scholarly journals Dissimilarity-based time–frequency distributions as features for epileptic EEG signal classification

2021 ◽  
Vol 64 ◽  
pp. 102268
Author(s):  
Y. Ech-Choudany ◽  
D. Scida ◽  
M. Assarar ◽  
J. Landré ◽  
B. Bellach ◽  
...  
Keyword(s):  
Signal Classification ◽  
Eeg Signal ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Eeg Signal Classification

Multi-category EEG signal classification developing time-frequency texture features based Fisher Vector encoding method

2016 ◽  
Vol 218 ◽  
pp. 251-258 ◽  
Author(s):  
Ömer F. Alçіn ◽  
Siuly Siuly ◽  
Varun Bajaj ◽  
Yanhui Guo ◽  
Abdulkadir Şengu¨r ◽  
...  
Keyword(s):  
Texture Features ◽  
Signal Classification ◽  
Eeg Signal ◽  
Time Frequency ◽  
Fisher Vector ◽  
Fisher Vector Encoding ◽  
Eeg Signal Classification ◽  
Encoding Method

EEG Signals Classification Based on Time Frequency Analysis

2017 ◽  
Vol 26 (12) ◽  
pp. 1750198 ◽  
Author(s):  
Abdelhakim Ridouh ◽  
Daoud Boutana ◽  
Salah Bourennane
Keyword(s):  
Moving Average ◽  
Real Life ◽  
Signal Classification ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Best Value ◽  
Electroencephalogram Eeg

This paper presents a method to characterize, identify and classify some pathological Electroencephalogram (EEG) signals. We use some Time Frequency Distributions (TFDs) to analyze its nonstationarity. The analysis is conducted by the spectrogram (SP), the Choi–Williams Distribution (CWD) and the Smoothed Pseudo Wigner Ville Distribution (SPWVD). The studies are carried on some real EEG signals collected from a known database. The estimation of the best value of parameters for each distribution is achieved using the Rényi entropy (RE). The time-frequency results have permitted to characterize some pathological EEG signals. In addition, the Rényi Marginal Entropy (RME) is used for the purpose of detecting the peak seizures and discriminates between normal and pathological EEG signals. The frequency bands are evaluated using the Marginal Frequency (MF). The EEG signal classification of two sets A and E containing normal and pathologic EEG signals, respectively, is performed using our proposed method based on energy extraction of signals from time-frequency plane. Also, the Moving Average (MA) is used as a tool to obtain better classification results. The results conducted on real-life EEG signals illustrate the effectiveness of the proposed method.

A Comprehensive Analysis on EEG Signal Classification on Human Brain

2021 ◽  
Author(s):  
Puranam Revanth Kumar ◽  
Sangeetha K ◽  
Rabi Narayan Satpathy ◽  
Sachi Nandan Mohanty
Keyword(s):  
Human Brain ◽  
Comprehensive Analysis ◽  
Signal Classification ◽  
Eeg Signal ◽  
Eeg Signal Classification
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