Self-supervised Contrastive Pre-training for Dry Electrode EEG Emotion Recognition via Cross Device Representation Consistency

Abstract

The use of dry electrode electroencephalography (EEG) systems holds significant importance in advancing the everyday application of emotion recognition. However, adapting it to real-world applications faces unique challenges due to low signal-to-noise ratios and unreliable emotion labels. To address these challenges, we propose a Cross-Device Representation Consistency (CDRC) pre-training paradigm for dry EEG emotion recognition, where the self-supervised signal is provided by the distance between representations embedded in wet and dry EEG components and trained via contrastive estimation. Specifically, we employ a dual-branch embedding prediction task coupled with contrastive feature alignment module to extract robust and distinctive features from dry electrode EEG signals. We evaluate our model on an available emotional dataset PaDWEED, extensive experiments demonstrate that CDRC performs comparably to fully supervised training and achieves state-of-the-art results compared to several self-supervised approaches. Moreover, the remarkable performance on subject-independent tasks highlights its effectiveness in addressing and mitigating subject variability.