Several methods for collecting psychophysiological data from humans have been developed, including galvanic skin response (GSR), electromyography (EMG), electroencephalography (EEG), and the electrocardiogram (ECG). This paper proposes a feature extraction method for emotion recognition in EEG-based human brain signals. In this research, emotions were elicited from subjects using emotion-related stimuli from the International Affective Picture System (IAPS) database. We selected four kinds of emotional stimuli in the arousal-valence domain. Raw brain signals were preprocessed using independent component analysis (ICA) to remove artifacts. We introduced a feature extraction method using LPP, and implemented a benchmark based on statistical and frequency domain features. The LPP-based results show the highest accuracy when using SVM in the all-selected feature set. The results also provide evidence and suggest a way for further developing a more specialized emotion recognition system using brain signals. 
A novel feature extraction method based on late positive potential for emotion recognition in human brain signal patterns