Cross-subject and Cross-gender Emotion Classification from EEG

This paper aims to explore whether different persons share similar patterns for EEG changing with emotions and examine the performance of cross-subject and cross-gender emotion classification from EEG. Movie clips are used to evoke three emotional states: positive, neutral, and negative. We adopt differential entropy (DE) as features, and apply linear dynamic system (LDS) to do feature smoothing. The average cross-subject classification accuracy is 64.82% with five frequency bands using data from 14 subjects as training set and data from the rest one subject as testing set. With the training set expanding from one subject to 14 subjects, the average accuracy will then continuously increase. Moreover, fuzzy-integral-based combination method is used to combine models across frequency bands and the average accuracy of 72.82% is obtained. The better performance of using training and testing data both from female subjects partly implies that there should be gender differences in EEG patterns when processing emotions.