The Role of Spectral Power Ratio in Characterizing Emotional EEG for Gender Identification

The motivation of this study was to perceive the gender variations by studying the emotional states (i.e. angry, anxiety, disgust, happiness, sadness and surprise). Emotional electroencephalography (EEG) data were recorded from ten healthful subjects whist the volunteers watched seven, short, emotional audio-visual video clips. Wavelet (WT) has been used as a denoising technique. The spectral relative power ratios (PR) features including (delta/theta), (theta/alpha), (alpha/beta), (beta/gamma) and (theta/gamma) were extracted from each EEG channel. In the subsequent step analysis of variance (ANOVA) has been performed to characterize the emotional EEG based on gender differences. Moreover, K-nearest neighbors (KNN) and support vector machine (SVM) classifiers were used to classify the emotional EEG based on gender differences. The results revealed that a relatively high PR for all emotional states in females compared to males particularly in anger, disgust, happiness and surprise emotional states compare to males' PR. Moreover, the females show relatively higher PR for anxiety, sadness and neutral in most cases. In contrast, the males show relatively higher PR particularly in theta/alpha and theta/gamma for anxiety emotional state, higher delta/theta and alpha/beta for sadness emotional state, and PR particularly had higher delta/theta and alpha/beta for neutral emotional state. The classification results were 90.4% for SVM and 92% for the KNN. Therefore, the proposed system using WT denoising method, spectral PR features, SVM and KNN classifiers were crucial role in gender identification and characterizing the emotional EEG signals.