Specificity Analysis of Picture-Induced Emotional EEG for Discrimination Between Schizophrenic and Control Participants

Emotion processing, playing an important role in our social interactions, is a sub-topic of social cognition. Significant differences in emotion perception and processing have been demonstrated between schizophrenia and normal people. Therefore, it is a very effective strategy to use the emotional stimulation as the core means to explore the difference between patients and normal people, and then to develop the discriminative model for patients with schizophrenia. In this paper, emotional images were used to stimulate the two groups (schizophrenia group and control group), and the electrophysiological signals during the experiment were recorded. In the feature extraction phase, the time-domain dynamics and the asymmetry of the hemisphere were considered at different stimulation stages. Finally, five effective machine learning methods were used to distinguish between schizophrenia and healthy controls under positive and negative emotional stimuli, respectively. The experimental results show that the two groups of event-related electrophysiological signals obtained by negative stimulation can be better distinguished than those obtained by positive stimulation. And, this phenomenon is more pronounced in the time window of first second after the stimulus appears. Meanwhile, the highest average F-score with 10-fold cross-validation strategy can reach 0.994 by combining both support vector machine classifier and grid search methods.