Discriminative Joint Knowledge Transfer With Online Updating Mechanism for EEG-Based Emotion Recognition
Xiaowei Zhang, Zhongyi Zhou, Qiqi Zhao, Kechen Hou, Xiangyu Wei, Sipo Zhang, Yikun Yang, Yanmeng Cui
Abstract
Domain adaptation (DA) has aroused a wide concern in electroencephalogram (EEG)-based cross-subject emotion recognition tasks. However, many existing DA algorithms focus more on transferability rather than discriminability. In addition, these algorithms typically rely on iterative optimization with pseudo-labels to attain the optimal model. In this study, a novel method with an online updating mechanism named discriminative joint knowledge transfer (DJKT) is proposed. A precise calculation of discriminative information for different emotional states within and across subjects is achieved by leveraging a small number of labeled target-domain samples. Furthermore, to accommodate the time-varying EEG, we extend the passive-aggressive (PA) algorithm to enable online adaptation of the emotion recognition model, thereby enhancing its suitability for real-world scenarios. Extensive experiments conducted on the SJTU emotion EEG dataset (SEED) and SEED-IV demonstrate the effectiveness of our approach. First, comprehensive incorporation of the discriminative information improves the performance of transfer learning significantly. In comparison with several state-of-the-art methods, DJKT exhibits significantly improved emotion recognition performance in both single-source to single-target (STS) and multisource to single-target (MTS) scenarios. Second, the online adjustment strategy effectively addresses the time-varying characteristics of EEG signals, leading to a more robust and stable model.