Cross-phase Emotion Recognition using Multiple Source Domain Adaptation

EEG signal, the brain wave, has been widely applied in detecting human emotion. Due to the human brain’s complexity, the EEG pattern varies from different individuals, leading to low cross-subject classification performance. What is more, even within the same subject, EEG data also shows diversity for the same reason. Many researchers have conducted experiments to deal with the variance between subjects by transfer learning or domain adaptation. However, most of them are still low-performance, especially when the new subject does not share generality with training samples. In this study, we examined using cross-phase data instead of cross-subject data because the discrepancy of different phase data should be smaller than that of different subjects. Different phases represent data recorded multiple times from the same subject with the same stimuli. Two neural networks are adopted to verify the effectiveness of the cross-phase domain adaptation. As a result, experiments on the public EEG dataset showed approximation level accuracy compared to the state-of-the-art method but much lower standard derivation. Moreover, multiple source domains promote accuracy in contrast to one single domain. This study helps develop a more robust and high-performance real-time EEG system by transferring knowledge from previous data phases.