Emotion classification based on gamma-band EEG.
In this paper, we use EEG signals to classify two emotions-happiness and sadness. These emotions are evoked by showing subjects pictures of smile and cry facial expressions. We propose a frequency band searching method to choose an optimal band into which the recorded EEG signal is filtered. We use common spatial patterns (CSP) and linear-SVM to classify these two emotions. To investigate the time resolution of classification, we explore two kinds of trials with lengths of 3s and 1s. Classification accuracies of 93.5% +/- 6.7% and 93.0%+/-6.2% are achieved on 10 subjects for 3s-trials and 1s-trials, respectively. Our experimental results indicate that the gamma band (roughly 30-100 Hz) is suitable for EEG-based emotion classification.
Li
Mu
M
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Lu
Bao-Liang
BL
D000328:Adult; D001185:Artificial Intelligence; D001698:Biomedical Engineering; D004569:Electroencephalography / Q000706:statistics & numerical data*; D004644:Emotions / Q000502:physiology*; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D010363:Pattern Recognition, Automated; D010775:Photic Stimulation; D012815:Signal Processing, Computer-Assisted