Online Cross-subject Emotion Recognition from ECG via Unsupervised Domain Adaptation

Abstract

Performing cross-subject emotion recognition (ER) using electrocardiogram (ECG) is challenging, since inter-subject discrepancy (caused by individual differences) between source and target subjects (new subjects) may hinder the generalization for new subjects. Recently, some ER methods based on unsupervised domain adaptation (UDA) are proposed to address inter-subject discrepancy. However, when being applied for online scenarios with time-varying ECG, existing methods may suffer performance degradation due to neglecting intra-subject discrepancy (caused by time-varying ECG) within target subjects, or need to re-train ER model, leading to time-and resource-consuming. In the paper, we propose an online cross-subject ER approach from ECG signals via UDA, consisting of two stages. In a training stage, we propose to train a classifier on a shared subspace with a lower inter-subject discrepancy. In an online recognition stage, an online data adaptation (ODA) method is introduced to adapt time-varying ECG via reducing the intra-subject discrepancy, and then online recognition results can be obtained by the trained classifier. Experimental results on Dreamer and Amigos with emotions of valence and arousal demonstrate that our proposed approach improves the classification accuracy by about 12% compared with the baseline method, and is robust to time-varying ECG in online scenarios.