Selection of Effective EEG Signal Channels for Recognizing Emotional States based on Frequency Sub-band Information
Shakiba Afsar
1
(
Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
)
Mahdi Khezri
2
(
Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
)
Keywords: Emotion recognition, EEG signal, Common spatial pattern, Power spectrum, Principal component analysis, Support vector machine.,
Abstract :
The design of a reliable emotion recognition system by selecting electrodes with more EEG signal information is considered in this study. The main challenge in using the EEG signal is the high number of recording electrodes, which results in an increase in the computational complexity and processing time. In order to reduce the impact of these cases in the present study, an emotion recognition system was presented using the common spatial pattern (CSP) algorithm to select effective channels. The EEG signals of the DEAP database were used to evaluate the proposed method. Theta, Alpha, Beta and Gamma frequency subbands were separated for EEG signals; And then the best channels were chosen by the CSP method for the subbands. By applying the method, the F4-F8-AF4-FP2-FZ-CZ electrodes were selected from the first 32 channels; And the maximum power spectrum for the frequency subbands of the selected channels was calculated as a feature. Then, using t-test and principal component analysis (PCA), the process of feature selection was done. The support vector machine (SVM) was used for emotion detection. The obtained results indicate the optimal performance of the proposed system in identifying four emotional states; So that the emotions were identified with 84% accuracy. The results show the effect of selecting EEG channels on the performance of the emotion detection system despite using only one feature of maximum power spectrum density. Obviously, as the number and type of features increase, the results will continue to improve.
The use of the CSP algorithm to select effective EEG channels based on frequency sub-band information.
Selection of prefrontal and frontal electrodes, which correspond to the brain lobes that control emotions.
Improved system performance in emotion recognition despite using only one power spectrum feature.
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