4.7 Article

Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition

期刊

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
卷 14, 期 3, 页码 2496-2511

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAFFC.2022.3164516

关键词

EEG; emotion recognition; brain-computer interface; cross-subject; contrastive learning

向作者/读者索取更多资源

EEG signals have been widely used for emotion recognition, but the inter-subject variability remains a challenge. In this study, we proposed a contrastive learning method called CLISA to address cross-subject emotion recognition problem. The experimental results showed that CLISA achieved state-of-the-art performance on different datasets and could generalize to unseen subjects and stimuli.
EEG signals have been reported to be informative and reliable for emotion recognition in recent years. However, the inter-subject variability of emotion-related EEG signals still poses a great challenge for the practical applications of EEG-based emotion recognition. Inspired by recent neuroscience studies on inter-subject correlation, we proposed a Contrastive Learning method for Inter-Subject Alignment (CLISA) to tackle the cross-subject emotion recognition problem. Contrastive learning was employed to minimize the inter-subject differences by maximizing the similarity in EEG signkal representations across subjects when they received the same emotional stimuli in contrast to different ones. Specifically, a convolutional neural network was applied to learn inter-subject aligned spatiotemporal representations from EEG time series in contrastive learning. The aligned representations were subsequently used to extract differential entropy features for emotion classification. CLISA achieved state-of-the-art cross-subject emotion recognition performance on our THU-EP dataset with 80 subjects and the publicly available SEED dataset with 15 subjects. It could generalize to unseen subjects or unseen emotional stimuli in testing. Furthermore, the spatiotemporal representations learned by CLISA could provide insights into the neural mechanisms of human emotion processing.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据