Human emotion recognition through short time Electroencephalogram (EEG) signals using Fast Fourier Transform (FFT)

Human emotion recognition plays a vital role in psychology, psycho-physiology and human machine interface (HMI) design. Electroencephalogram (EEG) reflects the internal emotional state changes of the subject compared to other conventional methods (face recognition, gestures, speech, etc). In this work, EEG signals are collected using 62 channels from 20 subjects in the age group of 21~39 years for determining discrete emotions. Audio-visual stimuli (video clips) is used for inducing five different emotions (happy, surprise, fear, disgust, neutral). EEG signals are preprocessed through Butterworth 4^th order filter with a cut off frequency of 0.5 Hz-60 Hz and smoothened using Surface Laplacian filter. EEG signals are framed into a short time duration of 5s and two statistical features (spectral centroid and spectral entropy) in four frequency bands namely alpha (8 Hz-16 Hz), beta (16 Hz-32 Hz), gamma (32 Hz-60 Hz) and alpha to gamma (8 Hz-60 Hz) are extracted using Fast Fourier Transform (FFT). These features are mapped into the corresponding emotions using two simple classifiers such as K Nearest Neighbor(KNN) and Probabilistic Neural Network (PNN). In this work, KNN outperforms PNN by offering the maximum mean classification accuracy of 91.33 % on beta band. This experimental results indicates the short time duration of EEG signals is highly essential for detecting the emotional state changes of the subjects.