  Emotion recognition is an important research direction in artificial
intelligence, helping machines understand and adapt to human emotional states.
Multimodal electrophysiological(ME) signals, such as EEG, GSR,
respiration(Resp), and temperature(Temp), are effective biomarkers for
reflecting changes in human emotions. However, using electrophysiological
signals for emotion recognition faces challenges such as data scarcity,
inconsistent labeling, and difficulty in cross-individual generalization. To
address these issues, we propose ME-MHACL, a self-supervised contrastive
learning-based multimodal emotion recognition method that can learn meaningful
feature representations from unlabeled electrophysiological signals and use
multi-head attention mechanisms for feature fusion to improve recognition
performance. Our method includes two stages: first, we use the Meiosis method
to group sample and augment unlabeled electrophysiological signals and design a
self-supervised contrastive learning task; second, we apply the trained feature
extractor to labeled electrophysiological signals and use multi-head attention
mechanisms for feature fusion. We conducted experiments on two public datasets,
DEAP and MAHNOB-HCI, and our method outperformed existing benchmark methods in
emotion recognition tasks and had good cross-individual generalization ability.
