practice on 12,160 EEG recordings. Standardized terms implemented in SCORE....... In the end, the diagnostic significance is scored, using a standardized list of terms. SCORE has specific modules for scoring seizures (including seizure semiology and ictal EEG patterns), neonatal recordings (including features specific for this age group), and for Critical Care EEG Terminology. SCORE...

Extended seizure detection algorithm for intracranial EEG recordings

DEFF Research Database (Denmark)

Kjaer, T. W.; Remvig, L. S.; Henriksen, J.

2010-01-01

Objective: We implemented and tested an existing seizure detection algorithm for scalp EEG (sEEG) with the purpose of improving it to intracranial EEG (iEEG) recordings. Method: iEEG was obtained from 16 patients with focal epilepsy undergoing work up for resective epilepsy surgery. Each patient...... had 4 or 5 recorded seizures and 24 hours of non-ictal data were used for evaluation. Data from three electrodes placed at the ictal focus were used for the analysis. A wavelet based feature extraction algorithm delivered input to a support vector machine (SVM) classifier for distinction between ictal...... and non-ictal iEEG. We compare our results to a method published by Shoeb in 2004. While the original method on sEEG was optimal with the use of only four subbands in the wavelet analysis, we found that better seizure detection could be made if all subbands were used for iEEG. Results: When using...

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.

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

International Nuclear Information System (INIS)

Freeborn, Danielle L.; McDaniel, Katherine L.; Moser, Virginia C.; Herr, David W.

2015-01-01

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 30 or an ED 50 –ED 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

No effects of a single 3G UMTS mobile phone exposure on spontaneous EEG activity, ERP correlates, and automatic deviance detection.

Science.gov (United States)

Trunk, Attila; Stefanics, Gábor; Zentai, Norbert; Kovács-Bálint, Zsófia; Thuróczy, György; Hernádi, István

2013-01-01

Potential effects of a 30 min exposure to third generation (3G) Universal Mobile Telecommunications System (UMTS) mobile phone-like electromagnetic fields (EMFs) were investigated on human brain electrical activity in two experiments. In the first experiment, spontaneous electroencephalography (sEEG) was analyzed (n = 17); in the second experiment, auditory event-related potentials (ERPs) and automatic deviance detection processes reflected by mismatch negativity (MMN) were investigated in a passive oddball paradigm (n = 26). Both sEEG and ERP experiments followed a double-blind protocol where subjects were exposed to either genuine or sham irradiation in two separate sessions. In both experiments, electroencephalograms (EEG) were recorded at midline electrode sites before and after exposure while subjects were watching a silent documentary. Spectral power of sEEG data was analyzed in the delta, theta, alpha, and beta frequency bands. In the ERP experiment, subjects were presented with a random series of standard (90%) and frequency-deviant (10%) tones in a passive binaural oddball paradigm. The amplitude and latency of the P50, N100, P200, MMN, and P3a components were analyzed. We found no measurable effects of a 30 min 3G mobile phone irradiation on the EEG spectral power in any frequency band studied. Also, we found no significant effects of EMF irradiation on the amplitude and latency of any of the ERP components. In summary, the present results do not support the notion that a 30 min unilateral 3G EMF exposure interferes with human sEEG activity, auditory evoked potentials or automatic deviance detection indexed by MMN. Copyright © 2012 Wiley Periodicals, Inc.

Comparison of different spatial transformations applied to EEG data: A case study of error processing.

Science.gov (United States)

Cohen, Michael X

2015-09-01

The purpose of this paper is to compare the effects of different spatial transformations applied to the same scalp-recorded EEG data. The spatial transformations applied are two referencing schemes (average and linked earlobes), the surface Laplacian, and beamforming (a distributed source localization procedure). EEG data were collected during a speeded reaction time task that provided a comparison of activity between error vs. correct responses. Analyses focused on time-frequency power, frequency band-specific inter-electrode connectivity, and within-subject cross-trial correlations between EEG activity and reaction time. Time-frequency power analyses showed similar patterns of midfrontal delta-theta power for errors compared to correct responses across all spatial transformations. Beamforming additionally revealed error-related anterior and lateral prefrontal beta-band activity. Within-subject brain-behavior correlations showed similar patterns of results across the spatial transformations, with the correlations being the weakest after beamforming. The most striking difference among the spatial transformations was seen in connectivity analyses: linked earlobe reference produced weak inter-site connectivity that was attributable to volume conduction (zero phase lag), while the average reference and Laplacian produced more interpretable connectivity results. Beamforming did not reveal any significant condition modulations of connectivity. Overall, these analyses show that some findings are robust to spatial transformations, while other findings, particularly those involving cross-trial analyses or connectivity, are more sensitive and may depend on the use of appropriate spatial transformations. Copyright © 2014 Elsevier B.V. All rights reserved.

EEG

African Journals Online (AJOL)

2017-09-03

Sep 3, 2017 ... However, very few studies have examined the use of EEG in developing countries, including Ni- ... of evoked potentials from brain neurons, referred to as .... Percentage. Gender. Male. 89. 62.7. Female. 53. 37.3. Age. 0-10. 59.

Aircraft noise effects on sleep: a systematic comparison of EEG awakenings and automatically detected cardiac activations

International Nuclear Information System (INIS)

Basner, Mathias; Müller, Uwe; Elmenhorst, Eva-Maria; Kluge, Götz; Griefahn, Barbara

2008-01-01

Polysomnography is the gold standard for investigating noise effects on sleep, but data collection and analysis are sumptuous and expensive. We recently developed an algorithm for the automatic identification of cardiac activations associated with cortical arousals, which uses heart rate information derived from a single electrocardiogram (ECG) channel. We hypothesized that cardiac activations can be used as estimates for EEG awakenings. Polysomnographic EEG awakenings and automatically detected cardiac activations were systematically compared using laboratory data of 112 subjects (47 male, mean ± SD age 37.9 ± 13 years), 985 nights and 23 855 aircraft noise events (ANEs). The probability of automatically detected cardiac activations increased monotonically with increasing maximum sound pressure levels of ANEs, exceeding the probability of EEG awakenings by up to 18.1%. If spontaneous reactions were taken into account, exposure–response curves were practically identical for EEG awakenings and cardiac activations. Automatically detected cardiac activations may be used as estimates for EEG awakenings. More investigations are needed to further validate the ECG algorithm in the field and to investigate inter-individual differences in its ability to predict EEG awakenings. This inexpensive, objective and non-invasive method facilitates large-scale field studies on the effects of traffic noise on sleep

Contribution of EEG in transient neurological deficits.

Science.gov (United States)

Lozeron, Pierre; Tcheumeni, Nadine Carole; Turki, Sahar; Amiel, Hélène; Meppiel, Elodie; Masmoudi, Sana; Roos, Caroline; Crassard, Isabelle; Plaisance, Patrick; Benbetka, Houria; Guichard, Jean-Pierre; Houdart, Emmanuel; Baudoin, Hélène; Kubis, Nathalie

2018-01-01

Identification of stroke mimics and 'chameleons' among transient neurological deficits (TND) is critical. Diagnostic workup consists of a brain imaging study, for a vascular disease or a brain tumour and EEG, for epileptiform discharges. The precise role of EEG in this diagnostic workup has, however, never been clearly delineated. However, this could be crucial in cases of atypical or incomplete presentation with consequences on disease management and treatment. We analysed the EEG patterns on 95 consecutive patients referred for an EEG within 7 days of a TND with diagnostic uncertainty. Patients were classified at the discharge or the 3-month follow-up visit as: 'ischemic origin', 'migraine aura', 'focal seizure', and 'other'. All patients had a brain imaging study. EEG characteristics were correlated to the TND symptoms, imaging study, and final diagnosis. Sixty four (67%) were of acute onset. Median symptom duration was 45 min. Thirty two % were 'ischemic', 14% 'migraine aura', 19% 'focal seizure', and 36% 'other' cause. EEGs were recorded with a median delay of 1.6 day after symptoms onset. Forty EEGs (42%) were abnormal. Focal slow waves were the most common finding (43%), also in the ischemic group (43%), whether patients had a typical presentation or not. Epileptiform discharges were found in three patients, one with focal seizure and two with migraine aura. Non-specific EEG focal slowing is commonly found in TND, and may last several days. We found no difference in EEG presentation between stroke mimics and stroke chameleons, and between other diagnoses.

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

A generalized parametric response mapping method for analysis of multi-parametric imaging: A feasibility study with application to glioblastoma.

Science.gov (United States)

Lausch, Anthony; Yeung, Timothy Pok-Chi; Chen, Jeff; Law, Elton; Wang, Yong; Urbini, Benedetta; Donelli, Filippo; Manco, Luigi; Fainardi, Enrico; Lee, Ting-Yim; Wong, Eugene

2017-11-01

Parametric response map (PRM) analysis of functional imaging has been shown to be an effective tool for early prediction of cancer treatment outcomes and may also be well-suited toward guiding personalized adaptive radiotherapy (RT) strategies such as sub-volume boosting. However, the PRM method was primarily designed for analysis of longitudinally acquired pairs of single-parameter image data. The purpose of this study was to demonstrate the feasibility of a generalized parametric response map analysis framework, which enables analysis of multi-parametric data while maintaining the key advantages of the original PRM method. MRI-derived apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) maps acquired at 1 and 3-months post-RT for 19 patients with high-grade glioma were used to demonstrate the algorithm. Images were first co-registered and then standardized using normal tissue image intensity values. Tumor voxels were then plotted in a four-dimensional Cartesian space with coordinate values equal to a voxel's image intensity in each of the image volumes and an origin defined as the multi-parametric mean of normal tissue image intensity values. Voxel positions were orthogonally projected onto a line defined by the origin and a pre-determined response vector. The voxels are subsequently classified as positive, negative or nil, according to whether projected positions along the response vector exceeded a threshold distance from the origin. The response vector was selected by identifying the direction in which the standard deviation of tumor image intensity values was maximally different between responding and non-responding patients within a training dataset. Voxel classifications were visualized via familiar three-class response maps and then the fraction of tumor voxels associated with each of the classes was investigated for predictive utility analogous to the original PRM method. Independent PRM and MPRM analyses of the contrast

EEG-informed fMRI analysis during a hand grip task: estimating the relationship between EEG rhythms and the BOLD signal

Directory of Open Access Journals (Sweden)

Roberta eSclocco

2014-04-01

Full Text Available In the last decade, an increasing interest has arisen in investigating the relationship between the electrophysiological and hemodynamic measurements of brain activity, such as EEG and (BOLD fMRI. In particular, changes in BOLD have been shown to be associated with changes in the spectral profile of neural activity, rather than with absolute power. Concurrently, recent findings showed that different EEG rhythms are independently related to changes in the BOLD signal: therefore, it would be important to distinguish between the contributions of the different EEG rhythms to BOLD fluctuations when modeling the relationship between the two signals. Here we propose a method to perform EEG-informed fMRI analysis, in which the EEG regressors take into account both the changes in the spectral profile and the rhythms distinction. We applied it to EEG-fMRI data during a hand grip task in healthy subjects, and compared the results with those obtained by two existing models found in literature. Our results showed that the proposed method better captures the correlations between BOLD signal and EEG rhythms modulations, identifying task-related, well localized activated volumes. Furthermore, we showed that including among the regressors also EEG rhythms not primarily involved in the task enhances the performance of the analysis, even when only correlations with BOLD signal and specific EEG rhythms are explored.

Preparing laboratory and real-world EEG data for large-scale analysis: A containerized approach

Directory of Open Access Journals (Sweden)

Nima eBigdely-Shamlo

2016-03-01

Full Text Available Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain-computer interface (BCI models.. However, the absence of standard-ized vocabularies for annotating events in a machine understandable manner, the welter of collection-specific data organizations, the diffi-culty in moving data across processing platforms, and the unavailability of agreed-upon standards for preprocessing have prevented large-scale analyses of EEG. Here we describe a containerized approach and freely available tools we have developed to facilitate the process of an-notating, packaging, and preprocessing EEG data collections to enable data sharing, archiving, large-scale machine learning/data mining and (meta-analysis. The EEG Study Schema (ESS comprises three data Levels, each with its own XML-document schema and file/folder convention, plus a standardized (PREP pipeline to move raw (Data Level 1 data to a basic preprocessed state (Data Level 2 suitable for application of a large class of EEG analysis methods. Researchers can ship a study as a single unit and operate on its data using a standardized interface. ESS does not require a central database and provides all the metadata data necessary to execute a wide variety of EEG processing pipelines. The primary focus of ESS is automated in-depth analysis and meta-analysis EEG studies. However, ESS can also encapsulate meta-information for the other modalities such as eye tracking, that are in-creasingly used in both laboratory and real-world neuroimaging. ESS schema and tools are freely available at eegstudy.org, and a central cata-log of over 850 GB of existing data in ESS format is available at study-catalog.org. These tools and resources are part of a larger effort to ena-ble data sharing at sufficient scale for researchers to engage in truly large-scale EEG analysis and data mining (BigEEG.org.

Improving the Specificity of EEG for Diagnosing Alzheimer's Disease

Directory of Open Access Journals (Sweden)

François-B. Vialatte

2011-01-01

Full Text Available Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD. However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment patients. Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest were θ (3.5–7.5 Hz, α1 (7.5–9.5 Hz, α2 (9.5–12.5 Hz, and β (12.5–25 Hz. The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models. Results. Enhanced EEG power in the θ range is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies. Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD.

The PREP Pipeline: Standardized preprocessing for large-scale EEG analysis

Directory of Open Access Journals (Sweden)

Nima eBigdelys Shamlo

2015-06-01

Full Text Available The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode/.

The PREP pipeline: standardized preprocessing for large-scale EEG analysis.

Science.gov (United States)

Bigdely-Shamlo, Nima; Mullen, Tim; Kothe, Christian; Su, Kyung-Min; Robbins, Kay A

2015-01-01

The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode.

Circulation and Directional Amplification in the Josephson Parametric Converter

Science.gov (United States)

Hatridge, Michael

Nonreciprocal transport and directional amplification of weak microwave signals are fundamental ingredients in performing efficient measurements of quantum states of flying microwave light. This challenge has been partly met, as quantum-limited amplification is now regularly achieved with parametrically-driven, Josephson-junction based superconducting circuits. However, these devices are typically non-directional, requiring external circulators to separate incoming and outgoing signals. Recently this limitation has been overcome by several proposals and experimental realizations of both directional amplifiers and circulators based on interference between several parametric processes in a single device. This new class of multi-parametrically driven devices holds the promise of achieving a variety of desirable characteristics simultaneously- directionality, reduced gain-bandwidth constraints and quantum-limited added noise, and are good candidates for on-chip integration with other superconducting circuits such as qubits.

Short pulse generation from a passively mode-locked fiber optical parametric oscillator with optical time-stretch.

Science.gov (United States)

Qiu, Yi; Wei, Xiaoming; Du, Shuxin; Wong, Kenneth K Y; Tsia, Kevin K; Xu, Yiqing

2018-04-16

We propose a passively mode-locked fiber optical parametric oscillator assisted with optical time-stretch. Thanks to the lately developed optical time-stretch technique, the onset oscillating spectral components can be temporally dispersed across the pump envelope and further compete for the parametric gain with the other parts of onset oscillating sidebands within the pump envelope. By matching the amount of dispersion in optical time-stretch with the pulse width of the quasi-CW pump and oscillating one of the parametric sidebands inside the fiber cavity, we numerically show that the fiber parametric oscillator can be operated in a single pulse regime. By varying the amount of the intracavity dispersion, we further verify that the origin of this single pulse mode-locking regime is due to the optical pulse stretching and compression.

EEG Changes Due to Experimentally Induced 3G Mobile Phone Radiation.

Science.gov (United States)

Roggeveen, Suzanne; van Os, Jim; Viechtbauer, Wolfgang; Lousberg, Richel

2015-01-01

The aim of this study was to investigate whether a 15-minute placement of a 3G dialing mobile phone causes direct changes in EEG activity compared to the placement of a sham phone. Furthermore, it was investigated whether placement of the mobile phone on the ear or the heart would result in different outcomes. Thirty-one healthy females participated. All subjects were measured twice: on one of the two days the mobile phone was attached to the ear, the other day to the chest. In this single-blind, cross-over design, assessments in the sham phone condition were conducted directly preceding and following the mobile phone exposure. During each assessment, EEG activity and radiofrequency radiation were recorded jointly. Delta, theta, alpha, slowbeta, fastbeta, and gamma activity was computed. The association between radiation exposure and the EEG was tested using multilevel random regression analyses with radiation as predictor of main interest. Significant radiation effects were found for the alpha, slowbeta, fastbeta, and gamma bands. When analyzed separately, ear location of the phone was associated with significant results, while chest placement was not. The results support the notion that EEG alterations are associated with mobile phone usage and that the effect is dependent on site of placement. Further studies are required to demonstrate the physiological relevance of these findings.

The added value of simultaneous EEG and amplitude-integrated EEG recordings in three newborn infants

NARCIS (Netherlands)

de Vries, Nathalie K. S.; ter Horst, Hendrik J.; Bos, Arend F.

2007-01-01

Amplitude-integrated electroencephalograms (aEEGs) recorded by cerebral function monitors (CFMs) are used increasingly to monitor the cerebral activity of newborn infants with encephalopathy. Recently, new CFM devices became available which also reveal the original EEG signals from the same leads.

Beamformer source analysis and connectivity on concurrent EEG and MEG data during voluntary movements.

Science.gov (United States)

Muthuraman, Muthuraman; Hellriegel, Helge; Hoogenboom, Nienke; Anwar, Abdul Rauf; Mideksa, Kidist Gebremariam; Krause, Holger; Schnitzler, Alfons; Deuschl, Günther; Raethjen, Jan

2014-01-01

Electroencephalography (EEG) and magnetoencephalography (MEG) are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis. In the present study, the cortical and sub-cortical network of coherent sources at the finger tapping task frequency (2-4 Hz) and the modes of interaction within this network were analysed in 15 healthy subjects using a beamformer approach called the dynamic imaging of coherent sources (DICS) with subsequent source signal reconstruction and renormalized partial directed coherence analysis (RPDC). MEG and EEG data were recorded simultaneously allowing the comparison of each of the modalities separately to that of the combined approach. We found the identified network of coherent sources for the finger tapping task as described in earlier studies when using only the MEG or combined MEG+EEG whereas the EEG data alone failed to detect single sub-cortical sources. The signal-to-noise ratio (SNR) level of the coherent rhythmic activity at the tapping frequency in MEG and combined MEG+EEG data was significantly higher than EEG alone. The functional connectivity analysis revealed that the combined approach had more active connections compared to either of the modalities during the finger tapping (FT) task. These results indicate that MEG is superior in the detection of deep coherent sources and that the SNR seems to be more vital than the sensitivity to theoretical dipole orientation and the volume conduction effect in the case of EEG.

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 %

Measurement of IR optics with linear coupling's action-angle parametrization

Science.gov (United States)

Luo, Y.; Bai, M.; Pilat, F.; Satogata, T.; Trbojevic, D.

2005-08-01

Linear coupling’s action-angle parametrization is convenient for interpretation of turn-by-turn beam position monitor (BPM) data. We demonstrate how to apply this parametrization to extract Twiss and coupling parameters in interaction regions (IRs), using BPMs on each side of a long IR drift region. Example data were acquired at the Relativistic Heavy Ion Collider, using an ac dipole to excite a single transverse eigenmode. We have measured the waist of the β function and its Twiss and coupling parameters.

Statistical models for brain signals with properties that evolve across trials.

Science.gov (United States)

Ombao, Hernando; Fiecas, Mark; Ting, Chee-Ming; Low, Yin Fen

2017-12-07

Most neuroscience cognitive experiments involve repeated presentations of various stimuli across several minutes or a few hours. It has been observed that brain responses, even to the same stimulus, evolve over the course of the experiment. These changes in brain activation and connectivity are believed to be associated with learning and/or habituation. In this paper, we present two general approaches to modeling dynamic brain connectivity using electroencephalograms (EEGs) recorded across replicated trials in an experiment. The first approach is the Markovian regime-switching vector autoregressive model (MS-VAR) which treats EEGs as realizations of an underlying brain process that switches between different states both within a trial and across trials in the entire experiment. The second is the slowly evolutionary locally stationary process (SEv-LSP) which characterizes the observed EEGs as a mixture of oscillatory activities at various frequency bands. The SEv-LSP model captures the dynamic nature of the amplitudes of the band-oscillations and cross-correlations between them. The MS-VAR model is able to capture abrupt changes in the dynamics while the SEv-LSP directly gives interpretable results. Moreover, it is nonparametric and hence does not suffer from model misspecification. For both of these models, time-evolving connectivity metrics in the frequency domain are derived from the model parameters for both functional and effective connectivity. We illustrate these two models for estimating cross-trial connectivity in selective attention using EEG data from an oddball paradigm auditory experiment where the goal is to characterize the evolution of brain responses to target stimuli and to standard tones presented randomly throughout the entire experiment. The results suggest dynamic changes in connectivity patterns over trials with inter-subject variability. Copyright © 2017. Published by Elsevier Inc.

Visual, Auditory, and Cross Modal Sensory Processing in Adults with Autism: An EEG Power and BOLD fMRI Investigation

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Hames, Elizabeth’ C.; Murphy, Brandi; Rajmohan, Ravi; Anderson, Ronald C.; Baker, Mary; Zupancic, Stephen; O’Boyle, Michael; Richman, David

2016-01-01

Electroencephalography (EEG) and blood oxygen level dependent functional magnetic resonance imagining (BOLD fMRI) assessed the neurocorrelates of sensory processing of visual and auditory stimuli in 11 adults with autism (ASD) and 10 neurotypical (NT) controls between the ages of 20–28. We hypothesized that ASD performance on combined audiovisual trials would be less accurate with observable decreased EEG power across frontal, temporal, and occipital channels and decreased BOLD fMRI activity in these same regions; reflecting deficits in key sensory processing areas. Analysis focused on EEG power, BOLD fMRI, and accuracy. Lower EEG beta power and lower left auditory cortex fMRI activity were seen in ASD compared to NT when they were presented with auditory stimuli as demonstrated by contrasting the activity from the second presentation of an auditory stimulus in an all auditory block vs. the second presentation of a visual stimulus in an all visual block (AA2-VV2).We conclude that in ASD, combined audiovisual processing is more similar than unimodal processing to NTs. PMID:27148020

Visual, Auditory, and Cross Modal Sensory Processing in Adults with Autism:An EEG Power and BOLD fMRI Investigation

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Elizabeth C Hames

2016-04-01

Full Text Available Electroencephalography (EEG and Blood Oxygen Level Dependent Functional Magnetic Resonance Imagining (BOLD fMRI assessed the neurocorrelates of sensory processing of visual and auditory stimuli in 11 adults with autism (ASD and 10 neurotypical (NT controls between the ages of 20-28. We hypothesized that ASD performance on combined audiovisual trials would be less accurate with observable decreased EEG power across frontal, temporal, and occipital channels and decreased BOLD fMRI activity in these same regions; reflecting deficits in key sensory processing areas. Analysis focused on EEG power, BOLD fMRI, and accuracy. Lower EEG beta power and lower left auditory cortex fMRI activity were seen in ASD compared to NT when they were presented with auditory stimuli as demonstrated by contrasting the activity from the second presentation of an auditory stimulus in an all auditory block versus the second presentation of a visual stimulus in an all visual block (AA2­VV2. We conclude that in ASD, combined audiovisual processing is more similar than unimodal processing to NTs.

The use of conventional EEG for the assessment of hypoxic ischaemic encephalopathy in the newborn: a review.

LENUS (Irish Health Repository)

Walsh, B H

2011-07-01

Neonatal hypoxic ischaemic encephalopathy continues to be one of the leading causes of morbidity and mortality among neonates around the globe. With the advent of therapeutic hypothermia, the need to accurately classify the severity of injury in the early neonatal period is of great importance. As clinical measures cannot always accurately estimate the severity early enough for treatment to be initiated, clinicians have become more dependent on conventional and amplitude integrated EEG. Despite this, there is currently no single agreed classification scheme for the neonatal EEG in hypoxic ischaemic encephalopathy. In this review we discuss classification schemes of neonatal background EEG, published over the past 35 years, highlighting the urgent need for a universal visual analysis scheme.

The use of conventional EEG for the assessment of hypoxic ischaemic encephalopathy in the newborn: a review.

LENUS (Irish Health Repository)

Walsh, B H

2012-01-31

Neonatal hypoxic ischaemic encephalopathy continues to be one of the leading causes of morbidity and mortality among neonates around the globe. With the advent of therapeutic hypothermia, the need to accurately classify the severity of injury in the early neonatal period is of great importance. As clinical measures cannot always accurately estimate the severity early enough for treatment to be initiated, clinicians have become more dependent on conventional and amplitude integrated EEG. Despite this, there is currently no single agreed classification scheme for the neonatal EEG in hypoxic ischaemic encephalopathy. In this review we discuss classification schemes of neonatal background EEG, published over the past 35 years, highlighting the urgent need for a universal visual analysis scheme.

Single-trial regression elucidates the role of prefrontal theta oscillations in response conflict

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Michael X Cohen

2011-02-01

Full Text Available In most cognitive neuroscience experiments there are many behavioral and experimental dynamics, and many indices of brain activity, that vary from trial to trial. For example, in studies of response conflict, conflict is usually treated as a binary variable (i.e., response conflict exists or does not in any given trial, whereas some evidence and intuition suggests that conflict may vary in intensity from trial to trial. Here we demonstrate that single-trial multiple regression of time-frequency electrophysiological activity reveals neural mechanisms of cognitive control that are not apparent in cross-trial averages. We also introduce a novel extension to oscillation phase coherence and synchronization analyses, based on weighted phase modulation, that has advantages over standard coherence measures in terms of linking electrophysiological dynamics to trial-varying behavior and experimental variables. After replicating previous response conflict findings using trial-averaged data, we extend these findings using single trial analytic methods to provide novel evidence for the role of medial frontal-lateral prefrontal theta-band synchronization in conflict-induced response time dynamics, including a role for lateral prefrontal theta-band activity in biasing response times according to perceptual conflict. Given that these methods shed new light on the prefrontal mechanisms of response conflict, they are also likely to be useful for investigating other neurocognitive processes.

EEG. Renewables Act. Comment. 3. new rev. and enl. ed.; EEG. Erneuerbare-Energien-Gesetz. Kommentar

Energy Technology Data Exchange (ETDEWEB)

Frenz, Walter [Rheinisch-Westfaelische Technische Hochschule (RWTH), Aachen (Germany). Lehr- und Forschungsgebiet Berg-, Umwelt- und Europarecht; Mueggenborg, Hans-Juergen (eds.) [Kassel Univ. (Germany)

2013-05-01

Like hardly any other law, the Renewable Energy Sources Law (EEG) is a subject to continuing modifications. This makes the application of the already complicated regulations even for experts to a special challenge. With the proven Berliner comment EEG, now a reliable companion through the bureaucratic jungle is available. All regulations of the EEG are commented precisely and easily to understand by profound experts. An extensive selection of terminology enables a rapid orientation within this book. In addition to the excursions to renewable energy technologies, this book also describes the structural aspects in the establishment of a photovoltaic system.

Standardized Computer-based Organized Reporting of EEG: SCORE

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Beniczky, Sándor; Aurlien, Harald; Brøgger, Jan C; Fuglsang-Frederiksen, Anders; Martins-da-Silva, António; Trinka, Eugen; Visser, Gerhard; Rubboli, Guido; Hjalgrim, Helle; Stefan, Hermann; Rosén, Ingmar; Zarubova, Jana; Dobesberger, Judith; Alving, Jørgen; Andersen, Kjeld V; Fabricius, Martin; Atkins, Mary D; Neufeld, Miri; Plouin, Perrine; Marusic, Petr; Pressler, Ronit; Mameniskiene, Ruta; Hopfengärtner, Rüdiger; Emde Boas, Walter; Wolf, Peter

2013-01-01

The electroencephalography (EEG) signal has a high complexity, and the process of extracting clinically relevant features is achieved by visual analysis of the recordings. The interobserver agreement in EEG interpretation is only moderate. This is partly due to the method of reporting the findings in free-text format. The purpose of our endeavor was to create a computer-based system for EEG assessment and reporting, where the physicians would construct the reports by choosing from predefined elements for each relevant EEG feature, as well as the clinical phenomena (for video-EEG recordings). A working group of EEG experts took part in consensus workshops in Dianalund, Denmark, in 2010 and 2011. The faculty was approved by the Commission on European Affairs of the International League Against Epilepsy (ILAE). The working group produced a consensus proposal that went through a pan-European review process, organized by the European Chapter of the International Federation of Clinical Neurophysiology. The Standardised Computer-based Organised Reporting of EEG (SCORE) software was constructed based on the terms and features of the consensus statement and it was tested in the clinical practice. The main elements of SCORE are the following: personal data of the patient, referral data, recording conditions, modulators, background activity, drowsiness and sleep, interictal findings, “episodes” (clinical or subclinical events), physiologic patterns, patterns of uncertain significance, artifacts, polygraphic channels, and diagnostic significance. The following specific aspects of the neonatal EEGs are scored: alertness, temporal organization, and spatial organization. For each EEG finding, relevant features are scored using predefined terms. Definitions are provided for all EEG terms and features. SCORE can potentially improve the quality of EEG assessment and reporting; it will help incorporate the results of computer-assisted analysis into the report, it will make

Acute confusional state of unknown cause in the elderly: a study with continuous EEG monitoring.

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Naeije, Gilles; Gaspard, Nicolas; Depondt, Chantal; Pepersack, Thierry; Legros, Benjamin

2012-03-01

Acute confusional state (ACS) is a frequent cause of emergency consultation in the elderly. Many causes of ACS are also risk factors for seizures. Both non-convulsive seizures and status epilepticus can cause acute confusion. The yield of routine EEG may not be optimal in case of prolonged post-ictal confusion. We thus, sought to evaluate the yield of CEEG in identifying seizures in elderly patients with ACS of unknown origin. We reviewed our CEEG database for patients over 75 years with ACS and collected EEG, CEEG and clinical information. Thirty-one percent (15/48) of the CEEG performed in elderly patients were done for ACS. Routine EEG did not reveal any epileptic anomalies in 7/15 patients. Among those, CEEG identified interictal epileptiform discharges (IED) in 2 and NCSE in 1. In 8/15 patients, routine EEG revealed epileptiform abnormalities: 3 with IED (including 1 with periodic lateralized discharges), 3 with non-convulsive seizures (NCSz) and 2 with non-convulsive status epilepticus (NCSE). Among patients with only IED, CEEG revealed NCSz in 1 and NCSE in 2. This retrospective study suggests that NCSz and NCSE may account for more cases of ACS than what was previously thought. A single negative routine EEG does not exclude this diagnosis. Continuous EEG (CEEG) monitoring is more revealing than routine EEG for the detection of NCSE and NCSz in confused elderly. The presence of IED in the first routine EEG strongly suggests concomitant NCSz or NCSE. Prospective studies are required to further determine the role of CEEG monitoring in the assessment of ACS in the elderly and to establish the incidence of NCSz and NCSE in this setting. Copyright © 2012 Elsevier Inc. All rights reserved.

Pharmaco-EEG: A Study of Individualized Medicine in Clinical Practice.

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Swatzyna, Ronald J; Kozlowski, Gerald P; Tarnow, Jay D

2015-07-01

Pharmaco-electroencephalography (Pharmaco-EEG) studies using clinical EEG and quantitative EEG (qEEG) technologies have existed for more than 4 decades. This is a promising area that could improve psychotropic intervention using neurological data. One of the objectives in our clinical practice has been to collect EEG and quantitative EEG (qEEG) data. In the past 5 years, we have identified a subset of refractory cases (n = 386) found to contain commonalities of a small number of electrophysiological features in the following diagnostic categories: mood, anxiety, autistic spectrum, and attention deficit disorders, Four abnormalities were noted in the majority of medication failure cases and these abnormalities did not appear to significantly align with their diagnoses. Those were the following: encephalopathy, focal slowing, beta spindles, and transient discharges. To analyze the relationship noted, they were tested for association with the assigned diagnoses. Fisher's exact test and binary logistics regression found very little (6%) association between particular EEG/qEEG abnormalities and diagnoses. Findings from studies of this type suggest that EEG/qEEG provides individualized understanding of pharmacotherapy failures and has the potential to improve medication selection. © EEG and Clinical Neuroscience Society (ECNS) 2014.

EEG signatures of arm isometric exertions in preparation, planning and execution.

Science.gov (United States)

Nasseroleslami, Bahman; Lakany, Heba; Conway, Bernard A

2014-04-15

-dependent planning and execution of isometric motor tasks. The results contribute to our understanding of the functions of different brain regions during voluntary motor tasks and their activity signatures in EEG can shed light on the relationships between large-scale recordings such as EEG and other recordings such as single unit activity and fMRI in this context. Copyright © 2013 Elsevier Inc. All rights reserved.

EEG activity during estral cycle in the rat.

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Corsi-Cabrera, M; Juárez, J; Ponce-de-León, M; Ramos, J; Velázquez, P N

1992-10-01

EEG activity was recorded from right and left parietal cortex in adult female rats daily during 6 days. Immediately after EEG recording vaginal smears were taken and were microscopically analyzed to determine the estral stage. Absolute and relative powers and interhemispheric correlation of EEG activity were calculated and compared between estral stages. Interhemispheric correlation was significantly lower during diestrous as compared to proestrous and estrous. Absolute and relative powers did not show significant differences between estral stages. Absolute powers of alpha1, alpha2, beta1 and beta2 bands were significantly higher at the right parietal cortex. Comparisons of the same EEG records with estral stages randomly grouped showed no significant differences for any of the EEG parameters. EEG activity is a sensitive tool to study functional changes related to the estral cycle.

Single-trial event-related potentials to significant stimuli.

Science.gov (United States)

Rushby, Jacqueline A; Barry, Robert J

2009-11-01

The stimulus-response pattern of the skin conductance response (SCR) was used as a model of the Orienting Reflex (OR) to assess the P1, N1, P2, N2 and late positive complex (LPC/P300) components of the ERP in a simple habituation paradigm, in which a single series of 12 innocuous tones were presented at a very long interstimulus interval (2 min). To maintain their waking state during this boring task, participants were instructed to alternately close or open their eyes to each stimulus. None of the baseline-to-peak ERP measures showed trials effects comparable with the marked habituation over trials shown by the SCRs. Principal Components Analysis was used to decompose the ERP, yielding factors identified as the N1, N2, P3a, P3b and Novelty P3 components. An additional factor represented later eye-movement activity. No trial effects were apparent for the N1, N2, P3a or P3b components. The Novelty P3 showed marked response decrement over trials. These results are discussed in relation to current conceptualisations of the OR.

Signal Quality Evaluation of Emerging EEG Devices

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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.

Design of a 32-channel EEG system for brain control interface applications.

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Wang, Ching-Sung

2012-01-01

This study integrates the hardware circuit design and the development support of the software interface to achieve a 32-channel EEG system for BCI applications. Since the EEG signals of human bodies are generally very weak, in addition to preventing noise interference, it also requires avoiding the waveform distortion as well as waveform offset and so on; therefore, 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 will generate DC bias which affects the measurement signals. For this reason, this study specially designs an improved 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. For the analog circuit, a frequency band will be taken out through the filtering circuit and then the digital filtering design will be used to adjust the extracted frequency band for the target frequency band, combining with MATLAB to design man-machine interface for displaying brain wave. Finally the measured signals are compared to the traditional 32-channel EEG signals. In addition to meeting the IFCN standards, the system design also conducted measurement verification in the standard EEG isolation room in order to demonstrate the accuracy and reliability of this system design.

Standardized computer-based organized reporting of EEG

DEFF Research Database (Denmark)

Beniczky, Sándor; Aurlien, Harald; Brøgger, Jan C.

2017-01-01

Standardized terminology for computer-based assessment and reporting of EEG has been previously developed in Europe. The International Federation of Clinical Neurophysiology established a taskforce in 2013 to develop this further, and to reach international consensus. This work resulted in the se......Standardized terminology for computer-based assessment and reporting of EEG has been previously developed in Europe. The International Federation of Clinical Neurophysiology established a taskforce in 2013 to develop this further, and to reach international consensus. This work resulted...... in the second, revised version of SCORE (Standardized Computer-based Organized Reporting of EEG), which is presented in this paper. The revised terminology was implemented in a software package (SCORE EEG), which was tested in clinical practice on 12,160 EEG recordings. Standardized terms implemented in SCORE...... are used to report the features of clinical relevance, extracted while assessing the EEGs. Selection of the terms is context sensitive: initial choices determine the subsequently presented sets of additional choices. This process automatically generates a report and feeds these features into a database...

Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors

Science.gov (United States)

Bhagat, Nikunj A.; Venkatakrishnan, Anusha; Abibullaev, Berdakh; Artz, Edward J.; Yozbatiran, Nuray; Blank, Amy A.; French, James; Karmonik, Christof; Grossman, Robert G.; O'Malley, Marcia K.; Francisco, Gerard E.; Contreras-Vidal, Jose L.

2016-01-01

This study demonstrates the feasibility of detecting motor intent from brain activity of chronic stroke patients using an asynchronous electroencephalography (EEG)-based brain machine interface (BMI). Intent was inferred from movement related cortical potentials (MRCPs) measured over an optimized set of EEG electrodes. Successful intent detection triggered the motion of an upper-limb exoskeleton (MAHI Exo-II), to guide movement and to encourage active user participation by providing instantaneous sensory feedback. Several BMI design features were optimized to increase system performance in the presence of single-trial variability of MRCPs in the injured brain: (1) an adaptive time window was used for extracting features during BMI calibration; (2) training data from two consecutive days were pooled for BMI calibration to increase robustness to handle the day-to-day variations typical of EEG, and (3) BMI predictions were gated by residual electromyography (EMG) activity from the impaired arm, to reduce the number of false positives. This patient-specific BMI calibration approach can accommodate a broad spectrum of stroke patients with diverse motor capabilities. Following BMI optimization on day 3, testing of the closed-loop BMI-MAHI exoskeleton, on 4th and 5th days of the study, showed consistent BMI performance with overall mean true positive rate (TPR) = 62.7 ± 21.4% on day 4 and 67.1 ± 14.6% on day 5. The overall false positive rate (FPR) across subjects was 27.74 ± 37.46% on day 4 and 27.5 ± 35.64% on day 5; however for two subjects who had residual motor function and could benefit from the EMG-gated BMI, the mean FPR was quite low (< 10%). On average, motor intent was detected −367 ± 328 ms before movement onset during closed-loop operation. These findings provide evidence that closed-loop EEG-based BMI for stroke patients can be designed and optimized to perform well across multiple days without system recalibration. PMID:27065787

Design and optimization of an EEG-based brain machine interface (BMI to an upper-limb exoskeleton for stroke survivors

Directory of Open Access Journals (Sweden)

Nikunj Arunkumar Bhagat

2016-03-01

Full Text Available This study demonstrates the feasibility of detecting motor intent from brain activity of chronic stroke patients using an asynchronous electroencephalography (EEG-based brain machine interface (BMI. Intent was inferred from movement related cortical potentials (MRCPs measured over an optimized set of EEG electrodes. Successful intent detection triggered the motion of an upper-limb exoskeleton (MAHI Exo-II, to guide movement and to encourage active user participation by providing instantaneous sensory feedback. Several BMI design features were optimized to increase system performance in the presence of single-trial variability of MRCPs in the injured brain: 1 an adaptive time window was used for extracting features during BMI calibration; 2 training data from two consecutive days were pooled for BMI calibration to increase robustness to handle the day-to-day variations typical of EEG, and 3 BMI predictions were gated by residual electromyography (EMG activity from the impaired arm, to reduce the number of false positives. This patient-specific BMI calibration approach can accommodate a broad spectrum of stroke patients with diverse motor capabilities. Following BMI optimization on day 3, testing of the closed-loop BMI-MAHI exoskeleton, on 4th and 5th days of the study, showed consistent BMI performance with overall mean true positive rate (TPR = 62.7 +/- 21.4 % on day 4 and 67.1 +/- 14.6 % on day 5. The overall false positive rate (FPR across subjects was 27.74 +/- 37.46 % on day 4 and 27.5 +/- 35.64 % on day 5; however for two subjects who had residual motor function and could benefit from the EMG-gated BMI, the mean FPR was quite low (< 10 %. On average, motor intent was detected -367 +/- 328 ms before movement onset during closed-loop operation. These findings provide evidence that closed-loop EEG-based BMI for stroke patients can be designed and optimized to perform well across multiple days without system recalibration.

Correlations of CT and EEG findings in brain affections

International Nuclear Information System (INIS)

Roth, B.; Nevsimalova, S.; Kvicala, V.

1984-01-01

The results were compared of electroencephalography (EEG) and computerized tomography (CT) examinations of 250 patients with different brain affections. In intracranial expansive processes the pre-operative CT findings were positive in 100% cases, the EEG findings in 89.7% of cases. In severe traumatic affections the EEG and CT findings were positive in all cases, in mild injuries and post-traumatic conditions the EEG findings were more frequently positive than the CT. In focal and diffuse vascular affections the EEG and CT findings were consistent, in transitory ischemic conditions the EEG findings were more frequently positive. In inflammatory cerebral affections and in paroxymal diseases the EEG findings were positive more frequently than the CT. The same applies for demyelinating and degenerative affections. Findings of other authors were confirmed to the effect that CT very reliably reveals morphological changes in cerebral tissue while EEG records the functional state of the central nervous system and its changes. The two methods are complementary. (author)

Identifying the effects of microsaccades in tripolar EEG signals.

Science.gov (United States)

Bellisle, Rachel; Steele, Preston; Bartels, Rachel; Lei Ding; Sunderam, Sridhar; Besio, Walter

2017-07-01

Microsaccades are tiny, involuntary eye movements that occur during fixation, and they are necessary to human sight to maintain a sharp image and correct the effects of other fixational movements. Researchers have theorized and studied the effects of microsaccades on electroencephalography (EEG) signals to understand and eliminate the unwanted artifacts from EEG. The tripolar concentric ring electrode (TCRE) sensors are used to acquire TCRE EEG (tEEG). The tEEG detects extremely focal signals from directly below the TCRE sensor. We have noticed a slow wave frequency found in some tEEG recordings. Therefore, we conducted the current work to determine if there was a correlation between the slow wave in the tEEG and the microsaccades. This was done by analyzing the coherence of the frequency spectrums of both tEEG and eye movement in recordings where microsaccades are present. Our preliminary findings show that there is a correlation between the two.

Within- and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory.

Science.gov (United States)

Collins, Anne G E; Frank, Michael J

2018-03-06

Learning from rewards and punishments is essential to survival and facilitates flexible human behavior. It is widely appreciated that multiple cognitive and reinforcement learning systems contribute to decision-making, but the nature of their interactions is elusive. Here, we leverage methods for extracting trial-by-trial indices of reinforcement learning (RL) and working memory (WM) in human electro-encephalography to reveal single-trial computations beyond that afforded by behavior alone. Neural dynamics confirmed that increases in neural expectation were predictive of reduced neural surprise in the following feedback period, supporting central tenets of RL models. Within- and cross-trial dynamics revealed a cooperative interplay between systems for learning, in which WM contributes expectations to guide RL, despite competition between systems during choice. Together, these results provide a deeper understanding of how multiple neural systems interact for learning and decision-making and facilitate analysis of their disruption in clinical populations.

EEG spectral analysis of attention in ADHD: implications for neurofeedback training?

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Hartmut eHeinrich

2014-08-01

Full Text Available Objective: In children with attention-deficit/hyperactivity disorder (ADHD, an increased theta/beta ratio in the resting EEG typically serves as a rationale to conduct theta/beta neurofeedback training. However, this finding is increasingly challenged. As neurofeedback may rather target an active than a passive state, we studied the EEG in a condition that requires attention.Methods: In children with ADHD of the DSM-IV combined type (ADHD-C; N=15 and of the predominantly inattentive type (ADHD-I; N=9 and in typically developing children (N=19, EEG spectral analysis was conducted for segments during the attention network test without processing of stimuli and overt behavior. Frontal (F3, Fz, F4, central (C3, Cz, C4 and parietal (P3, Pz, P4 electrodes were included in the statistical analysis. To investigate if EEG spectral parameters are related to performance measures, correlation coefficients were calculated.Results: Particularly in the ADHD-C group, higher theta and alpha activity was found with the most prominent effect in the upper-theta/lower-alpha (5.5-10.5 Hz range. In the ADHD-I group, a significantly higher theta/beta ratio was observed at single electrodes (F3, Fz and a tendency for a higher theta/beta ratio when considering all electrodes (large effect size. Higher 5.5-10.5 Hz activity was associated with higher reaction time variability with the effect most prominent in the ADHD-C group. A higher theta/beta ratio was associated with higher reaction times, particularly in the ADHD-I group.Conclusions: 1. In an attention demanding period, children with ADHD are characterized by an underactivated state in the EEG with subtype-specific differences. 2. The functional relevance of related EEG parameters is indicated by associations with performance (reaction time measures. 3. Findings provide a rationale for applying NF protocols targeting theta (and alpha activity and the theta/beta ratio in subgroups of children with ADHD.

Beamformer source analysis and connectivity on concurrent EEG and MEG data during voluntary movements.

Directory of Open Access Journals (Sweden)

Muthuraman Muthuraman

Full Text Available Electroencephalography (EEG and magnetoencephalography (MEG are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis. In the present study, the cortical and sub-cortical network of coherent sources at the finger tapping task frequency (2-4 Hz and the modes of interaction within this network were analysed in 15 healthy subjects using a beamformer approach called the dynamic imaging of coherent sources (DICS with subsequent source signal reconstruction and renormalized partial directed coherence analysis (RPDC. MEG and EEG data were recorded simultaneously allowing the comparison of each of the modalities separately to that of the combined approach. We found the identified network of coherent sources for the finger tapping task as described in earlier studies when using only the MEG or combined MEG+EEG whereas the EEG data alone failed to detect single sub-cortical sources. The signal-to-noise ratio (SNR level of the coherent rhythmic activity at the tapping frequency in MEG and combined MEG+EEG data was significantly higher than EEG alone. The functional connectivity analysis revealed that the combined approach had more active connections compared to either of the modalities during the finger tapping (FT task. These results indicate that MEG is superior in the detection of deep coherent sources and that the SNR seems to be more vital than the sensitivity to theoretical dipole orientation and the volume conduction effect in the case of EEG.

Parametric Study Of Window Frame Geometry

DEFF Research Database (Denmark)

Zajas, Jan Jakub; Heiselberg, Per

2013-01-01

This paper describes a parametric study on window frame geometry with the goal of designing frames with very good thermal properties. Three different parametric frame models are introduced, deseribed by a number of variables. In the first part of the study, a process of sensitivity analysis...... is conducted to determine which of the parameters describing the frame have the highest impact on its thermal performance. Afterwards, an optimization process is conducted on each frame in order to optimize the design with regard to three objectives: minimizing the thermal transmittance, maxim izing the net...... energy gain factor and minimizing the material use. Since the objectives contradiet each other, it was found that it is not possible to identifY a single solution that satisfies all these goals. lnstead, a compromise between the objectives has to be found....

Combined process automation for large-scale EEG analysis.

Science.gov (United States)

Sfondouris, John L; Quebedeaux, Tabitha M; Holdgraf, Chris; Musto, Alberto E

2012-01-01

Epileptogenesis is a dynamic process producing increased seizure susceptibility. Electroencephalography (EEG) data provides information critical in understanding the evolution of epileptiform changes throughout epileptic foci. We designed an algorithm to facilitate efficient large-scale EEG analysis via linked automation of multiple data processing steps. Using EEG recordings obtained from electrical stimulation studies, the following steps of EEG analysis were automated: (1) alignment and isolation of pre- and post-stimulation intervals, (2) generation of user-defined band frequency waveforms, (3) spike-sorting, (4) quantification of spike and burst data and (5) power spectral density analysis. This algorithm allows for quicker, more efficient EEG analysis. Copyright © 2011 Elsevier Ltd. All rights reserved.

The influence of vertical disparity gradient and cue conflict on EEG omega complexity in Panum's limiting case.

Science.gov (United States)

Li, Huayun; Jia, Huibin; Yu, Dongchuan

2018-03-01

Using behavioral measures and ERP technique, researchers discovered at least two factors could influence the final perception of depth in Panum's limiting case, which are the vertical disparity gradient and the degree of cue conflict between two- and three-dimensional shapes. Although certain event-related potential components have been proved to be sensitive to the different levels of these two factors, some methodological limitations existed in this technique. In this study, we proposed that the omega complexity of EEG signal may serve as an important supplement of the traditional event-related potential technique. We found that the trials with lower vertical gradient disparity have lower omega complexity (i.e., higher global functional connectivity) of the occipital region, especially that of the right-occipital hemisphere. Moreover, for occipital omega complexity, the trials with low-cue conflict have significantly larger omega complexity than those with medium- and high-cue conflict. It is also found that the electrodes located in the middle line of the occipital region (i.e., POz and Oz) are more crucial to the impact of different levels of cue conflict on omega complexity than the other electrodes located in the left- and right-occipital hemispheres. These evidences demonstrated that the EEG omega complexity could reflect distinct neural activities evoked by Panum's limiting case configurations, with different levels of vertical disparity gradient and cue conflict. Besides, the influence of vertical disparity gradient and cue conflict on omega complexity may be regional dependent. NEW & NOTEWORTHY The EEG omega complexity could reflect distinct neural activities evoked by Panum's limiting case configurations with different levels of vertical disparity gradient and cue conflict. The influence of vertical disparity gradient and cue conflict on omega complexity is regional dependent. The omega complexity of EEG signal can serve as an important supplement of the

Woods-Saxon potential parametrization at large deformations for plutonium odd isotopes

International Nuclear Information System (INIS)

Garcia, F.; Garrote, E.; Yoneama, M.L.; Arruda-Neto, J.D.T.; Mesa, J.; Bringas, F.; Likhachev, V.P.; Rodriguez, O.; Guzman, F.

1999-01-01

The structure of single-particle levels in the second minima of 237,239,241 Pu was analyzed with the help of an axially-deformed Woods-Saxon potential. The nuclear shape was parametrized in terms of the cassinian ovaloids. A parametrization of the spin-orbit part of the potential was obtained in the region corresponding to large deformations (second minimum), depending only on the nuclear surface area. With this parametrization, we were able to reproduce successfully the spin, parity and energies of the rotational band built on the 8 μ s isomeric state in 239 Pu and, also, a spin assignment for both isomeric states in 237 Pu and 241 Pu was carried out. (orig.)

Experimental investigation of saturation effect on pump-to-signal intensity modulation transfer in single-pump phase-insensitive fiber optic parametric amplifiers

DEFF Research Database (Denmark)

Cristofori, Valentina; Lali-Dastjerdi, Zohreh; Lund-Hansen, Toke

2013-01-01

We present an experimental characterization of how signal gain saturation affects the transfer of intensity modulation from the pump to the signal in single-pump, phase-insensitive fiber optic parametric amplifiers (FOPAs). In this work, we demonstrate experimentally for the first time, to our...... knowledge, how gain saturation of a FOPA reduces the noise contribution due to the transfer of pump power fluctuations to the signal. In a particular example, it is shown that the transferred noise is significantly reduced by a factor of 3, while the FOPA gain remains above 10 dB....

Multi-Variate EEG Analysis as a Novel Tool to Examine Brain Responses to Naturalistic Music Stimuli.

Directory of Open Access Journals (Sweden)

Irene Sturm

Full Text Available Note onsets in music are acoustic landmarks providing auditory cues that underlie the perception of more complex phenomena such as beat, rhythm, and meter. For naturalistic ongoing sounds a detailed view on the neural representation of onset structure is hard to obtain, since, typically, stimulus-related EEG signatures are derived by averaging a high number of identical stimulus presentations. Here, we propose a novel multivariate regression-based method extracting onset-related brain responses from the ongoing EEG. We analyse EEG recordings of nine subjects who passively listened to stimuli from various sound categories encompassing simple tone sequences, full-length romantic piano pieces and natural (non-music soundscapes. The regression approach reduces the 61-channel EEG to one time course optimally reflecting note onsets. The neural signatures derived by this procedure indeed resemble canonical onset-related ERPs, such as the N1-P2 complex. This EEG projection was then utilized to determine the Cortico-Acoustic Correlation (CACor, a measure of synchronization between EEG signal and stimulus. We demonstrate that a significant CACor (i can be detected in an individual listener's EEG of a single presentation of a full-length complex naturalistic music stimulus, and (ii it co-varies with the stimuli's average magnitudes of sharpness, spectral centroid, and rhythmic complexity. In particular, the subset of stimuli eliciting a strong CACor also produces strongly coordinated tension ratings obtained from an independent listener group in a separate behavioral experiment. Thus musical features that lead to a marked physiological reflection of tone onsets also contribute to perceived tension in music.

The role of the standard EEG in clinical psychiatry.

LENUS (Irish Health Repository)

O'Sullivan, S S

2012-02-03

BACKGROUND: The EEG is a commonly requested test on patients attending psychiatric services, predominantly to investigate for a possible organic brain syndrome causing behavioural changes. AIMS: To assess referrals for EEG from psychiatric services in comparison with those from other sources. We determine which clinical factors were associated with an abnormal EEG in patients referred from psychiatric sources. METHODS: A retrospective review of EEG requests in a 1-year period was performed. Analysis of referral reasons for psychiatric patients was undertaken, and outcome of patients referred from psychiatric services post-EEG was reviewed. RESULTS: One thousand four hundred and seventy EEGs were reviewed, of which 91 (6.2%) were referred from psychiatry. Neurology service referrals had detection rates of abnormal EEGs of 27%, with psychiatric referrals having the lowest abnormality detection rate of 17.6% (p < 0.1). In psychiatric-referred patients the only significant predictors found of an abnormal EEG were a known history of epilepsy (p < 0.001), being on clozapine (p < 0.05), and a possible convulsive seizure (RR = 6.51). Follow-up data of 53 patients did not reveal a significant clinical impact of EEG results on patient management. CONCLUSIONS: Many patients are referred for EEG from psychiatric sources despite a relatively low index of suspicion of an organic brain disorders, based on reasons for referral documented, with an unsurprising low clinical yield.

APT cost scaling: Preliminary indications from a Parametric Costing Model (PCM)

International Nuclear Information System (INIS)

Krakowski, R.A.

1995-01-01

A Parametric Costing Model has been created and evaluate as a first step in quantitatively understanding important design options for the Accelerator Production of Tritium (APT) concept. This model couples key economic and technical elements of APT in a two-parameter search of beam energy and beam power that minimizes costs within a range of operating constraints. The costing and engineering depth of the Parametric Costing Model is minimal at the present open-quotes entry levelclose quotes, and is intended only to demonstrate a potential for a more-detailed, cost-based integrating design tool. After describing the present basis of the Parametric Costing Model and giving an example of a single parametric scaling run derived therefrom, the impacts of choices related to resistive versus superconducting accelerator structures and cost of electricity versus plant availability (open-quotes load curveclose quotes) are reported. Areas of further development and application are suggested

Clusters of Insomnia Disorder: An Exploratory Cluster Analysis of Objective Sleep Parameters Reveals Differences in Neurocognitive Functioning, Quantitative EEG, and Heart Rate Variability

Science.gov (United States)

Miller, Christopher B.; Bartlett, Delwyn J.; Mullins, Anna E.; Dodds, Kirsty L.; Gordon, Christopher J.; Kyle, Simon D.; Kim, Jong Won; D'Rozario, Angela L.; Lee, Rico S.C.; Comas, Maria; Marshall, Nathaniel S.; Yee, Brendon J.; Espie, Colin A.; Grunstein, Ronald R.

2016-01-01

Study Objectives: To empirically derive and evaluate potential clusters of Insomnia Disorder through cluster analysis from polysomnography (PSG). We hypothesized that clusters would differ on neurocognitive performance, sleep-onset measures of quantitative (q)-EEG and heart rate variability (HRV). Methods: Research volunteers with Insomnia Disorder (DSM-5) completed a neurocognitive assessment and overnight PSG measures of total sleep time (TST), wake time after sleep onset (WASO), and sleep onset latency (SOL) were used to determine clusters. Results: From 96 volunteers with Insomnia Disorder, cluster analysis derived at least two clusters from objective sleep parameters: Insomnia with normal objective sleep duration (I-NSD: n = 53) and Insomnia with short sleep duration (I-SSD: n = 43). At sleep onset, differences in HRV between I-NSD and I-SSD clusters suggest attenuated parasympathetic activity in I-SSD (P insomnia clusters derived from cluster analysis differ in sleep onset HRV. Preliminary data suggest evidence for three clusters in insomnia with differences for sustained attention and sleep-onset q-EEG. Clinical Trial Registration: Insomnia 100 sleep study: Australia New Zealand Clinical Trials Registry (ANZCTR) identification number 12612000049875. URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=347742. Citation: Miller CB, Bartlett DJ, Mullins AE, Dodds KL, Gordon CJ, Kyle SD, Kim JW, D'Rozario AL, Lee RS, Comas M, Marshall NS, Yee BJ, Espie CA, Grunstein RR. Clusters of Insomnia Disorder: an exploratory cluster analysis of objective sleep parameters reveals differences in neurocognitive functioning, quantitative EEG, and heart rate variability. SLEEP 2016;39(11):1993–2004. PMID:27568796

The colorful brain: Visualization of EEG background patterns

NARCIS (Netherlands)

van Putten, Michel Johannes Antonius Maria

2008-01-01

This article presents a method to transform routine clinical EEG recordings to an alternative visual domain. The method is intended to support the classic visual interpretation of the EEG background pattern and to facilitate communication about relevant EEG characteristics. In addition, it provides

EEG, PET, SPET and MRI in intractable childhood epilepsies: possible surgical correlations.

Science.gov (United States)

Fois, A; Farnetani, M A; Balestri, P; Buoni, S; Di Cosmo, G; Vattimo, A; Guazzelli, M; Guzzardi, R; Salvadori, P A

1995-12-01

Magnetic resonance imaging (MRI), single photon emission tomography (SPET), and positron emission tomography (PET) using [18F]fluorodeoxyglucose were used in combination with scalp and scalp-video EEGs in a group of 30 pediatric patients with drug resistant epilepsy (DRE) in order to identify patients who could benefit from neurosurgical approach. Seizures were classified according to the consensus criteria of The International League Against Epilepsy. In three patients infantile spasms (IS) were diagnosed; 13 subjects were affected by different types of generalized seizures, associated with complex partial seizures (CPS) in three. In the other 14 patients partial seizures, either simple (SPS) or complex, were present. A localized abnormality was demonstrated in one patient with IS and in three patients with generalized seizures. Of the group of 14 subjects with CPS, MRI and CT were normal in 7, but SPET or PET indicated focal hypoperfusion or hypometabolism concordant with the localization of the EEG abnormalities. In 5 of the other 7 patients anatomical and functional imaging and EEG findings were concordant for a localized abnormality. It can be concluded that functional imaging combined with scalp EEGs appears to be superior to the use of only CT and MRI for selecting children with epilepsy in whom a surgical approach can be considered, in particular when CPS resistant to therapy are present.

[EEG changes in symptomatic headache caused by bruxism].

Science.gov (United States)

Wieselmann, G; Grabmair, W; Logar, C; Permann, R; Moser, F

1987-02-20

EEG recordings were carried out on 36 patients with the verified diagnosis of bruxism and unilateral headache. Occlusal splints were applied in the long-term management of these patients. Initial EEG recordings showed pathological changes in 56% of the patients. The EEG recordings were repeated two and six weeks later in these patients and following improvement in the clinical symptomatology pathological EEG patterns were detected in only 22% of all cases. This decrease is of statistical significance.

Research design considerations for single-dose analgesic clinical trials in acute pain

DEFF Research Database (Denmark)

Cooper, Stephen A; Desjardins, Paul J; Turk, Dennis C

2016-01-01

This article summarizes the results of a meeting convened by the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) on key considerations and best practices governing the design of acute pain clinical trials. We discuss the role of early phase clinical trials......, including pharmacokinetic-pharmacodynamic (PK-PD) trials, and the value of including both placebo and active standards of comparison in acute pain trials. This article focuses on single-dose and short-duration trials with emphasis on the perioperative and study design factors that influence assay...... sensitivity. Recommendations are presented on assessment measures, study designs, and operational factors. Although most of the methodological advances have come from studies of postoperative pain after dental impaction, bunionectomy, and other surgeries, the design considerations discussed are applicable...

Parametric interactions in high-Tc superconducting step edge junctions at X-band. [Y-Ba-Cu-O

Energy Technology Data Exchange (ETDEWEB)

Kain, A.Z. (TRW Space and Tech. Group, Redondo Beach, CA (United States)); Fetterman, H.R. (Electrical Engineering Dept., Univ. of California at Los Angeles (United States))

1993-04-20

We have fabricated and tested both single junctions and series arrays of YBCO step edge junctions for four photon parametric effects at X band as a first step in developing a parametric amplifier at 60 GHz. The series array of 25 junctions at 10.3 Ghz shows a 10 dB increase in reflected signal power as the pump power is increased, while the single junction at 12.2 GHz indicates a 2 dB change. The reflected power at the characteristic idler frequency of 2[omega][sub p]-[omega][sub s] is evidence of true Josephson junction parametric interaction. We are currently investigating the use of thallium based films at 60 GHz which offer a broader range of operating temperatures than does YBCO. Our design for a parametric amplifier at V band is a combination of microstrip based series arrays of junctions and an antipodal finline transition. (orig.)

Detuned mechanical parametric amplification as a quantum non-demolition measurement

International Nuclear Information System (INIS)

Szorkovszky, A; Bowen, W P; Clerk, A A; Doherty, A C

2014-01-01

Recently it has been demonstrated that the combination of continuous position detection with detuned parametric driving can lead to significant steady-state mechanical squeezing, far beyond the 3 dB limit normally associated with parametric driving. In this work, we show the close connection between this detuned scheme and quantum non-demolition (QND) measurement of a single mechanical quadrature. In particular, we show that applying an experimentally realistic detuned parametric drive to a cavity optomechanical system allows one to effectively realize a QND measurement despite being in the bad-cavity limit. In the limit of strong squeezing, we show that this scheme offers significant advantages over standard backaction evasion, not only by allowing operation in the weak measurement and low efficiency regimes, but also in terms of the purity of the mechanical state

Inferring Trial-to-Trial Excitatory and Inhibitory Synaptic Inputs from Membrane Potential using Gaussian Mixture Kalman Filtering

Directory of Open Access Journals (Sweden)

Milad eLankarany

2013-09-01

Full Text Available Time-varying excitatory and inhibitory synaptic inputs govern activity of neurons and process information in the brain. The importance of trial-to-trial fluctuations of synaptic inputs has recently been investigated in neuroscience. Such fluctuations are ignored in the most conventional techniques because they are removed when trials are averaged during linear regression techniques. Here, we propose a novel recursive algorithm based on Gaussian mixture Kalman filtering for estimating time-varying excitatory and inhibitory synaptic inputs from single trials of noisy membrane potential in current clamp recordings. The Kalman filtering is followed by an expectation maximization algorithm to infer the statistical parameters (time-varying mean and variance of the synaptic inputs in a non-parametric manner. As our proposed algorithm is repeated recursively, the inferred parameters of the mixtures are used to initiate the next iteration. Unlike other recent algorithms, our algorithm does not assume an a priori distribution from which the synaptic inputs are generated. Instead, the algorithm recursively estimates such a distribution by fitting a Gaussian mixture model. The performance of the proposed algorithms is compared to a previously proposed PF-based algorithm (Paninski et al., 2012 with several illustrative examples, assuming that the distribution of synaptic input is unknown. If noise is small, the performance of our algorithms is similar to that of the previous one. However, if noise is large, they can significantly outperform the previous proposal. These promising results suggest that our algorithm is a robust and efficient technique for estimating time varying excitatory and inhibitory synaptic conductances from single trials of membrane potential recordings.

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...... were sleep deprivation (84%), stress (70%), and alcohol consumption (51%). EEG findings included rapid spike-wave and polyspike-wave....

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....

EEG Correlates of Ten Positive Emotions.

Science.gov (United States)

Hu, Xin; Yu, Jianwen; Song, Mengdi; Yu, Chun; Wang, Fei; Sun, Pei; Wang, Daifa; Zhang, Dan

2017-01-01

Compared with the well documented neurophysiological findings on negative emotions, much less is known about positive emotions. In the present study, we explored the EEG correlates of ten different positive emotions (joy, gratitude, serenity, interest, hope, pride, amusement, inspiration, awe, and love). A group of 20 participants were invited to watch 30 short film clips with their EEGs simultaneously recorded. Distinct topographical patterns for different positive emotions were found for the correlation coefficients between the subjective ratings on the ten positive emotions per film clip and the corresponding EEG spectral powers in different frequency bands. Based on the similarities of the participants' ratings on the ten positive emotions, these emotions were further clustered into three representative clusters, as 'encouragement' for awe, gratitude, hope, inspiration, pride, 'playfulness' for amusement, joy, interest, and 'harmony' for love, serenity. Using the EEG spectral powers as features, both the binary classification on the higher and lower ratings on these positive emotions and the binary classification between the three positive emotion clusters, achieved accuracies of approximately 80% and above. To our knowledge, our study provides the first piece of evidence on the EEG correlates of different positive emotions.

EEG (Electroencephalogram)

Science.gov (United States)

... in diagnosing brain disorders, especially epilepsy or another seizure disorder. An EEG might also be helpful for diagnosing ... Sometimes seizures are intentionally triggered in people with epilepsy during the test, but appropriate medical care is ...

Detection of Paroxysms in Long-Term, Single Channel EEG-Monitoring of Patients with Typical Absence Seizures

DEFF Research Database (Denmark)

Kjær, Troels W.; Sørensen, Helge Bjarup Dissing; Groenborg, Sabine

2017-01-01

-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...

The Role of Skull Modeling in EEG Source Imaging for Patients with Refractory Temporal Lobe Epilepsy.

Science.gov (United States)

Montes-Restrepo, Victoria; Carrette, Evelien; Strobbe, Gregor; Gadeyne, Stefanie; Vandenberghe, Stefaan; Boon, Paul; Vonck, Kristl; Mierlo, Pieter van

2016-07-01

We investigated the influence of different skull modeling approaches on EEG source imaging (ESI), using data of six patients with refractory temporal lobe epilepsy who later underwent successful epilepsy surgery. Four realistic head models with different skull compartments, based on finite difference methods, were constructed for each patient: (i) Three models had skulls with compact and spongy bone compartments as well as air-filled cavities, segmented from either computed tomography (CT), magnetic resonance imaging (MRI) or a CT-template and (ii) one model included a MRI-based skull with a single compact bone compartment. In all patients we performed ESI of single and averaged spikes marked in the clinical 27-channel EEG by the epileptologist. To analyze at which time point the dipole estimations were closer to the resected zone, ESI was performed at two time instants: the half-rising phase and peak of the spike. The estimated sources for each model were validated against the resected area, as indicated by the postoperative MRI. Our results showed that single spike analysis was highly influenced by the signal-to-noise ratio (SNR), yielding estimations with smaller distances to the resected volume at the peak of the spike. Although averaging reduced the SNR effects, it did not always result in dipole estimations lying closer to the resection. The proposed skull modeling approaches did not lead to significant differences in the localization of the irritative zone from clinical EEG data with low spatial sampling density. Furthermore, we showed that a simple skull model (MRI-based) resulted in similar accuracy in dipole estimation compared to more complex head models (based on CT- or CT-template). Therefore, all the considered head models can be used in the presurgical evaluation of patients with temporal lobe epilepsy to localize the irritative zone from low-density clinical EEG recordings.

Quantitative topographic differentiation of the neonatal EEG.

Science.gov (United States)

Paul, Karel; Krajca, Vladimír; Roth, Zdenek; Melichar, Jan; Petránek, Svojmil

2006-09-01

To test the discriminatory topographic potential of a new method of the automatic EEG analysis in neonates. A quantitative description of the neonatal EEG can contribute to the objective assessment of the functional state of the brain, and may improve the precision of diagnosing cerebral dysfunctions manifested by 'disorganization', 'dysrhythmia' or 'dysmaturity'. 21 healthy, full-term newborns were examined polygraphically during sleep (EEG-8 referential derivations, respiration, ECG, EOG, EMG). From each EEG record, two 5-min samples (one from the middle of quiet sleep, the other from the middle of active sleep) were subject to subsequent automatic analysis and were described by 13 variables: spectral features and features describing shape and variability of the signal. The data from individual infants were averaged and the number of variables was reduced by factor analysis. All factors identified by factor analysis were statistically significantly influenced by the location of derivation. A large number of statistically significant differences were also established when comparing the effects of individual derivations on each of the 13 measured variables. Both spectral features and features describing shape and variability of the signal are largely accountable for the topographic differentiation of the neonatal EEG. The presented method of the automatic EEG analysis is capable to assess the topographic characteristics of the neonatal EEG, and it is adequately sensitive and describes the neonatal electroencephalogram with sufficient precision. The discriminatory capability of the used method represents a promise for their application in the clinical practice.

Studentized Continuous Wavelet Transform (t-CWT in the Analysis of Individual ERPs: Real and Simulated EEG Data

Directory of Open Access Journals (Sweden)

Ruben Gustav Leonhardt Real

2014-09-01

Full Text Available This study aimed at evaluating the performance of the Studentized Continuous Wavelet Transform (t-CWT as a method for the extraction and assessment of event-related brain potentials (ERP in data from a single subject. Sensitivity, specificity, positive (PPV and negative predictive values (NPV of the t-CWT were assessed and compared to a variety of competing procedures using simulated EEG data at six low signal-to-noise ratios. Results show that the t-CWT combines high sensitivity and specificity with favorable PPV and NPV. Applying the t-CWT to authentic EEG data obtained from 14 healthy participants confirmed its high sensitivity. The t-CWT may thus be well suited for the assessment of weak ERPs in single-subject settings.

Slater-Koster Tight-Binding parametrization of single and few-layer Black-Phosphorus from first-principles calculations

Science.gov (United States)

Menezes, Marcos; Capaz, Rodrigo

Black Phosphorus (BP) is a promising material for applications in electronics, especially due to the tuning of its band gap by increasing the number of layers. In single-layer BP, also called Phosphorene, the P atoms form two staggered chains bonded by sp3 hybridization, while neighboring layers are bonded by Van-der-Waals interactions. In this work, we present a Tight-Binding (TB) parametrization of the electronic structure of single and few-layer BP, based on the Slater-Koster model within the two-center approximation. Our model includes all 3s and 3p orbitals, which makes this problem more complex than that of graphene, where only 2pz orbitals are needed for most purposes. The TB parameters are obtained from a least-squares fit of DFT calculations carried on the SIESTA code. We compare the results for different basis-sets used to expand the ab-initio wavefunctions and discuss their applicability. Our model can fit a larger number of bands than previously reported calculations based on Wannier functions. Moreover, our parameters have a clear physical interpretation based on chemical bonding. As such, we expect our results to be useful in a further understanding of multilayer BP and other 2D-materials characterized by strong sp3 hybridization. CNPq, FAPERJ, INCT-Nanomateriais de Carbono.

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.

Connectivity Measures in EEG Microstructural Sleep Elements.

Science.gov (United States)

Sakellariou, Dimitris; Koupparis, Andreas M; Kokkinos, Vasileios; Koutroumanidis, Michalis; Kostopoulos, George K

2016-01-01

During Non-Rapid Eye Movement sleep (NREM) the brain is relatively disconnected from the environment, while connectedness between brain areas is also decreased. Evidence indicates, that these dynamic connectivity changes are delivered by microstructural elements of sleep: short periods of environmental stimuli evaluation followed by sleep promoting procedures. The connectivity patterns of the latter, among other aspects of sleep microstructure, are still to be fully elucidated. We suggest here a methodology for the assessment and investigation of the connectivity patterns of EEG microstructural elements, such as sleep spindles. The methodology combines techniques in the preprocessing, estimation, error assessing and visualization of results levels in order to allow the detailed examination of the connectivity aspects (levels and directionality of information flow) over frequency and time with notable resolution, while dealing with the volume conduction and EEG reference assessment. The high temporal and frequency resolution of the methodology will allow the association between the microelements and the dynamically forming networks that characterize them, and consequently possibly reveal aspects of the EEG microstructure. The proposed methodology is initially tested on artificially generated signals for proof of concept and subsequently applied to real EEG recordings via a custom built MATLAB-based tool developed for such studies. Preliminary results from 843 fast sleep spindles recorded in whole night sleep of 5 healthy volunteers indicate a prevailing pattern of interactions between centroparietal and frontal regions. We demonstrate hereby, an opening to our knowledge attempt to estimate the scalp EEG connectivity that characterizes fast sleep spindles via an "EEG-element connectivity" methodology we propose. The application of the latter, via a computational tool we developed suggests it is able to investigate the connectivity patterns related to the occurrence

Increased phase synchronization during continuous face integration measured simultaneously with EEG and fMRI.

Science.gov (United States)

Kottlow, Mara; Jann, Kay; Dierks, Thomas; Koenig, Thomas

2012-08-01

Gamma zero-lag phase synchronization has been measured in the animal brain during visual binding. Human scalp EEG studies used a phase locking factor (trial-to-trial phase-shift consistency) or gamma amplitude to measure binding but did not analyze common-phase signals so far. This study introduces a method to identify networks oscillating with near zero-lag phase synchronization in human subjects. We presented unpredictably moving face parts (NOFACE) which - during some periods - produced a complete schematic face (FACE). The amount of zero-lag phase synchronization was measured using global field synchronization (GFS). GFS provides global information on the amount of instantaneous coincidences in specific frequencies throughout the brain. Gamma GFS was increased during the FACE condition. To localize the underlying areas, we correlated gamma GFS with simultaneously recorded BOLD responses. Positive correlates comprised the bilateral middle fusiform gyrus and the left precuneus. These areas may form a network of areas transiently synchronized during face integration, including face-specific as well as binding-specific regions and regions for visual processing in general. Thus, the amount of zero-lag phase synchronization between remote regions of the human visual system can be measured with simultaneously acquired EEG/fMRI. Copyright © 2012 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Validating and improving the correction of ocular artifacts in electro-encephalography

NARCIS (Netherlands)

Kierkels, J.J.M.

2007-01-01

For modern applications of electro-encephalography, including brain computer interfaces and single-trial Event Related Potential detection, it is becoming increasingly important that artifacts are accurately removed from a recorded electro-encephalogram (EEG) without affecting the part of the EEG

Widespread EEG changes precede focal seizures.

Directory of Open Access Journals (Sweden)

Piero Perucca

Full Text Available The process by which the brain transitions into an epileptic seizure is unknown. In this study, we investigated whether the transition to seizure is associated with changes in brain dynamics detectable in the wideband EEG, and whether differences exist across underlying pathologies. Depth electrode ictal EEG recordings from 40 consecutive patients with pharmacoresistant lesional focal epilepsy were low-pass filtered at 500 Hz and sampled at 2,000 Hz. Predefined EEG sections were selected immediately before (immediate preictal, and 30 seconds before the earliest EEG sign suggestive of seizure activity (baseline. Spectral analysis, visual inspection and discrete wavelet transform were used to detect standard (delta, theta, alpha, beta and gamma and high-frequency bands (ripples and fast ripples. At the group level, each EEG frequency band activity increased significantly from baseline to the immediate preictal section, mostly in a progressive manner and independently of any modification in the state of vigilance. Preictal increases in each frequency band activity were widespread, being observed in the seizure-onset zone and lesional tissue, as well as in remote regions. These changes occurred in all the investigated pathologies (mesial temporal atrophy/sclerosis, local/regional cortical atrophy, and malformations of cortical development, but were more pronounced in mesial temporal atrophy/sclerosis. Our findings indicate that a brain state change with distinctive features, in the form of unidirectional changes across the entire EEG bandwidth, occurs immediately prior to seizure onset. We postulate that these changes might reflect a facilitating state of the brain which enables a susceptible region to generate seizures.

European Marketing Authorizations Granted Based on a Single Pivotal Clinical Trial: The Rule or the Exception?

Science.gov (United States)

Morant, Anne Vinther; Vestergaard, Henrik Tang

2018-07-01

A minimum of two positive, adequate, and well-controlled clinical trials has historically been the gold standard for providing substantial evidence to support regulatory approval of a new medicine. Nevertheless, the present analysis of European Marketing Authorizations granted between 2012 and 2016 showed that 45% of new active substances were approved based on a single pivotal clinical trial. For therapeutic areas such as oncology and cardiovascular diseases, approvals based on a single pivotal trial are the rule rather than the exception, whereas new medicines within the nervous system area were generally supported by two or more pivotal trials. While overall similar trends have been observed in the US, the recent US Food and Drug Administration approvals of nervous system medicines based on a single pivotal trial suggest that a case-by-case scientific evaluation of the totality of evidence is increasingly applied to facilitate faster access of new medicines to patients suffering from serious diseases. © 2017 American Society for Clinical Pharmacology and Therapeutics.

Novel screen printed electrode set for routine EEG recordings in patients with altered mental status.

Science.gov (United States)

Myllymaa, Sami; Lepola, Pasi; Hukkanen, Taina; Oun, Andre; Mervaala, Esa; Toyras, Juha; Lappalainen, Reijo; Myllymaa, Katja

2013-01-01

There is a growing need for an easy to use screening tool for the assessment of brain's electrical function in patients with altered mental status (AMS). The purpose of this study is to give a brief overview of the state-of-the-art in electrode technology, and to present a novel sub-hairline electrode set developed in our research group. Screen-printing technology was utilized to construct the electrode set consisting of ten electroencephalography (EEG) electrodes, two electrooculography (EOG) electrodes, two ground electrodes and two reference electrodes. Electrical characteristics of hydrogel-coated silver ink electrodes were found adequate for clinical EEG recordings as assessed by electrical impedance spectroscopy (EIS). The skin-electrode impedances remain stable and low enough at least two days enabling high-quality long-term recordings. Due to the proper material selection, thin ink layers and detachable zero insertion force (ZIF) - connector, electrode was observed to be CT- and MRI-compatible allowing imaging without removing the electrodes. Pilot EEG recordings gave very promising results and an on-going clinical trial with larger number of patients will show the true feasibility of this approach.

Discovering EEG resting state alterations of semantic dementia.

Science.gov (United States)

Grieder, Matthias; Koenig, Thomas; Kinoshita, Toshihiko; Utsunomiya, Keita; Wahlund, Lars-Olof; Dierks, Thomas; Nishida, Keiichiro

2016-05-01

Diagnosis of semantic dementia relies on cost-intensive MRI or PET, although resting EEG markers of other dementias have been reported. Yet the view still holds that resting EEG in patients with semantic dementia is normal. However, studies using increasingly sophisticated EEG analysis methods have demonstrated that slightest alterations of functional brain states can be detected. We analyzed the common four resting EEG microstates (A, B, C, and D) of 8 patients with semantic dementia in comparison with 8 healthy controls and 8 patients with Alzheimer's disease. Topographical differences between the groups were found in microstate classes B and C, while microstate classes A and D were comparable. The data showed that the semantic dementia group had a peculiar microstate E, but the commonly found microstate C was lacking. Furthermore, the presence of microstate E was significantly correlated with lower MMSE and language scores. Alterations in resting EEG can be found in semantic dementia. Topographical shifts in microstate C might be related to semantic memory deficits. This is the first study that discovered resting state EEG abnormality in semantic dementia. The notion that resting EEG in this dementia subtype is normal has to be revised. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Note: Pulsed single longitudinal mode optical parametric oscillator for sub-Doppler spectroscopy of jet cooled transient species

Science.gov (United States)

Zhang, Qiang; Zhu, Boxing; Zhang, Deping; Gu, Jingwang; Zhao, Dongfeng; Chen, Yang

2017-12-01

We present a pulsed single longitudinal mode optical parametric oscillator that was recently constructed for sub-Doppler spectroscopic studies of transient species in a supersonic slit jet expansion environment. The system consists of a Littman-type grazing-incidence-grating resonator and a KTP crystal and is pumped at 532 nm. By spatially filtering the pump laser beam and employing an active cavity-length-stabilization scheme, a frequency down-conversion efficiency up to 18% and generation of Fourier-transform limited pulses with a typical pulse duration of ˜5.5 ns and a bandwidth less than 120 MHz have been achieved. In combination with a slit jet expansion, a sub-Doppler spectrum of SiC2 has been recorded at ˜498 nm, showing a spectral resolution of Δν/ν ≈ 6.2 × 10-7.

Quantitative EEG analysis using error reduction ratio-causality test; validation on simulated and real EEG data.

Science.gov (United States)

Sarrigiannis, Ptolemaios G; Zhao, Yifan; Wei, Hua-Liang; Billings, Stephen A; Fotheringham, Jayne; Hadjivassiliou, Marios

2014-01-01

To introduce a new method of quantitative EEG analysis in the time domain, the error reduction ratio (ERR)-causality test. To compare performance against cross-correlation and coherence with phase measures. A simulation example was used as a gold standard to assess the performance of ERR-causality, against cross-correlation and coherence. The methods were then applied to real EEG data. Analysis of both simulated and real EEG data demonstrates that ERR-causality successfully detects dynamically evolving changes between two signals, with very high time resolution, dependent on the sampling rate of the data. Our method can properly detect both linear and non-linear effects, encountered during analysis of focal and generalised seizures. We introduce a new quantitative EEG method of analysis. It detects real time levels of synchronisation in the linear and non-linear domains. It computes directionality of information flow with corresponding time lags. This novel dynamic real time EEG signal analysis unveils hidden neural network interactions with a very high time resolution. These interactions cannot be adequately resolved by the traditional methods of coherence and cross-correlation, which provide limited results in the presence of non-linear effects and lack fidelity for changes appearing over small periods of time. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Measurement of IR optics with linear coupling’s action-angle parametrization

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Y. Luo

2005-08-01

Full Text Available Linear coupling’s action-angle parametrization is convenient for interpretation of turn-by-turn beam position monitor (BPM data. We demonstrate how to apply this parametrization to extract Twiss and coupling parameters in interaction regions (IRs, using BPMs on each side of a long IR drift region. Example data were acquired at the Relativistic Heavy Ion Collider, using an ac dipole to excite a single transverse eigenmode. We have measured the waist of the β function and its Twiss and coupling parameters.

A technique to consider mismatches between fMRI and EEG/MEG sources for fMRI-constrained EEG/MEG source imaging: a preliminary simulation study

International Nuclear Information System (INIS)

Im, Chang-Hwan; Lee, Soo Yeol

2006-01-01

fMRI-constrained EEG/MEG source imaging can be a powerful tool in studying human brain functions with enhanced spatial and temporal resolutions. Recent studies on the combination of fMRI and EEG/MEG have suggested that fMRI prior information could be readily implemented by simply imposing different weighting factors to cortical sources overlapping with the fMRI activations. It has been also reported, however, that such a hard constraint may cause severe distortions or elimination of meaningful EEG/MEG sources when there are distinct mismatches between the fMRI activations and the EEG/MEG sources. If one wants to obtain the actual EEG/MEG source locations and uses the fMRI prior information as just an auxiliary tool to enhance focality of the distributed EEG/MEG sources, it is reasonable to weaken the strength of fMRI constraint when severe mismatches between fMRI and EEG/MEG sources are observed. The present study suggests an efficient technique to automatically adjust the strength of fMRI constraint according to the mismatch level. The use of the proposed technique rarely affects the results of conventional fMRI-constrained EEG/MEG source imaging if no major mismatch between the two modalities is detected; while the new results become similar to those of typical EEG/MEG source imaging without fMRI constraint if the mismatch level is significant. A preliminary simulation study using realistic EEG signals demonstrated that the proposed technique can be a promising tool to selectively apply fMRI prior information to EEG/MEG source imaging

Global Manufacturing Research: Experience Exchange Group (EEG) contributions

DEFF Research Database (Denmark)

Bruun, Peter

1998-01-01

of preliminary studies found interesting to set upan EEG composed of representatives from industry and a researcher. Inthe paper some general research methods pertinent to the areaindustrial management is discussed. The EEG concept is introduced andcharacterised in comparison with the other methods. EEG...... activities aredescribed and a tentative coupling to the phases in a research processis proposed. Following this is a discussion of methodological andquality requirements. It is considered how EEG activities couldpossible contribute to an industrial rooted research. The paper endsup looking at future research......The intention of this paper is to clarify if and how an ExperienceExchange Group (EEG) can be involved in a research process in the areaof industrial management. For exemplification of the topic an ongoingresearch in global manufacturing is referred to. In this research itwas after a series...

Standardized computer-based organized reporting of EEG:SCORE

DEFF Research Database (Denmark)

Beniczky, Sandor; H, Aurlien,; JC, Brøgger,

2013-01-01

process, organized by the European Chapter of the International Federation of Clinical Neurophysiology. The Standardised Computer-based Organised Reporting of EEG (SCORE) software was constructed based on the terms and features of the consensus statement and it was tested in the clinical practice...... in free-text format. The purpose of our endeavor was to create a computer-based system for EEG assessment and reporting, where the physicians would construct the reports by choosing from predefined elements for each relevant EEG feature, as well as the clinical phenomena (for video-EEG recordings....... SCORE can potentially improve the quality of EEG assessment and reporting; it will help incorporate the results of computer-assisted analysis into the report, it will make possible the build-up of a multinational database, and it will help in training young neurophysiologists....

Aperture scaling effects with monolithic periodically poled lithium niobate optical parametric oscillators and generators.

Science.gov (United States)

Missey, M; Dominic, V; Powers, P; Schepler, K L

2000-02-15

We used elliptical beams to demonstrate aperture scaling effects in nanosecond single-grating and multigrating periodically poled lithium niobate (PPLN) monolithic optical parametric oscillators and generators. Increasing the cavity Fresnel number in single-grating crystals broadened both the beam divergence and the spectral bandwidth. Both effects are explained in terms of the phase-matching geometry. These effects are suppressed when a multigrating PPLN crystal is used because the individual gratings provide small effective subapertures. A flood-pumped multigrating optical parametric generator displayed a low output beam divergence and contained 19 pairs of signal and idler frequencies.

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.

Mean-field thalamocortical modeling of longitudinal EEG acquired during intensive meditation training.

Science.gov (United States)

Saggar, Manish; Zanesco, Anthony P; King, Brandon G; Bridwell, David A; MacLean, Katherine A; Aichele, Stephen R; Jacobs, Tonya L; Wallace, B Alan; Saron, Clifford D; Miikkulainen, Risto

2015-07-01

Meditation training has been shown to enhance attention and improve emotion regulation. However, the brain processes associated with such training are poorly understood and a computational modeling framework is lacking. Modeling approaches that can realistically simulate neurophysiological data while conforming to basic anatomical and physiological constraints can provide a unique opportunity to generate concrete and testable hypotheses about the mechanisms supporting complex cognitive tasks such as meditation. Here we applied the mean-field computational modeling approach using the scalp-recorded electroencephalogram (EEG) collected at three assessment points from meditating participants during two separate 3-month-long shamatha meditation retreats. We modeled cortical, corticothalamic, and intrathalamic interactions to generate a simulation of EEG signals recorded across the scalp. We also present two novel extensions to the mean-field approach that allow for: (a) non-parametric analysis of changes in model parameter values across all channels and assessments; and (b) examination of variation in modeled thalamic reticular nucleus (TRN) connectivity over the retreat period. After successfully fitting whole-brain EEG data across three assessment points within each retreat, two model parameters were found to replicably change across both meditation retreats. First, after training, we observed an increased temporal delay between modeled cortical and thalamic cells. This increase provides a putative neural mechanism for a previously observed reduction in individual alpha frequency in these same participants. Second, we found decreased inhibitory connection strength between the TRN and secondary relay nuclei (SRN) of the modeled thalamus after training. This reduction in inhibitory strength was found to be associated with increased dynamical stability of the model. Altogether, this paper presents the first computational approach, taking core aspects of physiology and

Transfer function between EEG and BOLD signals of epileptic activity

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Marco eLeite

2013-01-01

Full Text Available Simultaneous EEG-fMRI recordings have seen growing application in the evaluation of epilepsy, namely in the characterization of brain networks related to epileptic activity. In EEG-correlated fMRI studies, epileptic events are usually described as boxcar signals based on the timing information retrieved from the EEG, and subsequently convolved with a heamodynamic response function to model the associated BOLD changes. Although more flexible approaches may allow a higher degree of complexity for the haemodynamics, the issue of how to model these dynamics based on the EEG remains an open question. In this work, a new methodology for the integration of simultaneous EEG-fMRI data in epilepsy is proposed, which incorporates a transfer function from the EEG to the BOLD signal. Independent component analysis (ICA of the EEG is performed, and a number of metrics expressing different models of the EEG-BOLD transfer function are extracted from the resulting time courses. These metrics are then used to predict the fMRI data and to identify brain areas associated with the EEG epileptic activity. The methodology was tested on both ictal and interictal EEG-fMRI recordings from one patient with a hypothalamic hamartoma. When compared to the conventional analysis approach, plausible, consistent and more significant activations were obtained. Importantly, frequency-weighted EEG metrics yielded superior results than those weighted solely on the EEG power, which comes in agreement with previous literature. Reproducibility, specificity and sensitivity should be addressed in an extended group of patients in order to further validate the proposed methodology and generalize the presented proof of concept.

Information-Theoretical Analysis of EEG Microstate Sequences in Python

Directory of Open Access Journals (Sweden)

Frederic von Wegner

2018-06-01

Full Text Available We present an open-source Python package to compute information-theoretical quantities for electroencephalographic data. Electroencephalography (EEG measures the electrical potential generated by the cerebral cortex and the set of spatial patterns projected by the brain's electrical potential on the scalp surface can be clustered into a set of representative maps called EEG microstates. Microstate time series are obtained by competitively fitting the microstate maps back into the EEG data set, i.e., by substituting the EEG data at a given time with the label of the microstate that has the highest similarity with the actual EEG topography. As microstate sequences consist of non-metric random variables, e.g., the letters A–D, we recently introduced information-theoretical measures to quantify these time series. In wakeful resting state EEG recordings, we found new characteristics of microstate sequences such as periodicities related to EEG frequency bands. The algorithms used are here provided as an open-source package and their use is explained in a tutorial style. The package is self-contained and the programming style is procedural, focusing on code intelligibility and easy portability. Using a sample EEG file, we demonstrate how to perform EEG microstate segmentation using the modified K-means approach, and how to compute and visualize the recently introduced information-theoretical tests and quantities. The time-lagged mutual information function is derived as a discrete symbolic alternative to the autocorrelation function for metric time series and confidence intervals are computed from Markov chain surrogate data. The software package provides an open-source extension to the existing implementations of the microstate transform and is specifically designed to analyze resting state EEG recordings.

Automated approach to detecting behavioral states using EEG-DABS

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Zachary B. Loris

2017-07-01

Full Text Available Electrocorticographic (ECoG signals represent cortical electrical dipoles generated by synchronous local field potentials that result from simultaneous firing of neurons at distinct frequencies (brain waves. Since different brain waves correlate to different behavioral states, ECoG signals presents a novel strategy to detect complex behaviors. We developed a program, EEG Detection Analysis for Behavioral States (EEG-DABS that advances Fast Fourier Transforms through ECoG signals time series, separating it into (user defined frequency bands and normalizes them to reduce variability. EEG-DABS determines events if segments of an experimental ECoG record have significantly different power bands than a selected control pattern of EEG. Events are identified at every epoch and frequency band and then are displayed as output graphs by the program. Certain patterns of events correspond to specific behaviors. Once a predetermined pattern was selected for a behavioral state, EEG-DABS correctly identified the desired behavioral event. The selection of frequency band combinations for detection of the behavior affects accuracy of the method. All instances of certain behaviors, such as freezing, were correctly identified from the event patterns generated with EEG-DABS. Detecting behaviors is typically achieved by visually discerning unique animal phenotypes, a process that is time consuming, unreliable, and subjective. EEG-DABS removes variability by using defined parameters of EEG/ECoG for a desired behavior over chronic recordings. EEG-DABS presents a simple and automated approach to quantify different behavioral states from ECoG signals.

Plastic modulation of PTSD resting-state networks and subjective wellbeing by EEG neurofeedback.

Science.gov (United States)

Kluetsch, R C; Ros, T; Théberge, J; Frewen, P A; Calhoun, V D; Schmahl, C; Jetly, R; Lanius, R A

2014-08-01

Electroencephalographic (EEG) neurofeedback training has been shown to produce plastic modulations in salience network and default mode network functional connectivity in healthy individuals. In this study, we investigated whether a single session of neurofeedback training aimed at the voluntary reduction of alpha rhythm (8-12 Hz) amplitude would be related to differences in EEG network oscillations, functional MRI (fMRI) connectivity, and subjective measures of state anxiety and arousal in a group of individuals with post-traumatic stress disorder (PTSD). Twenty-one individuals with PTSD related to childhood abuse underwent 30 min of EEG neurofeedback training preceded and followed by a resting-state fMRI scan. Alpha desynchronizing neurofeedback was associated with decreased alpha amplitude during training, followed by a significant increase ('rebound') in resting-state alpha synchronization. This rebound was linked to increased calmness, greater salience network connectivity with the right insula, and enhanced default mode network connectivity with bilateral posterior cingulate, right middle frontal gyrus, and left medial prefrontal cortex. Our study represents a first step in elucidating the potential neurobehavioural mechanisms mediating the effects of neurofeedback treatment on regulatory systems in PTSD. Moreover, it documents for the first time a spontaneous EEG 'rebound' after neurofeedback, pointing to homeostatic/compensatory mechanisms operating in the brain. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Frequency comb generation in a continuously pumped optical parametric oscillator

Science.gov (United States)

Mosca, S.; Parisi, M.; Ricciardi, I.; Leo, F.; Hansson, T.; Erkintalo, M.; Maddaloni, P.; De Natale, P.; Wabnitz, S.; De Rosa, M.

2018-02-01

We demonstrate optical frequency comb generation in a continuously pumped optical parametric oscillator, in the parametric region around half of the pump frequency. We also model the dynamics of such quadratic combs using a single time-domain mean-field equation, and obtain simulation results that are in good agreement with experimentally observed spectra. Moreover, we numerically investigate the coherence properties of simulated combs, showing the existence of correlated and phase-locked combs. Our work could pave the way for a new class of frequency comb sources, which may enable straightforward access to new spectral regions and stimulate novel applications of frequency combs.

Absolute differential yield of parametric x-ray radiation

International Nuclear Information System (INIS)

Shchagin, A.V.; Pristupa, V.I.; Khizhnyak, N.A.

1993-01-01

The results of measurements of absolute differential yield of parametric X-ray radiation (PXR) in thin single crystal are presented for the first time. It has been established that the experimental results are in good agreement with theoretical calculations according with kinematical theory. The influence of density effect on PXR properties is discussed. (author). 19 refs., 7 figs

Analysis and correction of ballistocardiogram contamination of EEG recordings in MR

International Nuclear Information System (INIS)

Jaeger, L.; Hoffmann, A.; Reiser, M.F.; Werhahn, K.J.

2005-01-01

Purpose: to examine the influence of cardiac activity-related head movements and varying blood pulse frequencies on the shape of electroencephalography (EEG) recordings in a high magnetic field, and to implement a post-processing technique to eliminate cardiac activity-related artifacts. Material and methods: respiratory thoracic movements, changes of blood pulse frequency and passive head movements to 20 healthy subjects were examined outside and inside an MR magnet at rest in a simultaneously recorded 21-channel surface EEG. An electrocardiogram (ECG) was recorded simultaneously. On the basis of the correlation of the left ventricular ejection time (LVET) with the heart-rate, a post-processing heart-rate dependent subtraction of the cardiac activity-related artifacts of the EEG was developed. The quality of the post-processed EEG was tested by detecting alpha-activity in the pre- and post-processed EEGs. Results: inside the magnet, passive head motion but not respiratory thoracic movements resulted in EEG artifacts that correlated strongly with cardiac activity-related artifacts of the EEG. The blood pulse frequency influenced the appearance of the cardiac activity-related artifacts of the EEG. The removal of the cardiac activity-related artifacts of the EEG by the implemented post-processing algorithm resulted in an EEG of diagnostic quality with detected alpha-activity. Conclusion: when recording an EEG in MR environment, heart rate-dependent subtraction of EEG artifacts caused by ballistocardiogram contamination is essential to obtain EEG recordings of diagnostic quality and reliability. (orig.)

A Pilot Study of EEG Source Analysis Based Repetitive Transcranial Magnetic Stimulation for the Treatment of Tinnitus.

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Hui Wang

Full Text Available Repetitive Transcranial Magnetic Stimulation (rTMS is a novel therapeutic tool to induce a suppression of tinnitus. However, the optimal target sites are unknown. We aimed to determine whether low-frequency rTMS induced lasting suppression of tinnitus by decreasing neural activity in the cortex, navigated by high-density electroencephalogram (EEG source analysis, and the utility of EEG for targeting treatment.In this controlled three-armed trial, seven normal hearing patients with tonal tinnitus received a 10-day course of 1-Hz rTMS to the cortex, navigated by high-density EEG source analysis, to the left temporoparietal cortex region, and to the left temporoparietal with sham stimulation. The Tinnitus handicap inventory (THI and a visual analog scale (VAS were used to assess tinnitus severity and loudness. Measurements were taken before, and immediately, 2 weeks, and 4 weeks after the end of the interventions.Low-frequency rTMS decreased tinnitus significantly after active, but not sham, treatment. Responders in the EEG source analysis-based rTMS group, 71.4% (5/7 patients, experienced a significant reduction in tinnitus loudness, as evidenced by VAS scores. The target site of neuronal generators most consistently associated with a positive response was the frontal lobe in the right hemisphere, sourced using high-density EEG equipment, in the tinnitus patients. After left temporoparietal rTMS stimulation, 42.8% (3/7 patients experienced a decrease in tinnitus loudness.Active EEG source analysis based rTMS resulted in significant suppression in tinnitus loudness, showing the superiority of neuronavigation-guided coil positioning in dealing with tinnitus. Non-auditory areas should be considered in the pathophysiology of tinnitus. This knowledge in turn can contribute to investigate the pathophysiology of tinnitus.

Numerical Solution of Uncertain Beam Equations Using Double Parametric Form of Fuzzy Numbers

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Smita Tapaswini

2013-01-01

Full Text Available Present paper proposes a new technique to solve uncertain beam equation using double parametric form of fuzzy numbers. Uncertainties appearing in the initial conditions are taken in terms of triangular fuzzy number. Using the single parametric form, the fuzzy beam equation is converted first to an interval-based fuzzy differential equation. Next, this differential equation is transformed to crisp form by applying double parametric form of fuzzy number. Finally, the same is solved by homotopy perturbation method (HPM to obtain the uncertain responses subject to unit step and impulse loads. Obtained results are depicted in terms of plots to show the efficiency and powerfulness of the methodology.

Parametric Resonance in Dynamical Systems

CERN Document Server

Nijmeijer, Henk

2012-01-01

Parametric Resonance in Dynamical Systems discusses the phenomenon of parametric resonance and its occurrence in mechanical systems,vehicles, motorcycles, aircraft and marine craft, and micro-electro-mechanical systems. The contributors provide an introduction to the root causes of this phenomenon and its mathematical equivalent, the Mathieu-Hill equation. Also included is a discussion of how parametric resonance occurs on ships and offshore systems and its frequency in mechanical and electrical systems. This book also: Presents the theory and principles behind parametric resonance Provides a unique collection of the different fields where parametric resonance appears including ships and offshore structures, automotive vehicles and mechanical systems Discusses ways to combat, cope with and prevent parametric resonance including passive design measures and active control methods Parametric Resonance in Dynamical Systems is ideal for researchers and mechanical engineers working in application fields such as MEM...

The effects of cardiorespiratory fitness and acute aerobic exercise on executive functioning and EEG entropy in adolescents

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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.

EEG-Informed fMRI: A Review of Data Analysis Methods

Science.gov (United States)

Abreu, Rodolfo; Leal, Alberto; Figueiredo, Patrícia

2018-01-01

The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest. PMID:29467634

EEG-Informed fMRI: A Review of Data Analysis Methods

Directory of Open Access Journals (Sweden)

Rodolfo Abreu

2018-02-01

Full Text Available The simultaneous acquisition of electroencephalography (EEG with functional magnetic resonance imaging (fMRI is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest.

Trial-dependent psychometric functions accounting for perceptual learning in 2-AFC discrimination tasks.

Science.gov (United States)

Kattner, Florian; Cochrane, Aaron; Green, C Shawn

2017-09-01

The majority of theoretical models of learning consider learning to be a continuous function of experience. However, most perceptual learning studies use thresholds estimated by fitting psychometric functions to independent blocks, sometimes then fitting a parametric function to these block-wise estimated thresholds. Critically, such approaches tend to violate the basic principle that learning is continuous through time (e.g., by aggregating trials into large "blocks" for analysis that each assume stationarity, then fitting learning functions to these aggregated blocks). To address this discrepancy between base theory and analysis practice, here we instead propose fitting a parametric function to thresholds from each individual trial. In particular, we implemented a dynamic psychometric function whose parameters were allowed to change continuously with each trial, thus parameterizing nonstationarity. We fit the resulting continuous time parametric model to data from two different perceptual learning tasks. In nearly every case, the quality of the fits derived from the continuous time parametric model outperformed the fits derived from a nonparametric approach wherein separate psychometric functions were fit to blocks of trials. Because such a continuous trial-dependent model of perceptual learning also offers a number of additional advantages (e.g., the ability to extrapolate beyond the observed data; the ability to estimate performance on individual critical trials), we suggest that this technique would be a useful addition to each psychophysicist's analysis toolkit.

Interrater variability of EEG interpretation in comatose cardiac arrest patients

DEFF Research Database (Denmark)

Westhall, Erik; Rosén, Ingmar; Rossetti, Andrea O

2015-01-01

OBJECTIVE: EEG is widely used to predict outcome in comatose cardiac arrest patients, but its value has been limited by lack of a uniform classification. We used the EEG terminology proposed by the American Clinical Neurophysiology Society (ACNS) to assess interrater variability in a cohort...... who were blinded for patient outcome. Percent agreement and kappa (κ) for the categories in the ACNS EEG terminology and for prespecified malignant EEG-patterns were calculated. RESULTS: There was substantial interrater agreement (κ 0.71) for highly malignant patterns and moderate agreement (κ 0.......42) for malignant patterns. Substantial agreement was found for malignant periodic or rhythmic patterns (κ 0.72) while agreement for identifying an unreactive EEG was fair (κ 0.26). CONCLUSIONS: The ACNS EEG terminology can be used to identify highly malignant EEG-patterns in post cardiac arrest patients...

Low-Threshold Optical Parametric Oscillations in a Whispering Gallery Mode Resonator

DEFF Research Database (Denmark)

Fürst, J. U.; Strekalov, D. V.; Elser, D.

2010-01-01

In whispering gallery mode (WGM) resonator light is guided by continuous total internal reflection along a curved surface. Fabricating such resonators from an optically nonlinear material one takes advantage of their exceptionally high quality factors and small mode volumes to achieve extremely...... efficient optical frequency conversion. Our analysis of the phase-matching conditions for optical parametric down-conversion (PDC) in a spherical WGM resonator shows their direct relation to the sum rules for photons' angular momenta and predicts a very low parametric oscillation threshold. We realized...... such an optical parametric oscillator (OPO) based on naturally phase-matched PDC in lithium niobate. We demonstrated a single-mode, strongly nondegenerate OPO with a threshold of 6.7  μW and linewidth under 10 MHz. This work demonstrates the remarkable capabilities of WGM-based OPOs....

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.

Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: an offline study in patients with tetraplegia.

Science.gov (United States)

Blokland, Yvonne; Spyrou, Loukianos; Thijssen, Dick; Eijsvogels, Thijs; Colier, Willy; Floor-Westerdijk, Marianne; Vlek, Rutger; Bruhn, Jorgen; Farquhar, Jason

2014-03-01

Combining electrophysiological and hemodynamic features is a novel approach for improving current performance of brain switches based on sensorimotor rhythms (SMR). This study was conducted with a dual purpose: to test the feasibility of using a combined electroencephalogram/functional near-infrared spectroscopy (EEG-fNIRS) SMR-based brain switch in patients with tetraplegia, and to examine the performance difference between motor imagery and motor attempt for this user group. A general improvement was found when using both EEG and fNIRS features for classification as compared to using the single-modality EEG classifier, with average classification rates of 79% for attempted movement and 70% for imagined movement. For the control group, rates of 87% and 79% were obtained, respectively, where the "attempted movement" condition was replaced with "actual movement." A combined EEG-fNIRS system might be especially beneficial for users who lack sufficient control of current EEG-based brain switches. The average classification performance in the patient group for attempted movement was significantly higher than for imagined movement using the EEG-only as well as the combined classifier, arguing for the case of a paradigm shift in current brain switch research.

Non-invasive, home-based electroencephalography hypoglycaemia warning system for personal monitoring using skin surface electrodes: a single-case feasibility study.

Science.gov (United States)

Clewett, Christopher J; Langley, Phillip; Bateson, Anthony D; Asghar, Aziz; Wilkinson, Antony J

2016-03-01

Hypoglycaemia unawareness is a common condition associated with increased risk of severe hypoglycaemia. The purpose of the authors' study was to develop a simple to use, home-based and non-invasive hypoglycaemia warning system based on electroencephalography (EEG), and to demonstrate its use in a single-case feasibility study. A participant with type 1 diabetes forms a single-person case study where blood sugar levels and EEG were recorded. EEG was recorded using skin surface electrodes placed behind the ear located within the T3 region by the participant in the home. EEG was analysed retrospectively to develop an algorithm which would trigger a warning if EEG changes associated with hypoglycaemia onset were detected. All hypoglycaemia events were detected by the EEG hypoglycaemia warning algorithm. Warnings were triggered with blood glucose concentration levels at or below 4.2 mmol/l in this participant and no warnings were issued when in euglycaemia. The feasibility of a non-invasive EEG-based hypoglycaemia warning system for personal monitoring in the home has been demonstrated in a single case study. The results suggest that further studies are warranted to evaluate the system prospectively in a larger group of participants.

A comparison of EEG spectral entropy with conventional quantitative ...

African Journals Online (AJOL)

A comparison of EEG spectral entropy with conventional quantitative EEG at varying depths of sevoflurane anaesthesia. PR Bartel, FJ Smith, PJ Becker. Abstract. Background and Aim: Recently an electroencephalographic (EEG) spectral entropy module (M-ENTROPY) for an anaesthetic monitor has become commercially ...

Optical design and studies of a tiled single grating pulse compressor for enhanced parametric space and compensation of tiling errors

Science.gov (United States)

Daiya, D.; Patidar, R. K.; Sharma, J.; Joshi, A. S.; Naik, P. A.; Gupta, P. D.

2017-04-01

A new optical design of tiled single grating pulse compressor has been proposed, set-up and studied. The parametric space, i.e. the laser beam diameters that can be accommodated in the pulse compressor for the given range of compression lengths, has been calculated and shown to have up to two fold enhancement in comparison to our earlier proposed optical designs. The new optical design of the tiled single grating pulse compressor has an additional advantage of self compensation of various tiling errors like longitudinal and lateral piston, tip and groove density mismatch, compared to the earlier designs. Experiments have been carried out for temporal compression of 650 ps positively chirped laser pulses, at central wavelength 1054 nm, down to 235 fs in the tiled grating pulse compressor set up with the proposed design. Further, far field studies have been performed to show the desired compensation of the tiling errors takes place in the new compressor.

Removal of ocular artifacts in EEG--an improved approach combining DWT and ANC for portable applications.

Science.gov (United States)

Peng, Hong; Hu, Bin; Shi, Qiuxia; Ratcliffe, Martyn; Zhao, Qinglin; Qi, Yanbing; Gao, Guoping

2013-05-01

A new model to remove ocular artifacts (OA) from electroencephalograms (EEGs) is presented. The model is based on discrete wavelet transformation (DWT) and adaptive noise cancellation (ANC). Using simulated and measured data, the accuracy of the model is compared with the accuracy of other existing methods based on stationary wavelet transforms and our previous work based on wavelet packet transform and independent component analysis. A particularly novel feature of the new model is the use of DWTs to construct an OA reference signal, using the three lowest frequency wavelet coefficients of the EEGs. The results show that the new model demonstrates an improved performance with respect to the recovery of true EEG signals and also has a better tracking performance. Because the new model requires only single channel sources, it is well suited for use in portable environments where constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices. The model is also applied and evaluated against data recorded within the EUFP 7 Project--Online Predictive Tools for Intervention in Mental Illness (OPTIMI). The results show that the proposed model is effective in removing OAs and meets the requirements of portable systems used for patient monitoring as typified by the OPTIMI project.

The role of auditory cortices in the retrieval of single-trial auditory-visual object memories.

OpenAIRE

Matusz, P.J.; Thelen, A.; Amrein, S.; Geiser, E.; Anken, J.; Murray, M.M.

2015-01-01

Single-trial encounters with multisensory stimuli affect both memory performance and early-latency brain responses to visual stimuli. Whether and how auditory cortices support memory processes based on single-trial multisensory learning is unknown and may differ qualitatively and quantitatively from comparable processes within visual cortices due to purported differences in memory capacities across the senses. We recorded event-related potentials (ERPs) as healthy adults (n = 18) performed a ...

Taking into account latency, amplitude, and morphology: improved estimation of single-trial ERPs by wavelet filtering and multiple linear regression.

Science.gov (United States)

Hu, L; Liang, M; Mouraux, A; Wise, R G; Hu, Y; Iannetti, G D

2011-12-01

Across-trial averaging is a widely used approach to enhance the signal-to-noise ratio (SNR) of event-related potentials (ERPs). However, across-trial variability of ERP latency and amplitude may contain physiologically relevant information that is lost by across-trial averaging. Hence, we aimed to develop a novel method that uses 1) wavelet filtering (WF) to enhance the SNR of ERPs and 2) a multiple linear regression with a dispersion term (MLR(d)) that takes into account shape distortions to estimate the single-trial latency and amplitude of ERP peaks. Using simulated ERP data sets containing different levels of noise, we provide evidence that, compared with other approaches, the proposed WF+MLR(d) method yields the most accurate estimate of single-trial ERP features. When applied to a real laser-evoked potential data set, the WF+MLR(d) approach provides reliable estimation of single-trial latency, amplitude, and morphology of ERPs and thereby allows performing meaningful correlations at single-trial level. We obtained three main findings. First, WF significantly enhances the SNR of single-trial ERPs. Second, MLR(d) effectively captures and measures the variability in the morphology of single-trial ERPs, thus providing an accurate and unbiased estimate of their peak latency and amplitude. Third, intensity of pain perception significantly correlates with the single-trial estimates of N2 and P2 amplitude. These results indicate that WF+MLR(d) can be used to explore the dynamics between different ERP features, behavioral variables, and other neuroimaging measures of brain activity, thus providing new insights into the functional significance of the different brain processes underlying the brain responses to sensory stimuli.

Predicting EEG complexity from sleep macro and microstructure

International Nuclear Information System (INIS)

Chouvarda, I; Maglaveras, N; Mendez, M O; Rosso, V; Parrino, L; Grassi, A; Terzano, M; Bianchi, A M; Cerutti, S

2011-01-01

This work investigates the relation between the complexity of electroencephalography (EEG) signal, as measured by fractal dimension (FD), and normal sleep structure in terms of its macrostructure and microstructure. Sleep features are defined, encoding sleep stage and cyclic alternating pattern (CAP) related information, both in short and long term. The relevance of each sleep feature to the EEG FD is investigated, and the most informative ones are depicted. In order to quantitatively assess the relation between sleep characteristics and EEG dynamics, a modeling approach is proposed which employs subsets of the sleep macrostructure and microstructure features as input variables and predicts EEG FD based on these features of sleep micro/macrostructure. Different sleep feature sets are investigated along with linear and nonlinear models. Findings suggest that the EEG FD time series is best predicted by a nonlinear support vector machine (SVM) model, employing both sleep stage/transitions and CAP features at different time scales depending on the EEG activation subtype. This combination of features suggests that short-term and long-term history of macro and micro sleep events interact in a complex manner toward generating the dynamics of sleep

Rett syndrome: EEG presentation.

Science.gov (United States)

Robertson, R; Langill, L; Wong, P K; Ho, H H

1988-11-01

Rett syndrome, a degenerative neurological disorder of girls, has a classical presentation and typical EEG findings. The electroencephalograms (EEGs) of 7 girls whose records have been followed from the onset of symptoms to the age of 5 or more are presented. These findings are tabulated with the Clinical Staging System of Hagberg and Witt-Engerström (1986). The records show a progressive deterioration in background rhythms in waking and sleep. The abnormalities of the background activity may only become evident at 4-5 years of age or during stage 2--the Rapid Destructive Stage. The marked contrast between waking and sleep background may not occur until stage 3--the Pseudostationary Stage. In essence EEG changes appear to lag behind clinical symptomatology by 1-3 years. An unexpected, but frequent, abnormality was central spikes seen in 5 of 7 girls. They appeared to be age related and could be evoked by tactile stimulation in 2 patients. We hypothesize that the prominent 'hand washing' mannerism may be self-stimulating and related to the appearance of central spike discharges.

EEG simulation by 2D interconnected chaotic oscillators

Energy Technology Data Exchange (ETDEWEB)

Kubany, Adam, E-mail: adamku@bgu.ac.i [Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, P.O. Box 653, Beer-Sheva 84105 (Israel); Mhabary, Ziv; Gontar, Vladimir [Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, P.O. Box 653, Beer-Sheva 84105 (Israel)

2011-01-15

Research highlights: ANN of 2D interconnected chaotic oscillators is explored for EEG simulation. An inverse problem solution (PRCGA) is proposed. Good matching between the simulated and experimental EEG signals has been achieved. - Abstract: An artificial neuronal network composed by 2D interconnected chaotic oscillators is explored for brain waves (EEG) simulation. For the inverse problem solution a parallel real-coded genetic algorithm (PRCGA) is proposed. In order to conduct thorough comparison between the simulated and target signal characteristics, a spectrum analysis of the signals is undertaken. A good matching between the theoretical and experimental EEG signals has been achieved. Numerical results of calculations are presented and discussed.

EEG simulation by 2D interconnected chaotic oscillators

International Nuclear Information System (INIS)

Kubany, Adam; Mhabary, Ziv; Gontar, Vladimir

2011-01-01

Research highlights: → ANN of 2D interconnected chaotic oscillators is explored for EEG simulation. → An inverse problem solution (PRCGA) is proposed. → Good matching between the simulated and experimental EEG signals has been achieved. - Abstract: An artificial neuronal network composed by 2D interconnected chaotic oscillators is explored for brain waves (EEG) simulation. For the inverse problem solution a parallel real-coded genetic algorithm (PRCGA) is proposed. In order to conduct thorough comparison between the simulated and target signal characteristics, a spectrum analysis of the signals is undertaken. A good matching between the theoretical and experimental EEG signals has been achieved. Numerical results of calculations are presented and discussed.

Preoperational radiation surveillance of the WIPP project by EEG during 1992

International Nuclear Information System (INIS)

Kenney, J.W.

1994-02-01

The purpose of the EEG preoperational monitoring program is to document the existing concentrations of selected radionuclides in various environmental media collected from the vicinity of the WIPP site to provide a basis of comparison of any effects of future WT-PP operations. The basic methodology for conducting environmental surveillance both on-site and off-site was outlined by Spiegler (1984). This report represents a continuation of the EEG baseline data beginning in 1985, previously reported in EEG-43, EEG-47, EEG-49 and EEG-51. Such radionuclide baseline data are important in order to determine whether future WIPP operations with radioactive waste have affected concentrations of these radionuclides in the environment. EEG data are consistent with similar environmental measurements obtained by DOE beginning in 1985. Since late 1985, the EEG has collected or received as split samples 2 443 air filters with particulates, 202 water samples, 16 biota samples and 13 soil/sediment samples. A total of 5,946 specific radionuclide analyses have been performed on these samples. As reported previously by EEG (EEG-43, EEG-47, EEG-49 and EEG-51), observed concentrations of U-238 daughter radionuclides were not in equilibrium with the parent radionuclide in water samples. This observation is consistent with different radionuclide mobility in the environment. In a notice of proposed rule making for 40 CFR 141 (US EPA 1991), the Environmental Protection Agency (EPA) National Primary Drinking Water Regulations reflect this in the calculated activity-to-mass ratio of 1.3 pCi/μg of uranium using a geometric mean of the U-234:U-238 ratio in water supplies of 2.7. Ra-226 and Ra- 228 were reported in a number of water samples in concentrations similar to those previously published by EEG and DOE

How Long Should Routine EEG Be Recorded to Get Relevant Information?

Science.gov (United States)

Doudoux, Hannah; Skaare, Kristina; Geay, Thomas; Kahane, Philippe; Bosson, Jean L; Sabourdy, Cécile; Vercueil, Laurent

2017-03-01

The optimal duration of routine EEG (rEEG) has not been determined on a clinical basis. This study aims to determine the time required to obtain relevant information during rEEG with respect to the clinical request. All rEEGs performed over 3 months in unselected patients older than 14 years in an academic hospital were analyzed retrospectively. The latency required to obtain relevant information was determined for each rEEG by 2 independent readers blinded to the clinical data. EEG final diagnoses and latencies were analyzed with respect to the main clinical requests: subacute cognitive impairment, spells, transient focal neurologic manifestation or patients referred by epileptologists. From 430 rEEGs performed in the targeted period, 364 were analyzed: 92% of the pathological rEEGs were provided within the first 10 minutes of recording. Slowing background activity was diagnosed from the beginning, whereas interictal epileptiform discharges were recorded over time. Moreover, the time elapsed to demonstrate a pattern differed significantly in the clinical groups: in patients with subacute cognitive impairment, EEG abnormalities appeared within the first 10 minutes, whereas in the other groups, data could be provided over time. Patients with subacute cognitive impairment differed from those in the other groups significantly in the elapsed time required to obtain relevant information during rEEG, suggesting that 10-minute EEG recordings could be sufficient, arguing in favor of individualized rEEG. However, this conclusion does not apply to intensive care unit patients.

Super learning to hedge against incorrect inference from arbitrary parametric assumptions in marginal structural modeling.

Science.gov (United States)

Neugebauer, Romain; Fireman, Bruce; Roy, Jason A; Raebel, Marsha A; Nichols, Gregory A; O'Connor, Patrick J

2013-08-01

Clinical trials are unlikely to ever be launched for many comparative effectiveness research (CER) questions. Inferences from hypothetical randomized trials may however be emulated with marginal structural modeling (MSM) using observational data, but success in adjusting for time-dependent confounding and selection bias typically relies on parametric modeling assumptions. If these assumptions are violated, inferences from MSM may be inaccurate. In this article, we motivate the application of a data-adaptive estimation approach called super learning (SL) to avoid reliance on arbitrary parametric assumptions in CER. Using the electronic health records data from adults with new-onset type 2 diabetes, we implemented MSM with inverse probability weighting (IPW) estimation to evaluate the effect of three oral antidiabetic therapies on the worsening of glomerular filtration rate. Inferences from IPW estimation were noticeably sensitive to the parametric assumptions about the associations between both the exposure and censoring processes and the main suspected source of confounding, that is, time-dependent measurements of hemoglobin A1c. SL was successfully implemented to harness flexible confounding and selection bias adjustment from existing machine learning algorithms. Erroneous IPW inference about clinical effectiveness because of arbitrary and incorrect modeling decisions may be avoided with SL. Copyright © 2013 Elsevier Inc. All rights reserved.

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.

EEG-Annotate: Automated identification and labeling of events in continuous signals with applications to EEG.

Science.gov (United States)

Su, Kyung-Min; Hairston, W David; Robbins, Kay

2018-01-01

In controlled laboratory EEG experiments, researchers carefully mark events and analyze subject responses time-locked to these events. Unfortunately, such markers may not be available or may come with poor timing resolution for experiments conducted in less-controlled naturalistic environments. We present an integrated event-identification method for identifying particular responses that occur in unlabeled continuously recorded EEG signals based on information from recordings of other subjects potentially performing related tasks. We introduce the idea of timing slack and timing-tolerant performance measures to deal with jitter inherent in such non-time-locked systems. We have developed an implementation available as an open-source MATLAB toolbox (http://github.com/VisLab/EEG-Annotate) and have made test data available in a separate data note. We applied the method to identify visual presentation events (both target and non-target) in data from an unlabeled subject using labeled data from other subjects with good sensitivity and specificity. The method also identified actual visual presentation events in the data that were not previously marked in the experiment. Although the method uses traditional classifiers for initial stages, the problem of identifying events based on the presence of stereotypical EEG responses is the converse of the traditional stimulus-response paradigm and has not been addressed in its current form. In addition to identifying potential events in unlabeled or incompletely labeled EEG, these methods also allow researchers to investigate whether particular stereotypical neural responses are present in other circumstances. Timing-tolerance has the added benefit of accommodating inter- and intra- subject timing variations. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Resting state EEG correlates of memory consolidation.

Science.gov (United States)

Brokaw, Kate; Tishler, Ward; Manceor, Stephanie; Hamilton, Kelly; Gaulden, Andrew; Parr, Elaine; Wamsley, Erin J

2016-04-01

Numerous studies demonstrate that post-training sleep benefits human memory. At the same time, emerging data suggest that other resting states may similarly facilitate consolidation. In order to identify the conditions under which non-sleep resting states benefit memory, we conducted an EEG (electroencephalographic) study of verbal memory retention across 15min of eyes-closed rest. Participants (n=26) listened to a short story and then either rested with their eyes closed, or else completed a distractor task for 15min. A delayed recall test was administered immediately following the rest period. We found, first, that quiet rest enhanced memory for the short story. Improved memory was associated with a particular EEG signature of increased slow oscillatory activity (rest can facilitate memory, and that this may occur via an active process of consolidation supported by slow oscillatory EEG activity and characterized by decreased attention to the external environment. Slow oscillatory EEG rhythms are proposed to facilitate memory consolidation during sleep by promoting hippocampal-cortical communication. Our findings suggest that EEG slow oscillations could play a significant role in memory consolidation during other resting states as well. Copyright © 2016 Elsevier Inc. All rights reserved.

Enhanced brainstem and cortical evoked response amplitudes: single-trial covariance analysis.

Science.gov (United States)

Galbraith, G C

2001-06-01

The purpose of the present study was to develop analytic procedures that improve the definition of sensory evoked response components. Such procedures could benefit all recordings but would especially benefit difficult recordings where many trials are contaminated by muscle and movement artifacts. First, cross-correlation and latency adjustment analyses were applied to the human brainstem frequency-following response and cortical auditory evoked response recorded on the same trials. Lagged cross-correlation functions were computed, for each of 17 subjects, between single-trial data and templates consisting of the sinusoid stimulus waveform for the brainstem response and the subject's own smoothed averaged evoked response P2 component for the cortical response. Trials were considered in the analysis only if the maximum correlation-squared (r2) exceeded .5 (negatively correlated trials were thus included). Identical correlation coefficients may be based on signals with quite different amplitudes, but it is possible to assess amplitude by the nonnormalized covariance function. Next, an algorithm is applied in which each trial with negative covariance is matched to a trial with similar, but positive, covariance and these matched-trial pairs are deleted. When an evoked response signal is present in the data, the majority of trials positively correlate with the template. Thus, a residual of positively correlated trials remains after matched covariance trials are deleted. When these residual trials are averaged, the resulting brainstem and cortical responses show greatly enhanced amplitudes. This result supports the utility of this analysis technique in clarifying and assessing evoked response signals.

An automated and fast approach to detect single-trial visual evoked potentials with application to brain-computer interface.

Science.gov (United States)

Tu, Yiheng; Hung, Yeung Sam; Hu, Li; Huang, Gan; Hu, Yong; Zhang, Zhiguo

2014-12-01

This study aims (1) to develop an automated and fast approach for detecting visual evoked potentials (VEPs) in single trials and (2) to apply the single-trial VEP detection approach in designing a real-time and high-performance brain-computer interface (BCI) system. The single-trial VEP detection approach uses common spatial pattern (CSP) as a spatial filter and wavelet filtering (WF) a temporal-spectral filter to jointly enhance the signal-to-noise ratio (SNR) of single-trial VEPs. The performance of the joint spatial-temporal-spectral filtering approach was assessed in a four-command VEP-based BCI system. The offline classification accuracy of the BCI system was significantly improved from 67.6±12.5% (raw data) to 97.3±2.1% (data filtered by CSP and WF). The proposed approach was successfully implemented in an online BCI system, where subjects could make 20 decisions in one minute with classification accuracy of 90%. The proposed single-trial detection approach is able to obtain robust and reliable VEP waveform in an automatic and fast way and it is applicable in VEP based online BCI systems. This approach provides a real-time and automated solution for single-trial detection of evoked potentials or event-related potentials (EPs/ERPs) in various paradigms, which could benefit many applications such as BCI and intraoperative monitoring. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Variation in anticonvulsant selection and EEG monitoring following severe traumatic brain injury in children – Understanding resource availability in sites participating in a comparative effectiveness study

Science.gov (United States)

Kurz, Jonathan E.; Poloyac, Samuel M.; Abend, Nicholas S.; Fabio, Anthony; Bell, Michael J.; Wainwright, Mark S.

2016-01-01

Objective Early post-traumatic seizures (PTS) may contribute to worsened outcomes after traumatic brain injury (TBI). Evidence to guide the evaluation and management of early PTS in children is limited. We undertook a survey of current practices of continuous electroencephalographic monitoring (cEEG), seizure prophylaxis and the management of early PTS to provide essential information for trial design and the development of PTS management pathways. Design Surveys were sent to site principal investigators at all 43 sites participating in the ADAPT (Approaches and Decisions in Acute Pediatric TBI) trial at the time of the survey. Surveys consisted of 12 questions addressing strategies to (i) implement cEEG monitoring, (ii) PTS prophylaxis, (iii) treat acute PTS, (iv) treat status epilepticus (SE) and refractory status epilepticus (RSE) and (v) monitor anti-seizure drug levels. Setting Institutions comprised a mixture of free-standing children’s hospitals and university medical centers across the United States and Europe. Measurements and Main Results cEEG monitoring was available in the pediatric intensive care unit in the overwhelming majority of clinical sites (98%); however, the plans to operationalize such monitoring for children varied considerably. A similar majority of sites report that administration of prophylactic anti-seizure medications is anticipated in children (93%), yet a minority reports that a specified protocol for treatment of PTS is in place (43%). Reported medication choices varied substantially between sites, but the majority of sites reported pentobarbital for RSE (81%). Presence of an treatment protocols for seizure prophylaxis, early PTS, post-traumatic SE and RSE was associated with decreased reported medications (all p pediatric severe TBI. The substantial variation in cEEG implementation, choice of seizure prophylaxis medications, and management of early PTS across institutions was reported, signifying areas of clinical uncertainty that

Assessment of preconscious sucrose perception using EEG

DEFF Research Database (Denmark)

Rotvel, Camilla; Møller, Stine; Nielsen, Rene R

The objective of the current study is to develop a methodology for food ingredient screening based on Electro-Encephalo-Graphy (EEG). EEG measures electrical activity in the central nervous system, allowing assessment of activity in the ascending gustatory pathway from the taste buds on the tongue...... stimulus. The EEG was recorded using a 64 electrode setup, and gustatory evoked potentials (GEP) were estimated by coherent averaging across all 60 stimulations for each concentration. Cortical source localization based on the GEP was performed using a low resolution electromagnetic tomography (LORETA...

Unilateral pallidotomy in Parkinson's disease : a randomised, single-blind, multicentre trial

NARCIS (Netherlands)

de Bie, RMA; de Haan, RJ; Nijssen, PCG; Rutgers, AWF; Beute, GN; Haaxma, R; Schmand, B; Staal, MJ; Speelman, J.D.

1999-01-01

Background The results of several cohort studies suggest that patients with advanced Parkinson's disease would benefit from unilateral pallidotomy. We have assessed the efficacy of unilateral pallidotomy in a randomised, single-blind, multicentre trial. Methods We enrolled 37 patients with advanced

Detection of movement intention from single-trial movement-related cortical potentials

Science.gov (United States)

Niazi, Imran Khan; Jiang, Ning; Tiberghien, Olivier; Feldbæk Nielsen, Jørgen; Dremstrup, Kim; Farina, Dario

2011-10-01

Detection of movement intention from neural signals combined with assistive technologies may be used for effective neurofeedback in rehabilitation. In order to promote plasticity, a causal relation between intended actions (detected for example from the EEG) and the corresponding feedback should be established. This requires reliable detection of motor intentions. In this study, we propose a method to detect movements from EEG with limited latency. In a self-paced asynchronous BCI paradigm, the initial negative phase of the movement-related cortical potentials (MRCPs), extracted from multi-channel scalp EEG was used to detect motor execution/imagination in healthy subjects and stroke patients. For MRCP detection, it was demonstrated that a new optimized spatial filtering technique led to better accuracy than a large Laplacian spatial filter and common spatial pattern. With the optimized spatial filter, the true positive rate (TPR) for detection of movement execution in healthy subjects (n = 15) was 82.5 ± 7.8%, with latency of -66.6 ± 121 ms. Although TPR decreased with motor imagination in healthy subject (n = 10, 64.5 ± 5.33%) and with attempted movements in stroke patients (n = 5, 55.01 ± 12.01%), the results are promising for the application of this approach to provide patient-driven real-time neurofeedback.

Language Emptiness of Continuous-Time Parametric Timed Automata

DEFF Research Database (Denmark)

Benes, Nikola; Bezdek, Peter; Larsen, Kim Guldstrand

2015-01-01

Parametric timed automata extend the standard timed automata with the possibility to use parameters in the clock guards. In general, if the parameters are real-valued, the problem of language emptiness of such automata is undecidable even for various restricted subclasses. We thus focus on the case...... where parameters are assumed to be integer-valued, while the time still remains continuous. On the one hand, we show that the problem remains undecidable for parametric timed automata with three clocks and one parameter. On the other hand, for the case with arbitrary many clocks where only one......-time semantics only. To the best of our knowledge, this is the first positive result in the case of continuous-time and unbounded integer parameters, except for the rather simple case of single-clock automata....

Continuous EEG Monitoring in Aneurysmal Subarachnoid Hemorrhage

DEFF Research Database (Denmark)

Kondziella, Daniel; Friberg, Christian Kærsmose; Wellwood, Ian

2015-01-01

BACKGROUND: Continuous EEG (cEEG) may allow monitoring of patients with aneurysmal subarachnoid hemorrhage (SAH) for delayed cerebral ischemia (DCI) and seizures, including non-convulsive seizures (NCSz), and non-convulsive status epilepticus (NCSE). We aimed to evaluate: (a) the diagnostic...

Changes in EEG complexity with electroconvulsive therapy in a patient with autism spectrum disorders: a multiscale entropy approach

Directory of Open Access Journals (Sweden)

Ryoko eOkazaki

2015-02-01

Full Text Available Autism spectrum disorders (ASD are heterogeneous neurodevelopmental disorders that are reportedly characterized by aberrant neural networks. Recently developed multiscale entropy analysis (MSE can characterize the complexity inherent in EEG dynamics over multiple temporal scales in the dynamics of neural networks. We encountered an 18-year-old man with ASD whose refractory catatonic obsessive–compulsive symptoms were improved dramatically after electroconvulsive therapy (ECT. In this clinical case study, we strove to clarify the neurophysiological mechanism of ECT in ASD by assessing EEG complexity using MSE. Along with ECT, the frontocentral region showed decreased EEG complexity at higher temporal scales, whereas the occipital region expressed an increase at lower temporal scales. Furthermore, these changes were associated with clinical improvement associated with the elevation of brain-derived neurotrophic factor, which is a molecular hypothesis of ECT, playing key roles in ASD pathogenesis. Changes in EEG complexity in a region-specific and temporal scale-specific manner we found might reflect atypical EEG dynamics in ASD. Although MSE is not a direct approach to measuring neural connectivity and the results are from only a single case, they might reflect specific aberrant neural network activity and the therapeutic neurophysiological mechanism of ECT in ASD.

Music training is associated with cortical synchronization reflected in EEG coherence during verbal memory encoding

Science.gov (United States)

Cheung, Mei-chun; Chan, Agnes S.; Liu, Ying; Law, Derry; Wong, Christina W. Y.

2017-01-01

Music training can improve cognitive functions. Previous studies have shown that children and adults with music training demonstrate better verbal learning and memory performance than those without such training. Although prior studies have shown an association between music training and changes in the structural and functional organization of the brain, there is no concrete evidence of the underlying neural correlates of the verbal memory encoding phase involved in such enhanced memory performance. Therefore, we carried out an electroencephalography (EEG) study to investigate how music training was associated with brain activity during the verbal memory encoding phase. Sixty participants were recruited, 30 of whom had received music training for at least one year (the MT group) and 30 of whom had never received music training (the NMT group). The participants in the two groups were matched for age, education, gender distribution, and cognitive capability. Their verbal and visual memory functions were assessed using standardized neuropsychological tests and EEG was used to record their brain activity during the verbal memory encoding phase. Consistent with previous studies, the MT group demonstrated better verbal memory than the NMT group during both the learning and the delayed recall trials in the paper-and-pencil tests. The MT group also exhibited greater learning capacity during the learning trials. Compared with the NMT group, the MT group showed an increase in long-range left and right intrahemispheric EEG coherence in the theta frequency band during the verbal memory encoding phase. In addition, their event-related left intrahemispheric theta coherence was positively associated with subsequent verbal memory performance as measured by discrimination scores. These results suggest that music training may modulate the cortical synchronization of the neural networks involved in verbal memory formation. PMID:28358852

Music training is associated with cortical synchronization reflected in EEG coherence during verbal memory encoding.

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Mei-Chun Cheung

Full Text Available Music training can improve cognitive functions. Previous studies have shown that children and adults with music training demonstrate better verbal learning and memory performance than those without such training. Although prior studies have shown an association between music training and changes in the structural and functional organization of the brain, there is no concrete evidence of the underlying neural correlates of the verbal memory encoding phase involved in such enhanced memory performance. Therefore, we carried out an electroencephalography (EEG study to investigate how music training was associated with brain activity during the verbal memory encoding phase. Sixty participants were recruited, 30 of whom had received music training for at least one year (the MT group and 30 of whom had never received music training (the NMT group. The participants in the two groups were matched for age, education, gender distribution, and cognitive capability. Their verbal and visual memory functions were assessed using standardized neuropsychological tests and EEG was used to record their brain activity during the verbal memory encoding phase. Consistent with previous studies, the MT group demonstrated better verbal memory than the NMT group during both the learning and the delayed recall trials in the paper-and-pencil tests. The MT group also exhibited greater learning capacity during the learning trials. Compared with the NMT group, the MT group showed an increase in long-range left and right intrahemispheric EEG coherence in the theta frequency band during the verbal memory encoding phase. In addition, their event-related left intrahemispheric theta coherence was positively associated with subsequent verbal memory performance as measured by discrimination scores. These results suggest that music training may modulate the cortical synchronization of the neural networks involved in verbal memory formation.

Music training is associated with cortical synchronization reflected in EEG coherence during verbal memory encoding.

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Cheung, Mei-Chun; Chan, Agnes S; Liu, Ying; Law, Derry; Wong, Christina W Y

2017-01-01

Music training can improve cognitive functions. Previous studies have shown that children and adults with music training demonstrate better verbal learning and memory performance than those without such training. Although prior studies have shown an association between music training and changes in the structural and functional organization of the brain, there is no concrete evidence of the underlying neural correlates of the verbal memory encoding phase involved in such enhanced memory performance. Therefore, we carried out an electroencephalography (EEG) study to investigate how music training was associated with brain activity during the verbal memory encoding phase. Sixty participants were recruited, 30 of whom had received music training for at least one year (the MT group) and 30 of whom had never received music training (the NMT group). The participants in the two groups were matched for age, education, gender distribution, and cognitive capability. Their verbal and visual memory functions were assessed using standardized neuropsychological tests and EEG was used to record their brain activity during the verbal memory encoding phase. Consistent with previous studies, the MT group demonstrated better verbal memory than the NMT group during both the learning and the delayed recall trials in the paper-and-pencil tests. The MT group also exhibited greater learning capacity during the learning trials. Compared with the NMT group, the MT group showed an increase in long-range left and right intrahemispheric EEG coherence in the theta frequency band during the verbal memory encoding phase. In addition, their event-related left intrahemispheric theta coherence was positively associated with subsequent verbal memory performance as measured by discrimination scores. These results suggest that music training may modulate the cortical synchronization of the neural networks involved in verbal memory formation.

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