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Non-epileptiform EEG abnormalities: an overview
Andraus, Maria Emilia Cosenza;Alves-Leon, Soniza Vieira;
Arquivos de Neuro-Psiquiatria , 2011, DOI: 10.1590/S0004-282X2011000600020
Abstract: more than 80 years after its introduction by hans berger, the electroencephalogram (eeg) remains as an important supplementary examination in the investigation of neurological disorders and gives valuable and accurate information about cerebral function. abnormal eeg findings may include ictal patterns, interictal epileptiform activity and non-epileptiform abnormalities. the aim of this study is to make an overview on the main non-epileptiform eeg abnormalities, emphasizing the pathologic findings and the importance of their recognition, excluding periodic patterns and eeg physiologic changes. scientific articles were selected from medline and pubmed database. the presence of non-epileptiform eeg abnormalities provide evidence of brain dysfunction that are not specific to a particular etiology and may be related to a number of disorders affecting the brain. although these abnormalities are not specific, they can direct attention to the diagnostic possibilities and guide the best treatment choice.
Possibility for Recognition of Psychic Brain Activity with Continuous Wavelet Analysis of EEG  [PDF]
Evgeny A. Yumatov, Alexandr E. Hramov, Vadim V. Grubov, Oleg S. Glazachev, Elena N. Dudnik, Nikolay A. Karatygin
Journal of Behavioral and Brain Science (JBBS) , 2019, DOI: 10.4236/jbbs.2019.93006
Abstract: The brain is a unique organization in nature, possessing the ability for psychic activity, which manifests itself in thoughts, feelings and emotions. In present days, numbers of mathematical methods for analysis of electroencephalogram (EEG) were developed with continuous wavelet transform being one of the most successive approaches for studying of brain activity. This paper is aimed to develop methods for investigation of psychic brain activity with help of continuous wavelet analysis of EEG. Ability of human to realize semantic content of the image presented on the screen was tested. Experiment was accompanied with simultaneous EEG recording, which was held with developed software and PC-based experimental setup. The information capabilities of continuous wavelet transform-based method for EEG analysis were improved for the recognition of specific patterns in human brain activity. Comparative wavelet analysis was carried out for EEG recordings at the moment of awareness of the semantic content of the image and for EEG recordings in the absence of conscious (subjective) perception of the semantic content of the image. Significant differences were shown in the alpha rhythm of EEG for the moments of awareness of the semantic content of the image and for the moments of absence of conscious perception. Continuous wavelet analysis of EEG showed that the alpha rhythm is the main EEG rhythm, which can be used to estimate the presence of subjective perception of the visual image. Significant differences were shown in the alpha rhythm of EEG for the moments of awareness of the semantic content of the image and for the moments of absence of conscious perception. Conducted studies allow to conclude that revealing of brain activity related to visual image awareness is possible through analysis of EEG.
Recognition of Words from the EEG Laplacian  [PDF]
J. Acacio de Barros,C. G. Carvalhaes,J. P. R. F. de Mendon?a,P. Suppes
Physics , 2012,
Abstract: Recent works on the relationship between the electro-encephalogram (EEG) data and psychological stimuli show that EEG recordings can be used to recognize an auditory stimulus presented to a subject. The recognition rate is, however, strongly affected by technical and physiological artifacts. In this work, subjects were presented seven auditory simuli in the form of English words (first, second, third, left, right, yes, and no), and the time-locked electric field was recorded with a 64 channel Neuroscan EEG system. We used the surface Laplacian operator to eliminate artifacts due to sources located at regions far from the electrode. Our intent with the Laplacian was to improve the recognition rates of auditory stimuli from the electric field. To compute the Laplacian, we used a spline interpolation from spherical harmonics. The EEG Laplacian of the electric field were average over trials for the same auditory stimulus, and with those averages we constructed prototypes and test samples. In addition to the Laplacian, we applied Butterworth bandpass digital filters to the averaged prototypes and test samples, and compared the filtered test samples against the prototypes using a least squares metric in the time domain. We also analyzed the effects of the spline interpolation order and bandpass filter parameters in the recognition rates. Our results show that the use of the Laplacian improves the recognition rates and suggests a spatial isomorphism between both subjects.
Generalized periodic EEG activity in two cases of neurosyphilis
Anghinah, Renato;Camargo, érica C.S.;Braga, Nádia I.;Waksman, Simone;Nitrini, Ricardo;
Arquivos de Neuro-Psiquiatria , 2006, DOI: 10.1590/S0004-282X2006000100025
Abstract: neurosyphilis is a recognized cause of epileptic seizures and cognitive impairment, but is not usually associated with the finding of generalized periodic activity in the eeg. we report two similar cases characterized by progressive cognitive impairment followed by partial complex seizures, in whom the eeg showed generalized periodic activity. both cerebrospinal fluid and the response to penicillin therapy confirmed the diagnoses of neurosyphilis in the two cases. the finding of eeg generalized periodic activity in patients with cognitive or behavioral disorders is usually associated with creutzfeldt-jakob disease, although there are other conditions, some of them potentially reversible, which may also present this eeg abnormality. neurosyphilis has tended not to be included among them, and our present findings support the importance of first ruling out neurosyphilis in those patients with cognitive or behavioral disorders associated with generalized periodic epileptiform discharges.
Generalized periodic EEG activity in two cases of neurosyphilis  [cached]
Anghinah Renato,Camargo érica C.S.,Braga Nádia I.,Waksman Simone
Arquivos de Neuro-Psiquiatria , 2006,
Abstract: Neurosyphilis is a recognized cause of epileptic seizures and cognitive impairment, but is not usually associated with the finding of generalized periodic activity in the EEG. We report two similar cases characterized by progressive cognitive impairment followed by partial complex seizures, in whom the EEG showed generalized periodic activity. Both cerebrospinal fluid and the response to penicillin therapy confirmed the diagnoses of neurosyphilis in the two cases. The finding of EEG generalized periodic activity in patients with cognitive or behavioral disorders is usually associated with Creutzfeldt-Jakob disease, although there are other conditions, some of them potentially reversible, which may also present this EEG abnormality. Neurosyphilis has tended not to be included among them, and our present findings support the importance of first ruling out neurosyphilis in those patients with cognitive or behavioral disorders associated with generalized periodic epileptiform discharges.
NUMBER RECOGNITION SYSTEM USING ELECTROENCEPHALOGRAM (EEG) SIGNALS  [cached]
?Shashibala Rao, Bharti Gawali, Mehrotra S.C., Pramod Rokade and Rakesh Deore
Advances in Computational Research , 2012,
Abstract: This paper focuses on number recognition from feature extracted using Electroencephalogram (EEG) readings. EEG signals were recorded at Department of Computer Science and IT, Dr. B. A. M. University, India, from 6 volunteer subjects. A random number generator Graphics User Interface was developed in VB7. It is used to display numbers from 0 to 9 which worked as Visually Evoked Potential (VEP) for the experiment. The database of 6 male right-handed subjects in the age group of (20-25) was created and used as training data set. By exposing the same set of subjects to the GUI again, new EEG recordings were collected. This new set of EEG readings was considered as testing data set. The testing data was searched and matched with trained data set for recognizing pattern of each number. The experiments were conducted by concentrating on Beta signal and Linear discriminate analysis (LDA) was implemented to classify the data. The recognition rate observed was different for different numbers. Overall recognition rate observed was 68.33%. It is also seen that there exist a unique pattern for each number.
Single-trial EEG-based emotion recognition usingtemporally regularized common spatial pattern  [PDF]
Cheng Minmin, Lu Zuhong, Wang Haixian
- , 2015, DOI: 10.3969/j.issn.1003-7985.2015.01.010
Abstract: This study addresses the problem of classifying emotional words based on recorded electroencephalogram(EEG)signals by the single-trial EEG classification technique. Emotional two-character Chinese words are used as experimental materials. Positive words versus neutral words and negative words versus neutral words are classified, respectively, using the induced EEG signals. The method of temporally regularized common spatial patterns(TRCSP)is chosen to extract features from the EEG trials, and then single-trial EEG classification is achieved by linear discriminant analysis. Classification accuracies are between 55% and 65%. The statistical significance of the classification accuracies is confirmed by permutation tests, which shows the successful identification of emotional words and neutral ones, and also the ability to identify emotional words. In addition, 10 out of 15 subjects obtain significant classification accuracy for negative words versus neutral words while only 4 are significant for positive words versus neutral words, which demonstrate that negative emotions are more easily identified.
An Approach for Pattern Recognition of EEG Applied in Prosthetic Hand Drive
Xiao-Dong Zhang,Yun-Xia Wang,Yao-Nan Li,Jin-Jin Zhang
Journal of Systemics, Cybernetics and Informatics , 2011,
Abstract: For controlling the prosthetic hand by only electroencephalogram (EEG), it has become the hot spot in robotics research to set up a direct communication and control channel between human brain and prosthetic hand. In this paper, the EEG signal is analyzed based on multi-complicated hand activities. And then, two methods of EEG pattern recognition are investigated, a neural prosthesis hand system driven by BCI is set up, which can complete four kinds of actions (arm’s free state, arm movement, hand crawl, hand open). Through several times of off-line and on-line experiments, the result shows that the neural prosthesis hand system driven by BCI is reasonable and feasible, the C-support vector classifiers-based method is better than BP neural network on the EEG pattern recognition for multi-complicated hand activities.
Emotion recognition method using entropy analysis of EEG signals
Seyyed Abed Hosseini,Mohammad Bagher Naghibi-Sistani
International Journal of Image, Graphics and Signal Processing , 2011,
Abstract: This paper proposes an emotion recognition system using EEG signals, therefore a new approach to emotion state analysis by approximate (ApEn) and wavelet entropy (WE) is described. We have used EEG signals recorded during emotion in five channels (FP1, FP2, T3, T4 and Pz), under pictures induction environment (calm-neutral and negative excited) for participants. After a brief introduction to the concept, the ApEn and WE were extracted from two different EEG time series. The result showed that, the classification accuracy in two emotion states was 73.25% using the support vector machine (SVM) classifier. The simulations showed that the classification accuracy is good and the proposed methods are effective. During an emotion, the EEG is less complex compared to the normal, indicating reduction in active neuronal process in the brain.
Qualitative and Quantitative Evaluation of EEG Signals in Epileptic Seizure Recognition
S. A. Hosseini,M-R. Akbarzadeh-T,M-B. Naghibi-Sistani
International Journal of Intelligent Systems and Applications , 2013, DOI: 10.5815/ijisa.2013.06.05
Abstract: A chaos-ANFIS approach is presented for analysis of EEG signals for epileptic seizure recognition. The non-linear dynamics of the original EEGs are quantified in the form of the hurst exponent (H) and largest lyapunov exponent (λ). The process of EEG analysis consists of two phases, namely the qualitative and quantitative analysis. The classification ability of the H and λ measures is tested using ANFIS classifier. This method is evaluated with using a benchmark EEG dataset, and qualitative and quantitative results are presented. Our inter-ictal EEG based diagnostic approach achieves 97.4% accuracy with using 4-fold cross validation. Diagnosis based on ictal data is also tested in ANFIS classifier, reaching 96.9% accuracy. Therefore, our method can be successfully applied to both inter-ictal and ictal data.
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