Electroencephalography and evoked potentials: a PC-based analysis program for laboratory courses in physiology.

1992 ◽  
Vol 263 (6) ◽  
pp. S16
Author(s):  
M Illert ◽  
H Wiese ◽  
U Wolfram
Keyword(s):  
Evoked Potentials ◽  
Epileptic Seizures ◽  
Dental Students ◽  
Sleep Stages ◽  
Data Sets ◽  
Eeg Signals ◽  
Cerebral Activity ◽  
Laboratory Courses ◽  
Mental Activation ◽  
Eeg Recordings

A computer program (EEG Analysis) was developed for the preclinical laboratory course in physiology held for medical and dental students. It offers an off-line analysis of a set of typical and frequently occurring physiological and pathological electroencephalogram (EEG) and evoked potential (EP) recordings, which are stored in an IBM-compatible personal computer (PC) system. The users are requested to measure and analyze the data sets and to work through a base of questions relevant in the frame of the particular topic. The program is structured in several exercises: calibration, pickup of non-EEG signals (eye movements, chewing), waveforms in EEG recordings from awake subjects (alpha-waves, beta-waves), desynchronization of cerebral activity (visual activation, acoustic activation, mental activation), habituation of cerebral activity upon acoustic stimuli, EEG recordings from asleep subjects (different sleep stages, sleep-specific EEG signals), epileptic seizures, and EPs (principle of averaging, visually evoked potentials in different cortical areas). The program runs under MS-DOS and is network capable. The software structure ensures maximal flexibility for rapid changes and adaptations of the program to specific needs of a particular EEG course. The program has been used for three years, and the response from > 800 students has been consistently positive.

scholarly journals Voltage Distributions in Extracellular Brain Recordings

2021 ◽  
Author(s):  
Nicholas Vaughan Swindale ◽  
Peter Rowat ◽  
Matthew R Krause ◽  
Martin A Spacek ◽  
Catalin C Mitelut
Keyword(s):  
Action Potentials ◽  
Field Potential ◽  
Sleep Stages ◽  
Time Varying ◽  
Eeg Signals ◽  
Extracellular Recordings ◽  
Eeg Recordings ◽  
Log Linear ◽  
High Pass ◽  
Voltage Distributions

Extracellular recordings of brain voltage signals have many uses, including the identification of spikes and the characterization of brain states via analysis of local field potential (LFP) or EEG recordings. Though the factors underlying the generation of these signals are time-varying and complex, their analysis may be facilitated by an understanding of their statistical properties. To this end, we analyzed the voltage distributions of high-pass extracellular recordings from a variety of structures, including cortex, thalamus and hippocampus, in monkeys, cats and rodents. We additionally investigated LFP signals in these recordings as well as human EEG signals obtained during different sleep stages. In all cases, the distributions were accurately described by a Gaussian within ± 1.5 standard deviations from zero. Outside these limits, voltages tended to be distributed exponentially, i.e. they fell off linearly on log-linear frequency plots, with variable heights and slopes. A possible explanation for this is that sporadically and independently occurring events with individual Gaussian size distributions can sum to produce approximately exponential distributions. For the high-pass recordings, a second explanation results from a model of the noisy behaviour of ion channels which produce action potentials via Hodgkin-Huxley kinetics. The distributions produced by this model, relative to the averaged potential, were also Gaussian with approximately exponential flanks. The model also predicted time-varying noise distributions during action potentials, which were observed in the extracellular spike signals. These findings suggest a principled method for detecting spikes in high-pass recordings and transient events in LFP and EEG signals.

Epileptic EEG Classification by Using Time-Frequency Images for Deep Learning

2021 ◽  
pp. 2150026
Author(s):  
Mehmet Akif Ozdemir ◽  
Ozlem Karabiber Cura ◽  
Aydin Akan
Keyword(s):  
Epileptic Seizures ◽  
Eeg Signals ◽  
Clinical Tool ◽  
Energy Distributions ◽  
Time Frequency ◽  
Synchrosqueezing Transform ◽  
Novel Method ◽  
Eeg Recordings ◽  
Accuracy Rates ◽  
Aided Diagnosis

Epilepsy is one of the most common brain disorders worldwide. The most frequently used clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings. There have been proposed many computer-aided diagnosis systems using EEG signals for the detection and prediction of seizures. In this study, a novel method based on Fourier-based Synchrosqueezing Transform (SST), which is a high-resolution time-frequency (TF) representation, and Convolutional Neural Network (CNN) is proposed to detect and predict seizure segments. SST is based on the reassignment of signal components in the TF plane which provides highly localized TF energy distributions. Epileptic seizures cause sudden energy discharges which are well represented in the TF plane by using the SST method. The proposed SST-based CNN method is evaluated using the IKCU dataset we collected, and the publicly available CHB-MIT dataset. Experimental results demonstrate that the proposed approach yields high average segment-based seizure detection precision and accuracy rates for both datasets (IKCU: 98.99% PRE and 99.06% ACC; CHB-MIT: 99.81% PRE and 99.63% ACC). Additionally, SST-based CNN approach provides significantly higher segment-based seizure prediction performance with 98.54% PRE and 97.92% ACC than similar approaches presented in the literature using the CHB-MIT dataset.

DECOMPOSITION OF SEIZURES IN EEG SIGNALS USING DISCRETE WAVELET TRANSFORMS (DWT)

2021 ◽  
pp. 50-52
Author(s):  
N Shweta ◽  
Nagendra H
Keyword(s):  
Neurological Disorders ◽  
Wavelet Transforms ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Eeg Signals ◽  
Seizure Duration ◽  
Eeg Recordings ◽  
Electroencephalogram Eeg ◽  
The Brain

An electroencephalogram (EEG) is a test that records electrical activity in the brain. Epileptic seizures affect approximately 50 million people worldwide, making it one of the most serious neurological disorders. Seizures cause a loss of consciousness, but there are no specic signs associated with epileptic seizures. analysing the brain's activity during seizures and locating the seizure duration in EEG recordings is difcult and time consuming. A discrete wavelet transform (DWT), which is an effective tool for decomposing EEG signals into delta, theta, alpha, beta, and gamma ( and ) frequency bands. For research, the db4 is used, which has a morphological d,q,a,b g structure that is different to that of EEG.

scholarly journals Determinant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 74
Author(s):  
Shahab Abdulla ◽  
Mohammed Diykh ◽  
Sarmad K. D. Alkhafaji ◽  
Jonathan H. Greena ◽  
Hanan Al-Hadeethi ◽  
...  
Keyword(s):  
Covariance Matrix ◽  
Back Propagation ◽  
Epileptic Seizures ◽  
Back Propagation Neural Network ◽  
Machine Learning Techniques ◽  
Eeg Signals ◽  
Average Accuracy ◽  
Effective Selection ◽  
Kolmogorov Smirnov ◽  
Eeg Recordings

Experts usually inspect electroencephalogram (EEG) recordings page-by-page in order to identify epileptic seizures, which leads to heavy workloads and is time consuming. However, the efficient extraction and effective selection of informative EEG features is crucial in assisting clinicians to diagnose epilepsy accurately. In this paper, a determinant of covariance matrix (Cov–Det) model is suggested for reducing EEG dimensionality. First, EEG signals are segmented into intervals using a sliding window technique. Then, Cov–Det is applied to each interval. To construct a features vector, a set of statistical features are extracted from each interval. To eliminate redundant features, the Kolmogorov–Smirnov (KST) and Mann–Whitney U (MWUT) tests are integrated, the extracted features ranked based on KST and MWUT metrics, and arithmetic operators are adopted to construe the most pertinent classified features for each pair in the EEG signal group. The selected features are then fed into the proposed AdaBoost Back-Propagation neural network (AB_BP_NN) to effectively classify EEG signals into seizure and free seizure segments. Finally, the AB_BP_NN is compared with several classical machine learning techniques; the results demonstrate that the proposed mode of AB_BP_NN provides insignificant false positive rates, simpler design, and robustness in classifying epileptic signals. Two datasets, the Bern–Barcelona and Bonn datasets, are used for performance evaluation. The proposed technique achieved an average accuracy of 100% and 98.86%, respectively, for the Bern–Barcelona and Bonn datasets, which is considered a noteworthy improvement compared to the current state-of-the-art methods.

AUTOMATIC DETECTION OF EPILEPTIC EEG SIGNALS USING HIGHER ORDER CUMULANT FEATURES

2011 ◽  
Vol 21 (05) ◽  
pp. 403-414 ◽  
Author(s):  
U. RAJENDRA ACHARYA ◽  
S. VINITHA SREE ◽  
JASJIT S. SURI
Keyword(s):  
Clinical Information ◽  
Epileptic Seizures ◽  
Epileptic Activity ◽  
Higher Order ◽  
Support Vector ◽  
P Value ◽  
Detection Accuracy ◽  
Eeg Signals ◽  
The People ◽  
Eeg Recordings

The unpredictability of the occurrence of epileptic seizures makes it difficult to detect and treat this condition effectively. An automatic system that characterizes epileptic activities in EEG signals would allow patients or the people near them to take appropriate precautions, would allow clinicians to better manage the condition, and could provide more insight into these phenomena thereby revealing important clinical information. Various methods have been proposed to detect epileptic activity in EEG recordings. Because of the nonlinear and dynamic nature of EEG signals, the use of nonlinear Higher Order Spectra (HOS) features is a seemingly promising approach. This paper presents the methodology employed to extract HOS features (specifically, cumulants) from normal, interictal, and epileptic EEG segments and to use significant features in classifiers for the detection of these three classes. In this work, 300 sets of EEG data belonging to the three classes were used for feature extraction and classifier development and evaluation. The results show that the HOS based measures have unique ranges for the different classes with high confidence level (p-value < 0.0001). On evaluating several classifiers with the significant features, it was observed that the Support Vector Machine (SVM) presented a high detection accuracy of 98.5% thereby establishing the possibility of effective EEG segment classification using the proposed technique.

scholarly journals An electrophysiological marker of arousal level in humans

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Janna D Lendner ◽  
Randolph F Helfrich ◽  
Bryce A Mander ◽  
Luis Romundstad ◽  
Jack J Lin ◽  
...  
Keyword(s):  
Eye Movement ◽  
Rem Sleep ◽  
Oscillatory Activity ◽  
Sleep Stages ◽  
Brain State ◽  
Data Sets ◽  
Rapid Eye Movement ◽  
Spectral Slope ◽  
Scale Free ◽  
Eeg Recordings

Deep non-rapid eye movement sleep (NREM) and general anesthesia with propofol are prominent states of reduced arousal linked to the occurrence of synchronized oscillations in the electroencephalogram (EEG). Although rapid eye movement (REM) sleep is also associated with diminished arousal levels, it is characterized by a desynchronized, ‘wake-like’ EEG. This observation implies that reduced arousal states are not necessarily only defined by synchronous oscillatory activity. Using intracranial and surface EEG recordings in four independent data sets, we demonstrate that the 1/f spectral slope of the electrophysiological power spectrum, which reflects the non-oscillatory, scale-free component of neural activity, delineates wakefulness from propofol anesthesia, NREM and REM sleep. Critically, the spectral slope discriminates wakefulness from REM sleep solely based on the neurophysiological brain state. Taken together, our findings describe a common electrophysiological marker that tracks states of reduced arousal, including different sleep stages as well as anesthesia in humans.

scholarly journals Enhanced firefly optimizer with deep neural network for the detection of epileptic seizures using EEG signals

2020 ◽  
Vol 9 (5) ◽  
pp. 137-148
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Optimization Technique ◽  
Epileptic Seizures ◽  
Experimental Simulation ◽  
Series Data ◽  
Eeg Signals ◽  
Mode Decomposition ◽  
Eeg Recordings

Currently, Electroencephalogram (EEG) is extensively used for diagnosing the epilepsy. The objective of this research is to investigate the changes in epilepsy frequency by proposing a new optimization based deep learning model. At first, the EEG recordings were acquired from two online databases; Bern Barcelona (BB), and Bonn University (BU). Then, Chebyshev type two filter was implemented to remove the unwanted artifacts from the acquired EEG signals. Further, Multivariate Variational Mode Decomposition (MVMD) methodology was applied to decompose the denoised EEG signals. The signal decomposition helps in finding the necessary information, which required to model the complex time series data. Then, the features were extracted from decomposed signals by using fifteen entropy, linear and statistical features. In addition, enhanced firefly optimization technique was proposed for optimizing the extracted features. In the enhanced firefly optimizer, a crossover operator of genetic algorithm was added for enhancing the local convergence rate that gives better classification. At last, the optimized feature vectors were classified by Deep Neural Network (DNN) that includes two circumstances (seizure and healthy), and (Interictal, ictal, and normal). From the experimental simulation, the proposed model improvement maximum of 1.4%, and 8.82% of accuracy in BU and BB EEG datasets, respectively related to the existing models.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

Analysis of statistical coefficients and autoregressive parameters over intrinsic mode functions (IMFs) for epileptic seizure detection

2020 ◽  
Vol 65 (6) ◽  
pp. 693-704
Author(s):  
Rafik Djemili
Keyword(s):  
Involuntary Movement ◽  
Epileptic Seizures ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Time Segment ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Finite Set ◽  
Student’S T

AbstractEpilepsy is a persistent neurological disorder impacting over 50 million people around the world. It is characterized by repeated seizures defined as brief episodes of involuntary movement that might entail the human body. Electroencephalography (EEG) signals are usually used for the detection of epileptic seizures. This paper introduces a new feature extraction method for the classification of seizure and seizure-free EEG time segments. The proposed method relies on the empirical mode decomposition (EMD), statistics and autoregressive (AR) parameters. The EMD method decomposes an EEG time segment into a finite set of intrinsic mode functions (IMFs) from which statistical coefficients and autoregressive parameters are computed. Nevertheless, the calculated features could be of high dimension as the number of IMFs increases, the Student’s t-test and the Mann–Whitney U test were thus employed for features ranking in order to withdraw lower significant features. The obtained features have been used for the classification of seizure and seizure-free EEG signals by the application of a feed-forward multilayer perceptron neural network (MLPNN) classifier. Experimental results carried out on the EEG database provided by the University of Bonn, Germany, demonstrated the effectiveness of the proposed method which performance assessed by the classification accuracy (CA) is compared to other existing performances reported in the literature.

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