Emotion recognition method using entropy analysis of EEG signals
Автор: Seyyed Abed Hosseini, Mohammad Bagher Naghibi-Sistani
Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp
Статья в выпуске: 5 vol.3, 2011 года.
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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.
Approximate Entropy, EEG signals, Emotion recognition, Wavelet Entropy.
Короткий адрес: https://sciup.org/15012180
IDR: 15012180
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