Subdomain Adaption Method for Cross-Domain Emotion Recognition and Clinical Consciousness Detection

Emotion recognition and consciousness detection are critical in neuroscience. Patients with disorder of consciousness (DOC) often cannot express feelings or respond definitively to stimuli due to impaired functions, posing challenges for accurate annotation and model transfer from healthy individuals to patients with DOC. Existing domain adaptation methods struggle to capture inter-subdomain relationships, resulting in suboptimal cross-domain learning performance. This study proposes a novel Category Maximum Mean Discrepancy (CMMD) subdomain adaptation method for EEG-based emotion recognition and consciousness detection. The CMMD framework aligns subdomain distributions of the same category in target and source domains, capturing subtle EEG features in emotional states and improving cross-domain classification. On the emotion recognition benchmark datasets SEED and SEED-IV, our method outperforms existing baselines, achieving 85.67% and 74.44% accuracy. In clinical experiments on 25 DOC patients, our research explored within-group dynamics among 15 MCS and 10 UWS patients via EEG-based emotion recognition. Experiments revealed higher average emotion recognition accuracy in MCS patients (66.21%) than UWS patients (56.99%), with a positive correlation to CRS-R scores (p