Rhythmic EEG patterns in extremely preterm infants: Classification and association with brain injury and outcome.

Science.gov (United States)

Weeke, Lauren C; van Ooijen, Inge M; Groenendaal, Floris; van Huffelen, Alexander C; van Haastert, Ingrid C; van Stam, Carolien; Benders, Manon J; Toet, Mona C; Hellström-Westas, Lena; de Vries, Linda S

2017-12-01

Classify rhythmic EEG patterns in extremely preterm infants and relate these to brain injury and outcome. Retrospective analysis of 77 infants born Rhythmic patterns were observed in 62.3% (ictal 1.3%, PEDs 44%, other waveforms 86.3%) with multiple patterns in 36.4%. Ictal discharges were only observed in one and excluded from further analyses. The EEG location of the other waveforms (pRhythmic waveforms related to head position are likely artefacts. Rhythmic EEG patterns may have a different significance in extremely preterm infants. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Hidden pattern discovery on epileptic EEG with 1-D local binary patterns and epileptic seizures detection by grey relational analysis.

Science.gov (United States)

Kaya, Yılmaz

2015-09-01

This paper proposes a novel approach to detect epilepsy seizures by using Electroencephalography (EEG), which is one of the most common methods for the diagnosis of epilepsy, based on 1-Dimension Local Binary Pattern (1D-LBP) and grey relational analysis (GRA) methods. The main aim of this paper is to evaluate and validate a novel approach, which is a computer-based quantitative EEG analyzing method and based on grey systems, aimed to help decision-maker. In this study, 1D-LBP, which utilizes all data points, was employed for extracting features in raw EEG signals, Fisher score (FS) was employed to select the representative features, which can also be determined as hidden patterns. Additionally, GRA is performed to classify EEG signals through these Fisher scored features. The experimental results of the proposed approach, which was employed in a public dataset for validation, showed that it has a high accuracy in identifying epileptic EEG signals. For various combinations of epileptic EEG, such as A-E, B-E, C-E, D-E, and A-D clusters, 100, 96, 100, 99.00 and 100% were achieved, respectively. Also, this work presents an attempt to develop a new general-purpose hidden pattern determination scheme, which can be utilized for different categories of time-varying signals.

Recognizing of stereotypic patterns in epileptic EEG using empirical modes and wavelets

Science.gov (United States)

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

2017-11-01

Epileptic activity in the form of spike-wave discharges (SWD) appears in the electroencephalogram (EEG) during absence seizures. This paper evaluates two approaches for detecting stereotypic rhythmic activities in EEG, i.e., the continuous wavelet transform (CWT) and the empirical mode decomposition (EMD). The CWT is a well-known method of time-frequency analysis of EEG, whereas EMD is a relatively novel approach for extracting signal's waveforms. A new method for pattern recognition based on combination of CWT and EMD is proposed. It was found that this combined approach resulted to the sensitivity of 86.5% and specificity of 92.9% for sleep spindles and 97.6% and 93.2% for SWD, correspondingly. Considering strong within- and between-subjects variability of sleep spindles, the obtained efficiency in their detection was high in comparison with other methods based on CWT. It is concluded that the combination of a wavelet-based approach and empirical modes increases the quality of automatic detection of stereotypic patterns in rat's EEG.

Seizure and EEG patterns in Wolf-Hirschhorn (4p-) syndrome.

Science.gov (United States)

Battaglia, Agatino; Carey, John C

2005-08-01

Wolf-Hirschhorn syndrome (WHS) is a well-characterized chromosomal disorder that occurs due to partial deletion of the short arm of chromosome 4 (4p-). Although, about 300 cases have been reported to date, limited data are available on electroclinical findings. Information given to parents at the time of diagnosis tends to be skewed to the extreme negative. To delineate the natural history of seizures and EEG patterns in WHS, and obtain better information on diagnosis or outcome in a clinical setting, we reviewed the available literature on electroclinical findings of WHS. 4p- syndrome is characterized by distinctive seizure and EEG patterns that facilitate the early diagnosis and management of such patients.

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

The dynamics of visual experience, an EEG study of subjective pattern formation.

Directory of Open Access Journals (Sweden)

Mark A Elliott

Full Text Available BACKGROUND: Since the origin of psychological science a number of studies have reported visual pattern formation in the absence of either physiological stimulation or direct visual-spatial references. Subjective patterns range from simple phosphenes to complex patterns but are highly specific and reported reliably across studies. METHODOLOGY/PRINCIPAL FINDINGS: Using independent-component analysis (ICA we report a reduction in amplitude variance consistent with subjective-pattern formation in ventral posterior areas of the electroencephalogram (EEG. The EEG exhibits significantly increased power at delta/theta and gamma-frequencies (point and circle patterns or a series of high-frequency harmonics of a delta oscillation (spiral patterns. CONCLUSIONS/SIGNIFICANCE: Subjective-pattern formation may be described in a way entirely consistent with identical pattern formation in fluids or granular flows. In this manner, we propose subjective-pattern structure to be represented within a spatio-temporal lattice of harmonic oscillations which bind topographically organized visual-neuronal assemblies by virtue of low frequency modulation.

Déjà vu phenomenon-related EEG pattern. Case report

OpenAIRE

Vlasov, P.N.; Chervyakov, A.V.; Gnezditskii, V.V.

2013-01-01

Background D?j? vu (DV, from French d?j? vu ? ?already seen?) is an aberration of psychic activity associated with transitory erroneous perception of novel circumstances, objects, or people as already known. Objective This study aimed to record the EEG pattern of d?j? vu. Methods The subjects participated in a survey concerning d?j? vu characteristics and underwent ambulatory EEG monitoring (12?16?h). Results In patients with epilepsy, DV episodes began with polyspike activity in the right te...

Rhythmic EEG patterns in extremely preterm infants : Classification and association with brain injury and outcome

NARCIS (Netherlands)

Weeke, Lauren C; van Ooijen, Inge M; Groenendaal, Floris; van Huffelen, Alexander C.; van Haastert, Ingrid C; van Stam, Carolien; Benders, Manon J; Toet, Mona C; Hellström-Westas, Lena; de Vries, Linda S

2017-01-01

OBJECTIVE: Classify rhythmic EEG patterns in extremely preterm infants and relate these to brain injury and outcome. METHODS: Retrospective analysis of 77 infants born <28 weeks gestational age (GA) who had a 2-channel EEG during the first 72 h after birth. Patterns detected by the BrainZ seizure

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

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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.

Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space.

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Cichy, Radoslaw Martin; Pantazis, Dimitrios

2017-09-01

Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in low-level visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research. Copyright © 2017 Elsevier Inc. All rights reserved.

Qualitative ultrasound elastography assessment of benign thyroid nodules: Patterns and intra-observer acquisition variability

International Nuclear Information System (INIS)

Lacout, Alexis; Chevenet, Carole; Thariat, Juliette; Figl, Andrea; Marcy, Pierre-Yves

2013-01-01

To report and evaluate qualitative elastography patterns by using gray-scale and Doppler ultrasound (US) in patients presenting with benign thyroid nodules and to evaluate the reproducibility of US elastography examinations. Institutional review board approval was obtained, and all patients provided informed consent. Over a 3-month time period, all consecutive adult patients were referred to our institution to undergo a thyroid nodule fine-needle aspiration biopsy (FNAB) procedure. Patients presenting with benign cytology according to the Bethesda 2008 classification were prospectively enrolled in the study. Each thyroid nodule was assessed by using gray-scale, Doppler US, and elastography acquisitions by a single operator (A. L.). Multiple elastography acquisitions per thyroid nodule were performed and elastography scorings of the nodules were compared with each other. Nineteen patients (16 women and 3 men, mean age 58 years) with 22 thyroid nodules were included in the present study. Elastographic patterns 1, 2, and 3 were reported (23% nodules showed pattern 3). The elastography pattern showed a strong variability in 13 nodules (59%). The elastography acquisition result variability involved the “malignant” pattern 3 in 36% of cases. Almost one-third of benign thyroid nodules displayed pattern 3 on qualitative US elastography. The intra-observer variability of the benign thyroid elastography scoring is wide, thus limiting the thyroid nodule US examination accuracy. In FNAB-proven benign thyroid nodules, elastography pattern 3 is frequent and cannot be used as a strong indicator of thyroid malignancy

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...

Pattern of presentation and management of benign upper urinary ...

African Journals Online (AJOL)

Background: Upper urinary tract obstruction is a common cause of severe symptoms, recurrent urinary tract infection and deterioration or complete loss of renal function when intervention is not timely. Objective: To document the pattern of presentation and the management options for the benign upper urinary tract ...

fMRI activation patterns in an analytic reasoning task: consistency with EEG source localization

Science.gov (United States)

Li, Bian; Vasanta, Kalyana C.; O'Boyle, Michael; Baker, Mary C.; Nutter, Brian; Mitra, Sunanda

2010-03-01

Functional magnetic resonance imaging (fMRI) is used to model brain activation patterns associated with various perceptual and cognitive processes as reflected by the hemodynamic (BOLD) response. While many sensory and motor tasks are associated with relatively simple activation patterns in localized regions, higher-order cognitive tasks may produce activity in many different brain areas involving complex neural circuitry. We applied a recently proposed probabilistic independent component analysis technique (PICA) to determine the true dimensionality of the fMRI data and used EEG localization to identify the common activated patterns (mapped as Brodmann areas) associated with a complex cognitive task like analytic reasoning. Our preliminary study suggests that a hybrid GLM/PICA analysis may reveal additional regions of activation (beyond simple GLM) that are consistent with electroencephalography (EEG) source localization patterns.

EEG criteria predictive of complicated evolution in idiopathic rolandic epilepsy.

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Massa, R; de Saint-Martin, A; Carcangiu, R; Rudolf, G; Seegmuller, C; Kleitz, C; Metz-Lutz, M N; Hirsch, E; Marescaux, C

2001-09-25

Although so-called "benign" epilepsy with centrotemporal spikes (BECTS) always has an excellent prognosis with regard to seizure remission, behavioral problems and cognitive dysfunctions may sometimes develop in its course. To search for clinical or EEG markers allowing early detection of patients prone to such complications, the authors conducted a prospective study in a cohort of unselected patients with BECTS. In 35 children with BECTS, academic, familial, neurologic, neuropsychological, and wake and sleep EEG evaluations were repeated every 6 to 12 months from the beginning of the seizure disorder up to complete recovery. In 25 of 35 patients (72%), behavioral and intellectual functioning remained unimpaired. In 10 of 35 patients (28%), educational performance and familial maladjustment occurred. These sociofamilial problems were correlated with impulsivity, learning difficulties, attention disorders, and minor (7/35 cases, 20%) or serious (3/35 cases, 8%) auditory-verbal or visual-spatial deficits. Worsening phases started 2 to 36 months after onset and persisted for 9 to 39 months. Occurrence of atypical evolutions was significantly correlated with five qualitative and one quantitative interictal EEG pattern: intermittent slow-wave focus, multiple asynchronous spike-wave foci, long spike-wave clusters, generalized 3-c/s "absence-like" spike-wave discharges, conjunction of interictal paroxysms with negative or positive myoclonia, and abundance of interictal abnormalities during wakefulness and sleep. Clinical deterioration was not linked with seizure characteristics or treatment. Different combinations of at least three of six distinctive interictal EEG patterns and their long-lasting (> or =6-month) persistence seem to be the hallmarks of patients with BECTS at risk for neuropsychological impairments.

Which EEG patterns in coma are nonconvulsive status epilepticus?

Science.gov (United States)

Trinka, Eugen; Leitinger, Markus

2015-08-01

Nonconvulsive status epilepticus (NCSE) is common in patients with coma with a prevalence between 5% and 48%. Patients in deep coma may exhibit epileptiform EEG patterns, such as generalized periodic spikes, and there is an ongoing debate about the relationship of these patterns and NCSE. The purposes of this review are (i) to discuss the various EEG patterns found in coma, its fluctuations, and transitions and (ii) to propose modified criteria for NCSE in coma. Classical coma patterns such as diffuse polymorphic delta activity, spindle coma, alpha/theta coma, low output voltage, or burst suppression do not reflect NCSE. Any ictal patterns with a typical spatiotemporal evolution or epileptiform discharges faster than 2.5 Hz in a comatose patient reflect nonconvulsive seizures or NCSE and should be treated. Generalized periodic diacharges or lateralized periodic discharges (GPDs/LPDs) with a frequency of less than 2.5 Hz or rhythmic discharges (RDs) faster than 0.5 Hz are the borderland of NCSE in coma. In these cases, at least one of the additional criteria is needed to diagnose NCSE (a) subtle clinical ictal phenomena, (b) typical spatiotemporal evolution, or (c) response to antiepileptic drug treatment. There is currently no consensus about how long these patterns must be present to qualify for NCSE, and the distinction from nonconvulsive seizures in patients with critical illness or in comatose patients seems arbitrary. The Salzburg Consensus Criteria for NCSE [1] have been modified according to the Standardized Terminology of the American Clinical Neurophysiology Society [2] and validated in three different cohorts, with a sensitivity of 97.2%, a specificity of 95.9%, and a diagnostic accuracy of 96.3% in patients with clinical signs of NCSE. Their diagnostic utility in different cohorts with patients in deep coma has to be studied in the future. This article is part of a Special Issue entitled "Status Epilepticus". Copyright © 2015. Published by Elsevier Inc.

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.

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

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...

Modulation of EEG spectral edge frequency during patterned pneumatic oral stimulation in preterm infants

Science.gov (United States)

Song, Dongli; Jegatheesan, Priya; Weiss, Sunshine; Govindaswami, Balaji; Wang, Jingyan; Lee, Jaehoon; Oder, Austin; Barlow, Steven M

2014-01-01

Background Stimulation of the nervous system plays a central role in brain development and neurodevelopmental outcome. Thalamocortical and corticocortical development is diminished in premature infants and correlated to electroencephalography (EEG) progression. The purpose of this study was to determine the effects of orocutaneous stimulation on the modulation of spectral edge frequency, fc=90% (SEF-90) derived from EEG recordings in preterm infants. Methods Twenty two preterm infants were randomized to experimental and control conditions. Pulsed orocutaneous stimulation was presented during gavage feedings begun at around 32 weeks postmenstrual age (PMA). The SEF-90 was derived from 2-channel EEG recordings. Results Compared to the control condition, the pulsed orocutaneous stimulation produced a significant reorganization of SEF-90 in the left (p = 0.005) and right (p stimulation also produced a significant pattern of short term cortical adaptation and a long term neural adaptation manifest as a 0.5 Hz elevation in SEF-90 after repeated stimulation sessions. Conclusion This is the first study to demonstrate the modulating effects of a servo-controlled oral somatosensory input on the spectral features of EEG activity in preterm infants. PMID:24129553

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

Directory of Open Access Journals (Sweden)

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

Peak and averaged bicoherence for different EEG patterns during general anaesthesia

Directory of Open Access Journals (Sweden)

Myles Paul

2010-11-01

levels (comparable BIS estimates were 0.928(0.905-0.950 and 0.801(0.786-0.816. Estimates of linear regression and areas under ROC curves supported Pk findings. Bicoherence for eye movement artifacts were the most distinctive with respect to other EEG patterns (average |z| value 13.233. Conclusions This study quantified associations between deepening anaesthesia and increase in bicoherence for different frequency components and bicoherence estimates. Increase in bicoherence was also established for eye movement artifacts. While identified associations extend earlier findings of bicoherence changes with increases in anaesthetic drug concentration, results indicate that the unequal band bifrequency region, δ_θ, provides better predictive capabilities than equal band bifrequency regions.

A Novel Method for Detection of Epilepsy in Short and Noisy EEG Signals Using Ordinal Pattern Analysis

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Iman Veisi

2010-03-01

Full Text Available Introduction: In this paper, a novel complexity measure is proposed to detect dynamical changes in nonlinear systems using ordinal pattern analysis of time series data taken from the system. Epilepsy is considered as a dynamical change in nonlinear and complex brain system. The ability of the proposed measure for characterizing the normal and epileptic EEG signals when the signal is short or is contaminated with noise is investigated and compared with some traditional chaos-based measures. Materials and Methods: In the proposed method, the phase space of the time series is reconstructed and then partitioned using ordinal patterns. The partitions can be labeled using a set of symbols. Therefore, the state trajectory is converted to a symbol sequence. A finite state machine is then constructed to model the sequence. A new complexity measure is proposed to detect dynamical changes using the state transition matrix of the state machine. The proposed complexity measure was applied to detect epilepsy in short and noisy EEG signals and the results were compared with some chaotic measures. Results: The results indicate that this complexity measure can distinguish normal and epileptic EEG signals with an accuracy of more than 97% for clean EEG and more than 75% for highly noised EEG signals. Discussion and Conclusion: The complexity measure can be computed in a very fast and easy way and, unlike traditional chaotic measures, is robust with respect to noise corrupting the data. This measure is also capable of dynamical change detection in short time series data.

Lateralization patterns of covert but not overt movements change with age: An EEG neurofeedback study.

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Zich, Catharina; Debener, Stefan; De Vos, Maarten; Frerichs, Stella; Maurer, Stefanie; Kranczioch, Cornelia

2015-08-01

The mental practice of movements has been suggested as a promising add-on therapy to facilitate motor recovery after stroke. In the case of mentally practised movements, electroencephalogram (EEG) can be utilized to provide feedback about an otherwise covert act. The main target group for such an intervention are elderly patients, though research so far is largely focused on young populations (study therefore aimed to examine the influence of age on the neural correlates of covert movements (CMs) in a real-time EEG neurofeedback framework. CM-induced event-related desynchronization (ERD) was studied in young (mean age: 23.6 years) and elderly (mean age: 62.7 years) healthy adults. Participants performed covert and overt hand movements. CMs were based on kinesthetic motor imagery (MI) or quasi-movements (QM). Based on previous studies investigating QM in the mu frequency range (8-13Hz) QM were expected to result in more lateralized ERD% patterns and accordingly higher classification accuracies. Independent of CM strategy the elderly were characterized by a significantly reduced lateralization of ERD%, due to stronger ipsilateral ERD%, and in consequence, reduced classification accuracies. QM were generally perceived as more vivid, but no differences were evident between MI and QM in ERD% or classification accuracies. EEG feedback enhanced task-related activity independently of strategy and age. ERD% measures of overt and covert movements were strongly related in young adults, whereas in the elderly ERD% lateralization is dissociated. In summary, we did not find evidence in support of more pronounced ERD% lateralization patterns in QM. Our finding of a less lateralized activation pattern in the elderly is in accordance to previous research and with the idea that compensatory processes help to overcome neurodegenerative changes related to normal ageing. Importantly, it indicates that EEG neurofeedback studies should place more emphasis on the age of the potential end

A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study

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Duffy Frank H

2012-06-01

Full Text Available Abstract Background The autism rate has recently increased to 1 in 100 children. Genetic studies demonstrate poorly understood complexity. Environmental factors apparently also play a role. Magnetic resonance imaging (MRI studies demonstrate increased brain sizes and altered connectivity. Electroencephalogram (EEG coherence studies confirm connectivity changes. However, genetic-, MRI- and/or EEG-based diagnostic tests are not yet available. The varied study results likely reflect methodological and population differences, small samples and, for EEG, lack of attention to group-specific artifact. Methods Of the 1,304 subjects who participated in this study, with ages ranging from 1 to 18 years old and assessed with comparable EEG studies, 463 children were diagnosed with autism spectrum disorder (ASD; 571 children were neuro-typical controls (C. After artifact management, principal components analysis (PCA identified EEG spectral coherence factors with corresponding loading patterns. The 2- to 12-year-old subsample consisted of 430 ASD- and 554 C-group subjects (n = 984. Discriminant function analysis (DFA determined the spectral coherence factors' discrimination success for the two groups. Loading patterns on the DFA-selected coherence factors described ASD-specific coherence differences when compared to controls. Results Total sample PCA of coherence data identified 40 factors which explained 50.8% of the total population variance. For the 2- to 12-year-olds, the 40 factors showed highly significant group differences (P Conclusions Classification success suggests a stable coherence loading pattern that differentiates ASD- from C-group subjects. This might constitute an EEG coherence-based phenotype of childhood autism. The predominantly reduced short-distance coherences may indicate poor local network function. The increased long-distance coherences may represent compensatory processes or reduced neural pruning. The wide average spectral range

Benign occipital epilepsy of childhood: Panayiotopoulos syndrome in a 3 year old child

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Menon Narayanankutty Sunilkumar , Vadakut Krishnan Parvathy

2014-11-01

Full Text Available Panayiotopoulos syndrome (PS is a relatively frequent and benign epileptic syndrome seen in children in the age group of 3-6 years and is characterised by predominantly autonomic symptoms and/or simple motor focal seizures followed or not by impairment of consciousness. Although multifocal spikes with high amplitude sharp-slow wave complexes at various locations can be present in the EEG, interictal electroencephalogram (EEG in children with this particular type of epilepsy characteristically shows occipital spikes. This syndrome has known to be a masquerader and can imitate gastroenteritis, encephalitis, syncope, migraine, sleep disorders or metabolic diseases. In the absence of thorough knowledge of types of benign epilepsy syndromes and their various clinical presentations, epilepsy such as PS can be easily missed. The peculiar aspects of this type of epilepsy in children should be known not only by paediatricians but also by general doctors because a correct diagnosis would avoid aggressive interventions and concerns on account of its benign outcome. In this case study, we report a case of PS in a 3 year old child.

Benign occipital lobe seizures: Natural progression and atypical evolution

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Prithika Chary

2013-01-01

Full Text Available Benign occipital seizure syndromes are benign childhood epilepsy syndromes and are mainly of two types, Panayiotopoulos syndrome, an autonomic epilepsy and idiopathic childhood occipital epilepsy of Gastaut (ICOE-G including the idiopathic photosensitive occipital lobe epilepsy. Although both these types are categorized as occipital seizures, they are distinct in presentation and management. They can also be tricky to diagnose as visual symptoms may not always be the presenting feature and it is also not very easy to elicit visual hallucinations during history taking. These seizures have a good response to treatment; however, there could be atypical evolution and refractoriness to treatment especially with ICOE-G. We describe three children who presented with visual and non-visual symptoms and the electroencephalography (EEG in all the three cases showed occipital paroxysms. We have emphasized the clues in the clinical history and EEG leading to the diagnosis of these distinct epilepsy syndromes. We have also discussed the natural course of these epilepsy syndromes with some atypical evolution, which clinicians need to be aware of during treatment of these children.

Benign occipital lobe seizures: Natural progression and atypical evolution.

Science.gov (United States)

Chary, Prithika; Rajendran, Bhuvaneshwari

2013-10-01

Benign occipital seizure syndromes are benign childhood epilepsy syndromes and are mainly of two types, Panayiotopoulos syndrome, an autonomic epilepsy and idiopathic childhood occipital epilepsy of Gastaut (ICOE-G) including the idiopathic photosensitive occipital lobe epilepsy. Although both these types are categorized as occipital seizures, they are distinct in presentation and management. They can also be tricky to diagnose as visual symptoms may not always be the presenting feature and it is also not very easy to elicit visual hallucinations during history taking. These seizures have a good response to treatment; however, there could be atypical evolution and refractoriness to treatment especially with ICOE-G. We describe three children who presented with visual and non-visual symptoms and the electroencephalography (EEG) in all the three cases showed occipital paroxysms. We have emphasized the clues in the clinical history and EEG leading to the diagnosis of these distinct epilepsy syndromes. We have also discussed the natural course of these epilepsy syndromes with some atypical evolution, which clinicians need to be aware of during treatment of these children.

Wicket spikes: a case-control study of a benign eletroencephalografic variant pattern "Wicket spikes": estudo de variante eletrográfica benigna

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MARCUS SABRY AZAR BATISTA

1999-09-01

Full Text Available Wicket spikes (WS are a benign eletroencephalogram (EEG variant, seen mainly in adults, during somnolence, in the temporal regions, in many clinical situations. WS can appear in trains or isolatedly, sometimes being difficult to differentiate from epileptiform activity. We reviewed 2,000 EEG's, found 65 with WS (3.25% and compared them with 65 normal EEG without WS. There was statistically significant (SS association between WS and age over 33; adolescent age was correlated to absence of WS and age over 65, to the presence of WS; there was an inverse correlation between WS and epilepsy, related to differences in age; a SS association with cerebrovascular disorders disappeared after controlling for age; a SS correlation with headache was also related to age; female predominance was not SS. There was a great variety of clinical situation associated with WS. We conclude that WS are a inespecific normal variant of the EEG that is age-related.As Wicket spikes (WS são um padrão benigno, variante da normalidade do eletrencefalograma (EEG, vistas principalmente em adultos, durante a sonolência, nas regiões temporais, em situações clínicas variadas. WS aparecem em "trens" ou isoladamente, podendo ser difícil diferenciá-las de atividade epileptiforme. Nós revisamos 2.000 EEG e encontramos 65 com WS (3,25% e os comparamos a 65 EEG 's normais sem WS. Encontramos associação estatisticamente significante (ES entre WS e idade acima de 33 anos; adolescência e ausência de WS e idade acima de 65 e presença de WS. Houve correlação inversa entre WS e epilepsia, explicada por diferenças nas médias de idade. A correlação ES entre WS e doença cerebrovascular desapareceu após controlarmos a idade. A correlação ES a cefaléia dependeu de sua relação à idade. A predominância do sexo feminino não foi ES. Houve maior variedade de situações clínicas associadas a WS. WS são uma variante normal do EEG, idade-relacionada.

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.

Prognostic EEG patterns in patients resuscitated from cardiac arrest with particular focus on Generalized Periodic Epileptiform Discharges (GPEDs).

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Milani, P; Malissin, I; Tran-Dinh, Y R; Deye, N; Baud, F; Lévy, B I; Kubis, N

2014-04-01

We assessed clinical and early electrophysiological characteristics, in particular Generalized Periodic Epileptiform Discharges (GPEDs) patterns, of consecutive patients during a 1-year period, hospitalized in the Intensive Care Unit (ICU) after resuscitation following cardiac arrest (CA). Consecutive patients resuscitated from cardiac arrest (CA) with first EEG recordings within 48hours were included. Clinical data were collected from hospital records, in particular therapeutic hypothermia. Electroencephalograms (EEGs) were re-analyzed retrospectively. Sixty-two patients were included. Forty-two patients (68%) were treated with therapeutic hypothermia according to international guidelines. Global mortality was 74% but not significantly different between patients who benefited from therapeutic hypothermia compared to those who did not. All the patients who did not have an initial background activity (36/62; 58%) died. By contrast, initial background activity was present in 26/62 (42%) and among these patients, 16/26 (61%) survived. Electroencephalography demonstrated GPEDs patterns in 5 patients, all treated by therapeutic hypothermia and antiepileptic drugs. One of these survived and showed persistent background activity with responsiveness to benzodiazepine intravenous injection. Patients presenting suppressed background activity, even when treated by hypothermia, have a high probability of poor outcome. Thorough analysis of EEG patterns might help to identify patients with a better chance of survival. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Burst suppression in sleep in a routine outpatient EEG

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Ammar Kheder

2014-01-01

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

Correlation between temporal pole MRI abnormalities and surface ictal EEG patterns in patients with unilateral mesial temporal lobe epilepsy.

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Caboclo, Luís Otávio S F; Garzon, Eliana; Oliveira, Pedro A L; Carrete, Henrique; Centeno, Ricardo S; Bianchin, Marino M; Yacubian, Elza Márcia T; Sakamoto, Américo C

2007-01-01

The objective of this retrospective study is to analyze ictal patterns observed during continuous Video-EEG monitoring in patients with temporal lobe epilepsy (TLE) due to unilateral hippocampal sclerosis (HS), and to correlate these EEG patterns to temporal pole abnormalities observed on magnetic resonance imaging exams. We analyzed 147 seizures from 35 patients with TLE and unilateral HS. Ictal patterns were classified and correlated to signal abnormalities and volumetric measures of the temporal poles. Volume differences over 10% were considered abnormal. The most frequent type of ictal pattern was rhythmic theta activity (RTA), encountered in 65.5% of the seizures. Rhythmic beta activity (RBA) was observed in 11% of the seizures, localized attenuation in 8%, interruption of epileptiform discharges in 6%, repetitive discharges in 5.5%, and rhythmic delta activity (RDA) in 4%. Sixty-six percent of the patients presented signal abnormalities in the temporal pole that were always ipsilateral to the HS. Sixty percent presented significant asymmetry of the temporal poles consisting of reduced volume that was also always ipsilateral to HS. Although patients with RTA as the predominant ictal pattern tended to present asymmetry of temporal poles (p=0.305), the ictal EEG pattern did not correlate with temporal pole asymmetry or signal abnormalities. RTA is the most frequent initial ictal pattern in patients with TLE due to unilateral HS. Temporal pole signal changes and volumetric reduction were commonly found in this group of patients, both abnormalities appearing always ipsilateral to the HS. However, neither temporal pole volume reduction nor signal abnormalities correlated with the predominant ictal pattern, suggesting that the temporal poles are not crucially involved in the process of epileptogenesis.

Connectivity Measures in EEG Microstructural Sleep Elements.

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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

EEG in connection with coma.

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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.

Unsupervised EEG analysis for automated epileptic seizure detection

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Birjandtalab, Javad; Pouyan, Maziyar Baran; Nourani, Mehrdad

2016-07-01

Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.

A fingertip force prediction model for grasp patterns characterised from the chaotic behaviour of EEG.

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Roy, Rinku; Sikdar, Debdeep; Mahadevappa, Manjunatha; Kumar, C S

2018-05-19

A stable grasp is attained through appropriate hand preshaping and precise fingertip forces. Here, we have proposed a method to decode grasp patterns from motor imagery and subsequent fingertip force estimation model with a slippage avoidance strategy. We have developed a feature-based classification of electroencephalography (EEG) associated with imagination of the grasping postures. Chaotic behaviour of EEG for different grasping patterns has been utilised to capture the dynamics of associated motor activities. We have computed correlation dimension (CD) as the feature and classified with "one against one" multiclass support vector machine (SVM) to discriminate between different grasping patterns. The result of the analysis showed varying classification accuracies at different subband levels. Broad categories of grasping patterns, namely, power grasp and precision grasp, were classified at a 96.0% accuracy rate in the alpha subband. Furthermore, power grasp subtypes were classified with an accuracy of 97.2% in the upper beta subband, whereas precision grasp subtypes showed relatively lower 75.0% accuracy in the alpha subband. Following assessment of fingertip force distributions while grasping, a nonlinear autoregressive (NAR) model with proper prediction of fingertip forces was proposed for each grasp pattern. A slippage detection strategy has been incorporated with automatic recalibration of the regripping force. Intention of each grasp pattern associated with corresponding fingertip force model was virtualised in this work. This integrated system can be utilised as the control strategy for prosthetic hand in the future. The model to virtualise motor imagery based fingertip force prediction with inherent slippage correction for different grasp types ᅟ.

Changes in the frequency of benign focal spikes accompany changes in central information processing speed : a prospective 2-year follow-up study

NARCIS (Netherlands)

Ebus, S.C.M.; IJff, D.M.; den Boer, J.T.; Debeij-van Hall, M.H.J.A.; Klinkenberg, S.; van der Does, A.; Boon, P.J.; Arends, J.B.A.M.; Aldenkamp, A.P.

We prospectively examined whether changes in the frequency of benign focal spikes accompany changes in cognition. Twenty-six children with benign focal spikes (19 with Rolandic epilepsy) and learning difficulties were examined with repeated 24-hour EEG recordings, three cognitive tests on central

Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN.

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Bascil, M Serdar; Tesneli, Ahmet Y; Temurtas, Feyzullah

2016-09-01

Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha-beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.

A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study.

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Duffy, Frank H; Als, Heidelise

2012-06-26

The autism rate has recently increased to 1 in 100 children. Genetic studies demonstrate poorly understood complexity. Environmental factors apparently also play a role. Magnetic resonance imaging (MRI) studies demonstrate increased brain sizes and altered connectivity. Electroencephalogram (EEG) coherence studies confirm connectivity changes. However, genetic-, MRI- and/or EEG-based diagnostic tests are not yet available. The varied study results likely reflect methodological and population differences, small samples and, for EEG, lack of attention to group-specific artifact. Of the 1,304 subjects who participated in this study, with ages ranging from 1 to 18 years old and assessed with comparable EEG studies, 463 children were diagnosed with autism spectrum disorder (ASD); 571 children were neuro-typical controls (C). After artifact management, principal components analysis (PCA) identified EEG spectral coherence factors with corresponding loading patterns. The 2- to 12-year-old subsample consisted of 430 ASD- and 554 C-group subjects (n = 984). Discriminant function analysis (DFA) determined the spectral coherence factors' discrimination success for the two groups. Loading patterns on the DFA-selected coherence factors described ASD-specific coherence differences when compared to controls. Total sample PCA of coherence data identified 40 factors which explained 50.8% of the total population variance. For the 2- to 12-year-olds, the 40 factors showed highly significant group differences (P EEG coherence-based phenotype of childhood autism. The predominantly reduced short-distance coherences may indicate poor local network function. The increased long-distance coherences may represent compensatory processes or reduced neural pruning. The wide average spectral range of factor loadings may suggest over-damped neural networks.

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

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Yi-Hung Liu

2014-07-01

Full Text Available Electroencephalogram-based emotion recognition (EEG-ER has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI. However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher’s discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher’s emotion pattern (KFEP, and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68% and arousal (84.79% among all testing methods.

Detection of artifacts from high energy bursts in neonatal EEG.

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Bhattacharyya, Sourya; Biswas, Arunava; Mukherjee, Jayanta; Majumdar, Arun Kumar; Majumdar, Bandana; Mukherjee, Suchandra; Singh, Arun Kumar

2013-11-01

Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the

Higher-Order Spectrum in Understanding Nonlinearity in EEG Rhythms

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Cauchy Pradhan

2012-01-01

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

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...

[EEG changes in symptomatic headache caused by bruxism].

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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.

Predictive value of EEG in postanoxic encephalopathy: A quantitative model-based approach.

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Efthymiou, Evdokia; Renzel, Roland; Baumann, Christian R; Poryazova, Rositsa; Imbach, Lukas L

2017-10-01

The majority of comatose patients after cardiac arrest do not regain consciousness due to severe postanoxic encephalopathy. Early and accurate outcome prediction is therefore essential in determining further therapeutic interventions. The electroencephalogram is a standardized and commonly available tool used to estimate prognosis in postanoxic patients. The identification of pathological EEG patterns with poor prognosis relies however primarily on visual EEG scoring by experts. We introduced a model-based approach of EEG analysis (state space model) that allows for an objective and quantitative description of spectral EEG variability. We retrospectively analyzed standard EEG recordings in 83 comatose patients after cardiac arrest between 2005 and 2013 in the intensive care unit of the University Hospital Zürich. Neurological outcome was assessed one month after cardiac arrest using the Cerebral Performance Category. For a dynamic and quantitative EEG analysis, we implemented a model-based approach (state space analysis) to quantify EEG background variability independent from visual scoring of EEG epochs. Spectral variability was compared between groups and correlated with clinical outcome parameters and visual EEG patterns. Quantitative assessment of spectral EEG variability (state space velocity) revealed significant differences between patients with poor and good outcome after cardiac arrest: Lower mean velocity in temporal electrodes (T4 and T5) was significantly associated with poor prognostic outcome (pEEG patterns such as generalized periodic discharges (pEEG analysis (state space analysis) provides a novel, complementary marker for prognosis in postanoxic encephalopathy. Copyright © 2017 Elsevier B.V. All rights reserved.

Index finger motor imagery EEG pattern recognition in BCI applications using dictionary cleaned sparse representation-based classification for healthy people

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Miao, Minmin; Zeng, Hong; Wang, Aimin; Zhao, Fengkui; Liu, Feixiang

2017-09-01

Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface (BCI) has shown its effectiveness for the control of rehabilitation devices designed for large body parts of the patients with neurologic impairments. In order to validate the feasibility of using EEG to decode the MI of a single index finger and constructing a BCI-enhanced finger rehabilitation system, we collected EEG data during right hand index finger MI and rest state for five healthy subjects and proposed a pattern recognition approach for classifying these two mental states. First, Fisher's linear discriminant criteria and power spectral density analysis were used to analyze the event-related desynchronization patterns. Second, both band power and approximate entropy were extracted as features. Third, aiming to eliminate the abnormal samples in the dictionary and improve the classification performance of the conventional sparse representation-based classification (SRC) method, we proposed a novel dictionary cleaned sparse representation-based classification (DCSRC) method for final classification. The experimental results show that the proposed DCSRC method gives better classification accuracies than SRC and an average classification accuracy of 81.32% is obtained for five subjects. Thus, it is demonstrated that single right hand index finger MI can be decoded from the sensorimotor rhythms, and the feature patterns of index finger MI and rest state can be well recognized for robotic exoskeleton initiation.

Burst suppression in sleep in a routine outpatient EEG ?

OpenAIRE

Kheder, Ammar; Bianchi, Matt T.; Westover, M. Brandon

2014-01-01

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

Standardized Computer-based Organized Reporting of EEG: SCORE

Science.gov (United States)

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

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

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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.

Synaptic damage underlies EEG abnormalities in postanoxic encephalopathy: A computational study.

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Ruijter, B J; Hofmeijer, J; Meijer, H G E; van Putten, M J A M

2017-09-01

In postanoxic coma, EEG patterns indicate the severity of encephalopathy and typically evolve in time. We aim to improve the understanding of pathophysiological mechanisms underlying these EEG abnormalities. We used a mean field model comprising excitatory and inhibitory neurons, local synaptic connections, and input from thalamic afferents. Anoxic damage is modeled as aggravated short-term synaptic depression, with gradual recovery over many hours. Additionally, excitatory neurotransmission is potentiated, scaling with the severity of anoxic encephalopathy. Simulations were compared with continuous EEG recordings of 155 comatose patients after cardiac arrest. The simulations agree well with six common categories of EEG rhythms in postanoxic encephalopathy, including typical transitions in time. Plausible results were only obtained if excitatory synapses were more severely affected by short-term synaptic depression than inhibitory synapses. In postanoxic encephalopathy, the evolution of EEG patterns presumably results from gradual improvement of complete synaptic failure, where excitatory synapses are more severely affected than inhibitory synapses. The range of EEG patterns depends on the excitation-inhibition imbalance, probably resulting from long-term potentiation of excitatory neurotransmission. Our study is the first to relate microscopic synaptic dynamics in anoxic brain injury to both typical EEG observations and their evolution in time. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

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.

Specific features of cytological and colposcopical pattern in pregnant women with benign cervix uteri pathology in anamnesis.

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Bysaha, Nataliya Yu

2016-01-01

a tendency of increasing incidence of the cervix uteri precancer and cancer in women of reproductive age is noticed recently being related to the growth of number of the sexually-transmitted infections. The cervix uteri pathology incidence in women of fertile age is 20-25%. to study the specific features of the cytological and colposcopical pattern in pregnant patients with benign cervix uteri pathology in the anamnesis and the character of its change post partum. we have examined 195 women during their pregnancy and 3-5 months post partum. All pregnant women, alongside with generally accepted clinical and laboratory examinations, were subjected to the simple and extended colposcopy, cytology of the targeted smears and, according to indications, the histological studies of bioptate. according to the results of the colcoscopical studies and the signs of the cervix uteri pathology found, the patients were divided into several groups. A control group included 49 pregnant women. The clinical and instrumental examination of 146 women with cervix uteri pathology has been carried out both during pregnancy and post partum. the structure of the clinical forms of benign and premalignant changes in the cervix uteri epithelium in pregnant patients has been found. Specific features of the cytological and colposcopical pattern in pregnant patients with benign cervix uteri pathology in anamnesis have been studied. The relationship between the parity of pregnancy, delivery, route of delivery and regress of both benign and premalignant changes in the cervix uteri epithelium 3-5 months post partum has been determined.

Study on patterns and prevalence of EEG abnormalities in children presenting with behavioural disturbances in psychiatry OPD, Gauhati Medical College and Hospital

Directory of Open Access Journals (Sweden)

Bobby Hmar

2016-01-01

Full Text Available Background of the study: Children with behavioural abnormalities and developmental disorders are often advised electroencephalography (EEG for evaluation of electrophysiological process of the brain to rule out any organic pathology. Various studies have reported abnormal EEG in these groups of children without history of clinical seizure on routine EEG and sleep EEG. Aim of the study: To study pattern and prevalence of EEG abnormalities in children with behavioural abnormalities without history of clinical seizure. Materials and methods: The study is a retrospective study. Ethical clearance has been obtained from institutional ethical committee for the study. To collect data, socio-demographic and clinical data proforma has been used. Data has been evaluated during the period from June 2011 to June 2014 as per selection criteria from the case history record of children with behavioural abnormalities attending child guidance clinic (CGC. Associations of abnormal EEG with various psychiatric diagnoses has been analysed and chi-square test has been used. p value <0.05 has been taken as test of significance. Result: Total 2011 children attended CGC from 2011 June to 2014 June. One hundred and ninety two children of various psychiatric diagnoses as per the tenth revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10 criteria had fulfilled the selection criteria and 113 children had done EEG. Abnormal EEG was found in 26.54% of children with various psychiatric diagnoses. Association was statistically significant (p<0.05. The EEG abnormalities were found more in male gender than female (p<0.05 and more in younger age group (four to ten years, p<0.05. Conclusion: Children with various psychiatric diagnoses have significant association with abnormal EEG without history of clinical seizure.

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...

The prognostic value of amplitude-integrated EEG in full-term neonates with seizures.

Directory of Open Access Journals (Sweden)

Dandan Zhang

Full Text Available Neonatal seizures pose a high risk for adverse outcome in survived infants. While the prognostic value of amplitude-integrated electroencephalogram (aEEG is well established in neonates with encephalopathy and asphyxia, neonatal seizure studies focusing on the direct correlation between early aEEG measurement and subsequent neurologic outcome are scarce. In this study, the prognostic value of aEEG features was systematically analyzed in 143 full-term neonates to identify prognostic indicators of neurodevelopmental outcome. Neonatal aEEG features of background pattern, cyclicity, and seizure activity, as well as the etiology of neonatal seizures, were significantly associated with neurodevelopmental outcome at one year of age. aEEG background pattern was highly associated with neurologic outcomes (χ² = 116.9, followed by aEEG cyclicity (χ² = 87.2 and seizure etiology (χ² = 79.3. Multiple linear regression showed that the four predictors explained 71.2% of the variation in neurological outcome, with standardized β coefficients of 0.44, 0.24, 0.22, and 0.14 for the predictors of aEEG background pattern, cyclicity, etiology, and aEEG seizure activity, respectively. This clinically applicable scoring system based on etiology and three aEEG indices would allow pediatricians to assess the risk for neurodevelopmental impairment and facilitate an early intervention in newborns developing seizures.

Automatic burst detection for the EEG of the preterm infant.

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Jennekens, Ward; Ruijs, Loes S; Lommen, Charlotte M L; Niemarkt, Hendrik J; Pasman, Jaco W; van Kranen-Mastenbroek, Vivianne H J M; Wijn, Pieter F F; van Pul, Carola; Andriessen, Peter

2011-10-01

To aid with prognosis and stratification of clinical treatment for preterm infants, a method for automated detection of bursts, interburst-intervals (IBIs) and continuous patterns in the electroencephalogram (EEG) is developed. Results are evaluated for preterm infants with normal neurological follow-up at 2 years. The detection algorithm (MATLAB®) for burst, IBI and continuous pattern is based on selection by amplitude, time span, number of channels and numbers of active electrodes. Annotations of two neurophysiologists were used to determine threshold values. The training set consisted of EEG recordings of four preterm infants with postmenstrual age (PMA, gestational age + postnatal age) of 29-34 weeks. Optimal threshold values were based on overall highest sensitivity. For evaluation, both observers verified detections in an independent dataset of four EEG recordings with comparable PMA. Algorithm performance was assessed by calculation of sensitivity and positive predictive value. The results of algorithm evaluation are as follows: sensitivity values of 90% ± 6%, 80% ± 9% and 97% ± 5% for burst, IBI and continuous patterns, respectively. Corresponding positive predictive values were 88% ± 8%, 96% ± 3% and 85% ± 15%, respectively. In conclusion, the algorithm showed high sensitivity and positive predictive values for bursts, IBIs and continuous patterns in preterm EEG. Computer-assisted analysis of EEG may allow objective and reproducible analysis for clinical treatment.

Prognostic value of EEG in different etiological types of coma.

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Khaburzania, M; Beridze, M

2013-06-01

Study aimed at evaluation of prognostic value of standard EEG in different etiology of coma and the influence of etiological factor on the EEG patterns and coma outcome. Totally 175 coma patients were investigated. Patients were evaluated by Glasgow Coma Scale (GCS), clinically and by 16 channel electroencephalography. Auditory evoked potentials studied by EEG -regime for evoked potentials in patients with vegetative state (VS). Patients divided in 8 groups according to coma etiology. All patients were studied for photoreaction, brainstem reflexes, localization of sound and pain, length of coma state and outcome. Brain injury visualized by conventional CT. Outcome defined as death, VS, recovery with disability and without disability. Disability was rated by Disability Rating Scale (DRS). Recovered patients assessed by Mini Mental State Examination (MMSE) scale. Statistics performed by SPSS-11.0. From 175 coma patients 55 patients died, 23 patients found in VS, 97 patients recovered with and without disability. In all etiological groups of coma the background EEG patterns were established. Correspondence analysis of all investigated factors revealed that sound localization had the significant association with EEG delta and theta rhythms and with recovery from coma state (Chi-sqr. =31.10493; p= 0.000001). Among 23 VS patients 9 patients had the signs of MCS and showed the long latency waves (p300) after binaural stimulation. The high amplitude theta frequencies in frontal and temporal lobes significantly correlated with prolongation of latency of cognitive evoked potentials (r=+0.47; pEEG patterns' association with coma outcome only in hemorrhagic and traumatic coma (chi-sqr.=12.95; pEEG patterns and coma outcome. Low amplitude decreased power delta and theta frequencies correlated with SND in survived coma patients (r=+0.21; pEEG is the useful tool for elucidation of coma patients with a high probability to recover as well as those patients, who are at high risk of

EEG as an Indicator of Cerebral Functioning in Postanoxic Coma.

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Juan, Elsa; Kaplan, Peter W; Oddo, Mauro; Rossetti, Andrea O

2015-12-01

Postanoxic coma after cardiac arrest is one of the most serious acute cerebral conditions and a frequent cause of admission to critical care units. Given substantial improvement of outcome over the recent years, a reliable and timely assessment of clinical evolution and prognosis is essential in this context, but may be challenging. In addition to the classic neurologic examination, EEG is increasingly emerging as an important tool to assess cerebral functions noninvasively. Although targeted temperature management and related sedation may delay clinical assessment, EEG provides accurate prognostic information in the early phase of coma. Here, the most frequently encountered EEG patterns in postanoxic coma are summarized and their relations with outcome prediction are discussed. This article also addresses the influence of targeted temperature management on brain signals and the implication of the evolution of EEG patterns over time. Finally, the article ends with a view of the future prospects for EEG in postanoxic management and prognostication.

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

Demonstration of brain noise on human EEG signals in perception of bistable images

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Grubov, Vadim V.; Runnova, Anastasiya E.; Kurovskaya, Maria K.; Pavlov, Alexey N.; Koronovskii, Alexey A.; Hramov, Alexander E.

2016-03-01

In this report we studied human brain activity in the case of bistable visual perception. We proposed a new approach for quantitative characterization of this activity based on analysis of EEG oscillatory patterns and evoked potentials. Accordingly to theoretical background, obtained experimental EEG data and results of its analysis we studied a characteristics of brain activity during decision-making. Also we have shown that decisionmaking process has the special patterns on the EEG data.

Measurement and modification of the EEG and related behavior

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Sterman, M. B.

1991-01-01

Electrophysiological changes in the sensorimotor pathways were found to accompany the effect of rhythmic EEG patterns in the sensorimotor cortex. Additionally, several striking behavioral changes were seen, including in particular an enhancement of sleep and an elevation of seizure threshold to epileptogenic agents. This raised the possibility that human seizure disorders might be influenced therapeutically by similar training. Our objective in human EEG feedback training became not only the facilitation of normal rhythmic patterns, but also the suppression of abnormal activity, thus requiring complex contingencies directed to the normalization of the sensorimotor EEG. To achieve this, a multicomponent frequency analysis was developed to extract and separate normal and abnormal elements of the EEG signal. Each of these elements was transduced to a specific component of a visual display system, and these were combined through logic circuits to present the subject with a symbolic display. Variable criteria provided for the gradual shaping of EEG elements towards the desired normal pattern. Some 50-70% of patients with poorly controlled seizure disorders experienced therapeutic benefits from this approach in our laboratory, and subsequently in many others. A more recent application of this approach to the modification of human brain function in our lab has been directed to the dichotomous problems of task overload and underload in the contemporary aviation environment. At least 70% of all aviation accidents have been attributed to the impact of these kinds of problems on crew performance. The use of EEG in this context has required many technical innovations and the application of the latest advances in EEG signal analysis. Our first goal has been the identification of relevant EEG characteristics. Additionally, we have developed a portable recording and analysis system for application in this context. Findings from laboratory and in-flight studies suggest that we

Multi-resolution analysis using integrated microscopic configuration with local patterns for benign-malignant mass classification

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Rabidas, Rinku; Midya, Abhishek; Chakraborty, Jayasree; Sadhu, Anup; Arif, Wasim

2018-02-01

In this paper, Curvelet based local attributes, Curvelet-Local configuration pattern (C-LCP), is introduced for the characterization of mammographic masses as benign or malignant. Amid different anomalies such as micro- calcification, bilateral asymmetry, architectural distortion, and masses, the reason for targeting the mass lesions is due to their variation in shape, size, and margin which makes the diagnosis a challenging task. Being efficient in classification, multi-resolution property of the Curvelet transform is exploited and local information is extracted from the coefficients of each subband using Local configuration pattern (LCP). The microscopic measures in concatenation with the local textural information provide more discriminating capability than individual. The measures embody the magnitude information along with the pixel-wise relationships among the neighboring pixels. The performance analysis is conducted with 200 mammograms of the DDSM database containing 100 mass cases of each benign and malignant. The optimal set of features is acquired via stepwise logistic regression method and the classification is carried out with Fisher linear discriminant analysis. The best area under the receiver operating characteristic curve and accuracy of 0.95 and 87.55% are achieved with the proposed method, which is further compared with some of the state-of-the-art competing methods.

A Ternary Hybrid EEG-NIRS Brain-Computer Interface for the Classification of Brain Activation Patterns during Mental Arithmetic, Motor Imagery, and Idle State.

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Shin, Jaeyoung; Kwon, Jinuk; Im, Chang-Hwan

2018-01-01

The performance of a brain-computer interface (BCI) can be enhanced by simultaneously using two or more modalities to record brain activity, which is generally referred to as a hybrid BCI. To date, many BCI researchers have tried to implement a hybrid BCI system by combining electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS) to improve the overall accuracy of binary classification. However, since hybrid EEG-NIRS BCI, which will be denoted by hBCI in this paper, has not been applied to ternary classification problems, paradigms and classification strategies appropriate for ternary classification using hBCI are not well investigated. Here we propose the use of an hBCI for the classification of three brain activation patterns elicited by mental arithmetic, motor imagery, and idle state, with the aim to elevate the information transfer rate (ITR) of hBCI by increasing the number of classes while minimizing the loss of accuracy. EEG electrodes were placed over the prefrontal cortex and the central cortex, and NIRS optodes were placed only on the forehead. The ternary classification problem was decomposed into three binary classification problems using the "one-versus-one" (OVO) classification strategy to apply the filter-bank common spatial patterns filter to EEG data. A 10 × 10-fold cross validation was performed using shrinkage linear discriminant analysis (sLDA) to evaluate the average classification accuracies for EEG-BCI, NIRS-BCI, and hBCI when the meta-classification method was adopted to enhance classification accuracy. The ternary classification accuracies for EEG-BCI, NIRS-BCI, and hBCI were 76.1 ± 12.8, 64.1 ± 9.7, and 82.2 ± 10.2%, respectively. The classification accuracy of the proposed hBCI was thus significantly higher than those of the other BCIs ( p < 0.005). The average ITR for the proposed hBCI was calculated to be 4.70 ± 1.92 bits/minute, which was 34.3% higher than that reported for a previous binary hBCI study.

Epilepsy in fragile-X-syndrome mimicking panayiotopoulos syndrome: Description of three patients.

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Bonanni, Paolo; Casellato, Susanna; Fabbro, Franco; Negrin, Susanna

2017-10-01

Fragile-X-syndrome is the most common cause of inherited intellectual disability. Epilepsy is reported to occur in 10-20% of individuals with Fragile-X-syndrome. A frequent seizure/electroencephalogram (EEG) pattern resembles that of benign rolandic epilepsy. We describe the clinical features, EEG findings and evolution in three patients affected by Fragile-X-syndrome and epilepsy mimicking Panayiotopoulos syndrome. Age at seizure onset was between 4 and about 7 years. Seizures pattern comprised a constellation of autonomic symptoms with unilateral deviation of the eyes and ictal syncope. Duration of the seizures could be brief or lengthy. Interictal EEGs revealed functional multifocal abnormalities. The evolution was benign in all patients with seizures remission before the age of 14. This observation expands the spectrum of benign epileptic phenotypes present in Fragile-X-syndrome and may be quite helpful in guiding anticonvulsant management and counseling families as to expectations regarding seizure remission. © 2017 Wiley Periodicals, Inc.

Prevalence and etiology of false normal aEEG recordings in neonatal hypoxic-ischaemic encephalopathy

OpenAIRE

Marics, Gábor; Csekő, Anna; Vásárhelyi, Barna; Zakariás, Dávid; Schuster, György; Szabó, Miklós

2013-01-01

Background Amplitude-integrated electroencephalography (aEEG) is a useful tool to determine the severity of neonatal hypoxic-ischemic encephalopathy (HIE). Our aim was to assess the prevalence and study the origin of false normal aEEG recordings based on 85 aEEG recordings registered before six hours of age. Methods Raw EEG recordings were reevaluated retrospectively with Fourier analysis to identify and describe the frequency patterns of the raw EEG signal, in cases with inconsistent aEEG re...

Filtration of human EEG recordings from physiological artifacts with empirical mode method

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Grubov, Vadim V.; Runnova, Anastasiya E.; Khramova, Marina V.

2017-03-01

In the paper we propose the new method for dealing with noise and physiological artifacts in experimental human EEG recordings. The method is based on analysis of EEG signals with empirical mode decomposition (Hilbert-Huang transform). We consider noises and physiological artifacts on EEG as specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the method with following steps: empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing empirical modes with artifacts, reconstruction of the initial EEG signal. We test the method on filtration of experimental human EEG signals from eye-moving artifacts and show high efficiency of the method.

Ear-EEG detects ictal and interictal abnormalities in focal and generalized epilepsy

DEFF Research Database (Denmark)

Zibrandtsen, I. C.; Kidmose, P.; Christensen, C. B.

2017-01-01

-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...... 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. Conclusions 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. Significance Ear-EEG is likely to become an important tool in clinical epilepsy monitoring and diagnosis....

EEG Correlates of Ten Positive Emotions.

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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.

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. ...

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

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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.

[Clinical and electrophysiologic studies on epileptic negative myoclonus in atypical benign partial epilepsy of childhood].

Science.gov (United States)

Yang, Zhi-xian; Liu, Xiao-yan; Qin, Jiong; Zhang, Yue-hua; Bao, Xin-hua; Chang, Xing-zhi; Wu, Ye; Xiong, Hui

2008-12-01

To investigate the clinical, neurophysiologic characteristics and therapeutic considerations of epileptic negative myoclonus (ENM) in atypical benign partial epilepsy of childhood (ABPE). Video-EEG monitoring with outstretched arm tests were carried out in 17 patients, and 9 of them were examined with simultaneous electromyography (EMG). The ENM manifestations, electrophysiologic features and responses to antiepileptic drugs (AED) were analyzed. Seventeen patients were diagnosed as having benign childhood epilepsy with centrotemporal spikes (BECT) during the early course of the disease and were treated with AED. During the course of the disease, hand trembling, objects dropping, head nodding and instability during standing might be clues for ENM occurrence. ENM had been confirmed in our patients by outstretched arm tests during video-EEG recording. The ictal EEG showed that high-amplitude spikes followed by a slow wave over the contralateral motor areas. This was further confirmed by time-locked silent EMG in 9 patients. During ENM occurrence or recurrence, the habitual seizures and interictal discharges were exaggerated. Atypical absence seizures also occurred in 6 patients. The alteration of therapeutic options of AED relating to ENM appearance in some patients included the add-on therapy with carbamazepine (CBZ), oxcarbazepine, phenobarbital, or withdrawal of valproate (VPA). ENM was controlled in most cases by using VPA, clonazepam (CZP) and corticosteroid with different combination. ENM could occur during the course of ABPE. Outstretching arm tests during video-EEG monitoring in combination with EMG was essential to confirm ENM. The ENM occurrence was always associated with the frequency increasing of habitual seizures and the aggravation of interictal discharges. Some AED such as CBZ might induce ENM. VPA, benzodiazepines and corticosteroid with different combination were relatively effective in treatment of ENM.

Contribution of EEG in transient neurological deficits.

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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.

Automatic classification of background EEG activity in healthy and sick neonates

Science.gov (United States)

Löfhede, Johan; Thordstein, Magnus; Löfgren, Nils; Flisberg, Anders; Rosa-Zurera, Manuel; Kjellmer, Ingemar; Lindecrantz, Kaj

2010-02-01

The overall aim of our research is to develop methods for a monitoring system to be used at neonatal intensive care units. When monitoring a baby, a range of different types of background activity needs to be considered. In this work, we have developed a scheme for automatic classification of background EEG activity in newborn babies. EEG from six full-term babies who were displaying a burst suppression pattern while suffering from the after-effects of asphyxia during birth was included along with EEG from 20 full-term healthy newborn babies. The signals from the healthy babies were divided into four behavioural states: active awake, quiet awake, active sleep and quiet sleep. By using a number of features extracted from the EEG together with Fisher's linear discriminant classifier we have managed to achieve 100% correct classification when separating burst suppression EEG from all four healthy EEG types and 93% true positive classification when separating quiet sleep from the other types. The other three sleep stages could not be classified. When the pathological burst suppression pattern was detected, the analysis was taken one step further and the signal was segmented into burst and suppression, allowing clinically relevant parameters such as suppression length and burst suppression ratio to be calculated. The segmentation of the burst suppression EEG works well, with a probability of error around 4%.

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

The electroencephalographic (EEG) activity patterns in humans during motor behaviour provide insight into normal motor control processes and for diagnostic and rehabilitation applications. While the patterns preceding brisk voluntary movements, and especially movement execution, are well described, there are few EEG studies that address the cortical activation patterns seen in isometric exertions and their planning. In this paper, we report on time and time-frequency EEG signatures in experiments in normal subjects (n=8), using multichannel EEG during motor preparation, planning and execution of directional centre-out arm isometric exertions performed at the wrist in the horizontal plane, in response to instruction-delay visual cues. Our observations suggest that isometric force exertions are accompanied by transient and sustained event-related potentials (ERP) and event-related (de-)synchronisations (ERD/ERS), comparable to those of a movement task. Furthermore, the ERPs and ERD/ERS are also observed during preparation and planning of the isometric task. Comparison of ear-lobe-referenced and surface Laplacian ERPs indicates the contribution of superficial sources in supplementary and pre-motor (FC(z)), parietal (CP(z)) and primary motor cortical areas (C₁ and FC₁) to ERPs (primarily negative peaks in frontal and positive peaks in parietal areas), but contribution of deep sources to sustained time-domain potentials (negativity in planning and positivity in execution). Transient and sustained ERD patterns in μ and β frequency bands of ear-lobe-referenced and surface Laplacian EEG indicate the contribution of both superficial and deep sources to ERD/ERS. As no physical displacement happens during the task, we can infer that the underlying mechanisms of motor-related ERPs and ERD/ERS patterns do not only depend on change in limb coordinate or muscle-length-dependent ascending sensory information and are primary generated by motor preparation, direction

The clinical factors associated with benign renal tumors

International Nuclear Information System (INIS)

Yamashita, Ryo; Nakamura, Masafumi; Matsuzaki, Masato; Matsui, Takashi; Yamaguchi, Raizo; Niwakawa, Masashi; Tobisu, Kenichi; Asakura, Koiku; Ito, Ichiro

2009-01-01

In this study, we sought to define the incidence of benign renal tumors in our institute and to clarify the clinical factors associated with benign renal tumors, in order to assist in forming preoperative differential diagnoses. From October 2002 to July 2007, we performed 157 nephrectomies in patients preoperatively diagnosed with renal cell carcinoma. We chose 81 tumors, all of which were less than 5 cm, for further study. We reviewed double-phase helical CT imaging retrospectively, specifically focusing on attenuation patterns and homogeneity. We also compared clinical factors, including age, sex and tumor size, between the benign and malignant renal tumors. The patient's median age was 67 years (mean age, 63 years), and the median tumor diameter was 3.0 cm (mean, 3.2 cm). Benign renal tumors were found in 10 (12%) of the 81 tumors; these included seven cases of oncocytoma and three cases of angiomyolipoma with minimal fat. Several factors were significant clinical determinants of differentiation between benign and malignant renal tumors: homogeneity in CT, female gender, and small tumor size all predominated in cases of benign tumors. Attenuation pattern in CT, however, was not a significant factor (p=0.344). When a patient, especially a female, presents with a small and homogeneous renal tumor, careful consideration should be given to the possibility of a benign process, which needs further consideration before performing excessive surgery. (author)

Correlation between intra- and extracranial background EEG

DEFF Research Database (Denmark)

Duun-Henriksen, Jonas; Kjaer, Troels W.; Madsen, Rasmus E.

2012-01-01

Scalp EEG is the most widely used modality to record the electrical signals of the brain. It is well known that the volume conduction of these brain waves through the brain, cerebrospinal fluid, skull and scalp reduces the spatial resolution and the signal amplitude. So far the volume conduction...... has primarily been investigated by realistic head models or interictal spike analysis. We have set up a novel and more realistic experiment that made it possible to compare the information in the intra- and extracranial EEG. We found that intracranial EEG channels contained correlated patterns when...... placed less than 30 mm apart, that intra- and extracranial channels were partly correlated when placed less than 40 mm apart, and that extracranial channels probably were correlated over larger distances. The underlying cortical area that influences the extracranial EEG is found to be up to 45 cm2...

A study on epileptic negative myoclonus in atypical benign partial epilepsy of childhood.

Science.gov (United States)

Yang, Zhixian; Liu, Xiaoyan; Qin, Jiong; Zhang, Yuehua; Bao, Xinhua; Chang, Xingzhi; Wang, Shuang; Wu, Ye; Xiong, Hui

2009-04-01

To investigate the clinical and neurophysiological characteristics, particularly therapeutic considerations, of epileptic negative myoclonus (ENM) in atypical benign partial epilepsy (ABPE) of childhood. From 1998 to 2006, 14/242 patients with benign children epilepsy with centrotemporal spikes (BECTS) were diagnosed as having ABPE with ENM. In all 14 patients, we performed video-EEG monitoring along with tests with the patient's arms outstretched; 6/14 patients were also simultaneously underwent surface electromyogram (EMG). ENM manifestations, electrophysiological features, and responses to antiepileptic drugs were analyzed. In all cases, ENM developed after the onset of epilepsy and during antiepileptic drug therapy, and the appearance of ENM were corresponding to EEG findings of high-amplitude spikes followed by a slow wave in the contralateral motor areas with secondary generalization. This was further confirmed by time-locked silent EMG. During ENM occurrence or recurrence, habitual seizures and interictal discharges were exaggerated. In some patients, the changes in antiepileptic drug regimens in relation to ENM appearance included add-on therapy with carbamazepine, oxcarbazepine, and phenobarbital or withdrawal of valproate. ENM was controlled in most cases by administration of various combinations of valproate, clonazepam, and corticosteroids. The incidence of ENM or ABPE in our center was approximately 5.79%. A combination of video-EEG monitoring with the patient's arms outstretched and EMG is essential to identify ENM. The aggravation of habitual seizures and interictal discharges indicate ENM. Some antiepileptic drugs, such as carbamazepine, oxcarbazepine, and phenobarbital, may be related to ENM occurrence during spontaneous aggravation of ABPE. Various combinations of valproate, benzodiazepines, and corticosteroids are relatively effective for treating ENM that occurs in ABPE.

Early EEG for outcome prediction of postanoxic coma: prospective cohort study with cost-minimization analysis.

Science.gov (United States)

Sondag, Lotte; Ruijter, Barry J; Tjepkema-Cloostermans, Marleen C; Beishuizen, Albertus; Bosch, Frank H; van Til, Janine A; van Putten, Michel J A M; Hofmeijer, Jeannette

2017-05-15

We recently showed that electroencephalography (EEG) patterns within the first 24 hours robustly contribute to multimodal prediction of poor or good neurological outcome of comatose patients after cardiac arrest. Here, we confirm these results and present a cost-minimization analysis. Early prognosis contributes to communication between doctors and family, and may prevent inappropriate treatment. A prospective cohort study including 430 subsequent comatose patients after cardiac arrest was conducted at intensive care units of two teaching hospitals. Continuous EEG was started within 12 hours after cardiac arrest and continued up to 3 days. EEG patterns were visually classified as unfavorable (isoelectric, low-voltage, or burst suppression with identical bursts) or favorable (continuous patterns) at 12 and 24 hours after cardiac arrest. Outcome at 6 months was classified as good (cerebral performance category (CPC) 1 or 2) or poor (CPC 3, 4, or 5). Predictive values of EEG measures and cost-consequences from a hospital perspective were investigated, assuming EEG-based decision- making about withdrawal of life-sustaining treatment in the case of a poor predicted outcome. Poor outcome occurred in 197 patients (51% of those included in the analyses). Unfavorable EEG patterns at 24 hours predicted a poor outcome with specificity of 100% (95% CI 98-100%) and sensitivity of 29% (95% CI 22-36%). Favorable patterns at 12 hours predicted good outcome with specificity of 88% (95% CI 81-93%) and sensitivity of 51% (95% CI 42-60%). Treatment withdrawal based on an unfavorable EEG pattern at 24 hours resulted in a reduced mean ICU length of stay without increased mortality in the long term. This gave small cost reductions, depending on the timing of withdrawal. Early EEG contributes to reliable prediction of good or poor outcome of postanoxic coma and may lead to reduced length of ICU stay. In turn, this may bring small cost reductions.

EEG and CT findings of infant partial seizures

International Nuclear Information System (INIS)

Kajitani, Takashi; Kumanomido, Yoshiaki; Nakamura, Makoto; Ueoka, Kiyotaka

1981-01-01

Examination of EEG and cranial CT were performed in 19 cases of partial seizures with elementary symptomatology (PSES), 6 cases of partial seizures with complex symptomatology (PSCS), and 17 cases of benign focal pilepsy of childhood with Rolandic spikes (BFECRS). The results were as follows. 1) In 16 of 19 cases of PSES (84%), various abnormal CT findings such as localized cerebral atrophy (7 cases), localized cerebral atrophy complicated with porencephaly (4 cases), porencephaly alone (2 cases), and diffuse cerebral atrophy (3 cases) were found. 2) Of 6 cases of PSCS localized cerebral atrophy was found in 3 cases, porencephaly in one case, and localized calcification in one case. Normal CT findings were obtained in one case. 3) In comparison of EEG findings with CT findings in 25 cases of partial seizures CT findings correlated with the basic waves rather than the paroxysmal ones. 4) The fact that CT findings in patients with BFECRS were mostly normal suggests the functional origin of the seizures. 5) CT was valuable in partial seizures for detecting underlying disorders and predicting the prognosis. (Ueda, J.)

The characteristics of SPECT images in childhood benign partial epilepsy

International Nuclear Information System (INIS)

Jia Shaowei; Liao Jianxiang; Liu Xiaoyan; Zheng Xiyuan; Qin Jiong; Pan Zhongyun; Zuo Qihua

1998-01-01

Purpose: To investigate childhood benign partial epilepsy (BPE) with SPECT. Methods: Double SPECT imaging was performed on 21 cases of BPE at the stage of wake (interval spike discharge) and sleep (spike discharge), under EEG monitoring. The transverse images were reconstructed after digital image subtraction. The quantitative analysis was conducted with brain flow change rate (BFCR) % mathematical model. Results: EEG monitoring demonstrated approximately normal background of 21 cases of BPE during the stage of wake, and spike discharge frequency markedly increased during the stage of sleep, 117 foci were showed by SPeCT in cases of BPE, and the average was 5.6 +- 1.6 foci/case. The characteristics of SPECT transverse images were 1) multiple foci of mirror, 2) mostly seen in Rolandic region, 3) circular symbol, 4) the radioactivity in foci decreased during the stage of wake (interval spike discharge) and increased during the stage of sleep (spike discharge). The concordance of SPECT and EEG was 93.1% (109/117 foci). The BFCR% of all epileptogenic foci exceeded normal limit (99% confidence interval). There was no correlation between the spike discharge frequency and BFCR% (r = 0.45, P>0.05). Conclusions: Regional cerebral blood flow and function were abnormal during the epileptogenic foci were discharging abnormally in BPE

Dealing with noise and physiological artifacts in human EEG recordings: empirical mode methods

Science.gov (United States)

Runnova, Anastasiya E.; Grubov, Vadim V.; Khramova, Marina V.; Hramov, Alexander E.

2017-04-01

In the paper we propose the new method for removing noise and physiological artifacts in human EEG recordings based on empirical mode decomposition (Hilbert-Huang transform). As physiological artifacts we consider specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the proposed method with steps including empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing these empirical modes and reconstructing of initial EEG signal. We show the efficiency of the method on the example of filtration of human EEG signal from eye-moving artifacts.

Self-induced stretch syncope of adolescence: a video-EEG documentation.

Science.gov (United States)

Mazzuca, Michel; Thomas, Pierre

2007-12-01

We present the first video-EEG documentation, with ECG and EMG features, of stretch syncope of adolescence in a young, healthy 16-year-old boy. Stretch syncope of adolescence is a rarely reported, benign cause of fainting in young patients, which can be confused with epileptic seizures. In our patient, syncopes were self-induced to avoid school. Dynamic transcranial Doppler showed evidence of blood flow decrease in both posterior cerebral arteries mimicking effects of a Valsalva manoeuvre. Dynamic angiogram of the vertebral arteries was normal. Hypotheses concerning the physiopathology are discussed. [Published with video sequences].

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

Radiation therapy for benign diseases: patterns of care study in Germany

International Nuclear Information System (INIS)

Seegenschmiedt, M. Heinrich; Katalinic, Alexander; Makoski, Hans-Bruno; Haase, Wulf; Gademann, Guenther; Hassenstein, Eckhard

2000-01-01

Background: Radiotherapy of benign diseases is controversial and rarely applied in Anglo-American countries, whereas in other parts of the world it is commonly practiced for several benign disorders. Similar to a European survey, a pattern of care study was conducted in Germany. Method: Using a mailed questionnaire, radiation equipment, treatment indication, number of patients, and treatment concepts were assessed in 1994, 1995, and 1996 in 134 of 152 German institutions (88%): 22 in East and 112 in West Germany; 30 in university hospitals and 104 in community hospitals. Average numbers of each institution and of all institutions were analyzed for frequencies and ratios between regions and among institutions. Radiation treatment concepts were analyzed. Results: A mean of 2 (range 1-7) megavoltage and 1.4 (range 0-4) orthovoltage units were available per institution; 32 institutions (24%) had no orthovoltage equipment. A mean of 20,082 patients were treated annually: 456 (2%) for inflammatory diseases (221 hidradenitis, 78 local infection, 23 parotitis; 134 not specified) 12,600 (63%) for degenerative diseases (2711 peritendinitis humeroscapularis, 1555 epicondylitis humeri; 1382 plantar/dorsal heel spur; 2434 degenerative osteoarthritis; 4518 not specified); 927 (5%) for hyperproliferative diseases (146 Dupuytren's contracture, 382 keloids; 155 Peyronie's disease; 244 not specified); 1210 (6%) for functional disorders (853 Graves' orbitopathy; 357 not specified); and 4889 (24%) for other disorders (e.g., 3680 heterotopic ossification prophylaxis). In univariate analysis, there were geographic (West vs. East Germany) differences in using radiation therapy (RT) for inflammatory and degenerative disorders, and institutional differences (university versus community hospitals) in using RT for hyperproliferative and functional disorders (p < 0.05). The prescribed dose concepts were mostly in the low dose range, <10 Gy but varied widely and inconsistently within

Clinicopathological pattern of benign breast diseases among female ...

African Journals Online (AJOL)

This study describes our experience in the management of benign ... assessment consisting of clinical evaluation, breast imaging and fine needle aspiration cytology. (FNAC) has been recommended as a diagnostic tool for evaluation of patients with .... week for breast pain, breast lump and nipple discharge, respectively.

Statistical features of hypnagogic EEG measured by a new scoring system.

Science.gov (United States)

Tanaka, H; Hayashi, M; Hori, T

1996-11-01

The purpose of this study was to examine the durations of individual occurrences of each of nine hypnagogic electroencephalographic (EEG) stages and the interchange relationship among these stages. Most of the alpha patterns (stages 1, 2, and 3), ripples (stage 5), and spindles (stage 9) tended to last > 2 minutes. On the other hand, histograms of the durations of time in EEG flattening (stage 4) and vertex sharp wave (stages 6, 7, and 8) patterns had peaks that lasted hypnagogic state. This was especially true for the first five stages. EEG stages with vertex sharp waves (stages 6, 7, and 8), however, showed less-smooth changes, with approximately 20% of all changes involving a jump of more than one stage. These results show that the basic EEG activities in the sleep onset period are the alpha, theta, and sleep spindles activities, whereas the activities of vertex sharp waves seem to have a secondary or enhancing role, instead of independent characteristics.

Perfusion pattern and time of vascularisation with CEUS increase accuracy in differentiating between benign and malignant tumours in 216 musculoskeletal soft tissue masses

Energy Technology Data Exchange (ETDEWEB)

De Marchi, Armanda, E-mail: armanda.demarchi@tiscali.it [Department of Imaging, Azienda Ospedaliera Città della Salute e della Scienza, CTO Hospital, Via Zuretti 29, 10126 Torino (Italy); Prever, Elena Brach del, E-mail: elena.brach@unito.it [Department of OrthopaedicOncology and ReconstructiveSurgery, Azienda Ospedaliero Universitaria Città della Salute e della Scienza, CTO Hospital, Via Zuretti 29, 10126 Torino (Italy); Cavallo, Franco, E-mail: franco.cavallo@unito.it [Department of Public health and Paediatrics, University of Turin, Via Santena 5-bis, 10126 Torino (Italy); Pozza, Simona, E-mail: simona.pozza@tin.it [Department of Imaging, Azienda Ospedaliera Città della Salute e della Scienza, CTO Hospital, Via Zuretti 29, 10126 Torino (Italy); Linari, Alessandra, E-mail: linaralessandra@libero.it [Department of Pathology, Azienda Ospedaliero Universitaria Città della Salute e della Scienza, Regina Margherita Hospital, Piazza Polonia, 10126 Torino (Italy); Lombardo, Paolo, E-mail: pao.lombardo82@gmail.com [Department of DiagnosticImaging and Radiotherapy of the University of Turin, Azienda Ospedaliero-Universitaria Città della Salute e della Scienza di Torino, Via Genova 3, 10126 Torino (Italy); Comandone, Alessandro, E-mail: alessandro.comandone@gradenigo.it [Department of Oncology, Gradenigo Hospital, Corso Regina Margherita, 8/10.10153 Torino (Italy); Piana, Raimondo, E-mail: raimondo.piana@libero.it [Department of OrthopaedicOncology and ReconstructiveSurgery, Azienda Ospedaliero Universitaria Città della Salute e della Scienza, CTO Hospital, Via Zuretti 29, 10126 Torino (Italy); Faletti, Carlo [Department of Imaging, Azienda Ospedaliera Città della Salute e della Scienza, CTO Hospital, Via Zuretti 29, 10126 Torino (Italy)

2015-01-15

Introduction: Musculoskeletal Soft Tissue Tumours (STT) are frequent heterogeneous lesions. Guidelines consider a mass larger than 5 cm and deep with respect to the deep fascia potentially malignant. Contrast Enhanced Ultrasound (CEUS) can detect both vascularity and tumour neoangiogenesis. We hypothesised that perfusion patterns and vascularisation time could improve the accuracy of CEUS in discriminating malignant tumours from benign lesions. Materials and methods: 216 STT were studied: 40% benign lesions, 60% malignant tumours, 56% in the lower limbs. Seven CEUS perfusion patterns and three types of vascularisation (arterial-venous uptake, absence of uptake) were applied. Accuracy was evaluated by comparing imaging with the histological diagnosis. Univariate and multivariate analysis, Chi-square test and t-test for independent variables were applied; significance was set at p < 0.05 level, 95% computed CI. Results: CEUS pattern 6 (inhomogeneous perfusion), arterial uptake and location in the lower limb were associated with high risk of malignancy. CEUS pattern has PPV 77%, rapidity of vascularisation PPV 69%; location in the limbs is the most sensitive indicator, but NPV 52%, PPV 65%. The combination of CEUS-pattern and vascularisation has 74% PPV, 60% NPV, 70% sensitivity. No correlation with size and location in relation to the deep fascia was found. Conclusion: US with CEUS qualitative analysis could be an accurate technique to identify potentially malignant STT, for which second line imaging and biopsy are indicated in Referral Centers. Intense inhomogeneous enhancement with avascular areas and rapid vascularisation time could be useful in discriminating benign from malignant SST, overall when the lower limbs are involved.

Patterns of EEG Activity in Individuals with Autism Spectrum Disorders

Directory of Open Access Journals (Sweden)

Zhukova M.A.,

2016-10-01

Full Text Available The article reviews most recent findings on neural activity in children and adults with autism spectrum disorders (ASD. Most of the studies demonstrate decreased connectivity in cortical regions, excitatory/inhibitory imbalance and atypical processing of language in people with ASD. It is argued that difficulties in semantic integration are connected to selective insensitivity to language, which is manifested in atypical N400 ERP component. In the article we analyze the data suggesting a strong relationship between ASD and epilepsy and argue that the comorbidity is more prevalent among individuals who have cognitive dysfunction. The EEG profile of people with ASD suggests U-shaped alterations with excess in high- and low-frequency EEG bands. We critically analyze the “broken mirror” hypothesis of ASD and demonstrate findings which challenge this theory.

Standardized computer-based organized reporting of EEG SCORE - Second version

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...

Schizophrenia patients and 22q11.2 deletion syndrome adolescents at risk express the same deviant patterns of resting state EEG microstates: A candidate endophenotype of schizophrenia

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Miralena I. Tomescu

2015-09-01

Full Text Available Schizophrenia is a complex psychiatric disorder and many of the factors contributing to its pathogenesis are poorly understood. In addition, identifying reliable neurophysiological markers would improve diagnosis and early identification of this disease. The 22q11.2 deletion syndrome (22q11DS is one major risk factor for schizophrenia. Here, we show further evidence that deviant temporal dynamics of EEG microstates are a potential neurophysiological marker by showing that the resting state patterns of 22q11DS are similar to those found in schizophrenia patients. The EEG microstates are recurrent topographic distributions of the ongoing scalp potential fields with temporal stability of around 80 ms that are mapping the fast reconfiguration of resting state networks. Five minutes of high-density EEG recordings was analysed from 27 adult chronic schizophrenia patients, 27 adult controls, 30 adolescents with 22q11DS, and 28 adolescent controls. In both patient groups we found increased class C, but decreased class D presence and high transition probabilities towards the class C microstates. Moreover, these aberrant temporal dynamics in the two patient groups were also expressed by perturbations of the long-range dependency of the EEG microstates. These findings point to a deficient function of the salience and attention resting state networks in schizophrenia and 22q11DS as class C and class D microstates were previously associated with these networks, respectively. These findings elucidate similarities between individuals at risk and schizophrenia patients and support the notion that abnormal temporal patterns of EEG microstates might constitute a marker for developing schizophrenia.

Benign ovarian and endometrial uptake on FDG PET-CT. Patterns and pitfalls

International Nuclear Information System (INIS)

Liu, Y.

2009-01-01

Increased ovarian or endometrial uptake may cause a dilemma in the interpretation of whole body F18-fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging or even misdiagnosis of malignant disease. Knowledge of benign FDG uptake of the ovaries and uterus is important for daily practice of nuclear medicine radiologists. Increased uptake in the ovaries or uterus indicates a pathologic or neoplastic process in postmenopausal patients. In premenopausal women, increased ovarian or endometrial uptake can be functional or malignant. Benign functional uptake of premenopausal ovaries or uterus is related to the menstrual cycle; therefore, information about the patient's menstrual status is crucial for interpretation. In addition, correlation with computed tomography (CT), especially diagnostic CT acquired at the same time of PET/CT is very useful in clarifying the location of the uptake and the existence or disappearance of the discrete lesion. Increased ovarian uptake may also be identified in histologically different benign tumor entities. Nonmenstrual-related endometrial uptake may be present in many benign diseases as well. (author)

Calibrating EEG-based motor imagery brain-computer interface from passive movement.

Science.gov (United States)

Ang, Kai Keng; Guan, Cuntai; Wang, Chuanchu; Phua, Kok Soon; Tan, Adrian Hock Guan; Chin, Zheng Yang

2011-01-01

EEG data from performing motor imagery are usually collected to calibrate a subject-specific model for classifying the EEG data during the evaluation phase of motor imagery Brain-Computer Interface (BCI). However, there is no direct objective measure to determine if a subject is performing motor imagery correctly for proper calibration. Studies have shown that passive movement, which is directly observable, induces Event-Related Synchronization patterns that are similar to those induced from motor imagery. Hence, this paper investigates the feasibility of calibrating EEG-based motor imagery BCI from passive movement. EEG data of 12 healthy subjects were collected during motor imagery and passive movement of the hand by a haptic knob robot. The calibration models using the Filter Bank Common Spatial Pattern algorithm on the EEG data from motor imagery were compared against using the EEG data from passive movement. The performances were compared based on the 10×10-fold cross-validation accuracies of the calibration data, and off-line session-to-session transfer kappa values to other sessions of motor imagery performed on another day. The results showed that the calibration performed using passive movement yielded higher model accuracy and off-line session-to-session transfer (73.6% and 0.354) than the calibration performed using motor imagery (71.3% and 0.311), and no significant differences were observed between the two groups (p=0.20, 0.23). Hence, this study shows that it is feasible to calibrate EEG-based motor imagery BCI from passive movement.

The EEG as an index of neuromodulator balance in memory and mental illness

Directory of Open Access Journals (Sweden)

Costa eVakalopoulos

2014-04-01

Full Text Available There is a strong correlation between signature EEG frequency patterns and the relative levels of distinct neuromodulators. These associations become particularly evident during the sleep-wake cycle. The monoamine-acetylcholine balance hypothesis is a theory of neurophysiological markers of the EEG and a detailed description of the findings that support this proposal are presented in this paper. According to this model alpha rhythm reflects the relative predominance of cholinergic muscarinic signals and delta rhythm that of monoaminergic receptor effects. Both high voltage synchronized rhythms are likely mediated by inhibitory Gαi/o-mediated transduction of inhibitory interneurons. Cognitively, alpha and delta EEG measures are proposed to indicate automatic and flexible strategies, respectively. Sleep is associated with marked changes in relative neuromodulator levels corresponding to EEG markers of distinct stages. Sleep studies on memory consolidation present some of the strongest evidence yet for the respective roles of monoaminergic and cholinergic projections in declarative and non-declarative memory processes, a key theoretical premise for understanding the data. Affective dysregulation is reflected in altered EEG patterns during sleep.

The EEG as an index of neuromodulator balance in memory and mental illness.

Science.gov (United States)

Vakalopoulos, Costa

2014-01-01

There is a strong correlation between signature EEG frequency patterns and the relative levels of distinct neuromodulators. These associations become particularly evident during the sleep-wake cycle. The monoamine-acetylcholine balance hypothesis is a theory of neurophysiological markers of the EEG and a detailed description of the findings that support this proposal are presented in this paper. According to this model alpha rhythm reflects the relative predominance of cholinergic muscarinic signals and delta rhythm that of monoaminergic receptor effects. Both high voltage synchronized rhythms are likely mediated by inhibitory Gαi/o-mediated transduction of inhibitory interneurons. Cognitively, alpha and delta EEG measures are proposed to indicate automatic and flexible strategies, respectively. Sleep is associated with marked changes in relative neuromodulator levels corresponding to EEG markers of distinct stages. Sleep studies on memory consolidation present some of the strongest evidence yet for the respective roles of monoaminergic and cholinergic projections in declarative and non-declarative memory processes, a key theoretical premise for understanding the data. Affective dysregulation is reflected in altered EEG patterns during sleep.

Sleep EEG Fingerprints Reveal Accelerated Thalamocortical Oscillatory Dynamics in Williams Syndrome

Science.gov (United States)

Bodizs, Robert; Gombos, Ferenc; Kovacs, Ilona

2012-01-01

Sleep EEG alterations are emerging features of several developmental disabilities, but detailed quantitative EEG data on the sleep phenotype of patients with Williams syndrome (WS, 7q11.23 microdeletion) is still lacking. Based on laboratory (Study I) and home sleep records (Study II) here we report WS-related features of the patterns of…

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.

Clinical and electroencephalographic characteristics of benign occipital epilepsy of childhood in two tertiary Brazilian hospitals

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Soniza Vieira Alves-Leon

2011-08-01

Full Text Available This study intended to investigate the clinical and electroencephalographic benign occipital epilepsy of childhood (BOEC characteristics in a population sample of patients from two tertiary Brazilian hospitals. We analyzed retrospectively 4912 electroencephalograms (EEGs records, and the included patients were submitted to a new clinical and EEG evaluation. Were included 12 (0.92% patients; 4 (33.3% with criteria for early BOEC; 6 (50% for late form and 2 (16.7% with superimposed early and late onset forms. After new investigation, 2 (16.7% had normal EEG; 4 (33.3% had paroxysms over the occipital region; 3 (25% over the temporal posterior regions and 3 (25% over the posterior regions. Sharp waves were the predominant change, occurring in 8 (66.6%; spike and slow wave complexes in 1 (8.3% and sharp and slow wave complexes in 1 (8.3%. Vomiting, headache and visual hallucinations were the most common ictal manifestations, presented in 100% of patients with superimposed forms. Vomiting were absent in the late form and headache was present in all forms of BOEC.

Information theoretic measures of network coordination in high-frequency scalp EEG reveal dynamic patterns associated with seizure termination.

Science.gov (United States)

Stamoulis, Catherine; Schomer, Donald L; Chang, Bernard S

2013-08-01

How a seizure terminates is still under-studied and, despite its clinical importance, remains an obscure phase of seizure evolution. Recent studies of seizure-related scalp EEGs at frequencies >100 Hz suggest that neural activity, in the form of oscillations and/or neuronal network interactions, may play an important role in preictal/ictal seizure evolution (Andrade-Valenca et al., 2011; Stamoulis et al., 2012). However, the role of high-frequency activity in seizure termination, is unknown, if it exists at all. Using information theoretic measures of network coordination, this study investigated ictal and immediate postictal neurodynamic interactions encoded in scalp EEGs from a relatively small sample of 8 patients with focal epilepsy and multiple seizures originating in temporal and/or frontal brain regions, at frequencies ≤ 100 Hz and >100 Hz, respectively. Despite some heterogeneity in the dynamics of these interactions, consistent patterns were also estimated. Specifically, in several seizures, linear or non-linear increase in high-frequency neuronal coordination during ictal intervals, coincided with a corresponding decrease in coordination at frequencies interval, which continues during the postictal interval. This may be one of several possible mechanisms that facilitate seizure termination. In fact, inhibition of pairwise interactions between EEGs by other signals in their spatial neighborhood, quantified by negative interaction information, was estimated at frequencies ≤ 100 Hz, at least in some seizures. Copyright © 2013 Elsevier B.V. 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.

Information-Theoretical Analysis of EEG Microstate Sequences in Python

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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.

Maturation of EEG Power Spectra in Early Adolescence: A Longitudinal Study

Science.gov (United States)

Cragg, Lucy; Kovacevic, Natasa; McIntosh, Anthony Randal; Poulsen, Catherine; Martinu, Kristina; Leonard, Gabriel; Paus, Tomas

2011-01-01

This study investigated the fine-grained development of the EEG power spectra in early adolescence, and the extent to which it is reflected in changes in peak frequency. It also sought to determine whether sex differences in the EEG power spectra reflect differential patterns of maturation. A group of 56 adolescents were tested at age 10 years and…

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.

Structure constrained semi-nonnegative matrix factorization for EEG-based motor imagery classification.

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Lu, Na; Li, Tengfei; Pan, Jinjin; Ren, Xiaodong; Feng, Zuren; Miao, Hongyu

2015-05-01

Electroencephalogram (EEG) provides a non-invasive approach to measure the electrical activities of brain neurons and has long been employed for the development of brain-computer interface (BCI). For this purpose, various patterns/features of EEG data need to be extracted and associated with specific events like cue-paced motor imagery. However, this is a challenging task since EEG data are usually non-stationary time series with a low signal-to-noise ratio. In this study, we propose a novel method, called structure constrained semi-nonnegative matrix factorization (SCS-NMF), to extract the key patterns of EEG data in time domain by imposing the mean envelopes of event-related potentials (ERPs) as constraints on the semi-NMF procedure. The proposed method is applicable to general EEG time series, and the extracted temporal features by SCS-NMF can also be combined with other features in frequency domain to improve the performance of motor imagery classification. Real data experiments have been performed using the SCS-NMF approach for motor imagery classification, and the results clearly suggest the superiority of the proposed method. Comparison experiments have also been conducted. The compared methods include ICA, PCA, Semi-NMF, Wavelets, EMD and CSP, which further verified the effectivity of SCS-NMF. The SCS-NMF method could obtain better or competitive performance over the state of the art methods, which provides a novel solution for brain pattern analysis from the perspective of structure constraint. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

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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

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.

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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.

Can visual assessment of blood flow patterns by dynamic contrast-enhanced computed tomography distinguish between malignant and benign lung tumors?

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Harders, Stefan Walbom; Madsen, Hans Henrik; Nellemann, Hanne Marie; Rasmussen, Torben Riis; Thygesen, Jesper; Hager, Henrik; Andersen, Niels Trolle; Rasmussen, Finn

2017-05-01

Dynamic contrast-enhanced computed tomography (DCE-CT) is a tool, which, in theory, can quantify the blood flow and blood volume of tissues. In structured qualitative analysis, parametric color maps yield a visual impression of the blood flow and blood volume within the tissue being studied, allowing for quick identification of the areas with the highest or lowest blood flow and blood volume. To examine whether DCE-CT could be used to distinguish between malignant and benign lung tumors in patients with suspected lung cancer. Fifty-nine patients with suspected lung cancer and a lung tumor on their chest radiograph were included for DCE-CT. The tumors were categorized using structured qualitative analysis of tumor blood flow patterns. Histopathology was used as reference standard. Using structured qualitative analysis of tumor blood flow patterns, it was possible to distinguish between malignant and benign lung tumors (Fisher-Freeman-Halton exact test, P  = 0.022). The inter-reader agreement of this method of analysis was slight to moderate (kappa = 0.30; 95% confidence interval [CI] = 0.13-0.46). DCE-CT in suspected lung cancer using structured qualitative analysis of tumor blood flow patterns is accurate as well as somewhat reproducible. However, there are significant limitations to DCE-CT.

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...

Postnatal development of EEG patterns, catecholamine contents and myelination, and effect of hyperthyroidism in Suncus brain.

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Takeuchi, T; Sitizyo, K; Harada, E

1998-03-01

The postnatal development of the central nervous system (CNS) in house musk shrew in the early stage of maturation was studied. The electroencephalogram (EEG) and visual evoked potential (VEP) in association with catecholamine contents and myelin basic protein (MBP) immunoreactivity were carried out from the 1st to the 20th day of postnatal age. Different EEG patterns which were specific to behavioral states (awake and drowsy) were first recorded on the 5th day, and the total power which was obtained by power spectrum analysis increased after this stage. The latencies of all peaks in VEP markedly shortened between the 5th and the 7th day. Noradrenalin (NA) content of the brain showed a slight increase after the 3rd day, and reached maximum levels on the 7th day, which was delayed a few days compared to dopamine (DA). In hyperthyroidism, the peak latency of VEP was shortened and biosynthesis of NA in cerebral cortex and DA in hippocampus was accelerated. The most obvious change in MBP-immunoreactivity of the telencephalon occurred from the 7th to the 10th day. These morphological changes in the brain advanced at the identical time-course to those in the electrophysiological development and increment of DA and NA contents.

Multifractal analysis of real and imaginary movements: EEG study

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Pavlov, Alexey N.; Maksimenko, Vladimir A.; Runnova, Anastasiya E.; Khramova, Marina V.; Pisarchik, Alexander N.

2018-04-01

We study abilities of the wavelet-based multifractal analysis in recognition specific dynamics of electrical brain activity associated with real and imaginary movements. Based on the singularity spectra we analyze electroencephalograms (EEGs) acquired in untrained humans (operators) during imagination of hands movements, and show a possibility to distinguish between the related EEG patterns and the recordings performed during real movements or the background electrical brain activity. We discuss how such recognition depends on the selected brain region.

The effect of CPAP treatment on EEG of OSAS patients.

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Zhang, Cheng; Lv, Jun; Zhou, Junhong; Su, Li; Feng, Liping; Ma, Jing; Wang, Guangfa; Zhang, Jue

2015-12-01

Continuous positive airway pressure (CPAP) is currently the most effective treatment method for obstructive sleep apnea syndrome (OSAS). The purpose of this study was to compare the sleep electroencephalogram (EEG) changes before and after the application of CPAP to OSAS patients. A retrospective study was conducted and 45 sequential patients who received both polysomnography (PSG) and CPAP titration were included. The raw data of sleep EEG were extracted and analyzed by engineers using two main factors: fractal dimension (FD) and the zero-crossing rate of detrended FD (zDFD). FD was an effective indicator reflecting the EEG complexity and zDFD was useful to reflect the variability of the EEG complexity. The FD and zDFD indexes of sleep EEG of 45 OSAS patients before and after CPAP titration were analyzed. The age of 45 OSAS patients was 52.7 ± 5.6 years old and the patients include 12 females and 33 males. After CPAP treatment, FD of EEG in non-rapid eye movement (NREM) sleep decreased significantly (P CPAP therapy (P CPAP therapy had a significant influence on sleep EEG in patients with OSAHS, which lead to a more stable EEG pattern. This may be one of the mechanisms that CPAP could improve sleep quality and brain function of OSAS patients.

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

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Vitaly Schetinin

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

Mimickers of generalized spike and wave discharges.

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Azzam, Raed; Bhatt, Amar B

2014-06-01

Overinterpretation of benign EEG variants is a common problem that can lead to the misdiagnosis of epilepsy. We review four normal patterns that mimic generalized spike and wave discharges: phantom spike-and-wave, hyperventilation hypersynchrony, hypnagogic/ hypnopompic hypersynchrony, and mitten patterns.

Seizures and EEG features in 74 patients with genetic-dysmorphic syndromes.

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Alfei, Enrico; Raviglione, Federico; Franceschetti, Silvana; D'Arrigo, Stefano; Milani, Donatella; Selicorni, Angelo; Riva, Daria; Zuffardi, Orsetta; Pantaleoni, Chiara; Binelli, Simona

2014-12-01

Epilepsy is one of the most common findings in chromosome aberrations. Types of seizures and severity may significantly vary both between different conditions and within the same aberration. Hitherto specific seizures and EEG patterns are identified for only few syndromes. We studied 74 patients with defined genetic-dysmorphic syndromes with and without epilepsy in order to assess clinical and electroencephalographic features, to compare our observation with already described electro-clinical phenotypes, and to identify putative electroencephalographic and/or seizure characteristics useful to address the diagnosis. In our population, 10 patients had chromosomal disorders, 19 microdeletion or microduplication syndromes, and 32 monogenic syndromes. In the remaining 13, syndrome diagnosis was assessed on clinical grounds. Our study confirmed the high incidence of epilepsy in genetic-dysmorphic syndromes. Moreover, febrile seizures and neonatal seizures had a higher incidence compared to general population. In addition, more than one third of epileptic patients had drug-resistant epilepsy. EEG study revealed poor background organization in 42 patients, an excess of diffuse rhythmic activities in beta, alpha or theta frequency bands in 34, and epileptiform patterns in 36. EEG was completely normal only in 20 patients. No specific electro-clinical pattern was identified, except for inv-dup15, Angelman, and Rett syndromes. Nevertheless some specific conditions are described in detail, because of notable differences from what previously reported. Regarding the diagnostic role of EEG, we found that--even without any epileptiform pattern--the generation of excessive rhythmic activities in different frequency bandwidths might support the diagnosis of a genetic syndrome. © 2014 Wiley Periodicals, Inc.

Quantitative analysis of enhanced malignant and benign lesions on contrast-enhanced spectral mammography.

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Deng, Chih-Ying; Juan, Yu-Hsiang; Cheung, Yun-Chung; Lin, Yu-Ching; Lo, Yung-Feng; Lin, GiGin; Chen, Shin-Cheh; Ng, Shu-Hang

2018-02-27

To retrospectively analyze the quantitative measurement and kinetic enhancement among pathologically proven benign and malignant lesions using contrast-enhanced spectral mammography (CESM). We investigated the differences in enhancement between 44 benign and 108 malignant breast lesions in CESM, quantifying the extent of enhancements and the relative enhancements between early (between 2-3 min after contrast medium injection) and late (3-6 min) phases. The enhancement was statistically stronger in malignancies compared to benign lesions, with good performance by the receiver operating characteristic curve [0.877, 95% confidence interval (0.813-0.941)]. Using optimal cut-off value at 220.94 according to Youden index, the sensitivity was 75.9%, specificity 88.6%, positive likelihood ratio 6.681, negative likelihood ratio 0.272 and accuracy 82.3%. The relative enhancement patterns of benign and malignant lesions, showing 29.92 vs 73.08% in the elevated pattern, 7.14 vs 92.86% in the steady pattern, 5.71 vs 94.29% in the depressed pattern, and 80.00 vs 20.00% in non-enhanced lesions (p < 0.0001), respectively. Despite variations in the degree of tumour angiogenesis, quantitative analysis of the breast lesions on CESM documented the malignancies had distinctive stronger enhancement and depressed relative enhancement patterns than benign lesions. Advances in knowledge: To our knowledge, this is the first study evaluating the feasibility of quantifying lesion enhancement on CESM. The quantities of enhancement were informative for assessing breast lesions in which the malignancies had stronger enhancement and more relative depressed enhancement than the benign lesions.

Concordance and discordance between PET images and foci of scalp EEG

International Nuclear Information System (INIS)

Iinuma, Kazuie; Haginoya, Kazuhiro; Yanai, Kazuhiko; Hatazawa, Jun; Ito, Masatoshi.

1989-01-01

Epileptic foci were determined by scalp EEG and positron emission tomography (PET) with fluorine 18 in 22 children with partial epilepsy (PE, n=13) and Lennoxy-Gastaut syndrome (LGS, n=9). The patients ranged in age from 6 to 18 years. The pattern of hypometabolism was classified into the following 4 categories: non-focal, localized, hemispheric, and diffuse. In the group of PE patients, 11 showed a relative agreement between the EEG foci and region of a low cerebral metabolic rate for glucose (CMRglc) determined by PET. A decreased CMRglc was matched with the EEG foci in 4 patients with LGS. A tendency of a higher relationship between the EEG foci and PET images was significant in PE than LGS. (N.K.)

Generalized Information Equilibrium Approaches to EEG Sleep Stage Discrimination

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Todd Zorick

2016-01-01

Full Text Available Recent advances in neuroscience have raised the hypothesis that the underlying pattern of neuronal activation which results in electroencephalography (EEG signals is via power-law distributed neuronal avalanches, while EEG signals are nonstationary. Therefore, spectral analysis of EEG may miss many properties inherent in such signals. A complete understanding of such dynamical systems requires knowledge of the underlying nonequilibrium thermodynamics. In recent work by Fielitz and Borchardt (2011, 2014, the concept of information equilibrium (IE in information transfer processes has successfully characterized many different systems far from thermodynamic equilibrium. We utilized a publicly available database of polysomnogram EEG data from fourteen subjects with eight different one-minute tracings of sleep stage 2 and waking and an overlapping set of eleven subjects with eight different one-minute tracings of sleep stage 3. We applied principles of IE to model EEG as a system that transfers (equilibrates information from the time domain to scalp-recorded voltages. We find that waking consciousness is readily distinguished from sleep stages 2 and 3 by several differences in mean information transfer constants. Principles of IE applied to EEG may therefore prove to be useful in the study of changes in brain function more generally.

The Mozart Effect: A quantitative EEG study.

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Verrusio, Walter; Ettorre, Evaristo; Vicenzini, Edoardo; Vanacore, Nicola; Cacciafesta, Mauro; Mecarelli, Oriano

2015-09-01

The aim of this study is to investigate the influence of Mozart's music on brain activity through spectral analysis of the EEG in young healthy adults (Adults), in healthy elderly (Elderly) and in elderly with Mild Cognitive Impairment (MCI). EEG recording was performed at basal rest conditions and after listening to Mozart's K448 or "Fur Elise" Beethoven's sonatas. After listening to Mozart, an increase of alpha band and median frequency index of background alpha rhythm activity (a pattern of brain wave activity linked to memory, cognition and open mind to problem solving) was observed both in Adults and in Elderly. No changes were observed in MCI. After listening to Beethoven, no changes in EEG activity were detected. This results may be representative of the fact that said Mozart's music is able to "activate" neuronal cortical circuits related to attentive and cognitive functions. Copyright © 2015 Elsevier Inc. All rights reserved.

EEG entropy measures in anesthesia

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Liang, Zhenhu; Wang, Yinghua; Sun, Xue; Li, Duan; Voss, Logan J.; Sleigh, Jamie W.; Hagihira, Satoshi; Li, Xiaoli

2015-01-01

Highlights: ► Twelve entropy indices were systematically compared in monitoring depth of anesthesia and detecting burst suppression.► Renyi permutation entropy performed best in tracking EEG changes associated with different anesthesia states.► Approximate Entropy and Sample Entropy performed best in detecting burst suppression. Objective: Entropy algorithms have been widely used in analyzing EEG signals during anesthesia. However, a systematic comparison of these entropy algorithms in assessing anesthesia drugs' effect is lacking. In this study, we compare the capability of 12 entropy indices for monitoring depth of anesthesia (DoA) and detecting the burst suppression pattern (BSP), in anesthesia induced by GABAergic agents. Methods: Twelve indices were investigated, namely Response Entropy (RE) and State entropy (SE), three wavelet entropy (WE) measures [Shannon WE (SWE), Tsallis WE (TWE), and Renyi WE (RWE)], Hilbert-Huang spectral entropy (HHSE), approximate entropy (ApEn), sample entropy (SampEn), Fuzzy entropy, and three permutation entropy (PE) measures [Shannon PE (SPE), Tsallis PE (TPE) and Renyi PE (RPE)]. Two EEG data sets from sevoflurane-induced and isoflurane-induced anesthesia respectively were selected to assess the capability of each entropy index in DoA monitoring and BSP detection. To validate the effectiveness of these entropy algorithms, pharmacokinetic/pharmacodynamic (PK/PD) modeling and prediction probability (Pk) analysis were applied. The multifractal detrended fluctuation analysis (MDFA) as a non-entropy measure was compared. Results: All the entropy and MDFA indices could track the changes in EEG pattern during different anesthesia states. Three PE measures outperformed the other entropy indices, with less baseline variability, higher coefficient of determination (R2) and prediction probability, and RPE performed best; ApEn and SampEn discriminated BSP best. Additionally, these entropy measures showed an advantage in computation

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.

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

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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

Effects of magnesium sulphate on amplitude-integrated continuous EEG in asphyxiated term neonates

NARCIS (Netherlands)

Groenendaal, F; Rademaker, CMA; Toet, MC; de Vries, LS

2002-01-01

In this study it is hypothesized that magnesium sulphate in asphyxiated full-term neonates could lead to a gradual improvement in background pattern of the amplitude integrated EEG (aEEG), an early marker of hypoxic-ischaemic brain injury. In a double-blind, randomized, controlled pilot study of 22

Screening EEG in Aircrew Selection: Clinical Aerospace Neurology Perspective

Science.gov (United States)

Clark, Jonathan B.; Riley, Terrence

2001-01-01

.0139% (4/28627). After review of the value of the EEG as a screening tool, the US Navy now uses EEG only for certain clinical indications (head injury, unexplained loss of consciousness, family history of epilepsy, and abnormal neurological exam). Currently the US Navy does not use EEG for screening for any flight applicant without a neurologic indication. In the US Navy, an electroencephalographic pattern is determined to be epileptiform by a neurologist.

Decoding English Alphabet Letters Using EEG Phase Information

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YiYan Wang

2018-02-01

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

Rhythms of EEG and cognitive processes

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Novikova S.I.

2015-06-01

Full Text Available The study of cognitive processes is regarded to be more effective if it combines a psychological approach with a neurophysiological one. This approach makes it possible to come closer to understanding of the basic mechanisms of different cognitive processes, to describe the patterns of forming these mechanisms in ontogenesis, to investigate the origin of cognitive impairments, and to develop intervention techniques. The promising way of investigating the mechanisms of cognitive functions is the electroencephalography (EEG. This is a non-invasive, safe, and relatively cheap method of research of the functional condition of the brain. The characteristics of EEG rhythms, recorded with different cognitive loads, reflect the processes of functional modulation of neural network activity of the cortex, which serves the neurophysiologic basis for attention, memory and other cognitive processes. The article provides an overview of works containing the analysis of the alpha and theta rhythms’ dynamics in various states of wakefulness. It also introduces the substantiation of methodology of functional regulatory approach to the interpretation of behaviors of EEG rhythms.

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.

Responses to tonic heat pain in the ongoing EEG under conditions of controlled attention.

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Giehl, Janet; Meyer-Brandis, Gesa; Kunz, Miriam; Lautenbacher, Stefan

2014-03-01

To confirm the existence of an ongoing electroencephalogram (EEG) pattern that is truly suggestive of pain, tonic heat pain was induced by small heat pulses at 1 °C above the pain threshold and compared to slightly less intense tonic non-painful heat pulses at 1 °C below the pain threshold. Twenty healthy subjects rated the sensation intensity during thermal stimulation. Possible confounding effects of attention were thoroughly controlled for by testing in four conditions: (1) focus of attention directed ipsilateral or (2) contralateral to the side of the stimulation, (3) control without a side preference, and (4) no control of attention at all. EEG was recorded via eight leads according to the 10/20 convention. Absolute power was computed for the frequency bands delta (0.5-4 Hz), theta (4-8 Hz), alpha1 (8-11 Hz), alpha2 (11-14 Hz), beta1 (14-25 Hz), and beta2 (25-35 Hz). Ratings were clearly distinct between the heat and pain conditions and suggestive for heat and pain sensations. Manipulation of attention proved to be successful by producing effects on the ratings and on the EEG activity (with lower ratings and lower EEG activity (theta, beta1, 2) over central areas for side-focused attention). During pain stimulation, lower central alpha1 and alpha2 activity and higher right-parietal and right-occipital delta power were observed compared to heat stimulation. This EEG pattern was not influenced by the manipulation of attention. Since the two types of stimuli (pain, heat) were subjectively felt differently although stimulation intensities were nearby, we conclude that this EEG pattern is clearly suggestive of pain.

From swing to cane: Sex differences of EEG resting-state temporal patterns during maturation and aging

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M.I. Tomescu

2018-06-01

Full Text Available While many insights on brain development and aging have been gained by studying resting-state networks with fMRI, relating these changes to cognitive functions is limited by the temporal resolution of fMRI. In order to better grasp short-lasting and dynamically changing mental activities, an increasing number of studies utilize EEG to define resting-state networks, thereby often using the concept of EEG microstates. These are brief (around 100 ms periods of stable scalp potential fields that are influenced by cognitive states and are sensitive to neuropsychiatric diseases. Despite the rising popularity of the EEG microstate approach, information about age changes is sparse and nothing is known about sex differences. Here we investigated age and sex related changes of the temporal dynamics of EEG microstates in 179 healthy individuals (6–87 years old, 90 females, 204-channel EEG. We show strong sex-specific changes in microstate dynamics during adolescence as well as at older age. In addition, males and females differ in the duration and occurrence of specific microstates. These results are of relevance for the comparison of studies in populations of different age and sex and for the understanding of the changes in neuropsychiatric diseases.

The value of ultrasonography combined with compression technique in differentiation between benign and malignant breast masses

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Yoon, Seong Kuk; Lee, Ki Nam; Nam, Kyung Jin; Jung, Won Jung

2001-01-01

To determine whether the compression technique is a valuable additional method for differentiating between benign and malignant breast masses. The ultrasonographic findings of 95 benign and 53 malignant masses, all pathologically proven, were prospectively analyzed with regard to five diagnostic criteria: shape (regular/irregular), retrotumoral acoustic phenomena (posterior enhancement/posterior attenuation), internal echo pattern (homogeneous/inhomogeneous), compression effect on shape (distortion/no change), and compression effect on internal echo pattern (more homogeneous/no change). The number of cases of benign and malignant masses, respectively, was as follows: regular/irregular shape: 84/11, 9/44; posterior acoustic enhancement/posterior attenuation: 82/13, 16/37; homogeneous/inhomogeneous internal echo pattern: 78/17, 14/39; distortion/no change in shape: 76/19, 5/48; and more homogeneous/no change in internal echo pattern: 71/24, 3/50. For all diagnostic criteria for the differentiation of benign and malignant masses, the differences were statistically significant (p<.05). Ultrasonography is helpful for differentiating between benign and malignant breast masses. The compression technique is a valuable additional diagnostic method

Local and Widely Distributed EEG Activity in Schizophrenia With Prevalence of Negative Symptoms.

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Grin-Yatsenko, Vera A; Ponomarev, Valery A; Pronina, Marina V; Poliakov, Yury I; Plotnikova, Irina V; Kropotov, Juri D

2017-09-01

We evaluated EEG frequency abnormalities in resting state (eyes closed and eyes open) EEG in a group of chronic schizophrenia patients as compared with healthy subjects. The study included 3 methods of analysis of deviation of EEG characteristics: genuine EEG, current source density (CSD), and group independent component (gIC). All 3 methods have shown that the EEG in schizophrenia patients is characterized by enhanced low-frequency (delta and theta) and high-frequency (beta) activity in comparison with the control group. However, the spatial pattern of differences was dependent on the type of method used. Comparative analysis has shown that increased EEG power in schizophrenia patients apparently concerns both widely spatially distributed components and local components of signal. Furthermore, the observed differences in the delta and theta range can be described mainly by the local components, and those in the beta range mostly by spatially widely distributed ones. The possible nature of the widely distributed activity is discussed.

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

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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.

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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.

Prognostic value of non-reactive burst suppression EEG pattern associated to early neonatal seizures Valor prognóstico do EEG com padrão de surto-supressão não reativo associado a convulsões neonatais precoces

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Magda Lahorgue Nunes

2005-03-01

Full Text Available ABSTRACT Seizures are the most frequent neurological event in newborns and clinical data suggest that etiology is the dominant factor in long term outcome. However, there are consistent background EEG abnormalities associated to neonatal seizures that are usually related to unfavorable outcome as the burst - suppression pattern. OBJECTIVE: The objective of this study was to correlate clinical and EEG features associated to long - term outcome of newborns with non - reactive burst - suppression (BS EEG. METHOD: Newborns included in the study were selected from our database and had conceptional age (at the time of first EEG >37 weeks, EEG recordings with non - reactive BS available for review and clinical follow up. RESULTS: 12 newborns met inclusion criteria, 50% had seizures in the first day of life. Seizures became refractory to treatment in all of them. In 50% the etiology of seizures was considered cryptogenic, 33% had inborn errors of metabolism and 17% had clinical history and neuroimage suggestive of hypoxic-ischemic encephalopathy. The follow-up showed that 7/12 infants deceased, 3 during the first year of life, and one in the neonatal period. All the survivors had severe developmental delay and multifocal neurological impairment. 92% developed refractory epilepsy, 58% were latter diagnosed with West syndrome. CONCLUSION: The non-reactive BS pattern may appear related to many neonatal neurological disorders and is associated with early and refractory neonatal seizures. It is clearly associated with elevated morbidity and mortality and to the development of post-neonatal epilepsy.RESUMO Convulsões representam o evento neurológico mais freqüente no período neonatal e a etiologia das crises parece ser o aspecto clínico mais associado ao prognóstico a longo prazo. Entretanto, existem padrões anormais de EEG, que de forma consistente relacionam-se a prognóstico, entre eles o padrão de surto - supressão. OBJETIVO: Este estudo teve

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

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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

Fibronectin Pattern in Benign Hyperplasia and Cancer of the Prostate

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Miroslava M. Janković

2008-01-01

Full Text Available Fibronectin (FN is a multifunctional glycoprotein involved in cell-matrix interactions. It exhibits a complex pattern of forms differing in respect to aminoacid and oligosaccharide composition. In this study we examined glycobiochemical and functional properties of the FN in benign prostatic hyperplasia (BPH and prostatic cancer (PCa, attempting to resolve disease-related differences. Two BPH sera pools and three PCa sera pools were used as the FN source. The affinity-purified molecule was characterized by SDS-PAGE, immuno- and lectin blot, lectin-affinity chromatography and adhesion assay. BPH FN existed as intact molecule, giving the main immunoreactive band at 220 kDa. In contrast, PCa FN comprised three main immunoreactive fragments of 140, 110 and 90 kDa. As for glycosylation the ratio of altogether lectin-reactive PCa FN was different from that of BPH FN manifested as a decrease of Con A- and an increase of LCA-reactive moieties. Fibroblasts adhered to both FN preparations in a concentration dependent manner, but with a significantly lower efficiency to PCa FN. The results obtained showing distinct structural characteristics of PCa FN compared to BPH FN could be important for modulation of its ligand and recognition properties expressed as gain or loss of functions or as specific markers of its origin.

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

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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).

EEG and Coma.

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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.

Modulation of EEG Theta Band Signal Complexity by Music Therapy

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Bhattacharya, Joydeep; Lee, Eun-Jeong

The primary goal of this study was to investigate the impact of monochord (MC) sounds, a type of archaic sounds used in music therapy, on the neural complexity of EEG signals obtained from patients undergoing chemotherapy. The secondary goal was to compare the EEG signal complexity values for monochords with those for progressive muscle relaxation (PMR), an alternative therapy for relaxation. Forty cancer patients were randomly allocated to one of the two relaxation groups, MC and PMR, over a period of six months; continuous EEG signals were recorded during the first and last sessions. EEG signals were analyzed by applying signal mode complexity, a measure of complexity of neuronal oscillations. Across sessions, both groups showed a modulation of complexity of beta-2 band (20-29Hz) at midfrontal regions, but only MC group showed a modulation of complexity of theta band (3.5-7.5Hz) at posterior regions. Therefore, the neuronal complexity patterns showed different changes in EEG frequency band specific complexity resulting in two different types of interventions. Moreover, the different neural responses to listening to monochords and PMR were observed after regular relaxation interventions over a short time span.

Robot-Aided Upper-Limb Rehabilitation Based on Motor Imagery EEG

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Baoguo Xu

2011-09-01

Full Text Available Stroke is a leading cause of disability worldwide. In this paper, a novel robot-assisted rehabilitation system based on motor imagery electroencephalography (EEG is developed for regular training of neurological rehabilitation for upper limb stroke patients. Firstly, three-dimensional animation was used to guide the patient image the upper limb movement and EEG signals were acquired by EEG amplifier. Secondly, eigenvectors were extracted by harmonic wavelet transform (HWT and linear discriminant analysis (LDA classifier was utilized to classify the pattern of the left and right upper limb motor imagery EEG signals. Finally, PC triggered the upper limb rehabilitation robot to perform motor therapy and gave the virtual feedback. Using this robot-assisted upper limb rehabilitation system, the patient's EEG of upper limb movement imagination is translated to control rehabilitation robot directly. Consequently, the proposed rehabilitation system can fully explore the patient's motivation and attention and directly facilitate upper limb post-stroke rehabilitation therapy. Experimental results on unimpaired participants were presented to demonstrate the feasibility of the rehabilitation system. Combining robot-assisted training with motor imagery-based BCI will make future rehabilitation therapy more effective. Clinical testing is still required for further proving this assumption.

Human physiological benefits of viewing nature: EEG responses to exact and statistical fractal patterns.

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Hagerhall, C M; Laike, T; Küller, M; Marcheschi, E; Boydston, C; Taylor, R P

2015-01-01

Psychological and physiological benefits of viewing nature have been extensively studied for some time. More recently it has been suggested that some of these positive effects can be explained by nature's fractal properties. Virtually all studies on human responses to fractals have used stimuli that represent the specific form of fractal geometry found in nature, i.e. statistical fractals, as opposed to fractal patterns which repeat exactly at different scales. This raises the question of whether human responses like preference and relaxation are being driven by fractal geometry in general or by the specific form of fractal geometry found in nature. In this study we consider both types of fractals (statistical and exact) and morph one type into the other. Based on the Koch curve, nine visual stimuli were produced in which curves of three different fractal dimensions evolve gradually from an exact to a statistical fractal. The patterns were shown for one minute each to thirty-five subjects while qEEG was continuously recorded. The results showed that the responses to statistical and exact fractals differ, and that the natural form of the fractal is important for inducing alpha responses, an indicator of a wakefully relaxed state and internalized attention.

Diagnostic value of dynamic perfusion MR imaging in benign and malignant musculoskeletal lesions

International Nuclear Information System (INIS)

Choi, Byeong Kyoo; Lee, Sang Hoon; Cha, Ji Hyeon; Kim, Sung Moon; Shin, Myung Jin; Han, Heon; Kim, Sam Soo; Lee, Ji Yeon; Jeon, Yong Hwan

2008-01-01

To assess the diagnostic value of dynamic perfusion MR imaging for differentiation between benign and malignant musculoskeletal lesions. Dynamic perfusion MR imaging was performed using a 3.0 T system in 32 female and 30 male patients (aged 10-90 years, mean age, 43 years). Following the assessment of the precontrast imaging, a dynamic study was performed. This dynamic technique allowed for 638 images to be obtained at 11 levels throughout the lesion. Twenty-eight lesions originated within bone (8 benign, 20 malignant), whereas 34 lesions were of soft tissue origin (22 benign, 12 malignant). The final diagnosis was histopathologically confirmed in all patients. To differentiate between benign and malignant lesions, we analyzed the four parameters: (maximal relative enhancement (MRE), time to peak (TTP), wash in rate (WI), steepest slope (SS) and the distribution of time intensity curve (TIC) patterns. The TTP, WI, and SS values of malignant lesions were statistically significant from those of benign lesions(ρ < 0.05). However, the difference for the MRE values was not statistically significant. The distribution of TIC patterns was a helpful indicator of benign or malignant state, however the difference between the two states was not significant. Dynamic perfusion MR imaging is a helpful tool in differentiating benign and malignant musculoskeletal lesions

Safety of Simultaneous Scalp or Intracranial EEG during MRI: A Review

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Hassan B. Hawsawi

2017-10-01

Full Text Available Understanding the brain and its activity is one of the great challenges of modern science. Normal brain activity (cognitive processes, etc. has been extensively studied using electroencephalography (EEG since the 1930's, in the form of spontaneous fluctuations in rhythms, and patterns, and in a more experimentally-driven approach in the form of event-related potentials (ERPs allowing us to relate scalp voltage waveforms to brain states and behavior. The use of EEG recorded during functional magnetic resonance imaging (EEG-fMRI is a more recent development that has become an important tool in clinical neuroscience, for example for the study of epileptic activity. The purpose of this review is to explore the magnetic resonance imaging safety aspects specifically associated with the use of scalp EEG and other brain-implanted electrodes such as intracranial EEG electrodes when they are subjected to the MRI environment. We provide a theoretical overview of the mechanisms at play specifically associated with the presence of EEG equipment connected to the subject in the MR environment, and of the resulting health hazards. This is followed by a survey of the literature on the safety of scalp or invasive EEG-fMRI data acquisitions across field strengths, with emphasis on the practical implications for the safe application of the techniques; in particular, we attempt to summarize the findings in terms of acquisition protocols when possible.

Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm

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E. Parvinnia

2014-01-01

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

EEG entropy measures in anesthesia

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Zhenhu eLiang

2015-02-01

Full Text Available Objective: Entropy algorithms have been widely used in analyzing EEG signals during anesthesia. However, a systematic comparison of these entropy algorithms in assessing anesthesia drugs’ effect is lacking. In this study, we compare the capability of twelve entropy indices for monitoring depth of anesthesia (DoA and detecting the burst suppression pattern (BSP, in anesthesia induced by GA-BAergic agents.Methods: Twelve indices were investigated, namely Response Entropy (RE and State entropy (SE, three wavelet entropy (WE measures (Shannon WE (SWE, Tsallis WE (TWE and Renyi WE (RWE, Hilbert-Huang spectral entropy (HHSE, approximate entropy (ApEn, sample entropy (SampEn, Fuzzy entropy, and three permutation entropy (PE measures (Shannon PE (SPE, Tsallis PE (TPE and Renyi PE (RPE. Two EEG data sets from sevoflurane-induced and isoflu-rane-induced anesthesia respectively were selected to assess the capability of each entropy index in DoA monitoring and BSP detection. To validate the effectiveness of these entropy algorithms, phar-macokinetic / pharmacodynamic (PK/PD modeling and prediction probability analysis were applied. The multifractal detrended fluctuation analysis (MDFA as a non-entropy measure was compared.Results: All the entropy and MDFA indices could track the changes in EEG pattern during different anesthesia states. Three PE measures outperformed the other entropy indices, with less baseline vari-ability, higher coefficient of determination and prediction probability, and RPE performed best; ApEn and SampEn discriminated BSP best. Additionally, these entropy measures showed an ad-vantage in computation efficiency compared with MDFA.Conclusion: Each entropy index has its advantages and disadvantages in estimating DoA. Overall, it is suggested that the RPE index was a superior measure.Significance: Investigating the advantages and disadvantages of these entropy indices could help improve current clinical indices for monitoring DoA.

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.

EEG patterns from acute to chronic stroke phases in focal cerebral ischemic rats: correlations with functional recovery.

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Zhang, Shao-jie; Ke, Zheng; Li, Le; Yip, Shea-ping; Tong, Kai-yu

2013-04-01

Monitoring the neural activities from the ischemic penumbra provides critical information on neurological recovery after stroke. The purpose of this study is to evaluate the temporal alterations of neural activities using electroencephalography (EEG) from the acute phase to the chronic phase, and to compare EEG with the degree of post-stroke motor function recovery in a rat model of focal ischemic stroke. Male Sprague-Dawley rats were subjected to 90 min transient middle cerebral artery occlusion surgery followed by reperfusion for seven days (n = 58). The EEG signals were recorded at the pre-stroke phase (0 h), acute phase (3, 6 h), subacute phase (12, 24, 48, 72 h) and chronic phase (96, 120, 144, 168 h) (n = 8). This study analyzed post-stroke seizures and polymorphic delta activities (PDAs) and calculated quantitative EEG parameters such as the alpha-to-delta ratio (ADR). The ADR represented the ratio between alpha power and delta power, which indicated how fast the EEG activities were. Forelimb and hindlimb motor functions were measured by De Ryck's test and the beam walking test, respectively. In the acute phase, delta power increased fourfold with the occurrence of PDAs, and the histological staining showed that the infarct was limited to the striatum and secondary sensory cortex. In the subacute phase, the alpha power reduced to 50% of the baseline, and the infarct progressed to the forelimb cortical region. ADRs reduced from 0.23 ± 0.09 to 0.04 ± 0.01 at 3 h in the acute phase and gradually recovered to 0.22 ± 0.08 at 168 h in the chronic phase. In the comparison of correlations between the EEG parameters and the limb motor function from the acute phase to the chronic phase, ADRs were found to have the highest correlation coefficients with the beam walking test (r = 0.9524, p test (r = 0.8077, p < 0.05). This study measured EEG activities after focal cerebral ischemia and showed that functional recovery was closely correlated with the neural

EEG patterns from acute to chronic stroke phases in focal cerebral ischemic rats: correlations with functional recovery

International Nuclear Information System (INIS)

Zhang, Shao-jie; Ke, Zheng; Tong, Kai-yu; Li, Le; Yip, Shea-ping

2013-01-01

Monitoring the neural activities from the ischemic penumbra provides critical information on neurological recovery after stroke. The purpose of this study is to evaluate the temporal alterations of neural activities using electroencephalography (EEG) from the acute phase to the chronic phase, and to compare EEG with the degree of post-stroke motor function recovery in a rat model of focal ischemic stroke. Male Sprague–Dawley rats were subjected to 90 min transient middle cerebral artery occlusion surgery followed by reperfusion for seven days (n = 58). The EEG signals were recorded at the pre-stroke phase (0 h), acute phase (3, 6 h), subacute phase (12, 24, 48, 72 h) and chronic phase (96, 120, 144, 168 h) (n = 8). This study analyzed post-stroke seizures and polymorphic delta activities (PDAs) and calculated quantitative EEG parameters such as the alpha-to-delta ratio (ADR). The ADR represented the ratio between alpha power and delta power, which indicated how fast the EEG activities were. Forelimb and hindlimb motor functions were measured by De Ryck's test and the beam walking test, respectively. In the acute phase, delta power increased fourfold with the occurrence of PDAs, and the histological staining showed that the infarct was limited to the striatum and secondary sensory cortex. In the subacute phase, the alpha power reduced to 50% of the baseline, and the infarct progressed to the forelimb cortical region. ADRs reduced from 0.23 ± 0.09 to 0.04 ± 0.01 at 3 h in the acute phase and gradually recovered to 0.22 ± 0.08 at 168 h in the chronic phase. In the comparison of correlations between the EEG parameters and the limb motor function from the acute phase to the chronic phase, ADRs were found to have the highest correlation coefficients with the beam walking test (r = 0.9524, p < 0.05) and De Ryck's test (r = 0.8077, p < 0.05). This study measured EEG activities after focal cerebral ischemia and showed that functional recovery was closely

EEG Oscillatory States: Universality, Uniqueness and Specificity across Healthy-Normal, Altered and Pathological Brain Conditions

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Fingelkurts, Alexander A.; Fingelkurts, Andrew A.

2014-01-01

For the first time the dynamic repertoires and oscillatory types of local EEG states in 13 diverse conditions (examined over 9 studies) that covered healthy-normal, altered and pathological brain states were quantified within the same methodological and conceptual framework. EEG oscillatory states were assessed by the probability-classification analysis of short-term EEG spectral patterns. The results demonstrated that brain activity consists of a limited repertoire of local EEG states in any of the examined conditions. The size of the state repertoires was associated with changes in cognition and vigilance or neuropsychopathologic conditions. Additionally universal, optional and unique EEG states across 13 diverse conditions were observed. It was demonstrated also that EEG oscillations which constituted EEG states were characteristic for different groups of conditions in accordance to oscillations’ functional significance. The results suggested that (a) there is a limit in the number of local states available to the cortex and many ways in which these local states can rearrange themselves and still produce the same global state and (b) EEG individuality is determined by varying proportions of universal, optional and unique oscillatory states. The results enriched our understanding about dynamic microstructure of EEG-signal. PMID:24505292

Discrimination of Motor Imagery-Induced EEG Patterns in Patients with Complete Spinal Cord Injury

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G. Pfurtscheller

2009-01-01

Full Text Available EEG-based discrimination between different motor imagery states has been subject of a number of studies in healthy subjects. We investigated the EEG of 15 patients with complete spinal cord injury during imagined right hand, left hand, and feet movements. In detail we studied pair-wise discrimination functions between the 3 types of motor imagery. The following classification accuracies (mean ± SD were obtained: left versus right hand 65.03% ± 8.52, left hand versus feet 68.19% ± 11.08, and right hand versus feet 65.05% ± 9.25. In 5 out of 8 paralegic patients, the discrimination accuracy was greater than 70% but in only 1 out of 7 tetraplagic patients. The present findings provide evidence that in the majority of paraplegic patients an EEG-based BCI could achieve satisfied results. In tetraplegic patients, however, it is expected that extensive training-sessions are necessary to achieve a good BCI performance at least in some subjects.

Anterior EEG asymmetries and opponent process theory.

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Kline, John P; Blackhart, Ginette C; Williams, William C

2007-03-01

The opponent process theory of emotion [Solomon, R.L., and Corbit, J.D. (1974). An opponent-process theory of motivation: I. Temporal dynamics of affect. Psychological Review, 81, 119-143.] predicts a temporary reversal of emotional valence during the recovery from emotional stimulation. We hypothesized that this affective contrast would be apparent in asymmetrical activity patterns in the frontal lobes, and would be more apparent for left frontally active individuals. The present study tested this prediction by examining EEG asymmetries during and after blocked presentations of aversive pictures selected from the International Affective Picture System (IAPS). 12 neutral images, 12 aversive images, and 24 neutral images were presented in blocks. Participants who were right frontally active at baseline did not show changes in EEG asymmetry while viewing aversive slides or after cessation. Participants left frontally active at baseline, however, exhibited greater relative left frontal activity after aversive stimulation than before stimulation. Asymmetrical activity patterns in the frontal lobes may relate to affect regulatory processes, including contrasting opponent after-reactions to aversive stimuli.

Origin of frontal lobe spikes in the early onset benign occipital lobe epilepsy (Panayiotopoulos syndrome).

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Leal, Alberto J R; Ferreira, José C; Dias, Ana I; Calado, Eulália

2008-09-01

Early onset benign occipital lobe epilepsy (Panayiotopoulos syndrome [PS]) is a common and easily recognizable epilepsy. Interictal EEG spike activity is often multifocal but most frequently localized in the occipital lobes. The origin and clinical significance of the extra-occipital spikes remain poorly understood. Three patients with the PS and interictal EEG spikes with frontal lobe topography were studied using high-resolution EEG. Independent component analysis (ICA) was used to decompose the spikes in components with distinct temporal dynamics. The components were mapped in the scalp with a spline-laplacian algorithm. The change in scalp potential topography from spike onset to peak, suggests the contribution of several intracranial generators, with different kinetics of activation and significant overlap. ICA was able to separate the major contributors to frontal spikes and consistently revealed an early activating group of components over the occipital areas in all the patients. The local origin of these early potentials was established by the spline-laplacian montage. Frontal spikes in PS are consistently associated with early and unilateral occipital lobe activation, suggesting a postero-anterior spike propagation. Frontal spikes in the PS represent a secondary activation triggered by occipital interictal discharges and do not represent an independent focus.

EEG Controlled Wheelchair

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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.

Comparative sensitivity of quantitative EEG (QEEG) spectrograms for detecting seizure subtypes.

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Goenka, Ajay; Boro, Alexis; Yozawitz, Elissa

2018-02-01

To assess the sensitivity of Persyst version 12 QEEG spectrograms to detect focal, focal with secondarily generalized, and generalized onset seizures. A cohort of 562 seizures from 58 patients was analyzed. Successive recordings with 2 or more seizures during continuous EEG monitoring for clinical indications in the ICU or EMU between July 2016 and January 2017 were included. Patient ages ranged from 5 to 64 years (mean = 36 years). There were 125 focal seizures, 187 secondarily generalized and 250 generalized seizures from 58 patients analyzed. Seizures were identified and classified independently by two epileptologists. A correlate to the seizure pattern in the raw EEG was sought in the QEEG spectrograms in 4-6 h EEG epochs surrounding the identified seizures. A given spectrogram was interpreted as indicating a seizure, if at the time of a seizure it showed a visually significant departure from the pre-event baseline. Sensitivities for seizure detection using each spectrogram were determined for each seizure subtype. Overall sensitivities of the QEEG spectrograms for detecting seizures ranged from 43% to 72%, with highest sensitivity (402/562,72%) by the seizure detection trend. The asymmetry spectrogram had the highest sensitivity for detecting focal seizures (117/125,94%). The FFT spectrogram was most sensitive for detecting secondarily generalized seizures (158/187, 84%). The seizure detection trend was the most sensitive for generalized onset seizures (197/250,79%). Our study suggests that different seizure types have specific patterns in the Persyst QEEG spectrograms. Identifying these patterns in the EEG can significantly increase the sensitivity for seizure identification. Copyright © 2018 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Robot-Aided Upper-Limb Rehabilitation Based on Motor Imagery EEG

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Baoguo Xu

2011-09-01

Full Text Available Stroke is a leading cause of disability worldwide. In this paper, a novel robot‐assisted rehabilitation system based on motor imagery electroencephalography (EEG is developed for regular training of neurological rehabilitation for upper limb stroke patients. Firstly, three‐dimensional animation was used to guide the patient image the upper limb movement and EEG signals were acquired by EEG amplifier. Secondly, eigenvectors were extracted by harmonic wavelet transform (HWT and linear discriminant analysis (LDA classifier was utilized to classify the pattern of the left and right upper limb motor imagery EEG signals. Finally, PC triggered the upper limb rehabilitation robot to perform motor therapy and gave the virtual feedback. Using this robot‐assisted upper limb rehabilitation system, the patientʹs EEG of upper limb movement imagination is translated to control rehabilitation robot directly. Consequently, the proposed rehabilitation system can fully explore the patientʹs motivation and attention and directly facilitate upper limb post‐stroke rehabilitation therapy. Experimental results on unimpaired participants were presented to demonstrate the feasibility of the rehabilitation system. Combining robot‐assisted training with motor imagery‐ based BCI will make future rehabilitation therapy more effective. Clinical testing is still required for further proving this assumption.

Long-term EEG in patients with the ring chromosome 20 epilepsy syndrome.

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Freire de Moura, Maria; Flores-Guevara, Roberto; Gueguen, Bernard; Biraben, Arnaud; Renault, Francis

2016-05-01

The recognizable electroencephalography (EEG) pattern of ring chromosome 20 epilepsy syndrome can be missing in patients with r(20) chromosomal anomaly, and may be found in patients with frontal lobe epilepsy of other origin. This study aims to search for more specific EEG signs by using long-term recordings and measuring the duration of paroxysmal anomalies. The series included 12 adult patients with r(20) anomaly, and 12 controls without any chromosomal aberration. We measured the duration of every paroxysmal burst and calculated the sum of their durations for each long-term EEG recording. We compared patients to controls using the Mann-Whitney U-test. Every patient showed long-lasting paroxysmal EEG bursts, up to 60 min; controls did not show any bursts longer than 60 s (p < 0.0001). The total duration of paroxysmal anomalies was significantly longer in patients (31-692 min) compared to controls (0-48 min) (p < 0.0001). Thus, long-term recordings enhance the contribution of EEG methods for characterizing the ring 20 chromosome epilepsy syndrome. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

Topographical characteristics and principal component structure of the hypnagogic EEG.

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Tanaka, H; Hayashi, M; Hori, T

1997-07-01

The purpose of the present study was to identify the dominant topographic components of electroencephalographs (EEG) and their behavior during the waking-sleeping transition period. Somnography of nocturnal sleep was recorded on 10 male subjects. Each recording, from "lights-off" to 5 minutes after the appearance of the first sleep spindle, was analyzed. The typical EEG patterns during hypnagogic period were classified into nine EEG stages. Topographic maps demonstrated that the dominant areas of alpha-band activity moved from the posterior areas to anterior areas along the midline of the scalp. In delta-, theta-, and sigma-band activities, the differences of EEG amplitude between the focus areas (the dominant areas) and the surrounding areas increased as a function of EEG stage. To identify the dominant topographic components, a principal component analysis was carried out on a 12-channel EEG data set for each of six frequency bands. The dominant areas of alpha 2- (9.6-11.4 Hz) and alpha 3- (11.6-13.4 Hz) band activities moved from the posterior to anterior areas, respectively. The distribution of alpha 2-band activity on the scalp clearly changed just after EEG stage 3 (alpha intermittent, < 50%). On the other hand, alpha 3-band activity became dominant in anterior areas after the appearance of vertex sharp-wave bursts (EEG stage 7). For the sigma band, the amplitude of extensive areas from the frontal pole to the parietal showed a rapid rise after the onset of stage 7 (the appearance of vertex sharp-wave bursts). Based on the results, sleep onset process probably started before the onset of sleep stage 1 in standard criteria. On the other hand, the basic sleep process may start before the onset of sleep stage 2 or the manually scored spindles.

Correlation of EEG with neuropsychological status in children with epilepsy.

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Hsu, David A; Rayer, Katherine; Jackson, Daren C; Stafstrom, Carl E; Hsu, Murielle; Ferrazzano, Peter A; Dabbs, Kevin; Worrell, Gregory A; Jones, Jana E; Hermann, Bruce P

2016-02-01

To determine correlations of the EEG frequency spectrum with neuropsychological status in children with idiopathic epilepsy. Forty-six children ages 8-18 years old with idiopathic epilepsy were retrospectively identified and analyzed for correlations between EEG spectra and neuropsychological status using multivariate linear regression. In addition, the theta/beta ratio, which has been suggested as a clinically useful EEG marker of attention-deficit hyperactivity disorder (ADHD), and an EEG spike count were calculated for each subject. Neuropsychological status was highly correlated with posterior alpha (8-15 Hz) EEG activity in a complex way, with both positive and negative correlations at lower and higher alpha frequency sub-bands for each cognitive task in a pattern that depends on the specific cognitive task. In addition, the theta/beta ratio was a specific but insensitive indicator of ADHD status in children with epilepsy; most children both with and without epilepsy have normal theta/beta ratios. The spike count showed no correlations with neuropsychological status. (1) The alpha rhythm may have at least two sub-bands which serve different purposes. (2) The theta/beta ratio is not a sensitive indicator of ADHD status in children with epilepsy. (3) The EEG frequency spectrum correlates more robustly with neuropsychological status than spike count analysis in children with idiopathic epilepsy. (1) The role of posterior alpha rhythms in cognition is complex and can be overlooked if EEG spectral resolution is too coarse or if neuropsychological status is assessed too narrowly. (2) ADHD in children with idiopathic epilepsy may involve different mechanisms from those in children without epilepsy. (3) Reliable correlations with neuropsychological status require longer EEG samples when using spike count analysis than when using frequency spectra. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights

Odds Ratio Product of Sleep EEG as a Continuous Measure of Sleep State

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Younes, Magdy; Ostrowski, Michele; Soiferman, Marc; Younes, Henry; Younes, Mark; Raneri, Jill; Hanly, Patrick

2015-01-01

Study Objectives: To develop and validate an algorithm that provides a continuous estimate of sleep depth from the electroencephalogram (EEG). Design: Retrospective analysis of polysomnograms. Setting: Research laboratory. Participants: 114 patients who underwent clinical polysomnography in sleep centers at the University of Manitoba (n = 58) and the University of Calgary (n = 56). Interventions: None. Measurements and Results: Power spectrum of EEG was determined in 3-second epochs and divided into delta, theta, alpha-sigma, and beta frequency bands. The range of powers in each band was divided into 10 aliquots. EEG patterns were assigned a 4-digit number that reflects the relative power in the 4 frequency ranges (10,000 possible patterns). Probability of each pattern occurring in 30-s epochs staged awake was determined, resulting in a continuous probability value from 0% to 100%. This was divided by 40 (% of epochs staged awake) producing the odds ratio product (ORP), with a range of 0–2.5. In validation testing, average ORP decreased progressively as EEG progressed from wakefulness (2.19 ± 0.29) to stage N3 (0.13 ± 0.05). ORP sleep and ORP > 2.0 predicted wakefulness in > 95% of 30-s epochs. Epochs with intermediate ORP occurred in unstable sleep with a high arousal index (> 70/h) and were subject to much interrater scoring variability. There was an excellent correlation (r2 = 0.98) between ORP in current 30-s epochs and the likelihood of arousal or awakening occurring in the next 30-s epoch. Conclusions: Our results support the use of the odds ratio product (ORP) as a continuous measure of sleep depth. Citation: Younes M, Ostrowski M, Soiferman M, Younes H, Younes M, Raneri J, Hanly P. Odds ratio product of sleep EEG as a continuous measure of sleep state. SLEEP 2015;38(4):641–654. PMID:25348125

Benign fibroosseous lesions

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Cansu Köseoğlu Seçgin

2016-05-01

Full Text Available Benign fibroosseous lesions represent a group of lesions that share the same basic evolutive mechanism and are characterized by replacement of normal bone with a fibrous connective tissue that gradually undergoes mineralization. These lesions are presented by a variety of diseases including developmental, reactive-dysplastic processes and neoplasms. Depending on the nature and amount of calcified tissue, they can be observed as radiolucent, mixed or radiopaque. Their radiographic features could be well-defined or indistinguishable from the surrounding bone tissue. They can be asymptomatic as in osseous dysplasias and can be detected incidentally on radiographs, or they can lead to expansion in the affected bone as in ossifying fibroma. All fibroosseous lesions seen in the jaws and face are variations of the same histological pattern. Therefore, detailed clinical and radiographic evaluation in differential diagnosis is important. In this review, fibroosseous benign lesions are classified as osseous dysplasia, fibrous dysplasia and fibroosseous tumors; and radiographic features and differential diagnosis of these lesions are reviewed taking into account this classification.

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

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I. V. Kichuk

2016-01-01

Full Text Available Electroencephalogram (EEG is an important tool to study brain function. EEG can evaluate the current functional state of the brain with high temporal resolution and identify metabolic and ion disorders that cannot be detected by magnetic resonance imaging.Objective: to analyze the relationship between some neurophysiological and biochemical parameters with a Neuro-KM hardware-software complex for the topographic mapping of brain electrical activity.Patients and methods. 75 patients with depression, 101 with anxiety disorders (AD, and 86 control individuals were examined. EEG spectrum and coherence changes were estimated in the depression and AD groups versus the control group. Correlation analysis of EEG indices and blood serotonin concentrations was carried out.Results and discussion. The patients with depression and those with AD as compared to the controls were observed to have similar EEG spectral changes in the beta band. Coherence analysis in the beta-band showed that both disease groups versus the control group had oppositely directed changes: a reduction in intra- and interhemispheric coherence for depression and its increase for AD (p < 0.001. That in the theta and alpha bands revealed that both disease groups had unidirectional interhemispheric coherence changes: a decrease in integration in the alpha band and its increase in the theta and delta bands in the depression and AD groups (p < 0.05 and multidirectional changes in intrahemispheric coherence: its reduction in the depression group and an increase in the AD group (p < 0.05. Correlation analysis of EEG parameters and platelet serotonin concentrations showed opposite correlations of serotonin concentrations and EEG percentage power in the theta and beta bands. When there were higher serotonin concentrations in the patients with depression, EEG demonstrated a preponderance of a synchronization pattern; when these were in the patients with AD, there was a predominance

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

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

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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.

EEG-fMRI correlation patterns in the presurgical evaluation of focal epilepsy: A comparison with electrocorticographic data and surgical outcome measures

NARCIS (Netherlands)

van Houdt, P.J.; de Munck, J.C.; Leijten, F.S.S.; Huiskamp, G.J.M.; Colon, A.J.; Boon, P.A.J.M.; Ossenblok, P.P.W.

2013-01-01

EEG-correlated functional MRI (EEG-fMRI) visualizes brain regions associated with interictal epileptiform discharges (IEDs). This technique images the epileptiform network, including multifocal, superficial and deeply situated cortical areas. To understand the role of EEG-fMRI in presurgical

Prognostic value of electroencephalography (EEG) after out-of-hospital cardiac arrest in successfully resuscitated patients used in daily clinical practice.

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Søholm, Helle; Kjær, Troels Wesenberg; Kjaergaard, Jesper; Cronberg, Tobias; Bro-Jeppesen, John; Lippert, Freddy K; Køber, Lars; Wanscher, Michael; Hassager, Christian

2014-11-01

Out-of-hospital cardiac arrest (OHCA) is associated with a poor prognosis and predicting outcome is complex with neurophysiological testing and repeated clinical neurological examinations as key components of the assessment. In this study we examine the association between different electroencephalography (EEG) patterns and mortality in a clinical cohort of OHCA-patients. From 2002 to 2011 consecutive patients were admitted to an intensive-care-unit after resuscitation from OHCA. Utstein-criteria for pre-hospital data and review of individual patients' charts for post-resuscitation care were used. EEG reports were analysed according to the 2012 American Clinical Neurophysiology Society's guidelines. A total of 1076 patients were included, and EEG was performed in 20% (n=219) with a median of 3(IQR 2-4) days after OHCA. Rhythmic Delta Activity (RDA) was found in 71 patients (36%) and Periodic Discharges (PD) in 100 patients (45%). Background EEG frequency of Alpha+ or Theta was noted in 107 patients (49%), and change in cerebral EEG activity to stimulation (reactivity) was found in 38 patients (17%). Suppression (all activity <10 μV) was found in 26 (12%) and burst-suppression in 17 (8%) patients. A favourable EEG pattern (reactivity, favourable background frequency and RDA) was independently associated with reduced mortality with hazard ratio (HR) 0.43 (95%CI: 0.24-0.76), p=0.004 (false positive rate: 31%) and a non-favourable EEG pattern (no reactivity, unfavourable background frequency, and PD, suppressed voltage or burst-suppression) was associated with higher mortality (HR=1.62(1.09-2.41), p=0.02) after adjustment for known prognostic factors (false positive rate: 9%). EEG may be useful in work-up in prognostication of patients with OHCA. Findings such as Rhythmic Delta Activity (RDA) seem to be associated with a better prognosis, whereas suppressed voltage and burst-suppression patterns were associated with poor prognosis. Copyright © 2014 Elsevier Ireland

Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms.

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Liu, Rensong; Zhang, Zhiwen; Duan, Feng; Zhou, Xin; Meng, Zixuan

2017-01-01

Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K -nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance.

Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms

Science.gov (United States)

Zhang, Zhiwen; Duan, Feng; Zhou, Xin; Meng, Zixuan

2017-01-01

Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K-nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance. PMID:28874909

Automatic burst detection for the EEG of the preterm infant

NARCIS (Netherlands)

Jennekens, W.; Ruijs, L.S.; Lommen, Ch.M.L.; Niemarkt, H.J.; Pasman, J.W.; van Kranen-Mastenbroek, V.H.J.M.; Wijn, P.F.F.; van Pul, C.; Andriessen, P.

2011-01-01

To aid with prognosis and stratification of clinical treatment for preterm infants, a method for automated detection of bursts, interburst-intervals (IBIs) and continuous patterns in the electroencephalogram (EEG) is developed. Results are evaluated for preterm infants with normal neurological

Use of FDG-PET in differentiating benign from malignant compression fractures

International Nuclear Information System (INIS)

Bredella, Miriam A.; Essary, Brendan; Torriani, Martin; Ouellette, Hugue A.; Palmer, William E.

2008-01-01

The objective was to evaluate the use of fluorodeoxyglucose positron emission tomography (FDG-PET) in differentiating benign from malignant compression fractures. In a retrospective analysis, we identified 33 patients with 43 compression fractures who underwent FDG-PET. On FDG-PET the uptake pattern was recorded qualitatively and semiquantitatively and fractures were categorized as benign or malignant. Standardized uptake values (SUV) were obtained. MRI, CT, and biopsy results as well as clinical follow-up for 1-3 years served as standards of reference. The Student's t test was used to determine whether there was a statistically significant difference between the SUV for benign and malignant compression fractures. There were 14 malignant and 29 benign compression fractures, including 5 acute benign fractures. On FDG-PET, 5 benign fractures were falsely classified as malignant (false-positive). Three of these patients underwent prior treatment with bone marrow-stimulating agents. There were two false-negative results. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of FDG-PET in differentiating benign from malignant compression fractures were 86%, 83%, 84%, 71%, and 92% respectively. The difference between SUV values of benign and malignant fractures was statistically significant (1.9 ± 0.97 for benign and 3.9 ± 1.52 for malignant fractures, p < 0.001). SUV of benign acute and chronic fractures were not statistically significant. Fluorodeoxyglucose positron emission tomography is useful in differentiating benign from malignant compression fractures. Therapy with bone marrow-stimulating agents can mimic malignant involvement. (orig.)

Epileptic seizure detection in EEG signal with GModPCA and support vector machine.

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Jaiswal, Abeg Kumar; Banka, Haider

2017-01-01

Epilepsy is one of the most common neurological disorders caused by recurrent seizures. Electroencephalograms (EEGs) record neural activity and can detect epilepsy. Visual inspection of an EEG signal for epileptic seizure detection is a time-consuming process and may lead to human error; therefore, recently, a number of automated seizure detection frameworks were proposed to replace these traditional methods. Feature extraction and classification are two important steps in these procedures. Feature extraction focuses on finding the informative features that could be used for classification and correct decision-making. Therefore, proposing effective feature extraction techniques for seizure detection is of great significance. Principal Component Analysis (PCA) is a dimensionality reduction technique used in different fields of pattern recognition including EEG signal classification. Global modular PCA (GModPCA) is a variation of PCA. In this paper, an effective framework with GModPCA and Support Vector Machine (SVM) is presented for epileptic seizure detection in EEG signals. The feature extraction is performed with GModPCA, whereas SVM trained with radial basis function kernel performed the classification between seizure and nonseizure EEG signals. Seven different experimental cases were conducted on the benchmark epilepsy EEG dataset. The system performance was evaluated using 10-fold cross-validation. In addition, we prove analytically that GModPCA has less time and space complexities as compared to PCA. The experimental results show that EEG signals have strong inter-sub-pattern correlations. GModPCA and SVM have been able to achieve 100% accuracy for the classification between normal and epileptic signals. Along with this, seven different experimental cases were tested. The classification results of the proposed approach were better than were compared the results of some of the existing methods proposed in literature. It is also found that the time and space

Effects of action observation therapy on hand dexterity and EEG-based cortical activation patterns in patients with post-stroke hemiparesis.

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Kuk, Eun-Ju; Kim, Jong-Man; Oh, Duck-Won; Hwang, Han-Jeong

2016-10-01

Previous reports have suggested that action observation training (AOT) is beneficial in enhancing the early learning of new motor tasks; however, EEG-based investigation has received little attention for AOT. The purpose of this study was to illustrate the effects of AOT on hand dexterity and cortical activation in patients with post-stroke hemiparesis. Twenty patients with post-stroke hemiparesis were randomly divided into either the experimental group (EG) or control group (CG), with 10 patients in each group. Prior to the execution of motor tasks (carrying wooden blocks from one box to another), subjects in the EG and CG observed a video clip displaying the execution of the same motor task and pictures showing landscapes, respectively. Outcome measures included the box and block test (BBT) to evaluate hand dexterity and EEG-based brain mapping to detect changes in cortical activation. The BBT scores (EG: 20.50 ± 6.62 at pre-test and 24.40 ± 5.42 at post-test; CG: 20.20 ± 6.12 at pre-test and 20.60 ± 7.17 at post-test) revealed significant main effects for the time and group and significant time-by-group interactions (p < 0.05). For the subjects in the EG, topographical representations obtained with the EEG-based brain mapping system were different in each session of the AOT and remarkable changes occurred from the 2nd session of AOT. Furthermore, the middle frontal gyrus was less active at post-test than at pre-test. These findings support that AOT may be beneficial in altering cortical activation patterns and hand dexterity.

The early electroclinical manifestations of infantile spasms: A video EEG study

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Mary Iype

2016-01-01

Full Text Available Purpose: Infantile spasms are described as flexor extensor and mixed; but more features of their semiology and ictal electroencephalography (EEG changes are sparse in the literature. The purpose of the study was to describe the clinical and ictal video-EEG characteristics of consecutive cases with infantile spasms and to try to find an association with the etiology. Materials and Methods: The clinical phenomenology and EEG characteristics on video-EEG were analyzed in 16 babies with infantile spasms. Results: A total of 869 spasms were reviewed. Nine (56.3% showed focal seizures at least once during the recording and 1 (6.3% had multifocal myoclonus in addition to the spasms. The duration of the cluster and interval between spasms was totally variable in all patients. Lateralizing phenomena were present in at least some of the spasms in all patients. Unilateral manual automatism in the form of holding the pinna was noted in three patients following the spasm. The ictal EEG activity in the majority (75% was the slow wave. Four (25% showed fast generalized spindle-like ictal discharges. Spikes, spike and wave activity, or electrodecremental pattern alone during the ictus was seen in none. On bivariate analysis, no factor noted on the video EEG had association with the etiology. Conclusion: Infantile spasms could be associated with focal and other seizures, has unique, non-uniform and variable semiology from patient to patient. The ictal EEG manifestation in the majority (75% of our patients was the slow wave transient with 25% showing generalized fast spindle-like activity.

Increased EEG sigma and beta power during NREM sleep in primary insomnia.

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Spiegelhalder, Kai; Regen, Wolfram; Feige, Bernd; Holz, Johannes; Piosczyk, Hannah; Baglioni, Chiara; Riemann, Dieter; Nissen, Christoph

2012-12-01

The hyperarousal model of primary insomnia suggests that a deficit of attenuating arousal during sleep might cause the experience of non-restorative sleep. In the current study, we examined EEG spectral power values for standard frequency bands as indices of cortical arousal and sleep protecting mechanisms during sleep in 25 patients with primary insomnia and 29 good sleeper controls. Patients with primary insomnia demonstrated significantly elevated spectral power values in the EEG beta and sigma frequency band during NREM stage 2 sleep. No differences were observed in other frequency bands or during REM sleep. Based on prior studies suggesting that EEG beta activity represents a marker of cortical arousal and EEG sleep spindle (sigma) activity is an index of sleep protective mechanisms, our findings may provide further evidence for the concept that a simultaneous activation of wake-promoting and sleep-protecting neural activity patterns contributes to the experience of non-restorative sleep in primary insomnia. Copyright © 2012 Elsevier B.V. All rights reserved.

Joint optimization of algorithmic suites for EEG analysis.

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Santana, Eder; Brockmeier, Austin J; Principe, Jose C

2014-01-01

Electroencephalogram (EEG) data analysis algorithms consist of multiple processing steps each with a number of free parameters. A joint optimization methodology can be used as a wrapper to fine-tune these parameters for the patient or application. This approach is inspired by deep learning neural network models, but differs because the processing layers for EEG are heterogeneous with different approaches used for processing space and time. Nonetheless, we treat the processing stages as a neural network and apply backpropagation to jointly optimize the parameters. This approach outperforms previous results on the BCI Competition II - dataset IV; additionally, it outperforms the common spatial patterns (CSP) algorithm on the BCI Competition III dataset IV. In addition, the optimized parameters in the architecture are still interpretable.

High-Frequency EEG Variations in Children with Autism Spectrum Disorder during Human Faces Visualization

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Celina A. Reis Paula

2017-01-01

Full Text Available Autism spectrum disorder (ASD is a neuropsychiatric disorder characterized by the impairment in the social reciprocity, interaction/language, and behavior, with stereotypes and signs of sensory function deficits. Electroencephalography (EEG is a well-established and noninvasive tool for neurophysiological characterization and monitoring of the brain electrical activity, able to identify abnormalities related to frequency range, connectivity, and lateralization of brain functions. This research aims to evidence quantitative differences in the frequency spectrum pattern between EEG signals of children with and without ASD during visualization of human faces in three different expressions: neutral, happy, and angry. Quantitative clinical evaluations, neuropsychological evaluation, and EEG of children with and without ASD were analyzed paired by age and gender. The results showed stronger activation in higher frequencies (above 30 Hz in frontal, central, parietal, and occipital regions in the ASD group. This pattern of activation may correlate with developmental characteristics in the children with ASD.

Resting State EEG in Children With Learning Disabilities: An Independent Component Analysis Approach.

Science.gov (United States)

Jäncke, Lutz; Alahmadi, Nsreen

2016-01-01

In this study, the neurophysiological underpinnings of learning disabilities (LD) in children are examined using resting state EEG. We were particularly interested in the neurophysiological differences between children with learning disabilities not otherwise specified (LD-NOS), learning disabilities with verbal disabilities (LD-Verbal), and healthy control (HC) children. We applied 2 different approaches to examine the differences between the different groups. First, we calculated theta/beta and theta/alpha ratios in order to quantify the relationship between slow and fast EEG oscillations. Second, we used a recently developed method for analyzing spectral EEG, namely the group independent component analysis (gICA) model. Using these measures, we identified substantial differences between LD and HC children and between LD-NOS and LD-Verbal children in terms of their spectral EEG profiles. We obtained the following findings: (a) theta/beta and theta/alpha ratios were substantially larger in LD than in HC children, with no difference between LD-NOS and LD-Verbal children; (b) there was substantial slowing of EEG oscillations, especially for gICs located in frontal scalp positions, with LD-NOS children demonstrating the strongest slowing; (c) the estimated intracortical sources of these gICs were mostly located in brain areas involved in the control of executive functions, attention, planning, and language; and (d) the LD-Verbal children demonstrated substantial differences in EEG oscillations compared with LD-NOS children, and these differences were localized in language-related brain areas. The general pattern of atypical neurophysiological activation found in LD children suggests that they suffer from neurophysiological dysfunction in brain areas involved with the control of attention, executive functions, planning, and language functions. LD-Verbal children also demonstrate atypical activation, especially in language-related brain areas. These atypical

The effect of hypobaric hypoxia on multichannel EEG signal complexity.

Science.gov (United States)

Papadelis, Christos; Kourtidou-Papadeli, Chrysoula; Bamidis, Panagiotis D; Maglaveras, Nikos; Pappas, Konstantinos

2007-01-01

The objective of this study was the development and evaluation of nonlinear electroencephalography parameters which assess hypoxia-induced EEG alterations, and describe the temporal characteristics of different hypoxic levels' residual effect upon the brain electrical activity. Multichannel EEG, pO2, pCO2, ECG, and respiration measurements were recorded from 10 subjects exposed to three experimental conditions (100% oxygen, hypoxia, recovery) at three-levels of reduced barometric pressure. The mean spectral power of EEG under each session and altitude were estimated for the standard bands. Approximate Entropy (ApEn) of EEG segments was calculated, and the ApEn's time-courses were smoothed by a moving average filter. On the smoothed diagrams, parameters were defined. A significant increase in total power and power of theta and alpha bands was observed during hypoxia. Visual interpretation of ApEn time-courses revealed a characteristic pattern (decreasing during hypoxia and recovering after oxygen re-administration). The introduced qEEG parameters S1 and K1 distinguished successfully the three hypoxic conditions. The introduced parameters based on ApEn time-courses are assessing reliably and effectively the different hypoxic levels. ApEn decrease may be explained by neurons' functional isolation due to hypoxia since decreased complexity corresponds to greater autonomy of components, although this interpretation should be further supported by electrocorticographic animal studies. The introduced qEEG parameters seem to be appropriate for assessing the hypoxia-related neurophysiological state of patients in the hyperbaric chambers in the treatment of decompression sickness, carbon dioxide poisoning, and mountaineering.

Benign positional vertigo

Science.gov (United States)

Vertigo - positional; Benign paroxysmal positional vertigo; BPPV: dizziness- positional ... Benign positional vertigo is also called benign paroxysmal positional vertigo (BPPV). It is caused by a problem in the inner ear. ...

Distinct iEEG activity patterns in temporal-limbic and prefrontal sites induced by emotional intentionality.

Science.gov (United States)

Singer, Neomi; Podlipsky, Ilana; Esposito, Fabrizio; Okon-Singer, Hadas; Andelman, Fani; Kipervasser, Svetlana; Neufeld, Miri Y; Goebel, Rainer; Fried, Itzhak; Hendler, Talma

2014-11-01

Our emotions tend to be directed towards someone or something. Such emotional intentionality calls for the integration between two streams of information; abstract hedonic value and its associated concrete content. In a previous functional magnetic resonance imaging (fMRI) study we found that the combination of these two streams, as modeled by short emotional music excerpts and neutral film clips, was associated with synergistic activation in both temporal-limbic (TL) and ventral-lateral PFC (vLPFC) regions. This additive effect implies the integration of domain-specific 'affective' and 'cognitive' processes. Yet, the low temporal resolution of the fMRI limits the characterization of such cross-domain integration. To this end, we complemented the fMRI data with intracranial electroencephalogram (iEEG) recordings from twelve patients with intractable epilepsy. As expected, the additive fMRI activation in the amygdala and vLPFC was associated with distinct spatio-temporal iEEG patterns among electrodes situated within the vicinity of the fMRI activation foci. On the one hand, TL channels exhibited a transient (0-500 msec) increase in gamma power (61-69 Hz), possibly reflecting initial relevance detection or hedonic value tagging. On the other hand, vLPFC channels showed sustained (1-12 sec) suppression of low frequency power (2.3-24 Hz), possibly mediating changes in gating, enabling an on-going readiness for content-based processing of emotionally tagged signals. Moreover, an additive effect in delta-gamma phase-amplitude coupling (PAC) was found among the TL channels, possibly reflecting the integration between distinct domain specific processes. Together, this study provides a multi-faceted neurophysiological signature for computations that possibly underlie emotional intentionality in humans. Copyright © 2014 Elsevier Ltd. All rights reserved.

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...

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.

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

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

Directory of Open Access Journals (Sweden)

I. A. Sadekov

2017-01-01

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

SCOPE-mTL: A non-invasive tool for identifying and lateralizing mesial temporal lobe seizures prior to scalp EEG ictal onset.

Science.gov (United States)

Lam, Alice D; Maus, Douglas; Zafar, Sahar F; Cole, Andrew J; Cash, Sydney S

2017-09-01

In mesial temporal lobe (mTL) epilepsy, seizure onset can precede the appearance of a scalp EEG ictal pattern by many seconds. The ability to identify this early, occult mTL seizure activity could improve lateralization and localization of mTL seizures on scalp EEG. Using scalp EEG spectral features and machine learning approaches on a dataset of combined scalp EEG and foramen ovale electrode recordings in patients with mTL epilepsy, we developed an algorithm, SCOPE-mTL, to detect and lateralize early, occult mTL seizure activity, prior to the appearance of a scalp EEG ictal pattern. Using SCOPE-mTL, 73% of seizures with occult mTL onset were identified as such, and no seizures that lacked an occult mTL onset were identified as having one. Predicted mTL seizure onset times were highly correlated with actual mTL seizure onset times (r=0.69). 50% of seizures with early mTL onset were lateralizable prior to scalp ictal onset, with 94% accuracy. SCOPE-mTL can identify and lateralize mTL seizures prior to scalp EEG ictal onset, with high sensitivity, specificity, and accuracy. Quantitative analysis of scalp EEG can provide important information about mTL seizures, even in the absence of a visible scalp EEG ictal correlate. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. 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

Prevalence, phenotype and inheritance of benign neutropenia in Arabs

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Nagelkerke Nicollas

2009-03-01

Full Text Available Abstract Background Benign neutropenia, i.e., neutropenia not associated with an increased risk of infection, may result in serious medical consequences when a 'standard' definition of neutropenia (absolute neutrophil count (ANC 9cells/L is universally applied to all races. The aims of this study were to determine the prevalence of benign neutropenia among healthy Arabs and evaluate its mode of inheritance. Methods ANCs were studied prospectively amongst a healthy indigenous population (n = 1032 from the United Arab Emirates undergoing a nation-wide sickle-cell and thalassemia screening program. The mean neutrophil count and the prevalence of benign neutropenia were compared by age, sex and amongst various tribes. Results The mean neutrophil count (× 109cells/L was 3.3 (range 0.95–7.6. Benign neutropenia was present in 110 (10.7% subjects of whom 24 (2.3% individuals had moderate neutropenia (ANC 0.5 – 1.0 × 109 cells/L. In the 22 tribe-family groups, the prevalence of benign neutropenia varied between 0% and 38%. Benign neutropenia showed no difference in the frequency amongst the sexes (p = 0.23 and it was independent of age (Spearman's rho = 0.05, p = 0.13. The age-related mean neutrophil count was the lowest in Arabs when compared with other ethnic groups (Blacks, Europeans and Mexicans. The inheritance of benign neutropenia was consistent with an autosomal dominant pattern; however, the diversity of observed phenotypes suggested the presence of more than one genetic variant for this trait. Conclusion Arabs have a high prevalence of benign neutropenia that may be inherited as an autosomal dominant trait.

Improving Cross-Day EEG-Based Emotion Classification Using Robust Principal Component Analysis

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Yuan-Pin Lin

2017-07-01

Full Text Available Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability, arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18 and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40% as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing neither achieved the cross-day classification (the accuracy was around chance level nor replicated the encouraging improvement due to the inter-day EEG variability. This result

Driving behavior recognition using EEG data from a simulated car-following experiment.

Science.gov (United States)

Yang, Liu; Ma, Rui; Zhang, H Michael; Guan, Wei; Jiang, Shixiong

2017-11-23

Driving behavior recognition is the foundation of driver assistance systems, with potential applications in automated driving systems. Most prevailing studies have used subjective questionnaire data and objective driving data to classify driving behaviors, while few studies have used physiological signals such as electroencephalography (EEG) to gather data. To bridge this gap, this paper proposes a two-layer learning method for driving behavior recognition using EEG data. A simulated car-following driving experiment was designed and conducted to simultaneously collect data on the driving behaviors and EEG data of drivers. The proposed learning method consists of two layers. In Layer I, two-dimensional driving behavior features representing driving style and stability were selected and extracted from raw driving behavior data using K-means and support vector machine recursive feature elimination. Five groups of driving behaviors were classified based on these two-dimensional driving behavior features. In Layer II, the classification results from Layer I were utilized as inputs to generate a k-Nearest-Neighbor classifier identifying driving behavior groups using EEG data. Using independent component analysis, a fast Fourier transformation, and linear discriminant analysis sequentially, the raw EEG signals were processed to extract two core EEG features. Classifier performance was enhanced using the adaptive synthetic sampling approach. A leave-one-subject-out cross validation was conducted. The results showed that the average classification accuracy for all tested traffic states was 69.5% and the highest accuracy reached 83.5%, suggesting a significant correlation between EEG patterns and car-following behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

Human brain networks in physiological aging: a graph theoretical analysis of cortical connectivity from EEG data.

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Vecchio, Fabrizio; Miraglia, Francesca; Bramanti, Placido; Rossini, Paolo Maria

2014-01-01

Modern analysis of electroencephalographic (EEG) rhythms provides information on dynamic brain connectivity. To test the hypothesis that aging processes modulate the brain connectivity network, EEG recording was conducted on 113 healthy volunteers. They were divided into three groups in accordance with their ages: 36 Young (15-45 years), 46 Adult (50-70 years), and 31 Elderly (>70 years). To evaluate the stability of the investigated parameters, a subgroup of 10 subjects underwent a second EEG recording two weeks later. Graph theory functions were applied to the undirected and weighted networks obtained by the lagged linear coherence evaluated by eLORETA on cortical sources. EEG frequency bands of interest were: delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-40 Hz). The spectral connectivity analysis of cortical sources showed that the normalized Characteristic Path Length (λ) presented the pattern Young > Adult>Elderly in the higher alpha band. Elderly also showed a greater increase in delta and theta bands than Young. The correlation between age and λ showed that higher ages corresponded to higher λ in delta and theta and lower in the alpha2 band; this pattern reflects the age-related modulation of higher (alpha) and decreased (delta) connectivity. The Normalized Clustering coefficient (γ) and small-world network modeling (σ) showed non-significant age-modulation. Evidence from the present study suggests that graph theory can aid in the analysis of connectivity patterns estimated from EEG and can facilitate the study of the physiological and pathological brain aging features of functional connectivity networks.

Marked EEG worsening following Levetiracetam overdose: How a pharmacological issue can confound coma prognosis.

Science.gov (United States)

Bouchier, Baptiste; Demarquay, Geneviève; Guérin, Claude; André-Obadia, Nathalie; Gobert, Florent

2017-01-01

Levetiracetam is an anti-epileptic drug commonly used in intensive care when seizure is suspected as a possible cause of coma. We propose to question the cofounding effect of Levetiracetam during the prognostication process in a case of anoxic coma. We report the story of a young woman presenting a comatose state following a hypoxic cardiac arrest. After a first EEG presenting an intermediate EEG pattern, a seizure suspicion led to prescribe Levetiracetam. The EEG showed then the appearance of burst suppression, which was compatible with a very severe pattern of post-anoxic coma. This aggravation was in fact related to an overdose of Levetiracetam (the only medication introduced recently) and was reversible after Levetiracetam cessation. The increased plasmatic dosages of Levetiracetam confirming this overdose could have been favoured by a moderate reduction of renal clearance, previously underestimated because of a low body-weight. This EEG dynamic was unexpected under Levetiracetam and could sign a functional instability after anoxia. Burst suppression is classically observed with high doses of anaesthetics, but is not expected after a minor anti-epileptic drug. This report proposes that Levetiracetam tolerance might not be straightforward after brain lesions and engages us to avoid confounding factors during the awakening prognostication, which is mainly based on the severity of the EEG. Hence, prognosis should not be decided on an isolated parameter, especially if the dynamic is atypical after a new prescription, even for well-known drugs. For any suspicion, the drug's dosage and replacement should be managed before any premature care's withdrawal. Copyright © 2016 Elsevier B.V. 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.

Differential expression of p-ERM, a marker of cell polarity, in benign and neoplastic oviductal epithelium.

Science.gov (United States)

Ning, Gang; Bijron, Jonathan G; Yuan, Ju; Hirsch, Michelle S; McKeon, Frank D; Nucci, Marisa R; Crum, Christopher P; Xian, Wa

2013-07-01

Serous tubal intraepithelial carcinoma (STIC) is a noninvasive phase of pelvic serous cancer at risk for metastasizing. Because of its biologic significance, its accurate distinction from nonmalignant mimics is important. Loss of cell orientation is an important feature of STIC. We sought to determine whether the immunohistochemical localization of cytoskeletal-organizing proteins phospho-ezrin-radaxin-moesin (p-ERM) would be useful in making this distinction. The benign oviductal entities (normal and p53 signatures), premalignant atypias (tubal intraepithelial lesions in transition), serous intraepithelial carcinomas (STICs), and carcinomas were analyzed for 5 staining patterns and compared. Linear or uniform luminal p-ERM staining was strongly associated with benign mucosa in contrast to STICs, in which it was lost and often replaced by nonlinear or nonuniform patterns highlighting individually cell groups or single cells. Premalignant atypias were similar to benign mucosa by p-ERM staining and retained the linear luminal pattern. This study shows, for the first time, that patterns of staining for an immunohistochemical correlate of cell polarity (p-ERM) differ between STICs, their benign counterparts and premalignant atypias that do not fulfill the criteria for STICs. If confirmed, these findings warrant further analysis of indices of cell polarity as objective markers for the diagnosis and mapping of the evolution of pelvic serous precursors.

Resting and reactive frontal brain electrical activity (EEG among a non-clinical sample of socially anxious adults: Does concurrent depressive mood matter?

Directory of Open Access Journals (Sweden)

Elliott A Beaton

2008-03-01

Full Text Available Elliott A Beaton1, Louis A Schmidt2, Andrea R Ashbaugh2,5, Diane L Santesso2, Martin M Antony1,3,4, Randi E McCabe1,31Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada; 2Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario, Canada; 3Anxiety Treatment and Research Centre, St. Joseph’s Healthcare, Hamilton, Ontario, Canada; 4Department of Psychology, Ryerson University, Toronto, Ontario, Canada; 5Concordia University, Montreal, Quebec, CanadaAbstract: A number of studies have noted that the pattern of resting frontal brain electrical activity (EEG is related to individual differences in affective style in healthy infants, children, and adults and some clinical populations when symptoms are reduced or in remission. We measured self-reported trait shyness and sociability, concurrent depressive mood, and frontal brain electrical activity (EEG at rest and in anticipation of a speech task in a non-clinical sample of healthy young adults selected for high and low social anxiety. Although the patterns of resting and reactive frontal EEG asymmetry did not distinguish among individual differences in social anxiety, the pattern of resting frontal EEG asymmetry was related to trait shyness after controlling for concurrent depressive mood. Individuals who reported a higher degree of shyness were likely to exhibit greater relative right frontal EEG activity at rest. However, trait shyness was not related to frontal EEG asymmetry measured during the speech-preparation task, even after controlling for concurrent depressive mood. These findings replicate and extend prior work on resting frontal EEG asymmetry and individual differences in affective style in adults. Findings also highlight the importance of considering concurrent emotional states of participants when examining psychophysiological correlates of personality.Keywords: social anxiety, shyness, sociability

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...

Differentiation of benign and malignant solitary pulmonary nodules : value of contrast-enhanced dynamic MR imaging

International Nuclear Information System (INIS)

Kim, Jeong Ho; Kim, Hyung Jin; Han, Heon; Lee, Hong Lyeol; Kim, Kwang Ho; Suh, Chang Hae

1999-01-01

To evaluate the usefulness of contrast-enhanced dynamic MR imaging for differentiation of benign and malignant solitary pulmonary nodules (SPNs). Twenty-three patients with histologically or radiologically provened SPNs smaller than 40mm (14 benign, 9 malignant) underwent MR examination using the breath-hold fast multiplanar spoiled gradient echo (FMPSPGR) technique. Pre-enhancement MR examination was followed by serial scans obtained at one-minute intervals, beginning one-minute after the onset of bolus injection of paramagnetic contrast agent for a total of five scans. Signal intensities of SPNs were measured from pre- and post-contrast enhanced MR images and peak percentage increase in signal intensity (p%SI) was calculated. Mean percentage increase in signal intensity (m%SI) was also calculated and the time-m%SI curve was plotted. The enhancement patterns of SPNs were classified as homogeneous, peripheral rim-like, inhomogeneous, or no (or minimal) enhancement. We compared differences in p%SI, the pattern of the time-m%SI curve, and the pattern of enhancement between benign and malignant SPNs. On dynamic MR images, alignant SPNs (n=9) showed a significantly higher p%SI than benign SPNs (n=14) (malignant : mean 120.6, range 81.8-171.6; benign : mean 29.5, range 3.7-78.9)(p<0.0001). With 80 p%SI as the threshold for malignancy-positive, both sensitivity and specificity were 100%. The m%SI of malignant SPNs rapidly increased at one minute after enhancement and decreased gradually thereafter, whereas that of benign SPNs increased more slowly to form a plateau. Eighty-nine percent (8/9) of malignant SPNs showed homogeneous enhancement. In contrast, among benign SPNs, peripheral rim-like enhancement and no (or minimal) enhancement occurred in the same proportion of cases : 50%(7/14). The superb demonstration of different enhancement characteristics obtained using dynamic contrast-enhanced MR imaging is useful to discriminate malignant from benign SPNs

Benign childhood epilepsy with centro-temporal spikes: correlation between clinical, cognitive and EEG aspects Epilepsia benigna da infância com pontas centrotemporais: correlação entre aspectos clínicos, eletrencefalográficos e cognitivos

Directory of Open Access Journals (Sweden)

Lineu Corrêa Fonseca

2007-09-01

Full Text Available Benign childhood epilepsy with centro-temporal spikes (BECTS is a form of epilepsy with no demonstrable anatomical lesion showing spontaneous seizure remission. During the active phase of the disease the children may show cognitive deficits. The objective of this study was to assess, in children with BECTS, the relationship between clinical-EEG aspects and performance in the school performance test (SPT, Raven's progressive matrixes test and the Wechsler Intelligence Scale for Children (WISC-III. Forty-two 7 to 11 year old children were included and the following tests carried out: anamnesis, neurological examination, electroencephalogram (EEG, SPT, Raven's test and WISC-III. The children with BECTS had normal IQ values but showed inferior performance in the SPT more frequently than "healthy" children, paired with respect to age and maternal scholastic level. There was moderate positive correlation between WISC-III results and the age when the seizures started and the educational level of the parents. On the other hand, aspects linked to the epileptic nature of BECTS, such as the number of seizures, time since last seizure and the number and lateralization of the centro-temporal spikes on the EEG, showed no correlation with the neuropsychological tests.A epilepsia benigna da infância com pontas centrotemporais (EBICT é uma forma de epilepsia na qual não existem lesões anatômicas demonstráveis e há remissão espontânea das crises. Na fase ativa da epilepsia as crianças podem apresentar déficits cognitivos. O objetivo deste estudo foi avaliar, em crianças com EBICT, a relação entre aspectos clínico-eletrencefalográficos e o desempenho no teste de desempenho escolar (TDE, no teste das matrizes progressivas de Raven e na Escala Wechsler de Inteligência para Crianças (WISC-III. Foram incluídas 42 crianças de 7 a 11 anos de idade. Foram realizados: anamnese, exame neurológico, eletrencefalograma (EEG, TDE, teste de Raven e WISC

Radiological patterns of thyroid calcifications

International Nuclear Information System (INIS)

Lim, Jun; Sim, Do Chul; Park, Seog Hee; Kim, Choon Yul; Bahk, Yong Whee

1986-01-01

The purpose of this study was to analyse the various patterns of calcification demonstrated in the anterior and lateral neck roentgenograms of 213 unselected patients with thyroid enlargement. The patterns of thyroid calcifications were correlated with clinical, surgical and histological findings. The results were as follows: 1. Of 213 cases of thyroid enlargement, 180 cases were benign and 168 cases were female. 2. The calcification rate was high in the chronic thyroid enlargement. 3. The incidence of calcification was 30.2% in the malignancy and 17.2% in the benign disease. There was no calcification in the Hashimoto's disease. 4. The nodular calcification was demonstrated in the both benign and malignant disease but curvilinear calcification was predominantly seen in benign disease.

Diagnosis of autism through EEG processed by advanced computational algorithms: A pilot study.

Science.gov (United States)

Grossi, Enzo; Olivieri, Chiara; Buscema, Massimo

2017-04-01

Multi-Scale Ranked Organizing Map coupled with Implicit Function as Squashing Time algorithm(MS-ROM/I-FAST) is a new, complex system based on Artificial Neural networks (ANNs) able to extract features of interest in computerized EEG through the analysis of few minutes of their EEG without any preliminary pre-processing. A proof of concept study previously published showed accuracy values ranging from 94%-98% in discerning subjects with Mild Cognitive Impairment and/or Alzheimer's Disease from healthy elderly people. The presence of deviant patterns in simple resting state EEG recordings in autism, consistent with the atypical organization of the cerebral cortex present, prompted us in applying this potent analytical systems in search of a EEG signature of the disease. The aim of the study is to assess how effectively this methodology distinguishes subjects with autism from typically developing ones. Fifteen definite ASD subjects (13 males; 2 females; age range 7-14; mean value = 10.4) and ten typically developing subjects (4 males; 6 females; age range 7-12; mean value 9.2) were included in the study. Patients received Autism diagnoses according to DSM-V criteria, subsequently confirmed by the ADOS scale. A segment of artefact-free EEG lasting 60 seconds was used to compute input values for subsequent analyses. MS-ROM/I-FAST coupled with a well-documented evolutionary system able to select predictive features (TWIST) created an invariant features vector input of EEG on which supervised machine learning systems acted as blind classifiers. The overall predictive capability of machine learning system in sorting out autistic cases from normal control amounted consistently to 100% with all kind of systems employed using training-testing protocol and to 84% - 92.8% using Leave One Out protocol. The similarities among the ANN weight matrixes measured with apposite algorithms were not affected by the age of the subjects. This suggests that the ANNs do not read age

Test-retest reliability of cognitive EEG

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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.

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

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Cervantes Blanco Jorge Mauricio

2014-07-01

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

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

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Wu, Qunjian; Zeng, Ying; Zhang, Chi; Tong, Li; Yan, Bin

2018-01-24

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

Application of Sonoelastography in Differential Diagnosis of Benign and Malignant Thyroid Nodules.

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Esfahanian, Fatemeh; Aryan, Arvin; Ghajarzadeh, Mahsa; Yazdi, Meisam Hosein; Nobakht, Nasir; Burchi, Mehdi

2016-01-01

Sonoelastography is a new ultrasound method which could be helpful to determine which thyroid nodule is malignant. We designed this study to evaluate the accuracy of sonoelastography in differentiating of benign and malignant thyroid nodules in Iranian patients. Forty thyroid nodules in forty consecutive patients who had been referred for sonography-guided fine-needle aspiration biopsy were evaluated. Gray scale ultrasound and elastosonography by real-time, freehand technique applied for all patients. Elastography findings were classified into four groups. Nodules which were classified as patterns 1 or 2 in elastogram evaluation were classified as benign and probably malignant if elastogram scans were patterns 3 and 4 of elastogram scan. Mean age ± standard deviation (SD) was 42.2 ± 12.6 years, and mean ± SD thyroid-stimulating hormone level was 1.4 ± 1.9 IU/ml. Thirty-five cases (87.5%) were female and 5 (12.5%) were male. Histological examination indicated 27 (67.5%) benign and 13 (32.5%) malignant nodules. The most elastogram score was 2 (50%) followed by score 3. The cut-off point of 2 considered as the best value to differentiate benign and malignant thyroid nodules with sensitivity and specificity of 61% and 78% (area under the curve = 0.76, 95% confidence interval: 0.6-0.92, P = 0.007). Sonoelastography could help to differentiate benign and malignant thyroid nodules. As our sample size was limited, larger studies are recommended.

Postictal psychosis and its electrophysiological correlates in invasive EEG: a case report study and literature review.

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Kuba, Robert; Brázdil, Milan; Rektor, Ivan

2012-04-01

We identified two patients with medically refractory temporal lobe epilepsy, from whom intracranial EEG recordings were obtained at the time of postictal psychosis. Both patients had mesial temporal epilepsy associated with hippocampal sclerosis. In both patients, the postictal psychosis was associated with a continual "epileptiform" EEG pattern that differed from their interictal and ictal EEG findings (rhythmical slow wave and "abortive" spike-slow wave complex activity in the right hippocampus and lateral temporal cortex in case 1 and a periodic pattern of triphasic waves in the contacts recording activity from the left anterior cingulate gyrus). Some cases of postictal psychosis might be caused by the transient impairment of several limbic system structures due to the "continual epileptiform discharge" in some brain regions. Case 2 is the first report of a patient with TLE in whom psychotic symptoms were associated with the epileptiform impairment of the anterior cingulate gyrus. Copyright © 2012 Elsevier Inc. All rights reserved.

Hypnagogic imagery and EEG activity.

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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.

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

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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

EEG applications for sport and performance.

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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.

Stress assessment based on EEG univariate features and functional connectivity measures.

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Alonso, J F; Romero, S; Ballester, M R; Antonijoan, R M; Mañanas, M A

2015-07-01

The biological response to stress originates in the brain but involves different biochemical and physiological effects. Many common clinical methods to assess stress are based on the presence of specific hormones and on features extracted from different signals, including electrocardiogram, blood pressure, skin temperature, or galvanic skin response. The aim of this paper was to assess stress using EEG-based variables obtained from univariate analysis and functional connectivity evaluation. Two different stressors, the Stroop test and sleep deprivation, were applied to 30 volunteers to find common EEG patterns related to stress effects. Results showed a decrease of the high alpha power (11 to 12 Hz), an increase in the high beta band (23 to 36 Hz, considered a busy brain indicator), and a decrease in the approximate entropy. Moreover, connectivity showed that the high beta coherence and the interhemispheric nonlinear couplings, measured by the cross mutual information function, increased significantly for both stressors, suggesting that useful stress indexes may be obtained from EEG-based features.

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.

Decoding sequence learning from single-trial intracranial EEG in humans.

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Marzia De Lucia

Full Text Available We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM that captures spatio-temporal properties of the iEEG at the level of single trials. Continuous intracranial iEEG was acquired during two sessions (one before and one after a night of sleep in two patients with depth electrodes implanted in several brain areas. They performed a visuomotor sequence (serial reaction time task, SRTT using the fingers of their non-dominant hand. Our results show that the decoding algorithm correctly classified single iEEG trials from the trained sequence as belonging to either the initial training phase (day 1, before sleep or a later consolidated phase (day 2, after sleep, whereas it failed to do so for trials belonging to a control condition (pseudo-random sequence. Accurate single-trial classification was achieved by taking advantage of the distributed pattern of neural activity. However, across all the contacts the hippocampus contributed most significantly to the classification accuracy for both patients, and one fronto-striatal contact for one patient. Together, these human intracranial findings demonstrate that a multivariate decoding approach can detect learning-related changes at the level of single-trial iEEG. Because it allows an unbiased identification of brain sites contributing to a behavioral effect (or experimental condition at the level of single subject, this approach could be usefully applied to assess the neural correlates of other complex cognitive functions in patients implanted with multiple electrodes.

Improving Generalization Based on l1-Norm Regularization for EEG-Based Motor Imagery Classification

Directory of Open Access Journals (Sweden)

Yuwei Zhao

2018-05-01

Full Text Available Multichannel electroencephalography (EEG is widely used in typical brain-computer interface (BCI systems. In general, a number of parameters are essential for a EEG classification algorithm due to redundant features involved in EEG signals. However, the generalization of the EEG method is often adversely affected by the model complexity, considerably coherent with its number of undetermined parameters, further leading to heavy overfitting. To decrease the complexity and improve the generalization of EEG method, we present a novel l1-norm-based approach to combine the decision value obtained from each EEG channel directly. By extracting the information from different channels on independent frequency bands (FB with l1-norm regularization, the method proposed fits the training data with much less parameters compared to common spatial pattern (CSP methods in order to reduce overfitting. Moreover, an effective and efficient solution to minimize the optimization object is proposed. The experimental results on dataset IVa of BCI competition III and dataset I of BCI competition IV show that, the proposed method contributes to high classification accuracy and increases generalization performance for the classification of MI EEG. As the training set ratio decreases from 80 to 20%, the average classification accuracy on the two datasets changes from 85.86 and 86.13% to 84.81 and 76.59%, respectively. The classification performance and generalization of the proposed method contribute to the practical application of MI based BCI systems.

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.

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.

Modeling temporal sequences of cognitive state changes based on a combination of EEG-engagement, EEG-workload, and heart rate metrics

Directory of Open Access Journals (Sweden)

Maja eStikic

2014-11-01

Full Text Available The objective of this study was to investigate the feasibility of physiological metrics such as ECG-derived heart rate and EEG-derived cognitive workload and engagement as potential predictors of performance on different training tasks. An unsupervised approach based on self-organizing neural network (NN was utilized to model cognitive state changes over time. The feature vector comprised EEG-engagement, EEG-workload, and heart rate metrics, all self-normalized to account for individual differences. During the competitive training process, a linear topology was developed where the feature vectors similar to each other activated the same NN nodes. The NN model was trained and auto-validated on combat marksmanship training data from 51 participants that were required to make deadly force decisions in challenging combat scenarios. The trained NN model was cross validated using 10-fold cross-validation. It was also validated on a golf study in which additional 22 participants were asked to complete 10 sessions of 10 putts each. Temporal sequences of the activated nodes for both studies followed the same pattern of changes, demonstrating the generalization capabilities of the approach. Most node transition changes were local, but important events typically caused significant changes in the physiological metrics, as evidenced by larger state changes. This was investigated by calculating a transition score as the sum of subsequent state transitions between the activated NN nodes. Correlation analysis demonstrated statistically significant correlations between the transition scores and subjects’ performances in both studies. This paper explored the hypothesis that temporal sequences of physiological changes comprise the discriminative patterns for performance prediction. These physiological markers could be utilized in future training improvement systems (e.g., through neurofeedback, and applied across a variety of training environments.

Study of heart-brain interactions through EEG, ECG, and emotions

Science.gov (United States)

Ramasamy, Mouli; Varadan, Vijay K.

2017-04-01

Neurocardiology is the exploration of neurophysiological, neurological and neuroanatomical facets of neuroscience's influence in cardiology. The paraphernalia of emotions on the heart and brain are premeditated because of the interaction between the central and peripheral nervous system. This is an investigative attempt to study emotion based neurocardiology and the factors that influence this phenomenon. The factors include: interaction between sleep EEG (electroencephalogram) and ECG (electrocardiogram), relationship between emotion and music, psychophysiological coherence between the heart and brain, emotion recognition techniques, and biofeedback mechanisms. Emotions contribute vitally to the mundane life and are quintessential to a numerous biological and everyday-functional modality of a human being. Emotions are best represented through EEG signals, and to a certain extent, can be observed through ECG and body temperature. Confluence of medical and engineering science has enabled the monitoring and discrimination of emotions influenced by happiness, anxiety, distress, excitement and several other factors that influence the thinking patterns and the electrical activity of the brain. Similarly, HRV (Heart Rate Variability) widely investigated for its provision and discerning characteristics towards EEG and the perception in neurocardiology.

Mapping (and modeling) physiological movements during EEG-fMRI recordings: the added value of the video acquired simultaneously.

Science.gov (United States)

Ruggieri, Andrea; Vaudano, Anna Elisabetta; Benuzzi, Francesca; Serafini, Marco; Gessaroli, Giuliana; Farinelli, Valentina; Nichelli, Paolo Frigio; Meletti, Stefano

2015-01-15

During resting-state EEG-fMRI studies in epilepsy, patients' spontaneous head-face movements occur frequently. We tested the usefulness of synchronous video recording to identify and model the fMRI changes associated with non-epileptic movements to improve sensitivity and specificity of fMRI maps related to interictal epileptiform discharges (IED). Categorization of different facial/cranial movements during EEG-fMRI was obtained for 38 patients [with benign epilepsy with centro-temporal spikes (BECTS, n=16); with idiopathic generalized epilepsy (IGE, n=17); focal symptomatic/cryptogenic epilepsy (n=5)]. We compared at single subject- and at group-level the IED-related fMRI maps obtained with and without additional regressors related to spontaneous movements. As secondary aim, we considered facial movements as events of interest to test the usefulness of video information to obtain fMRI maps of the following face movements: swallowing, mouth-tongue movements, and blinking. Video information substantially improved the identification and classification of the artifacts with respect to the EEG observation alone (mean gain of 28 events per exam). Inclusion of physiological activities as additional regressors in the GLM model demonstrated an increased Z-score and number of voxels of the global maxima and/or new BOLD clusters in around three quarters of the patients. Video-related fMRI maps for swallowing, mouth-tongue movements, and blinking were comparable to the ones obtained in previous task-based fMRI studies. Video acquisition during EEG-fMRI is a useful source of information. Modeling physiological movements in EEG-fMRI studies for epilepsy will lead to more informative IED-related fMRI maps in different epileptic conditions. Copyright © 2014 Elsevier B.V. All rights reserved.

Morphological pattern of parotid gland tumors

International Nuclear Information System (INIS)

Musani, M.A.; Zafar, A.; Malik, S.

2008-01-01

To determine the morphological pattern of parotid tumours. During this study, 204 patients with parotid tumours were registered. The patients of all ages and both gender were included in this study. All patients were evaluated by history, clinical examination, F.N.A.C. and ultrasound, C.T/MRI was done in selected cases. All patients were surgically managed and their tumour specimen was sent for histopathology. Classification of individual tumour was based on 1991 World Health Organization Classification. Discrete data was presented in percentage and proportions. Out of 204 cases, 152 (74.5%) were benign and 52 (25.5%) were malignant. Of these, 117 (57.35%) patients were females and 87 (42.65%) males. Benign tumours were more common in females whereas malignant tumours were common in males. The mean age of patients was 34 years and 42 years for benign and malignant tumours respectively. Pleomorphic adenoma was most common benign tumor (83.5%), followed by Warthins tumour. The most common malignant tumour was mucoepidermoid carcinoma (60%), followed by adenoid cystic carcinoma. Superficial lobe of parotid gland was the commonest site, 120 benign and all 52 malignant tumours arising from it while 32 benign tumours originated from deep lobe. Parotid swelling for years was main feature of benign tumours, whereas malignant tumours presented with pain, fixation to skin or underlying structure, cervical lymphadenopathy and facial palsy. Pleomorphic adenoma was the most common benign tumour and mucoepidermoid carcinoma was most common malignant tumour. The morphological patterns and distribution followed the known pattern. (author)

Evidence for a neurophysiologic auditory deficit in children with benign epilepsy with centro-temporal spikes.

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Liasis, A; Bamiou, D E; Boyd, S; Towell, A

2006-07-01

Benign focal epilepsy in childhood with centro-temporal spikes (BECTS) is one of the most common forms of epilepsy. Recent studies have questioned the benign nature of BECTS, as they have revealed neuropsychological deficits in many domains including language. The aim of this study was to investigate whether the epileptic discharges during the night have long-term effects on auditory processing, as reflected on electrophysiological measures, during the day, which could underline the language deficits. In order to address these questions we recorded base line electroencephalograms (EEG), sleep EEG and auditory event related potentials in 12 children with BECTS and in age- and gender-matched controls. In the children with BECTS, 5 had unilateral and 3 had bilateral spikes. In the 5 patients with unilateral spikes present during sleep, an asymmetry of the auditory event related component (P85-120) was observed contralateral to the side of epileptiform activity compared to the normal symmetrical vertex distribution that was noted in all controls and in 3 the children with bilateral spikes. In all patients the peak to peak amplitude of this event related potential component was statistically greater compared to the controls. Analysis of subtraction waveforms (deviant - standard) revealed no evidence of a mismatch negativity component in any of the children with BECTS. We propose that the abnormality of P85-120 and the absence of mismatch negativity during wake recordings in this group may arise in response to the long-term effects of spikes occurring during sleep, resulting in disruption of the evolution and maintenance of echoic memory traces. These results may indicate that patients with BECTS have abnormal processing of auditory information at a sensory level ipsilateral to the hemisphere evoking spikes during sleep.

Infant EEG and temperament negative affectivity: Coherence of vulnerabilities to mothers' perinatal depression.

Science.gov (United States)

Lusby, Cara M; Goodman, Sherryl H; Yeung, Ellen W; Bell, Martha Ann; Stowe, Zachary N

2016-11-01

Associations between infants' frontal EEG asymmetry and temperamental negative affectivity (NA) across infants' first year of life and the potential moderating role of maternal prenatal depressive symptoms were examined prospectively in infants (n = 242) of mothers at elevated risk for perinatal depression. In predicting EEG, in the context of high prenatal depressive symptoms, infant NA and frontal EEG asymmetry were negatively associated at 3 months of age and positively associated by 12 months of age. By contrast, for low depression mothers, infant NA and EEG were not significantly associated at any age. Postnatal depressive symptoms did not add significantly to the models. Dose of infants' exposure to maternal depression mattered: infants exposed either pre- or postnatally shifted from a positive association at 3 months to a negative association at 12 months; those exposed both pre- and postnatally shifted from a negative association at 3 months to a positive association at 12 months. Prenatal relative to postnatal exposure did not matter for patterns of association between NA and EEG. The findings highlight the importance of exploring how vulnerabilities at two levels of analysis, behavioral and psychophysiological, co-occur over the course of infancy and in the context of mothers' depressive symptomatology.

Probabilistic M/EEG source imaging from sparse spatio-temporal event structure

DEFF Research Database (Denmark)

Stahlhut, Carsten; Attias, Hagai T.; Wipf, David

While MEG and EEG source imaging methods have to tackle a severely ill-posed problem their success can be stated as their ability to constrain the solutions using appropriate priors. In this paper we propose a hierarchical Bayesian model facilitating spatio-temporal patterns through the use of bo...

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%.

Mouse epileptic seizure detection with multiple EEG features and simple thresholding technique

Science.gov (United States)

Tieng, Quang M.; Anbazhagan, Ashwin; Chen, Min; Reutens, David C.

2017-12-01

Objective. Epilepsy is a common neurological disorder characterized by recurrent, unprovoked seizures. The search for new treatments for seizures and epilepsy relies upon studies in animal models of epilepsy. To capture data on seizures, many applications require prolonged electroencephalography (EEG) with recordings that generate voluminous data. The desire for efficient evaluation of these recordings motivates the development of automated seizure detection algorithms. Approach. A new seizure detection method is proposed, based on multiple features and a simple thresholding technique. The features are derived from chaos theory, information theory and the power spectrum of EEG recordings and optimally exploit both linear and nonlinear characteristics of EEG data. Main result. The proposed method was tested with real EEG data from an experimental mouse model of epilepsy and distinguished seizures from other patterns with high sensitivity and specificity. Significance. The proposed approach introduces two new features: negative logarithm of adaptive correlation integral and power spectral coherence ratio. The combination of these new features with two previously described features, entropy and phase coherence, improved seizure detection accuracy significantly. Negative logarithm of adaptive correlation integral can also be used to compute the duration of automatically detected seizures.

EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification

Directory of Open Access Journals (Sweden)

Ting Wang

2014-01-01

Full Text Available Eye state identification is a kind of common time-series classification problem which is also a hot spot in recent research. Electroencephalography (EEG is widely used in eye state classification to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper aims to propose a novel approach for EEG eye state identification using incremental attribute learning (IAL based on neural networks. IAL is a novel machine learning strategy which gradually imports and trains features one by one. Previous studies have verified that such an approach is applicable for solving a number of pattern recognition problems. However, in these previous works, little research on IAL focused on its application to time-series problems. Therefore, it is still unknown whether IAL can be employed to cope with time-series problems like EEG eye state classification. Experimental results in this study demonstrates that, with proper feature extraction and feature ordering, IAL can not only efficiently cope with time-series classification problems, but also exhibit better classification performance in terms of classification error rates in comparison with conventional and some other approaches.

Effect of carbamazepine (Tegretol) on seizure and EEG patterns in monkeys with alumina-induced focal motor and hippocampal foci.

Science.gov (United States)

David, J; Grewal, R S

1976-12-01

Qualitative and quantitative aspects of chronic carbamazepine (Tegretol) medication on focal seizures and associated interictal EEG abnormalities in Rhesus monkeys with alumina-induced foci in either the sensorimotor cortex or the hipocampus was investigated. In both groups of animals, carbamazepine produced qualitative control of visible seizures and reduced intracortical spike propagation, but did not cause complete normalization of the background EEG; quantitative indices, such as spike density and amount of paroxysmal discharge representative of abnormal EEG activity, were significantly reduced with respect to predrug values during medication and after cessation as well. Threshold to pentylenetetrazol was elevated by carbamazepine in both groups of epileptic monkeys. Aggressivity and other clinical manifestations in monekys with hippocampal foci were markedly reduced by carbamazepine.

Low-Rank Linear Dynamical Systems for Motor Imagery EEG.

Science.gov (United States)

Zhang, Wenchang; Sun, Fuchun; Tan, Chuanqi; Liu, Shaobo

2016-01-01

The common spatial pattern (CSP) and other spatiospectral feature extraction methods have become the most effective and successful approaches to solve the problem of motor imagery electroencephalography (MI-EEG) pattern recognition from multichannel neural activity in recent years. However, these methods need a lot of preprocessing and postprocessing such as filtering, demean, and spatiospectral feature fusion, which influence the classification accuracy easily. In this paper, we utilize linear dynamical systems (LDSs) for EEG signals feature extraction and classification. LDSs model has lots of advantages such as simultaneous spatial and temporal feature matrix generation, free of preprocessing or postprocessing, and low cost. Furthermore, a low-rank matrix decomposition approach is introduced to get rid of noise and resting state component in order to improve the robustness of the system. Then, we propose a low-rank LDSs algorithm to decompose feature subspace of LDSs on finite Grassmannian and obtain a better performance. Extensive experiments are carried out on public dataset from "BCI Competition III Dataset IVa" and "BCI Competition IV Database 2a." The results show that our proposed three methods yield higher accuracies compared with prevailing approaches such as CSP and CSSP.

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

EEG-vigilance differences between patients with borderline personality disorder, patients with obsessive-compulsive disorder and healthy controls.

Science.gov (United States)

Hegerl, Ulrich; Stein, Michael; Mulert, Christoph; Mergl, Roland; Olbrich, Sebastian; Dichgans, Eva; Rujescu, Dan; Pogarell, Oliver

2008-04-01

The regulation of brain activation, as assessed with the EEG, is a state modulated trait. A decline to lowered EEG-vigilance states has been found to be associated with emotional instability in older studies, but has not been systematically studied in patients with borderline personality disorder (BPD). Twenty unmedicated BPD patients were compared to 20 unmedicated patients with obsessive-compulsive disorder (OCD) as well as 20 healthy controls concerning their EEG-vigilance regulation over a 5-min period assessed with an algorithm classifying every artefact-free 2-s EEG segment into the EEG-vigilance state (A1-A3, B (=non-A)). If the alpha power was posterior more than 55% of the whole alpha power (anterior + posterior) in the artefact-free EEG-segments, that segment was marked as A1, if it was anterior more than 55% of the whole alpha power, as A3. For A2 the following rule was defined: Posterior or anterior alpha between 50 and 55% of the whole alpha power.BPD patients showed significantly lower rates of EEG-vigilance state A compared to OCD patients, indicating a lowered EEG-vigilance. All three groups showed a decrease in the rate of EEG-vigilance state A over the 5 min recording period in line with a lowering of vigilance. The study provides evidence for a less stable regulation of EEG-vigilance in BPD compared to OCD patients and is in line with concepts postulating that the behavioural pattern with sensation seeking and impulsivity in BPD has a compensatory and autoregulatory function to stabilize activation of the CNS.

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.

Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features

Directory of Open Access Journals (Sweden)

Keranmu Xielifuguli

2014-01-01

Full Text Available People often make decisions based on sensitivity rather than rationality. In the field of biological information processing, methods are available for analyzing biological information directly based on electroencephalogram: EEG to determine the pleasant/unpleasant reactions of users. In this study, we propose a sensitivity filtering technique for discriminating preferences (pleasant/unpleasant for images using a sensitivity image filtering system based on EEG. Using a set of images retrieved by similarity retrieval, we perform the sensitivity-based pleasant/unpleasant classification of images based on the affective features extracted from images with the maximum entropy method: MEM. In the present study, the affective features comprised cross-correlation features obtained from EEGs produced when an individual observed an image. However, it is difficult to measure the EEG when a subject visualizes an unknown image. Thus, we propose a solution where a linear regression method based on canonical correlation is used to estimate the cross-correlation features from image features. Experiments were conducted to evaluate the validity of sensitivity filtering compared with image similarity retrieval methods based on image features. We found that sensitivity filtering using color correlograms was suitable for the classification of preferred images, while sensitivity filtering using local binary patterns was suitable for the classification of unpleasant images. Moreover, sensitivity filtering using local binary patterns for unpleasant images had a 90% success rate. Thus, we conclude that the proposed method is efficient for filtering unpleasant images.

Imaging characteristics of malignant and benign lesions of skeletal muscle; Bildgebende Charakteristika maligner und benigner Laesionen der Skelettmuskulatur

Energy Technology Data Exchange (ETDEWEB)

Leonard, L.; Meyer, H.J.; Surov, A. [Universitaetsklinikum Leipzig, Klinik fuer Diagnostische und Interventionelle Radiologie, Leipzig (Germany)

2017-12-15

There are many different tumors and tumor-like lesions with variable biological behavior that may affect the skeletal musculature. The aim of this study was to review the different intramuscular lesions and to provide a classification based on their radiological patterns. Intramuscular lesions can present as solid, liquid, semiliquid or fat equivalent manifestations and also as diffuse muscle enlargement and muscle calcification. Additionally, lesions with mixed patterns of the aforementioned alterations can also occur. Benign and malignant muscle lesions can often manifest with identical radiological patterns, which is why a certain differentiation is often difficult. A systematic radiological description and when possible assignment with respect to etiology and dignity depending on the patient history is necessary in order to recommend a subsequent histological confirmation or to avoid unnecessary confirmation. (orig.) [German] Es gibt eine Vielzahl an Tumoren und tumorartigen Veraenderungen mit unterschiedlichem biologischem Verhalten, die die Skelettmuskulatur befallen. Das Ziel dieser Arbeit ist es, unterschiedliche Laesionen der Skelettmuskulatur aufzuzeigen und anhand ihrer radiologischen Muster zu klassifizieren. Laesionen der Skelettmuskulatur koennen sich solide, liquide/semiliquide oder fettaequivalent manifestieren sowie auch als diffuse Muskelauftreibungen und als Muskelkalzifikationen. Darueber hinaus gibt es Laesionen mit Mischverhalten der genannten Veraenderungen. Benigne und maligne Veraenderungen koennen die gleichen radiologischen Muster aufweisen, weshalb eine sichere Differenzierung haeufig schwierig ist. Eine systematische radiologische Beschreibung und wenn moeglich Einordnung bez. Aetiologie und Dignitaet ist in Abhaengigkeit der Anamnese notwendig, um eine konsekutive histologische Sicherung zu empfehlen oder unnoetige invasive Eingriffe zu verhindern. (orig.)

Ultrasonographic findings of benign soft tissue tumors

International Nuclear Information System (INIS)

Kim, Ki Sung; Oh, Dong Heon; Jung, Tae Gun; Kim, Yong Kil; Kwon, Jung Hyeok

1994-01-01

To clarify the characteristic sonographic features of benign soft tissue tumors and to evaluate the usefulness of sonographic imaging. We retrospectively reviewed ultrasonographic images of 70 cases in 68 patients with histologically proved benign soft tissue tumors. The tumors include 33 lipomas, 11 hemangiomas, 11 lymphangiomas, 7 neurilemmomas, 4 epidermoid cysts, 2 fibromas, 1 mesenchymoma, and 1 myxoma. The sonographic appearances of the lesions were mainly solid in 53 cases(33 lipomas, 8 hemangiomas, 2 lymphangiomas, 7 neurilemmomas, 2 fibromas and 1 mesenchymoma), mainly cystic in 14 cases(1 hemangioma, 8 lymphangiomas, 4 epidermoid cysts, and 1 myxomal), and mixed in 3 cases(2 hemangiomas and 1 lymphangioma). Although an accurate histologic prediction could not be made in most cases, certain patterns appeared to be characteristic of specific tumor types. 26 cases(78%) of lipoma were seen as lentiform, iso- or hyperechoic, solid mass. Hemangioma had variable appearance and characteristic calcifications were seen in 3 cases. Unicameral or multiseptated cystic mass with variable thickness of echogenic septa and solid portion was the characteristic finding of lymhangioma. Neurilemmoma showed lobulated, oval to round , relatively hypoechoic mass or with without internal cystic portion. Sonographic evaluation of benign soft tissue tumors is useful in demonstrating the location, size, extent, and internal characteristic of the mass. A relatively confident diagnosis can made when the characteristic features of the benign soft tissue tumor are present on sonographic imaging

Ultrasonographic findings of benign soft tissue tumors

Energy Technology Data Exchange (ETDEWEB)

Kim, Ki Sung; Oh, Dong Heon; Jung, Tae Gun; Kim, Yong Kil; Kwon, Jung Hyeok [Dongkang Genernal Hospital, Ulsan (Korea, Republic of)

1994-05-15

To clarify the characteristic sonographic features of benign soft tissue tumors and to evaluate the usefulness of sonographic imaging. We retrospectively reviewed ultrasonographic images of 70 cases in 68 patients with histologically proved benign soft tissue tumors. The tumors include 33 lipomas, 11 hemangiomas, 11 lymphangiomas, 7 neurilemmomas, 4 epidermoid cysts, 2 fibromas, 1 mesenchymoma, and 1 myxoma. The sonographic appearances of the lesions were mainly solid in 53 cases(33 lipomas, 8 hemangiomas, 2 lymphangiomas, 7 neurilemmomas, 2 fibromas and 1 mesenchymoma), mainly cystic in 14 cases(1 hemangioma, 8 lymphangiomas, 4 epidermoid cysts, and 1 myxomal), and mixed in 3 cases(2 hemangiomas and 1 lymphangioma). Although an accurate histologic prediction could not be made in most cases, certain patterns appeared to be characteristic of specific tumor types. 26 cases(78%) of lipoma were seen as lentiform, iso- or hyperechoic, solid mass. Hemangioma had variable appearance and characteristic calcifications were seen in 3 cases. Unicameral or multiseptated cystic mass with variable thickness of echogenic septa and solid portion was the characteristic finding of lymhangioma. Neurilemmoma showed lobulated, oval to round , relatively hypoechoic mass or with without internal cystic portion. Sonographic evaluation of benign soft tissue tumors is useful in demonstrating the location, size, extent, and internal characteristic of the mass. A relatively confident diagnosis can made when the characteristic features of the benign soft tissue tumor are present on sonographic imaging.

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

Directory of Open Access Journals (Sweden)

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

Deep learning with convolutional neural networks for EEG decoding and visualization.

Science.gov (United States)

Schirrmeister, Robin Tibor; Springenberg, Jost Tobias; Fiederer, Lukas Dominique Josef; Glasstetter, Martin; Eggensperger, Katharina; Tangermann, Michael; Hutter, Frank; Burgard, Wolfram; Ball, Tonio

2017-11-01

Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but a better understanding of how to design and train ConvNets for end-to-end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task-related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG-based brain mapping. Hum Brain Mapp 38:5391-5420, 2017. © 2017 Wiley Periodicals, Inc. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Classification of different reaching movements from the same limb using EEG

Science.gov (United States)

Shiman, Farid; López-Larraz, Eduardo; Sarasola-Sanz, Andrea; Irastorza-Landa, Nerea; Spüler, Martin; Birbaumer, Niels; Ramos-Murguialday, Ander

2017-08-01

Objective. Brain-computer-interfaces (BCIs) have been proposed not only as assistive technologies but also as rehabilitation tools for lost functions. However, due to the stochastic nature, poor spatial resolution and signal to noise ratio from electroencephalography (EEG), multidimensional decoding has been the main obstacle to implement non-invasive BCIs in real-live rehabilitation scenarios. This study explores the classification of several functional reaching movements from the same limb using EEG oscillations in order to create a more versatile BCI for rehabilitation. Approach. Nine healthy participants performed four 3D center-out reaching tasks in four different sessions while wearing a passive robotic exoskeleton at their right upper limb. Kinematics data were acquired from the robotic exoskeleton. Multiclass extensions of Filter Bank Common Spatial Patterns (FBCSP) and a linear discriminant analysis (LDA) classifier were used to classify the EEG activity into four forward reaching movements (from a starting position towards four target positions), a backward movement (from any of the targets to the starting position and rest). Recalibrating the classifier using data from previous or the same session was also investigated and compared. Main results. Average EEG decoding accuracy were significantly above chance with 67%, 62.75%, and 50.3% when decoding three, four and six tasks from the same limb, respectively. Furthermore, classification accuracy could be increased when using data from the beginning of each session as training data to recalibrate the classifier. Significance. Our results demonstrate that classification from several functional movements performed by the same limb is possible with acceptable accuracy using EEG oscillations, especially if data from the same session are used to recalibrate the classifier. Therefore, an ecologically valid decoding could be used to control assistive or rehabilitation mutli-degrees of freedom (DoF) robotic devices

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...

Behavioral and EEG evidence for auditory memory suppression

Directory of Open Access Journals (Sweden)

Maya Elizabeth Cano

2016-03-01

Full Text Available The neural basis of motivated forgetting using the Think/No-Think (TNT paradigm is receiving increased attention with a particular focus on the mechanisms that enable memory suppression. However, most TNT studies have been limited to the visual domain. To assess whether and to what extent direct memory suppression extends across sensory modalities, we examined behavioral and electroencephalographic (EEG effects of auditory Think/No-Think in healthy young adults by adapting the TNT paradigm to the auditory modality. Behaviorally, suppression of memory strength was indexed by prolonged response times during the retrieval of subsequently remembered No-Think words. We examined task-related EEG activity of both attempted memory retrieval and inhibition of a previously learned target word during the presentation of its paired associate. Event-related EEG responses revealed two main findings: 1 a centralized Think > No-Think positivity during auditory word presentation (from approximately 0-500ms, and 2 a sustained Think positivity over parietal electrodes beginning at approximately 600ms reflecting the memory retrieval effect which was significantly reduced for No-Think words. In addition, word-locked theta (4-8 Hz power was initially greater for No-Think compared to Think during auditory word presentation over fronto-central electrodes. This was followed by a posterior theta increase indexing successful memory retrieval in the Think condition.The observed event-related potential pattern and theta power analysis are similar to that reported in visual Think/No-Think studies and support a modality non-specific mechanism for memory inhibition. The EEG data also provide evidence supporting differing roles and time courses of frontal and parietal regions in the flexible control of auditory memory.

Behavioral and EEG Evidence for Auditory Memory Suppression.

Science.gov (United States)

Cano, Maya E; Knight, Robert T

2016-01-01

The neural basis of motivated forgetting using the Think/No-Think (TNT) paradigm is receiving increased attention with a particular focus on the mechanisms that enable memory suppression. However, most TNT studies have been limited to the visual domain. To assess whether and to what extent direct memory suppression extends across sensory modalities, we examined behavioral and electroencephalographic (EEG) effects of auditory TNT in healthy young adults by adapting the TNT paradigm to the auditory modality. Behaviorally, suppression of memory strength was indexed by prolonged response time (RTs) during the retrieval of subsequently remembered No-Think words. We examined task-related EEG activity of both attempted memory retrieval and inhibition of a previously learned target word during the presentation of its paired associate. Event-related EEG responses revealed two main findings: (1) a centralized Think > No-Think positivity during auditory word presentation (from approximately 0-500 ms); and (2) a sustained Think positivity over parietal electrodes beginning at approximately 600 ms reflecting the memory retrieval effect which was significantly reduced for No-Think words. In addition, word-locked theta (4-8 Hz) power was initially greater for No-Think compared to Think during auditory word presentation over fronto-central electrodes. This was followed by a posterior theta increase indexing successful memory retrieval in the Think condition. The observed event-related potential pattern and theta power analysis are similar to that reported in visual TNT studies and support a modality non-specific mechanism for memory inhibition. The EEG data also provide evidence supporting differing roles and time courses of frontal and parietal regions in the flexible control of auditory memory.

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

Science.gov (United States)

Reyes, Ana Calzada; Amador, Alfredo Alvarez

2009-02-01

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

Resting State EEG-based biometrics for individual identification using convolutional neural networks.

Science.gov (United States)

Lan Ma; Minett, James W; Blu, Thierry; Wang, William S-Y

2015-08-01

Biometrics is a growing field, which permits identification of individuals by means of unique physical features. Electroencephalography (EEG)-based biometrics utilizes the small intra-personal differences and large inter-personal differences between individuals' brainwave patterns. In the past, such methods have used features derived from manually-designed procedures for this purpose. Another possibility is to use convolutional neural networks (CNN) to automatically extract an individual's best and most unique neural features and conduct classification, using EEG data derived from both Resting State with Open Eyes (REO) and Resting State with Closed Eyes (REC). Results indicate that this CNN-based joint-optimized EEG-based Biometric System yields a high degree of accuracy of identification (88%) for 10-class classification. Furthermore, rich inter-personal difference can be found using a very low frequency band (0-2Hz). Additionally, results suggest that the temporal portions over which subjects can be individualized is less than 200 ms.

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.

Changes in Resting EEG in Colombian Ex-combatants ith Antisocial Personality Disorder.

Science.gov (United States)

Ramos, Claudia; Duque-Grajales, Jon; Rendón, Jorge; Montoya-Betancur, Alejandro; Baena, Ana; Pineda, David; Tobón, Carlos

Although the social and economic consequences of Colombian internal conflicts mainly affected the civilian population, they also had other implications. The ex-combatants, the other side of the conflict, have been the subject of many studies that question their personality structures and antisocial features. Results suggest that ex-combatants usually have characteristics of an antisocial personality disorder (ASPD) that is related with their behaviour. Quantitative EEG (qEEG) was used to evaluate differences in cortical activity patterns between an ex-combatants group and a control group. The Psychopathy Checklist-Revised (PCL-R) was used to assess the presence of ASPD in the ex-combatants group, as well as the Diagnostic Interview for Genetic Studies (DIGS) for other mental disorders classified in the DCI-10. There are significant differences in psychopathy levels between groups, as well as in alpha-2 and beta waves, especially in left temporal and frontal areas for alpha-2 waves and left temporal-central regions for beta waves. qEEG measurements allow spectral resting potential to be differentiated between groups that are related with features typically involved in antisocial personality disorder, and to correlate them with patterns in the questionnaires and clinical interview. Copyright © 2017 Asociación Colombiana de Psiquiatría. Publicado por Elsevier España. All rights reserved.

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.

Usefulness of FDG PET/CT in determining benign from malignant endobronchial obstruction

International Nuclear Information System (INIS)

Cho, Arthur; Kang, Won Jun; Cho, Ho Jin; Lee, Jae-hoon; Yun, Mijin; Lee, Jong Doo; Hur, Jin

2011-01-01

To evaluate the usefulness of FDG PET/CT to differentiate malignant endobronchial lesions with distal atelectasis from benign bronchial stenosis. This retrospective study reviewed 84 patients who underwent contrast-enhanced chest CT and then PET/CT and had histological (n = 81) or follow-up imaging (n = 3) confirmation. Two chest radiologists reviewed initial chest CT and determined endobronchial lesions to be malignant or benign. Two nuclear medicine physicians reviewed PET/CT for FDG uptake at the obstruction site and measured SUV. Malignancy was considered when increased FDG uptake was seen in the obstruction site, regardless of FDG within the atelectatic lung. The sensitivity, specificity and accuracy of chest CT was 95%, 48% and 84%, compared with 95%, 91% and 94% for PET/CT. Benign obstructive lesions showed statistically lower FDG uptake than malignant obstructions (benign SUV 2.5 ± 0.84; malignant SUV 11.8 ± 5.95, p < 0.001). ROC analysis showed an SUV cut-off value of 3.4 with highest sensitivity of 94% and specificity of 91%. Increased FDG PET/CT uptake at the obstruction site indicates a high probability of malignancy, while benign lesions show low FDG uptake. Careful evaluation of FDG uptake pattern at the obstruction site is helpful in the differentiation between benign and malignant endobronchial lesions. (orig.)

Usefulness of FDG PET/CT in determining benign from malignant endobronchial obstruction

Energy Technology Data Exchange (ETDEWEB)

Cho, Arthur; Kang, Won Jun; Cho, Ho Jin; Lee, Jae-hoon; Yun, Mijin; Lee, Jong Doo [Yonsei University Health System, Division of Nuclear Medicine, Department of Radiology, Seoul (Korea, Republic of); Hur, Jin [Yonsei University Health System, Department of Radiology, Seoul (Korea, Republic of)

2011-05-15

To evaluate the usefulness of FDG PET/CT to differentiate malignant endobronchial lesions with distal atelectasis from benign bronchial stenosis. This retrospective study reviewed 84 patients who underwent contrast-enhanced chest CT and then PET/CT and had histological (n = 81) or follow-up imaging (n = 3) confirmation. Two chest radiologists reviewed initial chest CT and determined endobronchial lesions to be malignant or benign. Two nuclear medicine physicians reviewed PET/CT for FDG uptake at the obstruction site and measured SUV. Malignancy was considered when increased FDG uptake was seen in the obstruction site, regardless of FDG within the atelectatic lung. The sensitivity, specificity and accuracy of chest CT was 95%, 48% and 84%, compared with 95%, 91% and 94% for PET/CT. Benign obstructive lesions showed statistically lower FDG uptake than malignant obstructions (benign SUV 2.5 {+-} 0.84; malignant SUV 11.8 {+-} 5.95, p < 0.001). ROC analysis showed an SUV cut-off value of 3.4 with highest sensitivity of 94% and specificity of 91%. Increased FDG PET/CT uptake at the obstruction site indicates a high probability of malignancy, while benign lesions show low FDG uptake. Careful evaluation of FDG uptake pattern at the obstruction site is helpful in the differentiation between benign and malignant endobronchial lesions. (orig.)

Comparative study of three sonoelastographic scores for differentiation between benign and malignant cervical lymph nodes

Energy Technology Data Exchange (ETDEWEB)

Lenghel, Lavinia Manuela, E-mail: pop.lavinia@umfcluj.ro [Iuliu Hatieganu University of Medicine and Pharmacy, Department of Radiology, Clinicilor Street, No. 3-5, 400006 Cluj-Napoca (Romania); Botar Jid, Carolina, E-mail: cbotar@umfcluj.ro [Iuliu Hatieganu University of Medicine and Pharmacy, Department of Radiology, Clinicilor Street, No. 3-5, 400006 Cluj-Napoca (Romania); Bolboaca, Sorana D., E-mail: sbolboaca@umfcluj.ro [Iuliu Hatieganu University of Medicine and Pharmacy, Department of Medical Informatics and Biostatistics, Louis Pasteur Street, No. 6, 400349 Cluj-Napoca (Romania); Ciortea, Cristiana, E-mail: cristianaciortea@yahoo.com [Cluj District University Emergency, Department of Radiology, Clinicilor Street, No. 3-5, 400006 Cluj-Napoca (Romania); Vasilescu, Dan, E-mail: vasilescu.dan@umfcluj.ro [Iuliu Hatieganu University of Medicine and Pharmacy, Department of Radiology, Clinicilor Street, No. 3-5, 400006 Cluj-Napoca (Romania); Baciut, Grigore, E-mail: gbaciut@umfcluj.ro [Iuliu Hatieganu University of Medicine and Pharmacy, Department of Cranio-Maxillo-Facial Surgery, Cardinal Iuliu Hossu Street, No. 37, 400029 Cluj-Napoca (Romania); Dudea, Sorin M., E-mail: sdudea@umfcluj.ro [Iuliu Hatieganu University of Medicine and Pharmacy, Department of Radiology, Clinicilor Street, No. 3-5, 400006 Cluj-Napoca (Romania)

2015-06-15

Highlights: • We compared three sonoelastographic scoring systems for neck lymphnode assessment. • We proposed a six pattern scoring system of the elastographic images. • No significant differences were found in the diagnostic value of the assessed scores. - Abstract: Purpose: The aim of the study was to explore the diagnostic value of three different sonoelastographic scoring systems (labeled S1–S3) for the differentiation between benign and malignant cervical lymph nodes. Materials and method: The authors propose a six pattern scoring system of the elastographic images with pattern 1 – representing purely soft nodes, pattern 2 – predominantly soft nodes, pattern 3 – predominantly soft nodes with focal had area, pattern 4 – predominantly hard node, pattern 5 – entirely hard node and pattern 6 – node with necrosis. The sonoelastographic images of 50 benign and 70 malignant lymph nodes were assessed. The area under the ROC curve (AUROC) for the differentiation between benign vs. malignant and benign vs. metastatic nodes were analyzed for the three scoring systems. Results: When all the malignant lymph nodes were considered, the S1 score showed an AUROC = 0.873 (95%CI [0.805–0.918], where CI = confidence interval; p < 0.001), sensibility (Se) = 58.57%, and specificity (Sp) = 96%. For S2 score the AUROC was 0.890 (95%CI [0.824–0.933], p < 0.001), Se = 92.86%, and Sp = 72%. For S3 score, the AUROC was 0.852 (95%CI [0.778–0.902], p < 0.001), Se = 64.29%, and Sp = 94%). When lymphomatous nodes were excluded, for S1 the AUROC was 0.884 (95%CI [0.809–0.932], p < 0.001), Se = 64%, and Sp = 96%. For S2 the AUROC was 0.894 (95%CI [0.818–0.939], p < 0.001), Se = 92%, and Sp = 72%. For S3, the AUROC was 0.856 (95%CI [0.771–0.911], p < 0.001), Se = 66%, and Sp = 94%. In the S3 scoring system, setting the benign vs. malignant cut off at pattern 3 increases the sensibility (41–65%) with minimal loss of specificity (96–94%). From the gray

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.

Deviant dynamics of EEG resting state pattern in 22q11.2 deletion syndrome adolescents: A vulnerability marker of schizophrenia?

Science.gov (United States)

Tomescu, Miralena I; Rihs, Tonia A; Becker, Robert; Britz, Juliane; Custo, Anna; Grouiller, Frédéric; Schneider, Maude; Debbané, Martin; Eliez, Stephan; Michel, Christoph M

2014-08-01

Previous studies have repeatedly found altered temporal characteristics of EEG microstates in schizophrenia. The aim of the present study was to investigate whether adolescents affected by the 22q11.2 deletion syndrome (22q11DS), known to have a 30 fold increased risk to develop schizophrenia, already show deviant EEG microstates. If this is the case, temporal alterations of EEG microstates in 22q11DS individuals could be considered as potential biomarkers for schizophrenia. We used high-density (204 channel) EEG to explore between-group microstate differences in 30 adolescents with 22q11DS and 28 age-matched controls. We found an increased presence of one microstate class (class C) in the 22q11DS adolescents with respect to controls that was associated with positive prodromal symptoms (hallucinations). A previous across-age study showed that the class C microstate was more present during adolescence and a combined EEG-fMRI study associated the class C microstate with the salience resting state network, a network known to be dysfunctional in schizophrenia. Therefore, the increased class C microstates could be indexing the increased risk of 22q11DS individuals to develop schizophrenia if confirmed by our ongoing longitudinal study comparing both the adult 22q11DS individuals with and without schizophrenia, as well as schizophrenic individuals with and without 22q11DS. Copyright © 2014 Elsevier B.V. All rights reserved.

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

TopoToolbox: using sensor topography to calculate psychologically meaningful measures from event-related EEG/MEG.

Science.gov (United States)

Tian, Xing; Poeppel, David; Huber, David E

2011-01-01

The open-source toolbox "TopoToolbox" is a suite of functions that use sensor topography to calculate psychologically meaningful measures (similarity, magnitude, and timing) from multisensor event-related EEG and MEG data. Using a GUI and data visualization, TopoToolbox can be used to calculate and test the topographic similarity between different conditions (Tian and Huber, 2008). This topographic similarity indicates whether different conditions involve a different distribution of underlying neural sources. Furthermore, this similarity calculation can be applied at different time points to discover when a response pattern emerges (Tian and Poeppel, 2010). Because the topographic patterns are obtained separately for each individual, these patterns are used to produce reliable measures of response magnitude that can be compared across individuals using conventional statistics (Davelaar et al. Submitted and Huber et al., 2008). TopoToolbox can be freely downloaded. It runs under MATLAB (The MathWorks, Inc.) and supports user-defined data structure as well as standard EEG/MEG data import using EEGLAB (Delorme and Makeig, 2004).

Bone tumors with an associated pathologic fracture: Differentiation between benign and malignant status using radiologic findings

International Nuclear Information System (INIS)

Bae, Ji Hyun; Lee, In Sook; Song, You Seon; Kim, Jeung Il; Lee, Moon Sung; Lee, Young Hwan; Song, Jong Woon

2015-01-01

To determine whether benign and malignant bone tumors with associated pathologic fractures can be differentiated using radiologic findings. Seventy-eight patients (47 men and 31 women, age range: 1-93 years) with a bone tumor and an associated pathologic fracture from 2004 to 2013 constituted the retrospective study cohort. The tumor size, margin, and enhancement patterns; the presence of sclerotic margin, the peritumoral bone marrow, soft tissue edema, extra-osseous soft tissue mass, intratumoral cystic/hemorrhagic/necrotic regions, mineralization/sclerotic regions, periosteal reaction and its appearance; and cortical change and its appearance were evaluated on all images. Differences between the imaging characteristics of malignant and benign pathologic fractures were compared using Pearson's chi-square test and the 2-sample t-test. There were 22 benign and 56 malignant bone tumors. Some factors were found to significantly differentiate between benign and malignant tumors; specifically, ill-defined tumor margin, the presence of sclerotic tumor margin and an extra-osseous soft tissue mass, the absence of cystic/necrotic/hemorrhagic portions in a mass, the homogeneous enhancement pattern, and the presence of a displaced fracture and of underlying cortical change were suggestive of malignant pathologic fractures. Some imaging findings were helpful for differentiating between benign and malignant pathologic fractures

Bone tumors with an associated pathologic fracture: Differentiation between benign and malignant status using radiologic findings

Energy Technology Data Exchange (ETDEWEB)

Bae, Ji Hyun; Lee, In Sook; Song, You Seon [Pusan National University School of Medicine, Pusan National University Hospital, Busan (Korea, Republic of); Kim, Jeung Il [Dept. of Radiology, Yeungnam University College of Medicine, Yeungnam University Medical Center, Daegu (Korea, Republic of); Lee, Moon Sung [Dept. of Radiology, Keimyung University College of Medicine, Dongsan Medical Center, Daegu (Korea, Republic of); Lee, Young Hwan [Dept. of Radiology, Catholic University of Daegu College of Medicine, Daegu Catholic University Hospital, Daegu (Korea, Republic of); Song, Jong Woon [Dept. of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Busan (Korea, Republic of)

2015-10-15

To determine whether benign and malignant bone tumors with associated pathologic fractures can be differentiated using radiologic findings. Seventy-eight patients (47 men and 31 women, age range: 1-93 years) with a bone tumor and an associated pathologic fracture from 2004 to 2013 constituted the retrospective study cohort. The tumor size, margin, and enhancement patterns; the presence of sclerotic margin, the peritumoral bone marrow, soft tissue edema, extra-osseous soft tissue mass, intratumoral cystic/hemorrhagic/necrotic regions, mineralization/sclerotic regions, periosteal reaction and its appearance; and cortical change and its appearance were evaluated on all images. Differences between the imaging characteristics of malignant and benign pathologic fractures were compared using Pearson's chi-square test and the 2-sample t-test. There were 22 benign and 56 malignant bone tumors. Some factors were found to significantly differentiate between benign and malignant tumors; specifically, ill-defined tumor margin, the presence of sclerotic tumor margin and an extra-osseous soft tissue mass, the absence of cystic/necrotic/hemorrhagic portions in a mass, the homogeneous enhancement pattern, and the presence of a displaced fracture and of underlying cortical change were suggestive of malignant pathologic fractures. Some imaging findings were helpful for differentiating between benign and malignant pathologic fractures.

Radiological and histopathological study of benign tumors of the mandible

Energy Technology Data Exchange (ETDEWEB)

Yoo, Seon Young; Baek, Seung Yon; Choi, Kyung Hee; Suh, Jeung Soo; Rhee, Chung Sik; Kim, Hee Seup [Ewha Womans University College of Medicine, Seoul (Korea, Republic of)

1984-06-15

Benign tumors of the mandible are uncommon lesions. That were classified into odontogenic tumors and nonodontogenic tumors. Author reviewed the radiological evaluation and pathological microscopic finding from 33 benign tumors of the mandible that were confirmed by the biopsy during last 10 years in Dental Clinics, Ewha Womans University Hospital and Seoul National University Dental Hospital. Following results were obtained; 1. Benign tumors of the mandible were classified into odontogenic (66.7%) and non-odontogenic tumors (33.3%). 2. The range of the age distribution was between 6 years and 67 years old. The commonest age group was the second decade (39.4%). 3. There was no difference to sex distribution. 4. The most frequent location was the body of the mandible (42.4%). 5. Radiographic findings were relatively characteristic in odontogenic tumors rather than non-odontogenic tumors. 1) Radiolucent cystic lesions-ameloblastoma, odontogenic myxoma, odontogenic fibtoma, aneurysmal bone cyst and neurofibroma. 2) Radiopaque mass lesions-odontoma, cementoma and osteoma. 3) Mixed patterns-ossifying fibroma, cementifying fibroma, calcifying odontogenic epithelial tumor and hemangioma. It was concluded that the radiographic examination was of value to diagnose the benign tumors of the mandible in symptomless patients.

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

Clinical utility of early amplitude integrated EEG in monitoring term newborns at risk of neurological injury

Directory of Open Access Journals (Sweden)

Paulina A. Toso

2014-04-01

Full Text Available OBJECTIVE: to test the clinical utility of an early amplitude-integrated electroencephalography (aEEG to predict short-term neurological outcome in term newborns at risk of neurology injury. METHODS: this was a prospective, descriptive study. The inclusion criteria were neonatal encephalopathy, neurologic disturbances, and severe respiratory distress syndrome. Sensitivity, specificity, positive and negative predictive values, and likelihood ratio (LR were calculated. Clinical and demographic data were analyzed. Neurological outcome was defined as the sum of clinical, electroimaging, and neuroimaging findings. RESULTS: ten of the 21 monitored infants (48% presented altered short-term neurologic outcome. The aEEG had 90% sensitivity, 82% specificity, 82% positive predictive value, and 90% negative predictive value. The positive LR was 4.95, and the negative LR was 0.12. In three of 12 (25% encephalopathic infants, the aEEG allowed for a better definition of the severity of their condition. Seizures were detected in eight infants (38%, all subclinical at baseline, and none had a normal aEEG background pattern. The status of three infants (43% evolved and required two or more drugs for treatment. CONCLUSIONS: in infants with encephalopathy or other severe illness, aEEG disturbances occur frequently. aEEG provided a better classification of the severity of encephalopathy, detected early subclinical seizures, and allowed for monitoring of the response to treatment. aEEG was a useful tool at the neonatal intensive care unit for predicting poor short-term neurological outcomes for all sick newborn.

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.

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...

EEG Derived Neuronal Dynamics during Meditation: Progress and Challenges

Directory of Open Access Journals (Sweden)

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.

Solitary, multiple, benign, atypical, or malignant: the "Granular Cell Tumor" puzzle.

Science.gov (United States)

Machado, Isidro; Cruz, Julia; Lavernia, Javier; Llombart-Bosch, Antonio

2016-05-01

The clinical evolution and biology of granular cell tumors (GCT) are poorly understood and treatment remains an issue of discussion. The majority of GCT are benign, although some display malignant behavior. The distinction between benign, atypical, and malignant GCT is controversial due to morphological and immunohistochemical overlap and lack of consistent histological and phenotypic criteria that predict behavior. Although histological criteria may indicate increased risk of malignant evolution, some GCT with evident benign appearance exceptionally progress towards metastatic disease. In this review, we discuss current knowledge on GCT, including histologic, immunophenotypic, and molecular characteristics and differential diagnosis. We focus on the following problematic items in GCT: (1) evolution of classification, (2) neural versus non-neural GCT, (3) neoplastic versus reactive disease, (4) malignant transformation of benign GCT, and (5) multiple versus metastatic GCT. We conclude that although a Ki-67 index >10 % and the presence of mitoses and/or of necrosis are frequently associated with malignant behavior, metastasis remains the only unequivocal sign of malignancy in GCT. An infiltrative growth pattern and vascular and/or perineural invasion are not indicative of malignancy. GCT with atypical/uncertain features almost never metastasize, and many of these tumors either behave in a benign fashion or only recur locally (similar to incompletely excised benign tumors). We therefore propose that classical and atypical histological variants form a single group of GCT. GCT with various unfavorable histological features might be labeled as "GCT with increased risk of metastasis" rather than malignant GCT.

Recognition of neural brain activity patterns correlated with complex motor activity

Science.gov (United States)

Kurkin, Semen; Musatov, Vyacheslav Yu.; Runnova, Anastasia E.; Grubov, Vadim V.; Efremova, Tatyana Yu.; Zhuravlev, Maxim O.

2018-04-01

In this paper, based on the apparatus of artificial neural networks, a technique for recognizing and classifying patterns corresponding to imaginary movements on electroencephalograms (EEGs) obtained from a group of untrained subjects was developed. The works on the selection of the optimal type, topology, training algorithms and neural network parameters were carried out from the point of view of the most accurate and fast recognition and classification of patterns on multi-channel EEGs associated with the imagination of movements. The influence of the number and choice of the analyzed channels of a multichannel EEG on the quality of recognition of imaginary movements was also studied, and optimal configurations of electrode arrangements were obtained. The effect of pre-processing of EEG signals is analyzed from the point of view of improving the accuracy of recognition of imaginary movements.

Ultrasound differentiation of benign and malignant cervical lymph nodes

Directory of Open Access Journals (Sweden)

Md. Mizanur Rahman

2009-01-01

Full Text Available This study was conducted to see whether the size (measured by maximal short axis diameter, shape (expressed in terms of ratio dividing long axis diameter of the node by short axis diameter or L/S ratio, marginal clarity (regular or irregular margin, internal echo-pattern (homogeneous hypoechoic or heterogeneous and hilar echogenicity (presence or absence of echo-genicity in hilum are good criteria for differentiating benign from malignant cervical lymph nodes using high frequency (high resolution ultrasound probe. The study was carried out from January 1998 to December 1998, among patients with enlarged cervical lymph nodes who were scanned with a high frequency (5.0 MHz curvilinear probe. Chi-Square test was done to see the statistical correlation between two groups of nodes. A p value of <0.05 was taken as significant. Out of 65 nodes studied, 26 (100% enlarged cervical nodes with short axis diameter more than 1 cm were all malignant. In contrast 31 (79.5% of 39 enlarged nodes with short axis diameter less than 1cm were benign and rest were malignant (p<0.001. Of 34 enlarged nodes with L/S ratio <2, 30 (88.2% nodes were found malignant and 4 (11.8% were benign. Among the rest 31 enlarged nodes, 27 (87.1% with L/S ratio  2 were benign while 4 (12.9% were malignant (p <0.001. Among the 39 nodes with regular margin 28 (71.8% were found benign, where as among 26 nodes with irregular margin 23 (88.5% were malignant (p <0.001. When the internal echopattern was taken in account, 32 nodes had homogeneous hypoechoic echo of which 28 (87.5% were benign and among 33 nodes with heterogeneous echopattern 30 (90.9% were malignant (p <0.001. Among the 43 enlarged nodes with presence of hilar echogenicity 31(72.1% were found benign and 22 (100% nodes with no hilar echogenicity were all malignant (p <0.001. Such findings suggest that real time high resolution ultrasound might assist in differentiation of benign and malignant enlarged cervical lymph nodes

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.

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.

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

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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.

Improving the Specificity of EEG for Diagnosing Alzheimer's Disease

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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.

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

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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.

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.

Elucidating the Dark Side of Envy: Distinctive Links of Benign and Malicious Envy With Dark Personalities

Science.gov (United States)

Lange, Jens; Paulhus, Delroy L.; Crusius, Jan

2017-01-01

Researchers have recently drawn a contrast between two forms of envy: benign and malicious envy. In three studies (total N = 3,123), we challenge the assumption that malicious envy is destructive, whereas benign envy is entirely constructive. Instead, both forms have links with the Dark Triad of personality. Benign envy is associated with Machiavellian behaviors, whereas malicious envy is associated with both Machiavellian and psychopathic behaviors. In Study 1, this pattern emerged from meta-analyzed trait correlations. In Study 2, a manipulation affecting the envy forms mediated an effect on antisocial behavioral intentions. Study 3 replicated these patterns by linking envy to specific antisocial behaviors and their impact on status in the workplace. Together, our correlational and experimental results suggest that the two forms of envy can both be malevolent. Instead of evaluating envy’s morality, we propose to focus on its functional value. PMID:29271287

EEG indices in patients with high risk of ischemic stroke as predictors of initial disturbed cerebral circulation

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N. A. Isaeva

2014-01-01

Full Text Available Abnormal changes were detected in EEG in patients with high risk of ischemic stroke (higher level than in the population. These changes show the disturbances in forming mechanisms of functional condition of cerebrum during the calm wakeful period. Changes were represented by: the registration of EEG IV- type (the E.A. Zhirmunsky type which was characterized by disorganization of alpha activity and of slow waves; the instability of pattern during the record of background activity; the paroxysmal activity in form of flashes of the bilateral synchronized waves; the strengthening of low-frequency and high-amplitude β-activity. Revealed changes in EEG show the presence of initial disturbed cerebral circulation and can be recommended as predictors of these disturbances.

Non-linear Analysis of Scalp EEG by Using Bispectra: The Effect of the Reference Choice

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Federico Chella

2017-05-01

Full Text Available Bispectral analysis is a signal processing technique that makes it possible to capture the non-linear and non-Gaussian properties of the EEG signals. It has found various applications in EEG research and clinical practice, including the assessment of anesthetic depth, the identification of epileptic seizures, and more recently, the evaluation of non-linear cross-frequency brain functional connectivity. However, the validity and reliability of the indices drawn from bispectral analysis of EEG signals are potentially biased by the use of a non-neutral EEG reference. The present study aims at investigating the effects of the reference choice on the analysis of the non-linear features of EEG signals through bicoherence, as well as on the estimation of cross-frequency EEG connectivity through two different non-linear measures, i.e., the cross-bicoherence and the antisymmetric cross-bicoherence. To this end, four commonly used reference schemes were considered: the vertex electrode (Cz, the digitally linked mastoids, the average reference, and the Reference Electrode Standardization Technique (REST. The reference effects were assessed both in simulations and in a real EEG experiment. The simulations allowed to investigated: (i the effects of the electrode density on the performance of the above references in the estimation of bispectral measures; and (ii the effects of the head model accuracy in the performance of the REST. For real data, the EEG signals recorded from 10 subjects during eyes open resting state were examined, and the distortions induced by the reference choice in the patterns of alpha-beta bicoherence, cross-bicoherence, and antisymmetric cross-bicoherence were assessed. The results showed significant differences in the findings depending on the chosen reference, with the REST providing superior performance than all the other references in approximating the ideal neutral reference. In conclusion, this study highlights the importance of

On the use of EEG or MEG brain imaging tools in neuromarketing research.

Science.gov (United States)

Vecchiato, Giovanni; Astolfi, Laura; De Vico Fallani, Fabrizio; Toppi, Jlenia; Aloise, Fabio; Bez, Francesco; Wei, Daming; Kong, Wanzeng; Dai, Jounging; Cincotti, Febo; Mattia, Donatella; Babiloni, Fabio

2011-01-01

Here we present an overview of some published papers of interest for the marketing research employing electroencephalogram (EEG) and magnetoencephalogram (MEG) methods. The interest for these methodologies relies in their high-temporal resolution as opposed to the investigation of such problem with the functional Magnetic Resonance Imaging (fMRI) methodology, also largely used in the marketing research. In addition, EEG and MEG technologies have greatly improved their spatial resolution in the last decades with the introduction of advanced signal processing methodologies. By presenting data gathered through MEG and high resolution EEG we will show which kind of information it is possible to gather with these methodologies while the persons are watching marketing relevant stimuli. Such information will be related to the memorization and pleasantness related to such stimuli. We noted that temporal and frequency patterns of brain signals are able to provide possible descriptors conveying information about the cognitive and emotional processes in subjects observing commercial advertisements. These information could be unobtainable through common tools used in standard marketing research. We also show an example of how an EEG methodology could be used to analyze cultural differences between fruition of video commercials of carbonated beverages in Western and Eastern countries.

On the Use of EEG or MEG Brain Imaging Tools in Neuromarketing Research

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Giovanni Vecchiato

2011-01-01

Full Text Available Here we present an overview of some published papers of interest for the marketing research employing electroencephalogram (EEG and magnetoencephalogram (MEG methods. The interest for these methodologies relies in their high-temporal resolution as opposed to the investigation of such problem with the functional Magnetic Resonance Imaging (fMRI methodology, also largely used in the marketing research. In addition, EEG and MEG technologies have greatly improved their spatial resolution in the last decades with the introduction of advanced signal processing methodologies. By presenting data gathered through MEG and high resolution EEG we will show which kind of information it is possible to gather with these methodologies while the persons are watching marketing relevant stimuli. Such information will be related to the memorization and pleasantness related to such stimuli. We noted that temporal and frequency patterns of brain signals are able to provide possible descriptors conveying information about the cognitive and emotional processes in subjects observing commercial advertisements. These information could be unobtainable through common tools used in standard marketing research. We also show an example of how an EEG methodology could be used to analyze cultural differences between fruition of video commercials of carbonated beverages in Western and Eastern countries.

Family History of Cancer in Benign Brain Tumor Subtypes Versus Gliomas

Energy Technology Data Exchange (ETDEWEB)

Ostrom, Quinn T. [Department of Anthropology, Case Western Reserve University, Cleveland, OH (United States); McCulloh, Christopher [Case Western Reserve University School of Medicine, Cleveland, OH (United States); Chen, Yanwen; Devine, Karen; Wolinsky, Yingli, E-mail: qto@case.edu [Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH (United States)

2012-02-28

Purpose: Family history is associated with gliomas, but this association has not been established for benign brain tumors. Using information from newly diagnosed primary brain tumor patients, we describe patterns of family cancer histories in patients with benign brain tumors and compare those to patients with gliomas. Methods: Newly diagnosed primary brain tumor patients were identified as part of the Ohio Brain Tumor Study. Each patient was asked to participate in a telephone interview about personal medical history, family history of cancer, and other exposures. Information was available from 33 acoustic neuroma (65%), 78 meningioma (65%), 49 pituitary adenoma (73.1%), and 152 glioma patients (58.2%). The association between family history of cancer and each subtype was compared with gliomas using unconditional logistic regression models generating odds ratios (ORs) and 95% confidence intervals. Results: There was no significant difference in family history of cancer between patients with glioma and benign subtypes. Conclusion: The results suggest that benign brain tumor may have an association with family history of cancer. More studies are warranted to disentangle the potential genetic and/or environmental causes for these diseases.

Family History of Cancer in Benign Brain Tumor Subtypes Versus Gliomas

International Nuclear Information System (INIS)

Ostrom, Quinn T.; McCulloh, Christopher; Chen, Yanwen; Devine, Karen; Wolinsky, Yingli

2012-01-01

Purpose: Family history is associated with gliomas, but this association has not been established for benign brain tumors. Using information from newly diagnosed primary brain tumor patients, we describe patterns of family cancer histories in patients with benign brain tumors and compare those to patients with gliomas. Methods: Newly diagnosed primary brain tumor patients were identified as part of the Ohio Brain Tumor Study. Each patient was asked to participate in a telephone interview about personal medical history, family history of cancer, and other exposures. Information was available from 33 acoustic neuroma (65%), 78 meningioma (65%), 49 pituitary adenoma (73.1%), and 152 glioma patients (58.2%). The association between family history of cancer and each subtype was compared with gliomas using unconditional logistic regression models generating odds ratios (ORs) and 95% confidence intervals. Results: There was no significant difference in family history of cancer between patients with glioma and benign subtypes. Conclusion: The results suggest that benign brain tumor may have an association with family history of cancer. More studies are warranted to disentangle the potential genetic and/or environmental causes for these diseases.

Family history of cancer in benign brain tumor subtypes versus gliomas

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Quinn eOstrom

2012-02-01

Full Text Available Purpose: Family history is associated with gliomas, but this association has not ben established for benign brain tumors. Using information from newly diagnosed primary brain tumor patients, we describe patterns of family cancer histories in patients with benign brain tumors and compare those to patients with gliomas. Methods: Newly diagnosed primary brain tumor patients were identified as part of the Ohio Brain Tumor Study (OBTS. Each patient was asked to participate in a telephone interview about personal medical history, family history of cancer, and other exposures. Information was available from 33 acoustic neuroma (65%, 78 meningioma (65%, 49 pituitary adenoma (73.1% and 152 glioma patients (58.2%. The association between family history of cancer and each subtype was compared with gliomas using unconditional logistic regression models generating odds ratios (ORs and 95% confidence intervals (95% CI. Results: There was no significant difference in family history of cancer between patients with glioma and benign subtypes. Conclusions: The results suggest that benign brain tumor may have an association with family history of cancer. More studies are warranted to disentangle the potential genetic and/or environmental causes for these diseases.

Early EEG for outcome prediction of postanoxic coma : Prospective cohort study with cost-minimization analysis

NARCIS (Netherlands)

Sondag, Lotte; Ruijter, Barry J.; Tjepkema-Cloostermans, Marleen C.; Beishuizen, Albertus; Bosch, Frank H.; van Til, Janine A.; van Putten, Michel J.A.M.; Hofmeijer, Jeannette

2017-01-01

Background: We recently showed that electroencephalography (EEG) patterns within the first 24 hours robustly contribute to multimodal prediction of poor or good neurological outcome of comatose patients after cardiac arrest. Here, we confirm these results and present a cost-minimization analysis.

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.

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.

Intracranial EEG fluctuates over months after implanting electrodes in human brain

Science.gov (United States)

Ung, Hoameng; Baldassano, Steven N.; Bink, Hank; Krieger, Abba M.; Williams, Shawniqua; Vitale, Flavia; Wu, Chengyuan; Freestone, Dean; Nurse, Ewan; Leyde, Kent; Davis, Kathryn A.; Cook, Mark; Litt, Brian

2017-10-01

Objective. Implanting subdural and penetrating electrodes in the brain causes acute trauma and inflammation that affect intracranial electroencephalographic (iEEG) recordings. This behavior and its potential impact on clinical decision-making and algorithms for implanted devices have not been assessed in detail. In this study we aim to characterize the temporal and spatial variability of continuous, prolonged human iEEG recordings. Approach. Intracranial electroencephalography from 15 patients with drug-refractory epilepsy, each implanted with 16 subdural electrodes and continuously monitored for an average of 18 months, was included in this study. Time and spectral domain features were computed each day for each channel for the duration of each patient’s recording. Metrics to capture post-implantation feature changes and inflexion points were computed on group and individual levels. A linear mixed model was used to characterize transient group-level changes in feature values post-implantation and independent linear models were used to describe individual variability. Main results. A significant decline in features important to seizure detection and prediction algorithms (mean line length, energy, and half-wave), as well as mean power in the Berger and high gamma bands, was observed in many patients over 100 d following implantation. In addition, spatial variability across electrodes declines post-implantation following a similar timeframe. All selected features decreased by 14-50% in the initial 75 d of recording on the group level, and at least one feature demonstrated this pattern in 13 of the 15 patients. Our findings indicate that iEEG signal features demonstrate increased variability following implantation, most notably in the weeks immediately post-implant. Significance. These findings suggest that conclusions drawn from iEEG, both clinically and for research, should account for spatiotemporal signal variability and that properly assessing the iEEG in

Deep learning with convolutional neural networks for EEG decoding and visualization

Science.gov (United States)

Springenberg, Jost Tobias; Fiederer, Lukas Dominique Josef; Glasstetter, Martin; Eggensperger, Katharina; Tangermann, Michael; Hutter, Frank; Burgard, Wolfram; Ball, Tonio

2017-01-01

Abstract Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end‐to‐end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end‐to‐end EEG analysis, but a better understanding of how to design and train ConvNets for end‐to‐end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task‐related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG‐based brain mapping. Hum Brain Mapp 38:5391–5420, 2017. © 2017 Wiley Periodicals, Inc. PMID:28782865

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

Science.gov (United States)

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.

The long-term course of temporal lobe epilepsy: From unilateral to bilateral interictal epileptiform discharges in repeated video-EEG monitorings.

Science.gov (United States)

Gollwitzer, Stephanie; Scott, Catherine A; Farrell, Fiona; Bell, Gail S; de Tisi, Jane; Walker, Matthew C; Wehner, Tim; Sander, Josemir W; Hamer, Hajo M; Diehl, Beate

2017-03-01

Bilateral interictal epileptiform discharges (IED) and ictal patterns are common in temporal lobe epilepsy (TLE) and have been associated with decreased chances of seizure freedom after epilepsy surgery. It is unclear whether secondary epileptogenesis, although demonstrated in experimental models, exists in humans and may account for progression of epilepsy. We reviewed consecutive video-EEG recordings from 1992 to 2014 repeated at least two years apart (mean interval 6.14years) in 100 people diagnosed with TLE. Ictal EEG patterns and IED remained restricted to one hemisphere in 36 people (group 1), 46 exhibited bilateral abnormalities from the first recording (group 2), 18 progressed from unilateral to bilateral EEG pathology over time (group 3). No significant differences between the three groups were seen with respect to age at epilepsy onset, duration, or underlying pathology. Extra-temporal IED during the first EEG recording were associated with an increased risk of developing bilateral epileptiform changes over time (hazard ratio 3.67; 95% CI 1.4, 9.4). Our findings provide some support of progression in TLE and raise the possibility of secondary epileptogenesis in humans. The development of an independent contra-lateral epileptogenic focus is known to be associated with a less favorable surgical outcome. We defined reliable EEG markers for an increased risk of progression to more widespread or independent bitemporal epileptogenicity at an early stage, thus allowing for individualized pre-surgical counselling. Copyright © 2016 Elsevier Inc. All rights reserved.

EEG activity during estral cycle in the rat.

Science.gov (United States)

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.

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.

Epileptic seizure detection from EEG signals with phase-amplitude cross-frequency coupling and support vector machine

Science.gov (United States)

Liu, Yang; Wang, Jiang; Cai, Lihui; Chen, Yingyuan; Qin, Yingmei

2018-03-01

As a pattern of cross-frequency coupling (CFC), phase-amplitude coupling (PAC) depicts the interaction between the phase and amplitude of distinct frequency bands from the same signal, and has been proved to be closely related to the brain’s cognitive and memory activities. This work utilized PAC and support vector machine (SVM) classifier to identify the epileptic seizures from electroencephalogram (EEG) data. The entropy-based modulation index (MI) matrixes are used to express the strength of PAC, from which we extracted features as the input for classifier. Based on the Bonn database, which contains five datasets of EEG segments obtained from healthy volunteers and epileptic subjects, a 100% classification accuracy is achieved for identifying seizure ictal from healthy data, and an accuracy of 97.67% is reached in the classification of ictal EEG signals from inter-ictal EEGs. Based on the CHB-MIT database which is a group of continuously recorded epileptic EEGs by scalp electrodes, a 97.50% classification accuracy is obtained and a raising sign of MI value is found at 6s before seizure onset. The classification performance in this work is effective, and PAC can be considered as a useful tool for detecting and predicting the epileptic seizures and providing reference for clinical diagnosis.

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...

Post-task Effects on EEG Brain Activity Differ for Various Differential Learning and Contextual Interference Protocols

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Diana Henz

2018-01-01

Full Text Available A large body of research has shown superior learning rates in variable practice compared to repetitive practice. More specifically, this has been demonstrated in the contextual interference (CI and in the differential learning (DL approach that are both representatives of variable practice. Behavioral studies have indicate different learning processes in CI and DL. Aim of the present study was to examine immediate post-task effects on electroencephalographic (EEG brain activation patterns after CI and DL protocols that reveal underlying neural processes at the early stage of motor consolidation. Additionally, we tested two DL protocols (gradual DL, chaotic DL to examine the effect of different degrees of stochastic fluctuations within the DL approach with a low degree of fluctuations in gradual DL and a high degree of fluctuations in chaotic DL. Twenty-two subjects performed badminton serves according to three variable practice protocols (CI, gradual DL, chaotic DL, and a repetitive learning protocol in a within-subjects design. Spontaneous EEG activity was measured before, and immediately after each 20-min practice session from 19 electrodes. Results showed distinguishable neural processes after CI, DL, and repetitive learning. Increases in EEG theta and alpha power were obtained in somatosensory regions (electrodes P3, P7, Pz, P4, P8 in both DL conditions compared to CI, and repetitive learning. Increases in theta and alpha activity in motor areas (electrodes C3, Cz, C4 were found after chaotic DL compared to gradual DL, and CI. Anterior areas (electrodes F3, F7, Fz, F4, F8 showed increased activity in the beta and gamma bands after CI. Alpha activity was increased in occipital areas (electrodes O1, O2 after repetitive learning. Post-task EEG brain activation patterns suggest that DL stimulates the somatosensory and motor system, and engages more regions of the cortex than repetitive learning due to a tighter stimulation of the motor and

Clinical neurophysiology referral patterns to a tertiary hospital--a prospective audit.

LENUS (Irish Health Repository)

Renganathan, R

2012-02-03

BACKGROUND: Cork University Hospital (CUH) provides a tertiary service for all neurophysiology referrals in the Southern Health Board region. AIM: To ascertain the number, source, symptoms and diagnosis of neurophysiology referrals at CUH. METHODS: We did a prospective audit of the referral patterns to the neurophysiology department over a 12 -week period. RESULTS: Of 635 referrals, 254 had electromyograms (EMG), 359 had electro-encephalograms (EEG), 18 had visual evoked potentials (VEP), three had somato-sensory evoked potentials (SSEP) and one had multiple sleep latency tests (MSLT). We analysed the demographic pattern, reason for referrals, the average waiting time for neurophysiology tests and the patterns of diagnosis in this audit. CONCLUSIONS: Patients from County Cork are making more use of the neurophysiology services than patients from other counties within the Southern Health Board. The average waiting time for an EEG was 32 days and for an EMG was 74 days. However, more than 35% of those patients waiting for an EEG or an EMG had their tests done within four weeks of referral. The appointments of EEG and EMG were assigned on the basis of clinical need.

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.

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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.

Guiding transcranial brain stimulation by EEG/MEG to interact with ongoing brain activity and associated functions

DEFF Research Database (Denmark)

Thut, Gregor; Bergmann, Til Ole; Fröhlich, Flavio

2017-01-01

of NTBS with respect to the ongoing brain activity. Temporal patterns of ongoing neuronal activity, in particular brain oscillations and their fluctuations, can be traced with electro- or magnetoencephalography (EEG/MEG), to guide the timing as well as the stimulation settings of NTBS. These novel, online...... and offline EEG/MEG-guided NTBS-approaches are tailored to specifically interact with the underlying brain activity. Online EEG/MEG has been used to guide the timing of NTBS (i.e., when to stimulate): by taking into account instantaneous phase or power of oscillatory brain activity, NTBS can be aligned......Non-invasive transcranial brain stimulation (NTBS) techniques have a wide range of applications but also suffer from a number of limitations mainly related to poor specificity of intervention and variable effect size. These limitations motivated recent efforts to focus on the temporal dimension...

Functional community analysis of brain: a new approach for EEG-based investigation of the brain pathology.

Science.gov (United States)

Ahmadlou, Mehran; Adeli, Hojjat

2011-09-15

Analysis of structure of the brain functional connectivity (SBFC) is a fundamental issue for understanding of the brain cognition as well as the pathology of brain disorders. Analysis of communities among sub-parts of a system is increasingly used for social, ecological, and other networks. This paper presents a new methodology for investigation of the SBFC and understanding of the brain based on graph theory and community pattern analysis of functional connectivity graph of the brain obtained from encephalograms (EEGs). The methodology consists of three main parts: fuzzy synchronization likelihood (FSL), community partitioning, and decisions based on partitions. As an example application, the methodology is applied to analysis of brain of patients with attention deficit/hyperactivity disorder (ADHD) and the problem of discrimination of ADHD EEGs from healthy (non-ADHD) EEGs. Copyright © 2011. Published by Elsevier Inc.

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.

MRI features in differentiation borderline from benign mucinous ovarian cystadenoma

International Nuclear Information System (INIS)

Zhao Shuhui; Qiang Jinwei; Zhang Guofu; Qiu Haiying; Wang Xuezhen; Wang Li

2012-01-01

Objective: To investigate MRI features for differentiating borderline from benign mucinous cystadenoma (MC) of the ovary. Methods: Twenty three patients with 23 benign MCs and 19 patients with 20 borderline mucinous cystadenomas (BMC)proven by surgery and pathology underwent MRI, with 23 benign MCs and 20 BMC. MRI features of tumor were evaluated and compared between two groups including location, shape, size, loculation, signal intensity of the fluid, thickness of septa and wall, and vegetations. The findings were correlated with those of pathology. The loculation, the signal intensity of the intracystic content, the thickness of the septation and the wall, and the vegetations between the benign MCs and the BMCs were compared using the Chi-square test.Results Homogenous low signal on T 1 WI and homogenous high signal on T 2 WI were the main signal patterns of benign MC seen more commonly in benign MC (18/23 and 17/23, respectively) than in BMC (5/20 and 8/20, respectively) (χ 2 =12.1979, 5.0553; P<0.05). The honeycomb loculi, high signal on T 1 WI, low signal on T 2 WI, thickened septa or wall (≥5 mm), and vegetations (≥5 mm) were significantly more common in BMC (10/20, 9/20, 8/20, 10/20 and 14/20, respectively) than in benign MC(4/23, 3/23, 1/23, 1/23 and 1/23, respectively) (χ 2 =5.1804, 5.4300, 8.2163, 11.7113 and 20.2990, P<0.05), with the sensitivity and specificity for characterizing BMC of 50.0% and 82.6%, 45.0% and 87.0%, 40.0% and 95.7%, 50.0% and 95.7%, and 70.0% and 95.7%, respectively. When one of honeycomb loculi with low signal on T 2 WI, thickened septa or wall (≥5 mm), and vegetations (≥5 mm) were found, the sensitivity, specificity and accuracy for characterizing BMC were 90.0%, 91.3% and 90.7% respectively. Conclusion: MRI is accurate for demonstrating morphological features of ovarian MC which well correlated to pathological characteristics, and for differentiating BMC from benign MC, thus helpful for making surgery strategy. (authors)

Role of MR imaging in the differentiation of benign and nonbenign intracranial meningiomas. The utility of contrast-enhanced T1-weighted images

International Nuclear Information System (INIS)

Tanaka, Yasunori; Matsuo, Michimasa

1996-01-01

The purpose of this study was to develop useful criteria for distinguishing nonbenign (atypical and malignant) primary intracranial meningiomas from their benign counterparts by using magnetic resonance imaging (MRI). To determine useful MRI findings for this purpose, 12 benign and five nonbenign meningiomas were retrospectively evaluated according to the following items: tumor signal intensity on plain T1-, T2- and proton density-weighted images, degree of perifocal edema on T2-weighted images, morphology of the tumor margin on contrast-enhanced T1-weighted images, presence of irregular nodule and/or mushrooming pattern on contrast-enhanced T1-weighted images, homogeneity of the tumor on contrast-enhanced T1-weighted images, and presence of marked skull destruction. Markedly irregular tumor margin, presence of irregular nodule and/or mushrooming pattern and markedly inhomogeneous enhancing pattern were significantly more frequent in nonbenign meningiomas. We defined these three MRI findings as nonbenign findings, and tried to categorize meningiomas by the number of nonbenign findings. It was found that 10 meningiomas with no or one nonbenign finding were benign lesions, of four meningiomas with two nonbenign findings two were benign lesions and two were nonbenign lesions, and three meningiomas with three nonbenign findings were nonbenign lesions. The two benign meningiomas with two nonbenign findings were accompanied by increased mitotic activity or brain invasion. Contrast-enhanced T1-weighted images were considered very useful in distinguishing benign and nonbenign meningiomas. (author)

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.

EEG frontal asymmetry related to pleasantness of music perception in healthy children and cochlear implanted users.

Science.gov (United States)

Vecchiato, G; Maglione, A G; Scorpecci, A; Malerba, P; Marsella, P; Di Francesco, G; Vitiello, S; Colosimo, A; Babiloni, Fabio

2012-01-01

Interestingly, the international debate about the quality of music fruition for cochlear implanted users does not take into account the hypothesis that bilateral users could perceive music in a more pleasant way with respect to monolateral users. In this scenario, the aim of the present study was to investigate if cerebral signs of pleasantness during music perception in healthy child are similar to those observed in monolateral and in bilateral cochlear implanted users. In fact, previous observations in literature on healthy subjects have indicated that variations of the frontal EEG alpha activity are correlated with the perceived pleasantness of the sensory stimulation received (approach-withdrawal theory). In particular, here we described differences between cortical activities estimated in the alpha frequency band for a healthy child and in patients having a monolateral or a bilateral cochlear implant during the fruition of a musical cartoon. The results of the present analysis showed that the alpha EEG asymmetry patterns observed in a healthy child and that of a bilateral cochlear implanted patient are congruent with the approach-withdrawal theory. Conversely, the scalp topographic distribution of EEG power spectra in the alpha band resulting from the monolateral cochlear user presents a different EEG pattern from the normal and bilateral implanted patients. Such differences could be explained at the light of the approach-withdrawal theory. In fact, the present findings support the hypothesis that a monolateral cochlear implanted user could perceive the music in a less pleasant way when compared to a healthy subject or to a bilateral cochlear user.

Estimating repetitive spatiotemporal patterns from resting-state brain activity data.

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Takeda, Yusuke; Hiroe, Nobuo; Yamashita, Okito; Sato, Masa-Aki

2016-06-01

Repetitive spatiotemporal patterns in spontaneous brain activities have been widely examined in non-human studies. These studies have reported that such patterns reflect past experiences embedded in neural circuits. In human magnetoencephalography (MEG) and electroencephalography (EEG) studies, however, spatiotemporal patterns in resting-state brain activities have not been extensively examined. This is because estimating spatiotemporal patterns from resting-state MEG/EEG data is difficult due to their unknown onsets. Here, we propose a method to estimate repetitive spatiotemporal patterns from resting-state brain activity data, including MEG/EEG. Without the information of onsets, the proposed method can estimate several spatiotemporal patterns, even if they are overlapping. We verified the performance of the method by detailed simulation tests. Furthermore, we examined whether the proposed method could estimate the visual evoked magnetic fields (VEFs) without using stimulus onset information. The proposed method successfully detected the stimulus onsets and estimated the VEFs, implying the applicability of this method to real MEG data. The proposed method was applied to resting-state functional magnetic resonance imaging (fMRI) data and MEG data. The results revealed informative spatiotemporal patterns representing consecutive brain activities that dynamically change with time. Using this method, it is possible to reveal discrete events spontaneously occurring in our brains, such as memory retrieval. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Analysis of EEG activity during sleep - brain hemisphere symmetry of two classes of sleep spindles

Science.gov (United States)

Smolen, Magdalena M.

2009-01-01

This paper presents automatic analysis of some selected human electroencephalographic patterns during deep sleep using the Matching Pursuit (MP) algorithm. The periodicity of deep sleep EEG patterns was observed by calculating autocorrelation functions of their percentage contributions. The study confirmed the increasing trend of amplitude-weighted average frequency of sleep spindles from frontal to posterior derivations. The dominant frequencies from the left and the right brain hemisphere were strongly correlated.

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.

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....

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.

Differentiation of specific ripple patterns helps to identify epileptogenic areas for surgical procedures.

Science.gov (United States)

Kerber, Karolin; Dümpelmann, Matthias; Schelter, Björn; Le Van, Pierre; Korinthenberg, Rudolf; Schulze-Bonhage, Andreas; Jacobs, Julia

2014-07-01

High frequency oscillations (HFOs) at 80-500 Hz are promising markers of epileptic areas. Several retrospective studies reported that surgical removal of areas generating HFOs was associated with a good seizure outcome. Recent reports suggested that ripple (80-200 Hz) HFO patterns co-existed with different background EEG activities. We hypothesized that the coexisting background EEG pattern may distinguish physiological from epileptic ripples. Rates of HFOs were analyzed in intracranial EEG recordings of 22 patients. Additionally, ripple patterns were classified for each channel depending either as coexisting with a flat or oscillatory background activity. A multi-variate analysis was performed to determine whether removal of areas showing the above EEG markers correlated with seizure outcome. Removal of areas generating high rates of 'fast ripples (>200 Hz)' and 'ripples on a flat background activity' showed a significant correlation with a seizure-free outcome. In contrast, removal of high rates of 'ripples' or 'ripple patterns in a continuously oscillating background' was not significantly associated with seizure outcome. Ripples occurring in an oscillatory background activity may be suggestive of physiological activity, while those on a flat background reflect epileptic activity. Consideration of coexisting background patterns may improve the delineation of the epileptogenic areas using ripple oscillations. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

EEG (Electroencephalogram)

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... 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 ...

Differences in the perceived music pleasantness between monolateral cochlear implanted and normal hearing children assessed by EEG.

Science.gov (United States)

Vecchiato, G; Maglione, A G; Scorpecci, A; Malerba, P; Graziani, I; Cherubino, P; Astolfi, L; Marsella, P; Colosimo, A; Babiloni, Fabio

2013-01-01

The perception of the music in cochlear implanted (CI) patients is an important aspect of their quality of life. In fact, the pleasantness of the music perception by such CI patients can be analyzed through a particular analysis of EEG rhythms. Studies on healthy subjects show that exists a particular frontal asymmetry of the EEG alpha rhythm which can be correlated with pleasantness of the perceived stimuli (approach-withdrawal theory). In particular, here we describe differences between EEG activities estimated in the alpha frequency band for a monolateral CI group of children and a normal hearing one during the fruition of a musical cartoon. The results of the present analysis showed that the alpha EEG asymmetry patterns related to the normal hearing group refers to a higher pleasantness perception when compared to the cerebral activity of the monolateral CI patients. In fact, the present results support the statement that a monolateral CI group could perceive the music in a less pleasant way when compared to normal hearing children.

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.

How about a Bayesian M/EEG imaging method correcting for incomplete spatio-temporal priors

DEFF Research Database (Denmark)

Stahlhut, Carsten; Attias, Hagai T.; Sekihara, Kensuke

2013-01-01

previous spatio-temporal inverse M/EEG models, the proposed model benefits of consisting of two source terms, namely, a spatio-temporal pattern term limiting the source configuration to a spatio-temporal subspace and a source correcting term to pick up source activity not covered by the spatio......-temporal prior belief. We have tested the model on both artificial data and real EEG data in order to demonstrate the efficacy of the model. The model was tested at different SNRs (-10.0,-5.2, -3.0, -1.0, 0, 0.8, 3.0 dB) using white noise. At all SNRs the sAquavit performs best in AUC measure, e.g. at SNR=0d...

FDG PET/CT detects benign neurofibromas presenting as nodal masses: Imaging hallmarks of a diagnostic “red herring”

International Nuclear Information System (INIS)

Puranik, Ameya D.; Purandare, Nilendu C.; Bal, Munita M.; Agrawal, Archi; Shah, Sneha; Rangarajan, Venkatesh

2015-01-01

Multi-modality positron emission tomography/computed tomography (PET/CT) with 18F-fluoro-deoxy-glucose (FDG) depicts the enhancement pattern and metabolic intensity of lesions. Benign lesions with multiplicity, like neurofibromas often mimic similar appearing malignant neoplasms. We, report, a similar case where FDG PET/CT shows imaging hallmarks for diagnosing benign neurofibromas, in a patient with clinical presentation of lymphoma

Clinical and EEG features of ischemic stroke patients with abnormal discharges

Directory of Open Access Journals (Sweden)

Jia-lei YANG

2016-05-01

generalized seizure.  Conclusions Infract site has an influence on the abnormal EEG pattern, and the unmatched location of ischemic region and abnormal EEG may be indicative for the seizure type of post-ischemic epilepsy. DOI: 10.3969/j.issn.1672-6731.2016.05.008

[EEG frequency and regional properties in patients with paranoid schizophrenia: effects of positive and negative symptomatology prevalence].

Science.gov (United States)

Bochkarev, V K; Kirenskaya, A V; Tkachenko, A A; Samylkin, D V; Novototsky-Vlasov, V Yu; Kovaleva, M E

2015-01-01

EEG changes in schizophrenic patients are caused by a multitude of factors related to clinical heterogeneity of the disease, current state of patients, and conducted therapy. EEG spectral analysis remains an actual methodical approach for the investigation of the neurophysiological mechanisms of the disease. The goal of the investigation was the study of frequency and regional EEG correlating with the intensity of productive and negative disorders. Models of summary prevalence of positive/negative disorders and evidence of concrete clinical indices of the PANSS scale were used. Spectral characteristics of background EEG in the frequency range of 1-60 Hz were studied in 35 patients with paranoid schizophrenia free from psychoactive medication and in 19 healthy volunteers. It was established that the main index of negative symptomatology in summary assessment was diffuse increase of spectral power of gamma and delta ranges. Deficient states with the predominance of volitional disorders were characterized by a lateralized increase of spectral power of beta-gamma ranges in the left hemisphere, and of delta range - in frontal areas of this hemisphere. Positive symptomatology was noticeably less reflected in EEG changes than negative ones. An analysis of psychopathological symptom complexes revealed the significance of spatially structured EEG patterns in the beta range: for the delusion disturbances with psychic automatism phenomena - in frontal areas of the left hemisphere, and for the paranoid syndrome with primary interpretative delusion - in cortical areas of the right hemisphere.

A Clinico-Pathological Study on Benign Breast Diseases

Science.gov (United States)

Sangma, Mima B. Maychet; Panda, Kishori; Dasiah, Simon

2013-01-01

Background: To study the patterns of clinically benign breast disease in females and to co-relate them with the pathological findings. Methods: One hundred females who attended the Surgery Outpatients Department in Indira Gandhi Medical College and Research Institute, Pondicherry, with various forms of benign breast diseases during the period from October 2011 to September 2012, were studied. Early diagnoses by doing a triple assessment like a clinical examination, FNAC or a core needle biopsy and imaging methods like ultrasonography or mammography, were made within 72 hrs from the first consultation. The clinical diagnoses were compared with the cytological or histological findings wherever possible and their accuracies were evaluated. Results: Out of the 100 female patients who were studied, 87 patients who presented with breast lumps and fibroadenoma, accounted for 48% of the cases, which was the highest number of patients. Fibrocystic changes and breast abscesses came next with 18% and 12% cases respectively. We detected 3 cases of proliferative disease with atypia and one case with florid hyperplasia, which had high and low risk factors respectively, for developing invasive carcinoma. The oldest lady of the group who was clinically diagnosed to have benign disease, was detected to have invasive ductal carcinoma. They were treated in our hospital and were advised follow up. Conclusion: Benign breast diseases are common in female patients and fibroadenoma is the commonest of them all. Triple assessment provided a quick diagnosis and it alleviated unnecessary anxiety from the patients about breast cancer. The clinical diagnosis of a breast lump, as confirmed by cytology and histology, was accurate in 91.95 % of the cases. PMID:23634406

Assessment of dynamic contrast-enhanced magnetic resonance imaging in the differentiation of malignant from benign orbital masses

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Yuan, Ying [Department of Radiology, Shanghai Ninth People' s Hospital, Affiliated to JiaoTong University School of Medicine, Shanghai 200011 (China); Kuai, Xin-Ping [Department of Radiology, Changshu Second People' s Hospital, Jiangsu Province 215500 (China); Department of Radiology, Changzheng Hospital, Affiliated to Second Military Medical University, Shanghai 200003 (China); Chen, Xiao-Song [Comprehensive Breast Health Center, Ruijin Hospital Shanghai Jiaotong University School of Medicine, 197 Ruijin Er Road, Shanghai 20025 (China); Tao, Xiao-Feng, E-mail: cjr.taoxiaofeng@vip.163.com [Department of Radiology, Shanghai Ninth People' s Hospital, Affiliated to JiaoTong University School of Medicine, Shanghai 200011 (China)

2013-09-15

Objective: Dynamic contrast enhanced MR imaging (DCE-MRI) allows imaging of the physiology of the microcirculation. The purpose of this study was to determine the diagnostic efficacy of time intensity curve (TIC) and DCE parameters for characterization of orbital masses. Methods: Fifty-nine patients with untreated orbital lesions underwent DCE-MRI before surgery. For each lesion, peak height (PH), maximum enhancement ratio (ER{sub max}), time of peak enhancement (T{sub peak}) and maximum rise slope (Slope{sub max}) were plotted and calculated. Receiver operator characteristics (ROC) analysis was conducted to assess the appropriate cut-off value. Results: All 26 lesions that demonstrated persistent pattern (type-I) TICs were benign. Most of the masses with the washout pattern (type-III) TIC were malignant (10/14), including lymphoma (n = 6) and melanoma (n = 4). The Slope{sub max} of benign lesions was statistically lower than malignant ones, while the ER{sub max} and T{sub peak} values of benign lesions were significantly higher. No statistical difference was found in PH (P = 0.121). The AUC for ER{sub max}, T{sub peak} and Slope{sub max} in differentiating benign orbital lesions from malignant ones were 0.683, 0.837 and 0.738, respectively. In the three DCE parameters, Slope{sub max} cut-off value of 1.10 provided the highest sensitivity of 93.8%; however, the corresponding specificity was low (58.1%). The ER{sub max} cut-off value of 1.37 and T{sub peak} cut-off value of 35.14 respectively offered the best diagnostic performances. Conclusion: DCE-MRI, especially the qualitative TIC pattern and quantitative value of Slope{sub max}, ER{sub max} and T{sub peak}, could be a complementary investigation in distinguishing malignant orbital tumor from benign ones.

Diagnostic value of p53 and M67 immunostaining for distinguishing benign from malignant serous effusions

International Nuclear Information System (INIS)

Hafez, N.H.; Tahoun, N.S.

2011-01-01

23 cases (65.2%) that showed ki67 immunostaining were benign. Conclusion: Benign and malignant effusions showed significantly different staining pattern for p53 and ki67. When used individually, p53 immunostaining can truly diagnose 75.6% and 94% of the malignant and benign cases, respectively. ki67 immunostaining can correctly identify 73.2% and 66% of the malignant and benign cases, respectively. When used in combination, 91.7% of p53 and ki67 positive cases were malignant while 94% of pS3 and ki67 negative cases were benign. Hence they could be used when the cytomorphology fails to provide a definitive diagnosis.malignant. Thirty two out of 34 cases (94.1%) that showed negative results for both antigens were benign. For the cases that showed p53 immunostaining only, 9 out of 10 cases (90%) were malignant. Fifteen out of 23 cases (65.2%) that showed ki67 immunostaining were benign. Conclusion: Benign and malignant effusions showed significantly different staining pattern for p53 and ki67. When used individually, p53 immunostaining can truly diagnose 75.6% and 94% of the malignant and benign cases, respectively. ki67 immunostaining can correctly identify 73.2% and 66% of the malignant and benign cases, respectively. When used in combination, 91.7% of p53 and ki67 positive cases were malignant while 94% of pS3 and ki67 negative cases were benign. Hence they could be used when the cytomorphology fails to provide a definitive diagnosis.

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.

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.

EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement

Science.gov (United States)

Shenoy Handiru, Vikram; Vinod, A. P.; Guan, Cuntai

2017-08-01

Objective. In electroencephalography (EEG)-based brain-computer interface (BCI) systems for motor control tasks the conventional practice is to decode motor intentions by using scalp EEG. However, scalp EEG only reveals certain limited information about the complex tasks of movement with a higher degree of freedom. Therefore, our objective is to investigate the effectiveness of source-space EEG in extracting relevant features that discriminate arm movement in multiple directions. Approach. We have proposed a novel feature extraction algorithm based on supervised factor analysis that models the data from source-space EEG. To this end, we computed the features from the source dipoles confined to Brodmann areas of interest (BA4a, BA4p and BA6). Further, we embedded class-wise labels of multi-direction (multi-class) source-space EEG to an unsupervised factor analysis to make it into a supervised learning method. Main Results. Our approach provided an average decoding accuracy of 71% for the classification of hand movement in four orthogonal directions, that is significantly higher (>10%) than the classification accuracy obtained using state-of-the-art spatial pattern features in sensor space. Also, the group analysis on the spectral characteristics of source-space EEG indicates that the slow cortical potentials from a set of cortical source dipoles reveal discriminative information regarding the movement parameter, direction. Significance. This study presents evidence that low-frequency components in the source space play an important role in movement kinematics, and thus it may lead to new strategies for BCI-based neurorehabilitation.

Discovering EEG resting state alterations of semantic dementia.

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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.

Stimulus-dependent spiking relationships with the EEG

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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

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

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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.

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....

Investigating social cognition in infants and adults using dense array electroencephalography ((d)EEG).

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Akano, Adekemi J; Haley, David W; Dudek, Joanna

2011-06-27

Dense array electroencephalography ((d)EEG), which provides a non-invasive window for measuring brain activity and a temporal resolution unsurpassed by any other current brain imaging technology¹, ² is being used increasingly in the study of social cognitive functioning in infants and adults. While (d)EEG is enabling researchers to examine brain activity patterns with unprecedented levels of sensitivity, conventional EEG recording systems continue to face certain limitations, including 1) poor spatial resolution and source localization³,⁴2) the physical discomfort for test subjects of enduring the individual application of numerous electrodes to the surface of the scalp, and 3) the complexity for researchers of learning to use multiple software packages to collect and process data. Here we present an overview of an established methodology that represents a significant improvement on conventional methodologies for studying EEG in infants and adults. Although several analytical software techniques can be used to establish indirect indices of source localization to improve the spatial resolution of (d)EEG, the HydroCel Geodesic Sensor Net (HCGSN) by Electrical Geodesics, Inc. (EGI), a dense sensory array that maintains equal distances among adjacent recording electrodes on all surfaces of the scalp, further enhances spatial resolution⁴,⁵(,)⁶ compared to standard (d)EEG systems. The sponge-based HCGSN can be applied rapidly and without scalp abrasion, making it ideal for use with adults⁷,⁸ children⁹,¹⁰, ¹¹,¹² and infants¹², in both research and clinical ⁴,⁵,⁶,¹³,¹⁴,¹⁵settings. This feature allows for considerable cost and time savings by decreasing the average net application time compared to other (d)EEG systems. Moreover, the HCGSN includes unified, seamless software applications for all phases of data, greatly simplifying the collection, processing, and analysis of (d)EEG data. The HCGSN features a low-profile electrode

Comparative neuroimaging in children with cerebral palsy using fMRI and a novel EEG-based brain mapping during a motor task--a preliminary investigation.

Science.gov (United States)

Lee, Jae Jin; Lee, Dong Ryul; Shin, Yoon Kyum; Lee, Nam Gi; Han, Bong S; You, Sung Joshua Hyun

2013-01-01

The purpose of this study was to compare topographical maps using a novel EEG-based brain mapping system with fMRI in normal and children with cerebral palsy (CP) during a grasping motor task. A normal child (mean ± SD = 13 ± 0 yrs) and four children with CP (mean ± SD = 10.25 ± 2.86 yrs) were recruited from a local community school and medical center. A novel EEG-based brain mapping system with 30 scalp sites (an extension of the 10-20 system) and a 3T MR scanner were used to observe cortical activation patterns during a grasping motor task. Descriptive analysis. In the EEG brain mapping data, the sensorimotor cortex (SMC) and inferior parietal cortex (IPC) were activated in all of the children. The children with CP showed additional activation areas in the premotor cortex (PMC), superior parietal cortex (SPC), and prefrontal cortex (PFC). In the fMRI brain mapping data, SMC activation was observed in all of the children, and the children with CP showed additional activation areas in the PMC and primary somatosensory cortex (PSC). The EEG-based topographical maps were equivalent to the maps obtained from fMRI during the grasping motor task. The results indicate that our novel EEG-based brain mapping system is useful for probing cortical activation patterns in normal children and children with CP.

Directed Motor-Auditory EEG Connectivity Is Modulated by Music Tempo.

Science.gov (United States)

Nicolaou, Nicoletta; Malik, Asad; Daly, Ian; Weaver, James; Hwang, Faustina; Kirke, Alexis; Roesch, Etienne B; Williams, Duncan; Miranda, Eduardo R; Nasuto, Slawomir J

2017-01-01

Beat perception is fundamental to how we experience music, and yet the mechanism behind this spontaneous building of the internal beat representation is largely unknown. Existing findings support links between the tempo (speed) of the beat and enhancement of electroencephalogram (EEG) activity at tempo-related frequencies, but there are no studies looking at how tempo may affect the underlying long-range interactions between EEG activity at different electrodes. The present study investigates these long-range interactions using EEG activity recorded from 21 volunteers listening to music stimuli played at 4 different tempi (50, 100, 150 and 200 beats per minute). The music stimuli consisted of piano excerpts designed to convey the emotion of "peacefulness". Noise stimuli with an identical acoustic content to the music excerpts were also presented for comparison purposes. The brain activity interactions were characterized with the imaginary part of coherence (iCOH) in the frequency range 1.5-18 Hz (δ, θ, α and lower β) between all pairs of EEG electrodes for the four tempi and the music/noise conditions, as well as a baseline resting state (RS) condition obtained at the start of the experimental task. Our findings can be summarized as follows: (a) there was an ongoing long-range interaction in the RS engaging fronto-posterior areas; (b) this interaction was maintained in both music and noise, but its strength and directionality were modulated as a result of acoustic stimulation; (c) the topological patterns of iCOH were similar for music, noise and RS, however statistically significant differences in strength and direction of iCOH were identified; and (d) tempo had an effect on the direction and strength of motor-auditory interactions. Our findings are in line with existing literature and illustrate a part of the mechanism by which musical stimuli with different tempi can entrain changes in cortical activity.

Directed Motor-Auditory EEG Connectivity Is Modulated by Music Tempo

Directory of Open Access Journals (Sweden)

Nicoletta Nicolaou

2017-10-01

Full Text Available Beat perception is fundamental to how we experience music, and yet the mechanism behind this spontaneous building of the internal beat representation is largely unknown. Existing findings support links between the tempo (speed of the beat and enhancement of electroencephalogram (EEG activity at tempo-related frequencies, but there are no studies looking at how tempo may affect the underlying long-range interactions between EEG activity at different electrodes. The present study investigates these long-range interactions using EEG activity recorded from 21 volunteers listening to music stimuli played at 4 different tempi (50, 100, 150 and 200 beats per minute. The music stimuli consisted of piano excerpts designed to convey the emotion of “peacefulness”. Noise stimuli with an identical acoustic content to the music excerpts were also presented for comparison purposes. The brain activity interactions were characterized with the imaginary part of coherence (iCOH in the frequency range 1.5–18 Hz (δ, θ, α and lower β between all pairs of EEG electrodes for the four tempi and the music/noise conditions, as well as a baseline resting state (RS condition obtained at the start of the experimental task. Our findings can be summarized as follows: (a there was an ongoing long-range interaction in the RS engaging fronto-posterior areas; (b this interaction was maintained in both music and noise, but its strength and directionality were modulated as a result of acoustic stimulation; (c the topological patterns of iCOH were similar for music, noise and RS, however statistically significant differences in strength and direction of iCOH were identified; and (d tempo had an effect on the direction and strength of motor-auditory interactions. Our findings are in line with existing literature and illustrate a part of the mechanism by which musical stimuli with different tempi can entrain changes in cortical activity.

Directed Motor-Auditory EEG Connectivity Is Modulated by Music Tempo

Science.gov (United States)

Nicolaou, Nicoletta; Malik, Asad; Daly, Ian; Weaver, James; Hwang, Faustina; Kirke, Alexis; Roesch, Etienne B.; Williams, Duncan; Miranda, Eduardo R.; Nasuto, Slawomir J.

2017-01-01

Beat perception is fundamental to how we experience music, and yet the mechanism behind this spontaneous building of the internal beat representation is largely unknown. Existing findings support links between the tempo (speed) of the beat and enhancement of electroencephalogram (EEG) activity at tempo-related frequencies, but there are no studies looking at how tempo may affect the underlying long-range interactions between EEG activity at different electrodes. The present study investigates these long-range interactions using EEG activity recorded from 21 volunteers listening to music stimuli played at 4 different tempi (50, 100, 150 and 200 beats per minute). The music stimuli consisted of piano excerpts designed to convey the emotion of “peacefulness”. Noise stimuli with an identical acoustic content to the music excerpts were also presented for comparison purposes. The brain activity interactions were characterized with the imaginary part of coherence (iCOH) in the frequency range 1.5–18 Hz (δ, θ, α and lower β) between all pairs of EEG electrodes for the four tempi and the music/noise conditions, as well as a baseline resting state (RS) condition obtained at the start of the experimental task. Our findings can be summarized as follows: (a) there was an ongoing long-range interaction in the RS engaging fronto-posterior areas; (b) this interaction was maintained in both music and noise, but its strength and directionality were modulated as a result of acoustic stimulation; (c) the topological patterns of iCOH were similar for music, noise and RS, however statistically significant differences in strength and direction of iCOH were identified; and (d) tempo had an effect on the direction and strength of motor-auditory interactions. Our findings are in line with existing literature and illustrate a part of the mechanism by which musical stimuli with different tempi can entrain changes in cortical activity. PMID:29093672

Transfer function between EEG and BOLD signals of epileptic activity

Directory of Open Access Journals (Sweden)

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.

Benign mesial temporal lobe epilepsy: A clinical cohort and literature review.

Science.gov (United States)

AlQassmi, Amal; Burneo, Jorge G; McLachlan, Richard S; Mirsattari, Seyed M

2016-12-01

We present a single-center retrospective study of benign mesial temporal lobe epilepsy (bMTLE) between 1995 and 2014. Hospital records and clinic charts were reviewed. The clinical, Eelectroencephalographic (EEG), imaging features, and response to treatment with antiepileptic drugs (AEDs) were documented. Patients were included in this study if they were seizure-free for a minimum of 24months with or without an AED. Twenty-seven patients were identified. There were 19 (70%) females, mean age at first seizure was 32.2 (range: 15-80years). In all patients, seizures were mild, and seizure freedom was readily achieved with the initiation of AED therapy. Sixteen patients (59%) had mesial temporal sclerosis (MTS). In three patients, we attempted to discontinue AED therapy after a prolonged period of remission (5-8years), but all had seizure recurrence within 2 to 4weeks. Not all temporal lobe epilepsy is refractory to medication, despite the presence of MTS. Until clinical trials indicate otherwise, surgery is not indicated but life-long medical treatment is advocated. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

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...

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.)

The EEG Split Alpha Peak: Phenomenological Origins and Methodological Aspects of Detection and Evaluation.

Science.gov (United States)

Olejarczyk, Elzbieta; Bogucki, Piotr; Sobieszek, Aleksander

2017-01-01

Electroencephalographic (EEG) patterns were analyzed in a group of ambulatory patients who ranged in age and sex using spectral analysis as well as Directed Transfer Function, a method used to evaluate functional brain connectivity. We tested the impact of window size and choice of reference electrode on the identification of two or more peaks with close frequencies in the spectral power distribution, so called "split alpha." Together with the connectivity analysis, examination of spatiotemporal maps showing the distribution of amplitudes of EEG patterns allowed for better explanation of the mechanisms underlying the generation of split alpha peaks. It was demonstrated that the split alpha spectrum can be generated by two or more independent and interconnected alpha wave generators located in different regions of the cerebral cortex, but not necessarily in the occipital cortex. We also demonstrated the importance of appropriate reference electrode choice during signal recording. In addition, results obtained using the original data were compared with results obtained using re-referenced data, using average reference electrode and reference electrode standardization techniques.

Real Time Hand Motion Reconstruction System for Trans-Humeral Amputees Using EEG and EMG

Directory of Open Access Journals (Sweden)

Jacobo Fernandez-Vargas

2016-08-01

Full Text Available Predicting a hand’s position using only biosignals is a complex problem that has not been completely solved. The only reliable solutions currently available require invasive surgery. The attempts using non-invasive technologies are rare, and usually have led to lower correlation values between the real and the reconstructed position than those required for real-world applications. In this study, we propose a solution for reconstructing the hand’s position in three dimensions using EEG and EMG to detect from the shoulder area. This approach would be valid for most trans-humeral amputees. In order to find the best solution, we tested four different architectures for the system based on artificial neural networks. Our results show that it is possible to reconstruct the hand’s motion trajectory with a correlation value up to 0.809 compared to a typical value in the literature of 0.6. We also demonstrated that both EEG and EMG contribute jointly to the motion reconstruction. Furthermore, we discovered that the system architectures do not change the results radically. In addition, our results suggest that different motions may have different brain activity patterns that could be detected through EEG. Finally, we suggest a method to study non-linear relations in the brain through the EEG signals, which may lead to a more accurate system.

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.

Paratesticular cysts with benign epithelial proliferations of wolffian origin.

Science.gov (United States)

Nistal, Manuel; González-Peramato, Pilar; Serrano, Alvaro; Vega-Perez, Maria; De Miguel, Maria P; Regadera, Javier

2005-08-01

Paratesticular cysts with benign epithelial proliferations (BEPs) are rare. Only 10 cases were found in a series of 431 paratesticular cysts and were classified as follows: cystadenoma, 5; papilloma, 2; and hamartoma, 3. Four cystadenomas showed multiple papillae lined by CD10+ epithelial cells with hyperchromatic nuclei. The remaining lesion showed areas with a microcystic, glandular, cribriform pattern, with small, benign glands without atypia. Urothelial papilloma presented BEPs with cytokeratin (CK) 7+ and CD10+ and CK20- umbrella-like cells. The mural papilloma was lined by proliferative cylindrical cells exhibiting strong CK7 and CD10 expression. The 3 Wolffian hamartomas were characterized by strongly CD10+ epithelium surrounded by smooth muscle cells. The consistent CD10 expression in BEPs of paratesticular cysts suggests a Wolffian origin. The differential diagnosis of paratesticular cysts with BEP vs metastatic prostatic and primary borderline or malignant tumors is discussed.

Analyzing Electroencephalogram Signal Using EEG Lab

Directory of Open Access Journals (Sweden)

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.

The changes in relation of auditory and visual input activity between hemispheres analized in cartographic EEG in a child with hyperactivity syndrome

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Radičević Zoran

2015-01-01

Full Text Available The paper discusses the changes in relations of visual and auditory inputs between the hemispheres in a child with hyperactive syndrome and its effects which may lead to better attention engagement in auditory and visual information analysis. The method included the use of cartographic EEG and clinical procedure in a 10-year-old boy with hyperactive syndrome and attention deficit disorder, who has theta dysfunction manifested in standard EEG. Cartographic EEG patterns was performed on NihonKohden Corporation, EEG - 1200K Neurofax apparatus in longitudinal bipolar electrode assembly schedule by utilizing10/20 International electrode positioning. Impedance was maintained below 5 kΩ, with not more than 1 kΩ differences between the electrodes. Lower filter was set at 0.53 Hz and higher filter at 35 Hz. Recording was performed in a quiet period and during stimulation procedures that include speech and language basis. Standard EEG and Neurofeedback (NFB treatment indicated higher theta load, alpha 2 and beta 1 activity measured in the cartographic EEG which was done after the relative failure of NFB treatment. After this, the NFB treatment was applied which lasted for six months, in a way that when the boy was reading, the visual input was enhanced to the left hemisphere and auditory input was reduced to the right hemisphere. Repeated EEG mapping analysis showed that there was a significant improvement, both in EEG findings as well as in attention, behavioural and learning disorders. The paper discusses some aspects of learning, attention and behaviour in relation to changes in the standard EEG, especially in cartographic EEG and NFB findings.

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 ...

Patterns recognition of electric brain activity using artificial neural networks

Science.gov (United States)

Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

2017-04-01

An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

Benign mixed tumor of the lacrimal sac

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Jong-Suk Lee

2015-01-01

Full Text Available Neoplasms of the lacrimal drainage system are uncommon, but potentially life-threatening and are often difficult to diagnose. Among primary lacrimal sac tumors, benign mixed tumors are extremely rare. Histologically, benign mixed tumors have been classified as a type of benign epithelial tumor. Here we report a case of benign mixed tumor of the lacrimal sac.

Proposal for best practice in the use of video-EEG when psychogenic non-epileptic seizures are a possible diagnosis

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Kimberley Whitehead

Full Text Available The gold-standard for the diagnosis of psychogenic non-epileptic seizures (PNES is capturing an attack with typical semiology and lack of epileptic ictal discharges on video-EEG. Despite the importance of this diagnostic test, lack of standardisation has resulted in a wide variety of protocols and reporting practices. The goal of this review is to provide an overview of research findings on the diagnostic video-EEG procedure, in both the adult and paediatric literature. We discuss how uncertainties about the ethical use of suggestion can be resolved, and consider what constitutes best clinical practice. We stress the importance of ictal observation and assessment and consider how diagnostically useful information is best obtained. We also discuss the optimal format of video-EEG reports; and of highlighting features with high sensitivity and specificity to reduce the risk of miscommunication. We suggest that over-interpretation of the interictal EEG, and the failure to recognise differences between typical epileptic and nonepileptic seizure manifestations are the greatest pitfalls in neurophysiological assessment of patients with PNES. Meanwhile, under-recognition of semiological pointers towards frontal lobe seizures and of the absence of epileptiform ictal EEG patterns during some epileptic seizure types (especially some seizures not associated with loss of awareness, may lead to erroneous PNES diagnoses. We propose that a standardised approach to the video-EEG examination and the subsequent written report will facilitate a clear communication of its import, improving diagnostic certainty and thereby promoting appropriate patient management. Keywords: Psychogenic nonepileptic seizures, Nonepileptic attack disorder, Suggestion, EEG

Brain order disorder 2nd group report of f-EEG

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

2014-05-01

responses, which is what we seek. The averaged traditional EEG measurements and are further improved by BSS decomposition into finer stimulus-response source mixing matrix [A] having finer & faster spatial grids with rapid temporal updates. Then, the functional EEG is the second order co-variance matrix defined as the electrode-pair fluctuation correlation function C(s~, s~') of independent thermodynamic source components. (1) We define a 1-D Space filling curve as a spiral curve without origin. This pattern is historically known as the Peano-Hilbert arc length a. By taking the most significant bits of the Cartesian product a≡ O(x * y * z), it represents the arc length in the numerical size with values that map the 3-D neighborhood proximity into a 1-D neighborhood arc length representation. (2) 1-D Fourier coefficients spectrum have no spurious high frequency contents, which typically arise in lexicographical (zig-zag scanning) discontinuity [Hsu & Szu, "Peano-Hilbert curve," SPIE 2014]. A simple Fourier spectrum histogram fits nicely with the Compressive Sensing CRDT Mathematics. (3) Stationary power spectral density is a reasonable approximation of EEG responses in striate layers in resonance feedback loops capable of producing a 100, 000 neuronal collective Impulse Response Function (IRF). The striate brain layer architecture represents an ensemble functional-EEG: After taking the 1-D space-filling curve, we compute the ensemble averaged 1-D Power Spectral Density (PSD) and then make use of the inverse FFT to generate f-EEG. (ii) Goal#2 individual wellness baseline (IWB): We need novel change detection, so we derive the ubiquitous fat-tail distributions for healthy brains PSD in outdoor environments (Signal=310°C Noise=27°C: SNR=310/300; 300°K=(1/40)eV). The departure from IWB might imply stress, fever, a sports injury, an unexpected fall, or numerous midnight excursions which may signal an onset of dementia in Home Alone Senior (HAS), discovered by telemedicine

Statistical feature extraction for artifact removal from concurrent fMRI-EEG recordings.

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Liu, Zhongming; de Zwart, Jacco A; van Gelderen, Peter; Kuo, Li-Wei; Duyn, Jeff H

2012-02-01

We propose a set of algorithms for sequentially removing artifacts related to MRI gradient switching and cardiac pulsations from electroencephalography (EEG) data recorded during functional magnetic resonance imaging (fMRI). Special emphasis is directed upon the use of statistical metrics and methods for the extraction and selection of features that characterize gradient and pulse artifacts. To remove gradient artifacts, we use channel-wise filtering based on singular value decomposition (SVD). To remove pulse artifacts, we first decompose data into temporally independent components and then select a compact cluster of components that possess sustained high mutual information with the electrocardiogram (ECG). After the removal of these components, the time courses of remaining components are filtered by SVD to remove the temporal patterns phase-locked to the cardiac timing markers derived from the ECG. The filtered component time courses are then inversely transformed into multi-channel EEG time series free of pulse artifacts. Evaluation based on a large set of simultaneous EEG-fMRI data obtained during a variety of behavioral tasks, sensory stimulations and resting conditions showed excellent data quality and robust performance attainable with the proposed methods. These algorithms have been implemented as a Matlab-based toolbox made freely available for public access and research use. Published by Elsevier Inc.

Disentangling Tinnitus Distress and Tinnitus Presence by Means of EEG Power Analysis

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Martin Meyer

2014-01-01

Full Text Available The present study investigated 24 individuals suffering from chronic tinnitus (TI and 24 nonaffected controls (CO. We recorded resting-state EEG and collected psychometric data to obtain information about how chronic tinnitus experience affects the cognitive and emotional state of TI. The study was meant to disentangle TI with high distress from those who suffer less from persistent tinnitus based on both neurophysiological and behavioral data. A principal component analysis of psychometric data uncovers two distinct independent dimensions characterizing the individual tinnitus experience. These independent states are distress and presence, the latter is described as the perceived intensity of sound experience that increases with tinnitus duration devoid of any considerable emotional burden. Neuroplastic changes correlate with the two independent components. TI with high distress display increased EEG activity in the oscillatory range around 25 Hz (upper β-band that agglomerates over frontal recording sites. TI with high presence show enhanced EEG signal strength in the δ-, α-, and lower γ-bands (30–40 Hz over bilateral temporal and left perisylvian electrodes. Based on these differential patterns we suggest that the two dimensions, namely, distress and presence, should be considered as independent dimensions of chronic subjective tinnitus.

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

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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.

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

Benign bone tumors

International Nuclear Information System (INIS)

Gilday, D.L.; Ash, J.M.

1976-01-01

There is little information in the literature concerning the role of bone scanning in benign bone neoplasms except for sporadic reports. Since the advent of /sup 99m/Tc-polyphosphate, bone imaging has proven feasible and useful in locating the cause of bone pain, such as in osteoid osteomas, which are not always radiologically apparent, and in evaluating whether or not a radiologic lesion is indeed benign and solitary. Blood-pool images are particularly important in neoplastic disease, since the absence of hyperemia in the immediate postinjection period favors the diagnosis of a benign neoplasm, as does low-grade uptake on the delayed study. The scan, including pinhole magnification images, is especially valuable in diagnosing lesions in the spine and pelvis, which are poorly seen radiologically. We have studied various types of benign bone tumors, including simple and aneurysmal bone cysts, fibrous cortical defects, and nonossifying fibromas, all of which had minimal or no increased uptake of the radiopharmaceutical, unless traumatized. Although osteochondromas and enchondromas showed varied accumulation of activity, the scan was useful in differentiating these from sarcomatous lesions. All osteoid osteomas demonstrated marked activity, and could be accurately located preoperatively, as could the extent of fibrous dysplasia. The bone scan in the reticuloses also showed abnormal accumulation of activity, and aided in arriving at the prognosis and treatment of histiocytic bone lesions

Imaging benign pathology and variants with uptake in 68ga-Dotatate PET/CT studies

International Nuclear Information System (INIS)

Servente, L.; Bianco, C.; Gigirey, V.; Alonso, O.

2017-01-01

Purpose: To evaluate the physiological, anatomical variants and benign lesions in positron emission computed tomography (PET/CT) studies with 68Ga-DOTATATE.Materials and methods: We retrospectively reviewed PET/CT reports scanned with 68Ga-DOTATATE and selected those that contained words in the report related to anatomical, physiological variants and benign tumors. The degree of 68Ga-DOTATATE uptake was evaluated qualitatively and quantitatively by measuring the standarized uptake max value (SUVmax value). The anatomical location, SUVmax value and morphological CT image findings were recorded. All cases had clinical and imaging follow-up. Results: From a total of 772 PET/CT reports, 28 patients were obtained with 33 benign variants or tumors, 14 females and 14 males with a median age of 63 years. Uptake patterns were classified into four groups: anatomic and physiological variants (15), dependent on osteoblastic activity (4), dependent on inflammatory activity (10) and non-neuro-endocrine benign tumors (4).Discussion: Somatostatin receptors are overexpressed not only in the neuroendocrine system but also in other tissues. Physiological, anatomical variants and benign tumors expressing these receptors may be misleading. In the present work the frequency of this finding is 5.1%.Conclusion: Physiological variants and benign lesions (tumor and inflammatory) can accumulate 68Ga-DOTATATE since their tissues can express somatostatin receptors. The semiologic analysis of the tomographic component of this hybrid method enhances the diagnostic efficacy, optimizing PET/CT study performance. (authors) [es

Benign fibrous histiocytoma of the lumbar vertebrae

International Nuclear Information System (INIS)

Demiralp, Bahtiyar; Oguz, Erbil; Sehirlioglu, Ali; Kose, Ozkan; Sanal, Tuba; Ozcan, Ayhan

2009-01-01

Benign fibrous histiocytoma is an extremely rare spinal tumor with ten reported cases in the literature. Benign fibrous histiocytoma constitutes a diagnostic challenge because it shares common clinical symptoms, radiological characteristics, and histological features with other benign lesions involving the spine. We present a case of benign fibrous histiocytoma of the lumbar spine and discuss its differential diagnosis and management. (orig.)

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-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.

Highly Efficient Compression Algorithms for Multichannel EEG.

Science.gov (United States)

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.

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.

Thalamic involvement in the regulation of alpha EEG activity in psychiatric patients

International Nuclear Information System (INIS)

Shirazi, S.P.; Pakula, J.; Young, I.J.; Crayton, J.W.; Konopka, L.M.; Rybak, M.

2002-01-01

Aim: The thalamus is considered to be an important sub-cortical system involved in modulation of cortical activities. A relationship between thalamic activity and surface EEG was recently reported. In this study we evaluated a group of patients with psychiatric disorders who presented with asymmetric perfusion of the thalamus based on brain SPECT HMPAO studies. We predicted that asymmetrical activity of the thalamus would have asymmetrically distributed surface qEEG activity patterns. Materials and Methods: Twenty-three male psychiatric patients (age 54±14) with a primary diagnosis of depression and co-morbid substance abuse (83%) were studied with qEEG and HMPAO brain SPECT. The HMPAO ligand was administered while the EEG activity was being recorded. The SPECT analysis was conducted by means of ROI and SPM. ROI regions were determined based on the Talairach atlas coordinate system. ROI locations were verified by the automated utility, Talairach Demon. QEEG data was analyzed by a standardized protocol involving the NxLink database. Correlations between SPECT findings and qEEG absolute power were calculated. Results: Patients were divided into two groups based on thalamic perfusion patterns. Group 1 (Gr 1) had decreased perfusion to the right thalamus whereas Group 2 (Gr 2) had decreased perfusion to the left thalamus. SPM comparison of the patient groups to normal control subjects indicated significant findings. Comparison of Gr 1 to controls showed increased activity in the left temporal lobe and vermis. Decreased activity was observed in the left and right medial frontal lobes (right Brodmann 9;left Brodmann 6) as well as the left (Brodmann 30) and right (Brodmann 24) cingulate. Gr 2 comparison showed increased activity in the right middle frontal gyrus (Brodmann 10) and left inferior parietal lobe. Decreased activity was found in the left inferior frontal lobe (Brodmann 47). A positive correlation between alpha power and thalamic perfusion was identified in Gr

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...

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...

Effectiveness of the Benign and Malignant Diagnosis of Mediastinal and Hilar Lymph Nodes by Endobronchial Ultrasound Elastography.

Science.gov (United States)

Huang, Haidong; Huang, Zhiang; Wang, Qin; Wang, Xinan; Dong, Yuchao; Zhang, Wei; Zarogoulidis, Paul; Man, Yan-Gao; Schmidt, Wolfgang Hohenforst; Bai, Chong

2017-01-01

Background and Objectives: Endobronchial ultrasound elastography is a new technique for describing the stiffness of tissue during endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA). The aims of this study were to investigate the diagnostic value of Endobronchial ultrasound (EBUS) elastography for distinguishing the difference between benign and malignant lymph nodes among mediastinal and hilar lymph node. Materials and Methods: From June 2015 to August 2015, 47 patients confirmed of mediastinal and hilar lymph node enlargement through examination of Computed tomography (CT) were enrolled, and a total of 78 lymph nodes were evaluated by endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA). EBUS-guided elastography of lymph nodes was performed prior to EBUS-TBNA. A convex probe EBUS was used with a new EBUS processor to assess elastographic patterns that were classified based on color distribution as follows: Type 1, predominantly non-blue (green, yellow and red); Type 2, part blue, part non-blue (green, yellow and red); Type 3, predominantly blue. Pathological determination of malignant or benign lymph nodes was used as the gold standard for this study. The elastographic patterns were compared with the final pathologic results from EBUS-TBNA. Results: On pathological evaluation of the lymph nodes, 45 were benign and 33 were malignant. The lymph nodes that were classified as Type 1 on endobronchial ultrasound elastography were benign in 26/27 (96.3%) and malignant in 1/27 (3.7%); for Type 2 lymph nodes, 15/20 (75.0%) were benign and 5/20 (25.0%) were malignant; Type 3 lymph nodes were benign in 4/31 (12.9%) and malignant in 27/31 (87.1%). In classifying Type 1 as 'benign' and Type 3 as 'malignant,' the sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy rates were 96.43%, 86.67%, 87.10%, 96.30%, 91.38%, respectively. Conclusion: EBUS elastography of mediastinal and

Benign core biopsy of probably benign breast lesions 2 cm or larger: correlation with excisional biopsy and long-term follow-up

International Nuclear Information System (INIS)

Jung, Hyun Kyung; Moon, Hee Jung; Kim, Min Jung; Kim, Eun Kyung

2014-01-01

To evaluate the accuracy of benign core biopsy of probably benign breast lesions (category 3) 2 cm or larger on the basis of excisional biopsy and long-term follow-up. We retrospectively reviewed 146 category 3 lesions in 146 patients 2 cm or larger which were diagnosed as benign by ultrasound (US)-guided core biopsy. Patients were initially diagnosed as benign at core needle biopsy and then followed up with excisional biopsy (surgical excision, n=91; US-guided vacuum assisted excision, n=35) or breast ultrasonography (n=20). Of the 126 patients who underwent surgical excision or US-guided vacuum-assisted excision, 114 patients were diagnosed with benign lesions, 10 patients with borderline lesions (benign phyllodes tumor), and two patients with malignant phyllodes tumors. The probabilities of lesions being benign, borderline and malignant were 91.8% (134/146), 6.8% (10/146), and 1.4% (2/146), respectively. Of 13 patients who had growing masses on follow-up ultrasonography, three (23.1%) were non-benign (two benign phyllodes tumors and one malignant phyllodes tumor). US-guided core needle biopsy of probably benign breast mass 2 cm or larger was accurate (98.6%) enough to rule out malignancy. But, it was difficult to rule out borderline lesions even when they were diagnosed as benign.

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...

Decoding attended information in short-term memory: an EEG study.

Science.gov (United States)

LaRocque, Joshua J; Lewis-Peacock, Jarrod A; Drysdale, Andrew T; Oberauer, Klaus; Postle, Bradley R

2013-01-01

For decades it has been assumed that sustained, elevated neural activity--the so-called active trace--is the neural correlate of the short-term retention of information. However, a recent fMRI study has suggested that this activity may be more related to attention than to retention. Specifically, a multivariate pattern analysis failed to find evidence that information that was outside the focus of attention, but nonetheless in STM, was retained in an active state. Here, we replicate and extend this finding by querying the neural signatures of attended versus unattended information within STM with electroencephalograpy (EEG), a method sensitive to oscillatory neural activity to which the previous fMRI study was insensitive. We demonstrate that in the delay-period EEG activity, there is information only about memory items that are also in the focus of attention. Information about items outside the focus of attention is not detectable. This result converges with the fMRI findings to suggest that, contrary to conventional wisdom, an active memory trace may be unnecessary for the short-term retention of information.

Clinical utility of early amplitude integrated EEG in monitoring term newborns at risk of neurological injury

Directory of Open Access Journals (Sweden)

Paulina A. Toso

2014-03-01

Full Text Available Objective: to test the clinical utility of an early amplitude-integrated electroencephalography (aEEG to predict short-term neurological outcome in term newborns at risk of neurology injury. Methods: this was a prospective, descriptive study. The inclusion criteria were neonatal encephalopathy, neurologic disturbances, and severe respiratory distress syndrome. Sensitivity, specificity, positive and negative predictive values, and likelihood ratio (LR were calculated. Clinical and demographic data were analyzed. Neurological outcome was defined as the sum of clinical, electroimaging, and neuroimaging findings. Results: ten of the 21 monitored infants (48% presented altered short-term neurologic outcome. The aEEG had 90% sensitivity, 82% specificity, 82% positive predictive value, and 90% negative predictive value. The positive LR was 4.95, and the negative LR was 0.12. In three of 12 (25% encephalopathic infants, the aEEG allowed for a better definition of the severity of their condition. Seizures were detected in eight infants (38%, all subclinical at baseline, and none had a normal aEEG background pattern. The status of three infants (43% evolved and required two or more drugs for treatment. Conclusions: in infants with encephalopathy or other severe illness, aEEG disturbances occur frequently. aEEG provided a better classification of the severity of encephalopathy, detected early subclinical seizures, and allowed for monitoring of the response to treatment. aEEG was a useful tool at the neonatal intensive care unit for predicting poor short-term neurological outcomes for all sick newborn. Resumo: Objetivo: testar a utilidade clínica do aEEG precoce em recém-nascidos a termo com risco de lesão neurológica, para prever resultados neurológicos de curto prazo. Métodos: estudo prospectivo e descritivo. Os critérios de inclusão foram encefalopatia neonatal, distúrbios neurológicos e bebês com SARA grave. Sensibilidade, especificidade

EEG classification of emotions using emotion-specific brain functional network.

Science.gov (United States)

Gonuguntla, V; Shafiq, G; Wang, Y; Veluvolu, K C

2015-08-01

The brain functional network perspective forms the basis to relate mechanisms of brain functions. This work analyzes the network mechanisms related to human emotion based on synchronization measure - phase-locking value in EEG to formulate the emotion specific brain functional network. Based on network dissimilarities between emotion and rest tasks, most reactive channel pairs and the reactive band corresponding to emotions are identified. With the identified most reactive pairs, the subject-specific functional network is formed. The identified subject-specific and emotion-specific dynamic network pattern show significant synchrony variation in line with the experiment protocol. The same network pattern are then employed for classification of emotions. With the study conducted on the 4 subjects, an average classification accuracy of 62 % was obtained with the proposed technique.

Radical pancreaticoduodenectomy for benign disease.

LENUS (Irish Health Repository)

Kavanagh, D O

2008-01-01

Whipple\\'s procedure is the treatment of choice for pancreatic and periampullary malignancies. Preoperative histological confirmation of malignancy is frequently unavailable and some patients will subsequently be found to have benign disease. Here, we review our experience with Whipple\\'s procedure for patients ultimately proven to have benign disease. The medical records of all patients who underwent Whipple\\'s procedure during a 15-year period (1987-2002) were reviewed; 112 patients underwent the procedure for suspected malignancy. In eight cases, the final histology was benign (7.1%). One additional patient was known to have benign disease at resection. The mean age was 50 years (range: 30-75). The major presenting features included jaundice (five), pain (two), gastric outlet obstruction (one), and recurrent gastrointestinal haemorrhage (one). Investigations included ultrasound (eight), computerised tomography (eight), endoscopic retrograde cholangiopancreatography (seven; of these, four patients had a stent inserted and three patients had sampling for cytology), and endoscopic ultrasound (two). The pathological diagnosis included benign biliary stricture (two), chronic pancreatitis (two), choledochal cyst (one), inflammatory pseudotumour (one), cystic duodenal wall dysplasia (one), duodenal angiodysplasia (one), and granular cell neoplasm (one). There was no operative mortality. Morbidity included intra-abdominal collection (one), anastomotic leak (one), liver abscess (one), and myocardial infarction (one). All patients remain alive and well at mean follow-up of 41 months. Despite recent advances in diagnostic imaging, 8% of the patients undergoing Whipple\\'s procedure had benign disease. A range of unusual pathological entities can mimic malignancy. Accurate preoperative histological diagnosis may have allowed a less radical operation to be performed. Endoscopic ultrasound-guided fine needle aspirate (EUS-FNA) may reduce the need for Whipple\\'s operation

Radical Pancreaticoduodenectomy for Benign Disease

Directory of Open Access Journals (Sweden)

D. O. Kavanagh

2008-01-01

Full Text Available Whipple's procedure is the treatment of choice for pancreatic and periampullary malignancies. Preoperative histological confirmation of malignancy is frequently unavailable and some patients will subsequently be found to have benign disease. Here, we review our experience with Whipple's procedure for patients ultimately proven to have benign disease. The medical records of all patients who underwent Whipple's procedure during a 15-year period (1987–2002 were reviewed; 112 patients underwent the procedure for suspected malignancy. In eight cases, the final histology was benign (7.1%. One additional patient was known to have benign disease at resection. The mean age was 50 years (range: 30–75. The major presenting features included jaundice (five, pain (two, gastric outlet obstruction (one, and recurrent gastrointestinal haemorrhage (one. Investigations included ultrasound (eight, computerised tomography (eight, endoscopic retrograde cholangiopancreatography (seven; of these, four patients had a stent inserted and three patients had sampling for cytology, and endoscopic ultrasound (two. The pathological diagnosis included benign biliary stricture (two, chronic pancreatitis (two, choledochal cyst (one, inflammatory pseudotumour (one, cystic duodenal wall dysplasia (one, duodenal angiodysplasia (one, and granular cell neoplasm (one. There was no operative mortality. Morbidity included intra-abdominal collection (one, anastomotic leak (one, liver abscess (one, and myocardial infarction (one. All patients remain alive and well at mean follow-up of 41 months. Despite recent advances in diagnostic imaging, 8% of the patients undergoing Whipple'’s procedure had benign disease. A range of unusual pathological entities can mimic malignancy. Accurate preoperative histological diagnosis may have allowed a less radical operation to be performed. Endoscopic ultrasound–guided fine needle aspirate (EUS-FNA may reduce the need for Whipple's operation in

Engagement Assessment Using EEG Signals

Science.gov (United States)

Li, Feng; Li, Jiang; McKenzie, Frederic; Zhang, Guangfan; Wang, Wei; Pepe, Aaron; Xu, Roger; Schnell, Thomas; Anderson, Nick; Heitkamp, Dean

2012-01-01

In this paper, we present methods to analyze and improve an EEG-based engagement assessment approach, consisting of data preprocessing, feature extraction and engagement state classification. During data preprocessing, spikes, baseline drift and saturation caused by recording devices in EEG signals are identified and eliminated, and a wavelet based method is utilized to remove ocular and muscular artifacts in the EEG recordings. In feature extraction, power spectrum densities with 1 Hz bin are calculated as features, and these features are analyzed using the Fisher score and the one way ANOVA method. In the classification step, a committee classifier is trained based on the extracted features to assess engagement status. Finally, experiment results showed that there exist significant differences in the extracted features among different subjects, and we have implemented a feature normalization procedure to mitigate the differences and significantly improved the engagement assessment performance.

Use Case Analysis: The Ambulatory EEG in Navy Medicine for Traumatic Brain Injuries

Science.gov (United States)

2016-12-01

science of binaural beats . Retrieved from http://binauralbrains.com/the-science-of- binaural - beats / Biosignal. (2016). MicroEEG. Retrieved from http...Cap. Source: Binaural Brains (n.d.). ....................................4  Figure 3.  EEG Machine. Source: Refine Medical Technology (n.d...EEG. Figures 2, 3, and 4 display images of a standard EEG cap, EEG machine, and an EEG recording. Figure 2. Standard EEG Cap. Source: Binaural Brains

Performance evaluation for epileptic electroencephalogram (EEG) detection by using Neyman-Pearson criteria and a support vector machine

Science.gov (United States)

Wang, Chun-mei; Zhang, Chong-ming; Zou, Jun-zhong; Zhang, Jian

2012-02-01

The diagnosis of several neurological disorders is based on the detection of typical pathological patterns in electroencephalograms (EEGs). This is a time-consuming task requiring significant training and experience. A lot of effort has been devoted to developing automatic detection techniques which might help not only in accelerating this process but also in avoiding the disagreement among readers of the same record. In this work, Neyman-Pearson criteria and a support vector machine (SVM) are applied for detecting an epileptic EEG. Decision making is performed in two stages: feature extraction by computing the wavelet coefficients and the approximate entropy (ApEn) and detection by using Neyman-Pearson criteria and an SVM. Then the detection performance of the proposed method is evaluated. Simulation results demonstrate that the wavelet coefficients and the ApEn are features that represent the EEG signals well. By comparison with Neyman-Pearson criteria, an SVM applied on these features achieved higher detection accuracies.

Orthopedic surgery-related benign uptake on FDG-PET. Case examples and pitfalls

International Nuclear Information System (INIS)

Liu, Y.

2009-01-01

Orthopedic surgical procedures often create some special postoperative complications, which may demonstrate abnormally increased or focal uptake for an extended period of time on fluorodeoxyglucose (FDG) positron emission tomography (PET)-CT images. The distinction of normal from pathologic, benign from malignant uptake is very important to minimize the number of false positive results. To date, very little data have been published regarding surgical-related benign musculoskeletal uptake on PET-CT imaging. In this paper, we present to the readers some case examples of FDG PET-CT imaging for postoperative fracture, infection or osteomyelitis, metallic implants, aggressive bone edge, heterotopic ossification, granuloma and neuroma. We also discuss potential pitfalls to recognize these orthopedic surgery-related complications and identify benign nature of increased FDG uptake. In all cases, the patient's medical and surgical history would be of paramount importance to the radiologists/nuclear medicine physicians who interprets the scan. It is also crucial to carefully correlate FDG uptake with the anatomy on the co-registered CT images in all transaxial, coronal and sagittal views in order to identify the location and pattern of uptake. (author)

EEG Oscillations Are Modulated in Different Behavior-Related Networks during Rhythmic Finger Movements.

Science.gov (United States)

Seeber, Martin; Scherer, Reinhold; Müller-Putz, Gernot R

2016-11-16

Sequencing and timing of body movements are essential to perform motoric tasks. In this study, we investigate the temporal relation between cortical oscillations and human motor behavior (i.e., rhythmic finger movements). High-density EEG recordings were used for source imaging based on individual anatomy. We separated sustained and movement phase-related EEG source amplitudes based on the actual finger movements recorded by a data glove. Sustained amplitude modulations in the contralateral hand area show decrease for α (10-12 Hz) and β (18-24 Hz), but increase for high γ (60-80 Hz) frequencies during the entire movement period. Additionally, we found movement phase-related amplitudes, which resembled the flexion and extension sequence of the fingers. Especially for faster movement cadences, movement phase-related amplitudes included high β (24-30 Hz) frequencies in prefrontal areas. Interestingly, the spectral profiles and source patterns of movement phase-related amplitudes differed from sustained activities, suggesting that they represent different frequency-specific large-scale networks. First, networks were signified by the sustained element, which statically modulate their synchrony levels during continuous movements. These networks may upregulate neuronal excitability in brain regions specific to the limb, in this study the right hand area. Second, movement phase-related networks, which modulate their synchrony in relation to the movement sequence. We suggest that these frequency-specific networks are associated with distinct functions, including top-down control, sensorimotor prediction, and integration. The separation of different large-scale networks, we applied in this work, improves the interpretation of EEG sources in relation to human motor behavior. EEG recordings provide high temporal resolution suitable to relate cortical oscillations to actual movements. Investigating EEG sources during rhythmic finger movements, we distinguish sustained from

A STUDY ON EEG ABNORMALITIES IN CHILDREN WITH MIGRAINE

Directory of Open Access Journals (Sweden)

Subinay Mandal

2017-04-01

Full Text Available BACKGROUND Migraine is one of the common causes of headache in children. Migraine and epilepsy are both common episodic neurological disorders. The comorbidity of these two conditions is well known. Many researcher have pointed out that neuronal hyperexcitability is the initiating event for occurrence of migraine attack. The aim of the paper was to evaluate the EEG in children with migraine. MATERIALS AND METHODS We retrospectively analysed records of children who attended our paediatric outpatient department with diagnoses as suffering from migraine based on International Headache Society (IHS diagnostic criteria. Apart from detailed clinical history, EEG of every patient was collected and analysed. EEG was performed interictally at least 24 hours after the last episode of headache attack in all the cases. RESULTS 56 children (age range, 4-14 years constituted our study group. 64.3% children had migraine without aura (common type and in 23.2% cases had migraine with aura (classic type other were with migraine variants. Abnormal EEG was reported in 30.3% children. 17% of children with migraine without history of seizure had abnormal EEG. Sixty one percent of patients with aura had abnormal EEG. History of either febrile fits or afebrile fits was present in total 17.1% of cases. The type of paroxysmal discharges we came across was- a Sharp waves, b Spikes and c Spike and slow wave complexes. Abnormal paroxysmal sharp and spike-wave complexes (also called spike-and-slow-wave complexes were the most common EEG abnormality. CONCLUSION EEG abnormality was found in significant number of children with migraine both with and without history of seizure in our study. This indicates neuronal hyperexcitability during episodes of migraine. So, EEG should be considered in patients with clinical diagnoses of migraine to exclude association of any seizure activity.

Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata.

Science.gov (United States)

Liu, Aiming; Chen, Kun; Liu, Quan; Ai, Qingsong; Xie, Yi; Chen, Anqi

2017-11-08

Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain-computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain-computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain-computer interface systems.

Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata

Directory of Open Access Journals (Sweden)

Aiming Liu

2017-11-01

Full Text Available Motor Imagery (MI electroencephalography (EEG is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP and local characteristic-scale decomposition (LCD algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA classifier. Both the fourth brain–computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain–computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain–computer interface systems.

[EEG-markers of vertical postural organization in healthy persons].

Science.gov (United States)

Zhavoronkova, L A; Zharikova, A V; Kushnir, E M; Mikhalkova, A A

2012-01-01

In 10 healthy persons (22.8 +/- 0.67 years) spectral-coherence parameters of EEG were analyzed in different steps of verticalizations--from gorizontal position to seat and stand one. Maximal changes of all EEG parameters were observed in state with absence of visual control. We observed an increase of power for fast spectral bands of EEG (beta- and gamma-bands) in all conditions and additional increase of these EEG parameters was observed at situation of complication of conditions of vertical pose supporting. Results of EEG coherent analysis in conditions of human verticalization showed specific increase of coherence for the majority of rhythm ranges in the right hemisphere especially in the central-frontal and in occipital-parietal areas and for interhemispheric pairs for these leads. This fact can reflect participation of cortical as well as subcortical structures in these processes. In conditions of complicate conditions of vertical pose supporting the additional increase of EEG coherence in fast bands (beta-rhythm) was observed at the frontal areas. This fact can testify about increasing of executive functions in this conditions.

Analysis of behavioral and EEG correlatives of attention in the dynamics of recovery of consciousness following severe brain injury

Directory of Open Access Journals (Sweden)

E. V. Sharova

2016-01-01

Full Text Available Objective: to determine the behavioral manifestations and electroencephalographic correlates of modality-nonspecific attention using a clinical model of severe brain injury (SBI.Patients and methods. 35 patients with SBI in the dynamics of post-coma recovery of mental activity (a study group and 23 healthy subjects (a control group were examined. The behavioral manifestations of NSA from coma to clear consciousness were analyzed in the patients. Changes in the pattern of EEG and in the indices of its coherence in the presence and activation of different forms of attention (an orienting response to the sound and eye opening; involuntary and voluntary visual forms, by applying specially developed computerized techniques, were investigated. The features of associated with attention changes in interhemispheric EEG coherence (IHC with the data of 3T diffusion tensor tractography of the corpus callosum (CC were compared.Results. Attention disorders were shown to be essential and an «axial disorder» in patients with SBI. There were statistically confirmed qualitative and quantitative differences attention-associated changes in the EEG pattern and IHC in reversible and chronic unconsciousness. The important favorable prognostic sign proved to be reactive changes in interhemispheric EEG relations, including frontal ones characterized by the absence of clear external manifestations of consciousness in the very earliest stages. There was a significant correlation between the preservation of CC tracts (primarily, the rostrum, anterior portion, and splenium and attention-related reactivity of IHC, which reflects the specific, though nonrigid, structural determinacy of the latter.

Absence of early epileptiform abnormalities predicts lack of seizures on continuous EEG.

Science.gov (United States)

Shafi, Mouhsin M; Westover, M Brandon; Cole, Andrew J; Kilbride, Ronan D; Hoch, Daniel B; Cash, Sydney S

2012-10-23

To determine whether the absence of early epileptiform abnormalities predicts absence of later seizures on continuous EEG monitoring of hospitalized patients. We retrospectively reviewed 242 consecutive patients without a prior generalized convulsive seizure or active epilepsy who underwent continuous EEG monitoring lasting at least 18 hours for detection of nonconvulsive seizures or evaluation of unexplained altered mental status. The findings on the initial 30-minute screening EEG, subsequent continuous EEG recordings, and baseline clinical data were analyzed. We identified early EEG findings associated with absence of seizures on subsequent continuous EEG. Seizures were detected in 70 (29%) patients. A total of 52 patients had their first seizure in the initial 30 minutes of continuous EEG monitoring. Of the remaining 190 patients, 63 had epileptiform discharges on their initial EEG, 24 had triphasic waves, while 103 had no epileptiform abnormalities. Seizures were later detected in 22% (n = 14) of studies with epileptiform discharges on their initial EEG, vs 3% (n = 3) of the studies without epileptiform abnormalities on initial EEG (p monitoring is necessary.

Effects of oral amines on the EEG.

Science.gov (United States)

Scott, D F; Moffett, A M; Swash, M

1977-02-01

Oral tyramine activated pre-existing episodic EEG abnormalities--namely, sharp waves, spike and wave, and localised theta activity--in epileptic patients. Little change was found in the EEGs of migrainous subjects after chocolate or beta-phenylethylamine. The implications of the findings with tyramine are discussed.

Changes of hypnagogic imagery and EEG stages

OpenAIRE

Hayashi, Mitsuo; Katoh, Kohichi; Hori, Tadao

1998-01-01

The aim of this study is to investigate the relationships between hypnagogic imagery and EEG stages. According to Hori, et al. (1994), the hypnagogic EEGs was classified into 9 stages, those were 1) alpha wave train, 2) alpha wave intermittent (>50%), 3) alpha wave intermittent (

Memories of attachment hamper EEG cortical connectivity in dissociative patients.

Science.gov (United States)

Farina, Benedetto; Speranza, Anna Maria; Dittoni, Serena; Gnoni, Valentina; Trentini, Cristina; Vergano, Carola Maggiora; Liotti, Giovanni; Brunetti, Riccardo; Testani, Elisa; Della Marca, Giacomo

2014-08-01

In this study, we evaluated cortical connectivity modifications by electroencephalography (EEG) lagged coherence analysis, in subjects with dissociative disorders and in controls, after retrieval of attachment memories. We asked thirteen patients with dissociative disorders and thirteen age- and sex-matched healthy controls to retrieve personal attachment-related autobiographical memories through adult attachment interviews (AAI). EEG was recorded in the closed eyes resting state before and after the AAI. EEG lagged coherence before and after AAI was compared in all subjects. In the control group, memories of attachment promoted a widespread increase in EEG connectivity, in particular in the high-frequency EEG bands. Compared to controls, dissociative patients did not show an increase in EEG connectivity after the AAI. Conclusions: These results shed light on the neurophysiology of the disintegrative effect of retrieval of traumatic attachment memories in dissociative patients.

Diagnostic Role of ECG Recording Simultaneously With EEG Testing.

Science.gov (United States)

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

2015-07-01

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

EEG feature selection method based on decision tree.

Science.gov (United States)

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

2015-01-01

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

Pattern Recognition

Directory of Open Access Journals (Sweden)

Aleš Procházka

2018-05-01

Full Text Available Multimodal signal analysis based on sophisticated sensors, efficient communicationsystems and fast parallel processing methods has a rapidly increasing range of multidisciplinaryapplications. The present paper is devoted to pattern recognition, machine learning, and the analysisof sleep stages in the detection of sleep disorders using polysomnography (PSG data, includingelectroencephalography (EEG, breathing (Flow, and electro-oculogram (EOG signals. The proposedmethod is based on the classification of selected features by a neural network system with sigmoidaland softmax transfer functions using Bayesian methods for the evaluation of the probabilities of theseparate classes. The application is devoted to the analysis of the sleep stages of 184 individualswith different diagnoses, using EEG and further PSG signals. Data analysis points to an averageincrease of the length of the Wake stage by 2.7% per 10 years and a decrease of the length of theRapid Eye Movement (REM stages by 0.8% per 10 years. The mean classification accuracy for givensets of records and single EEG and multimodal features is 88.7% ( standard deviation, STD: 2.1 and89.6% (STD:1.9, respectively. The proposed methods enable the use of adaptive learning processesfor the detection and classification of health disorders based on prior specialist experience andman–machine interaction.

Synchronization of EEG activity in patients with bipolar disorder

Science.gov (United States)

Panischev, O. Yu; Demin, S. A.; Muhametshin, I. G.; Demina, N. Yu

2015-12-01

In paper we apply the method based on the Flicker-Noise Spectroscopy (FNS) to determine the differences in frequency-phase synchronization of the cortical electroencephalographic (EEG) activities in patients with bipolar disorder (BD). We found that for healthy subjects the frequency-phase synchronization of EEGs from long-range electrodes was significantly better for BD patients. In BD patients a high synchronization of EEGs was observed only for short-range electrodes. Thus, the FNS is a simple graphical method for qualitative analysis can be applied to identify the synchronization effects in EEG activity and, probably, may be used for the diagnosis of this syndrome.

[The cause and efficacy of benign tracheal stenosis].

Science.gov (United States)

Su, Zhu-quan; Wei, Xiao-qun; Zhong, Chang-hao; Chen, Xiao-bo; Luo, Wei-zhan; Guo, Wen-liang; Wang, Ying-zhi; Li, Shi-yue

2013-09-01

To analysis the causes of benign tracheal stenosis and evaluate the curative effect of intraluminal bronchoscopic treatment. 158 patients with benign tracheal stenosis in our hospital from September 2005 to September 2012 were collected to retrospectively analysis the causes and clinic features of tracheal stenosis. Interventional treatments through bronchoscopy were used to treat the benign tracheal stenosis and the curative effects were evaluated. 158 cases of benign tracheal stenosis were recruited to our study, 69.6% of them were young and middle-aged. The main causes of benign tracheal stenosis were as follows: secondary to postintubation or tracheotomy in 61.4% (97/158), tuberculosis in 16% (26/158), benign tumor in 5.1% (8/158) and other 27 cases. 94.3% patients improved in symptoms with alleviation immediately after bronchoscopic treatment, the average tracheal diameter increased form (4.22 ± 2.06) mm to (10.16 ± 2.99) mm (t = 21.48, P benign tracheal stenosis were increasing year by year. The most common cause of benign tracheal stenosis was postintubation and tracheotomy. Interventional treatments through bronchoscopy is effective in treating benign tracheal stenosis, but repeated interventional procedures may be required to maintain the favorable long-term effects.

Safety and EEG data quality of concurrent high-density EEG and high-speed fMRI at 3 Tesla

DEFF Research Database (Denmark)

Foged, Mette Thrane; Lindberg, Ulrich; Vakamudi, Kishore

2017-01-01

) related heating, the effect of EEG on cortical signal-to-noise ratio (SNR) in fMRI, and assess EEG data quality. MATERIALS AND METHODS: The study compared EPI, multi-echo EPI, multi-band EPI and multi-slab echo-volumar imaging pulse sequences, using clinical 3 Tesla MR scanners from two different vendors...

Benign gastric filling defect

Energy Technology Data Exchange (ETDEWEB)

Oh, K K; Lee, Y H; Cho, O K; Park, C Y [Yonsei University College of Medicine, Seoul (Korea, Republic of)

1979-06-15

The gastric lesion is a common source of complaints to Orientals, however, evaluation of gastric symptoms and laboratory examination offer little specific aid in the diagnosis of gastric diseases. Thus roentgenography of gastrointestinal tract is one of the most reliable method for detail diagnosis. On double contract study of stomach, gastric filling defect is mostly caused by malignant gastric cancer, however, other benign lesions can cause similar pictures which can be successfully treated by surgery. 66 cases of benign causes of gastric filling defect were analyzed at this point of view, which was verified pathologically by endoscope or surgery during recent 7 years in Yensei University College of Medicine, Severance Hospital. The characteristic radiological picture of each disease was discussed for precise radiologic diagnosis. 1. Of total 66 cases, there were 52 cases of benign gastric tumor 10 cases of gastric varices, 5 cases of gastric bezoar, 5 cases of corrosive gastritis, 3 cases of granulomatous disease and one case of gastric hematoma. 2. The most frequent causes of benign tumors were adenomatous polyp (35/42) and the next was leiomyoma (4/42). Others were one of case of carcinoid, neurofibroma and cyst. 3. Characteristic of benign adenomatous polyp were relatively small in size, smooth surface and were observed that large size, benign polyp was frequently type IV lesion with a stalk. 4. Submucosal tumors such as leiomyoma needed differential diagnosis with polypoid malignant cancer. However, the characteristic points of differentiation was well circumscribed smooth margined filling defect without definite mucosal destruction on surface. 5. Gastric varices showed multiple lobulated filling defected especially on gastric fundus that changed its size and shape by respiration and posture of patients. Same varices lesions on esophagus and history of liver disease were helpful for easier diagnosis. 6. Gastric bezoar showed well defined movable mass

Benign gastric filling defect

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Oh, K. K.; Lee, Y. H.; Cho, O. K.; Park, C. Y.

1979-01-01

The gastric lesion is a common source of complaints to Orientals, however, evaluation of gastric symptoms and laboratory examination offer little specific aid in the diagnosis of gastric diseases. Thus roentgenography of gastrointestinal tract is one of the most reliable method for detail diagnosis. On double contract study of stomach, gastric filling defect is mostly caused by malignant gastric cancer, however, other benign lesions can cause similar pictures which can be successfully treated by surgery. 66 cases of benign causes of gastric filling defect were analyzed at this point of view, which was verified pathologically by endoscope or surgery during recent 7 years in Yensei University College of Medicine, Severance Hospital. The characteristic radiological picture of each disease was discussed for precise radiologic diagnosis. 1. Of total 66 cases, there were 52 cases of benign gastric tumor 10 cases of gastric varices, 5 cases of gastric bezoar, 5 cases of corrosive gastritis, 3 cases of granulomatous disease and one case of gastric hematoma. 2. The most frequent causes of benign tumors were adenomatous polyp (35/42) and the next was leiomyoma (4/42). Others were one of case of carcinoid, neurofibroma and cyst. 3. Characteristic of benign adenomatous polyp were relatively small in size, smooth surface and were observed that large size, benign polyp was frequently type IV lesion with a stalk. 4. Submucosal tumors such as leiomyoma needed differential diagnosis with polypoid malignant cancer. However, the characteristic points of differentiation was well circumscribed smooth margined filling defect without definite mucosal destruction on surface. 5. Gastric varices showed multiple lobulated filling defected especially on gastric fundus that changed its size and shape by respiration and posture of patients. Same varices lesions on esophagus and history of liver disease were helpful for easier diagnosis. 6. Gastric bezoar showed well defined movable mass

Benign gastric filling defect

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Oh, K. K.; Lee, Y. H.; Cho, O. K.; Park, C. Y. [Yonsei University College of Medicine, Seoul (Korea, Republic of)

1979-06-15

The gastric lesion is a common source of complaints to Orientals, however, evaluation of gastric symptoms and laboratory examination offer little specific aid in the diagnosis of gastric diseases. Thus roentgenography of gastrointestinal tract is one of the most reliable method for detail diagnosis. On double contract study of stomach, gastric filling defect is mostly caused by malignant gastric cancer, however, other benign lesions can cause similar pictures which can be successfully treated by surgery. 66 cases of benign causes of gastric filling defect were analyzed at this point of view, which was verified pathologically by endoscope or surgery during recent 7 years in Yensei University College of Medicine, Severance Hospital. The characteristic radiological picture of each disease was discussed for precise radiologic diagnosis. 1. Of total 66 cases, there were 52 cases of benign gastric tumor 10 cases of gastric varices, 5 cases of gastric bezoar, 5 cases of corrosive gastritis, 3 cases of granulomatous disease and one case of gastric hematoma. 2. The most frequent causes of benign tumors were adenomatous polyp (35/42) and the next was leiomyoma (4/42). Others were one of case of carcinoid, neurofibroma and cyst. 3. Characteristic of benign adenomatous polyp were relatively small in size, smooth surface and were observed that large size, benign polyp was frequently type IV lesion with a stalk. 4. Submucosal tumors such as leiomyoma needed differential diagnosis with polypoid malignant cancer. However, the characteristic points of differentiation was well circumscribed smooth margined filling defect without definite mucosal destruction on surface. 5. Gastric varices showed multiple lobulated filling defected especially on gastric fundus that changed its size and shape by respiration and posture of patients. Same varices lesions on esophagus and history of liver disease were helpful for easier diagnosis. 6. Gastric bezoar showed well defined movable mass

Singular spectrum analysis of sleep EEG in insomnia.

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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.

EEG Suppression Associated with Apneic Episodes in a Neonate

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Evonne Low

2012-01-01

Full Text Available We describe the EEG findings from an ex-preterm neonate at term equivalent age who presented with intermittent but prolonged apneic episodes which were presumed to be seizures. A total of 8 apneic episodes were captured (duration 23–376 seconds during EEG monitoring. The baseline EEG activity was appropriate for corrected gestational age and no electrographic seizure activity was recorded. The average baseline heart rate was 168 beats per minute (bpm and the baseline oxygen saturation level was in the mid-nineties. Periods of complete EEG suppression lasting 68 and 179 seconds, respectively, were recorded during 2 of these 8 apneic episodes. Both episodes were accompanied by bradycardia less than 70 bpm and oxygen saturation levels of less than 20%. Short but severe episodes of apnea can cause complete EEG suppression in the neonate.

An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information.

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Kumar, Shiu; Sharma, Alok; Tsunoda, Tatsuhiko

2017-12-28

Common spatial pattern (CSP) has been an effective technique for feature extraction in electroencephalography (EEG) based brain computer interfaces (BCIs). However, motor imagery EEG signal feature extraction using CSP generally depends on the selection of the frequency bands to a great extent. In this study, we propose a mutual information based frequency band selection approach. The idea of the proposed method is to utilize the information from all the available channels for effectively selecting the most discriminative filter banks. CSP features are extracted from multiple overlapping sub-bands. An additional sub-band has been introduced that cover the wide frequency band (7-30 Hz) and two different types of features are extracted using CSP and common spatio-spectral pattern techniques, respectively. Mutual information is then computed from the extracted features of each of these bands and the top filter banks are selected for further processing. Linear discriminant analysis is applied to the features extracted from each of the filter banks. The scores are fused together, and classification is done using support vector machine. The proposed method is evaluated using BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, and it outperformed all other competing methods achieving the lowest misclassification rate and the highest kappa coefficient on all three datasets. Introducing a wide sub-band and using mutual information for selecting the most discriminative sub-bands, the proposed method shows improvement in motor imagery EEG signal classification.

Benign tumors of the breast in Kano, Northern Nigeria: A 10-year experience and review of literature

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Mohammed Ibrahim Imam

2016-01-01

Full Text Available Background: Benign breast tumors are common worldwide and various reports suggest an increasing incidence in Nigeria which necessitates an urgent need to differentiate it from malignant tumors. The study was carried out to classify and determine the pattern, frequency, age, and sex distribution of benign breast tumors seen in a tertiary hospital. Materials and Methods: This was a 10-year retrospective study of all benign breast tumors diagnosed at the Pathology Department of a teaching hospital from January 1 2001 to December 31 2010. Results: A total of 1566 breast tumors were diagnosed during the study period, 1035 cases of benign breast tumors constituting 66.3% of all breast tumors were seen. The female to male ratio was 72.9:1. The overall mean age for benign breast tumor was 29 years with a peak age occurrence in the third decade. Fibroadenoma (FA was the most common benign breast tumor followed by fibrocystic change and they accounted for 47.1% and 25.4% of benign breast tumors with mean age of 24.7 years and 33.4 years, respectively. FA has a peak occurrence in the third decade while fibrocystic change has a peak occurrence in the fourth decade. Other major tumors encountered were tubular adenoma (6.0%, lactating adenoma (5.6%, benign phyllodes (4.8%, sclerosing adenoma (3.3%, and blunt duct adenoma (2.5%. Gynecomastia (1.4% was the only benign breast tumor seen in males.Conclusions: Benign breast tumors are quite common, presenting mostly as FA and fibrocystic change. The tumors are seen in both sexes with a striking female preponderance and occurred predominantly in young females with a peak in the third decade. The findings are generally similar to the most previous studies from Nigeria, Africa, and the Western world with minimal variations.

Imaging characteristics of malignant and benign lesions of skeletal muscle

International Nuclear Information System (INIS)

Leonard, L.; Meyer, H.J.; Surov, A.

2017-01-01

There are many different tumors and tumor-like lesions with variable biological behavior that may affect the skeletal musculature. The aim of this study was to review the different intramuscular lesions and to provide a classification based on their radiological patterns. Intramuscular lesions can present as solid, liquid, semiliquid or fat equivalent manifestations and also as diffuse muscle enlargement and muscle calcification. Additionally, lesions with mixed patterns of the aforementioned alterations can also occur. Benign and malignant muscle lesions can often manifest with identical radiological patterns, which is why a certain differentiation is often difficult. A systematic radiological description and when possible assignment with respect to etiology and dignity depending on the patient history is necessary in order to recommend a subsequent histological confirmation or to avoid unnecessary confirmation. (orig.) [de

Familial benign nonprogressive myoclonic epilepsies.

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Striano, Pasquale; de Falco, Fabrizio A; Minetti, Carlo; Zara, Federico

2009-05-01

Work on the classification of epileptic syndromes is ongoing, and many syndromes are still under discussion. In particular, special difficulty still persists in correctly classifying epilepsies with myoclonic seizures. The existence of special familial epileptic syndromes primarily showing myoclonic features has been recently suggested on the basis of a clear pattern of inheritance or on the identification of new chromosomal genetic loci linked to the disease. These forms in development include familial infantile myoclonic epilepsy (FIME), benign adult familial myoclonic epilepsy (BAFME), or autosomal dominant cortical myoclonus and epilepsy (ADCME), and, maybe, adult-onset myoclonic epilepsy (AME). In the future, the identification of responsible genes and the protein products will contribute to our understanding of the molecular pathways of epileptogenesis and provide neurobiologic criteria for the classification of epilepsies, beyond the different phenotypic expression.

Categorisation of Mobile EEG: A Researcher’s Perspective

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Anthony D. Bateson

2017-01-01

Full Text Available Researchers are increasingly attempting to undertake electroencephalography (EEG recordings in novel environments and contexts outside of the traditional static laboratory setting. The term “mobile EEG,” although commonly used to describe many of these undertakings, is ambiguous, since it attempts to encompass a wide range of EEG device mobility, participant mobility, and system specifications used across investigations. To provide quantitative parameters for “mobile EEG,” we developed a Categorisation of Mobile EEG (CoME scheme based upon scoring of device mobility (D, from 0, off-body, to 5, head-mounted with no additional equipment, participant mobility (P, from 0, static, to 5, unconstrained running, system specification (S, from 4, lowest, to 20, highest, and number of channels (C used. The CoME scheme was applied to twenty-nine published mobile EEG studies. Device mobility scores ranged from 0D to 4D, participant mobility scores from 0P to 4P, and system specification scores from 6S to 17S. The format of the scores for the four parameters is given, for example, as (2D, 4P, 17S, 32C and readily enables comparisons across studies. Our CoME scheme enables researchers to quantify the degree of device mobility, participant mobility, and system specification used in their “mobile EEG” investigations in a standardised way.

Frontal EEG asymmetry in borderline personality disorder is associated with alexithymia.

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Flasbeck, Vera; Popkirov, Stoyan; Brüne, Martin

2017-01-01

Frontal EEG asymmetry is a widely studied correlate of emotion processing and psychopathology. Recent research suggests that frontal EEG asymmetry during resting state is related to approach/withdrawal motivation and is also found in affective disorders such as major depressive disorder. Patients with borderline personality disorder (BPD) show aberrant behavior in relation to both approach and withdrawal motivation, which may arguably be associated with their difficulties in emotion processing. The occurrence and significance of frontal EEG asymmetry in BPD, however, has received little attention. Thirty-seven BPD patients and 39 controls underwent resting EEG and completed several psychometric questionnaires. While there were no between-group differences in frontal EEG asymmetry, in BPD frontal EEG asymmetry scores correlated significantly with alexithymia. That is, higher alexithymia scores were associated with relatively lower right-frontal activity. A subsequent analysis corroborated the significant interaction between frontal EEG asymmetry and alexithymia, which was moderated by group. Our findings reveal that lower right frontal EEG asymmetry is associated with alexithymia in patients with BPD. This finding is in accordance with neurophysiological models of alexithymia that implicate a right hemisphere impairment in emotion processing, and could suggest frontal EEG asymmetry as a potential biomarker of relevant psychopathology in these patients.

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