Emotion-related awareness detection for patients with disorders of consciousness via graph isomorphic network

The clinical diagnosis of patients with disorders of consciousness (DOC) mainly relies on behavioral scales. However, patients with DOC often have severe dyskinesia, which may lead to misdiagnosis of patients in the minimally conscious state (MCS) as patients in vegetative state (VS). In this paper, we propose an emotion-induced paradigm based on audio-visual stimulation, which can collect electroencephalogram (EEG) signals for consciousness detection without performing behavioral expression tasks. This paradigm exposes patients to emotional videos and stimulates them through video clips, which is more comfortable than event-related potential (ERP), steady-state visual evoked potential (SSVEP) and other paradigms. It effectively reduces the mental burden required by the patients. In terms of algorithms, a graph isomorphic network (GIN) is adopted to automatically classify VS and MCS, using emotional EEG signals from patients with DOC. The accuracy, precision, specificity, and sensitivity of our method are 93.70%, 93.77%, 89.44%, and 95.21%, respectively. Compared with the existing consciousness detection methods, our method has superior performance in consciousness detection for patients with DOC. The experimental results show that our method is feasible in distinguishing MCS and VS, and it is an effective extension of consciousness level detection in patients with DOC.