An Affective Brain-Computer Interface Based on a Transfer Learning Method

An affective brain-computer interface (aBCI) can detect affective states based on brain signals and might assist people in improving their emotion regulation abilities. However, individual differences in emotional brain patterns make cross-subject emotion identification extremely challenging. Traditional supervised single-subject classification schemes require considerable calibration samples from new individuals to train subject-dependent models. Individuals are easily fatigued with long-term EEG collection processes, which may affect performance in subsequent online experiments. In this study, we propose a real-time aBCI system using domain-fusion-based multisource style transfer mapping (DF-MS-STM) to detect positive, neutral, and negative emotional states without the need for additional training sessions. Sixteen subjects participated in our online experiments to test the performance of our aBCI system and an average online prediction accuracy of 72.17±12.25% was obtained for three-class emotion recognition tasks in the last three experimental sessions. Our proposed algorithm significantly outperformed numerous baseline methods in terms of cross-subject emotion classification. In addition, we identified distinct brain patterns in response to different emotional stimuli based on the results of event-related spectral perturbation (ERSP) analyses. These neural patterns might provide new insights for emotional brain mechanistic studies and related aBCIs.