scholarly journals A Survey Based on EEG Classification

2016 ◽  
Vol 5 (1) ◽  
pp. 01-07
Author(s):  
Deepthi M ◽  
Kavipriya P
Keyword(s):  
Eeg Classification

EEG classification for the detection of mental states

2015 ◽  
Vol 32 ◽  
pp. 113-131 ◽  
Author(s):  
Laurent Vézard ◽  
Pierrick Legrand ◽  
Marie Chavent ◽  
Frédérique Faïta-Aïnseba ◽  
Leonardo Trujillo
Keyword(s):  
Mental States ◽  
Eeg Classification

COMPARISON OF ICTAL AND INTERICTAL EEG SIGNALS USING FRACTAL FEATURES

2013 ◽  
Vol 23 (06) ◽  
pp. 1350028 ◽  
Author(s):  
YU WANG ◽  
WEIDONG ZHOU ◽  
QI YUAN ◽  
XUELI LI ◽  
QINGFANG MENG ◽  
...  
Keyword(s):  
Feature Analysis ◽  
Support Vector ◽  
Eeg Analysis ◽  
Eeg Signals ◽  
Eeg Classification ◽  
Significant Difference ◽  
Interictal Eeg ◽  
Nonlinear Features ◽  
The Difference ◽  
Multi Scales

The feature analysis of epileptic EEG is very significant in diagnosis of epilepsy. This paper introduces two nonlinear features derived from fractal geometry for epileptic EEG analysis. The features of blanket dimension and fractal intercept are extracted to characterize behavior of EEG activities, and then their discriminatory power for ictal and interictal EEGs are compared by means of statistical methods. It is found that there is significant difference of the blanket dimension and fractal intercept between interictal and ictal EEGs, and the difference of the fractal intercept feature between interictal and ictal EEGs is more noticeable than the blanket dimension feature. Furthermore, these two fractal features at multi-scales are combined with support vector machine (SVM) to achieve accuracies of 97.58% for ictal and interictal EEG classification and 97.13% for normal, ictal and interictal EEG classification.

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