EEG-based emotion recognition based on kernel Fisher´s discriminant analysis and spectral powers

In this paper, a feature extraction method called kernel Fisher´s emotion pattern (KFEP) based on the kernel Fisher´s discriminant analysis and spectral powers of multiple EEG rhythms is proposed for emotion recognition. An emotion-induction paradigm is designed for emotional EEG data collection, where a set of pictures selected from the International Affective Picture System (IAPS) are used as the emotion induction stimuli. Experimental results indicate that the KFEP feature performs better than the commonly used spectral power features. Our proposed KFEP achieves high classification accuracies of valence (78.49%) and arousal (81.93%).