Electroencephalographic Signal Processing for Detection and Prediction of Emotion
This chapter deals with processing of electroencephalographic signals for detection and prediction of emotion. Prediction of electroencephalographic signal form past samples are needed for early diagnosis of patients, suffering from frequent epileptic seizure and/or psychotherapeutic treatment. The chapter begins with comparing the performance of various digital filter algorithms to identify the right candidate for application in electroencephalographic signal prediction. It then considers clustering/classification of emotion from filter co-efficient, wavelet co-efficient and Fast Fourier Transform co-efficient. It also considers the scope of bio-potential signals, such as skin conductance and pulse count along with electroencephalographic signal on emotion detection of personals. Several algorithms of pattern classification have been examined to determine the best algorithm for the emotion classification problem.
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