Individual Similarity Guided Transfer Modeling for EEG-based Emotion Recognition

Intelligent recognition of electroencephalogram (EEG) signals has been an important means to recognize emotions. Traditional user-independent method, which treat^s each individual's EEG data as independent and identically distributed (i.i.d.) samples and ignores destruction on i.i.d. condition caused by individual differences, usually has lower generalization performance. Although user-dependent method could alleviate abovementioned problem, it faces difficulty in collection of sufficient training EEG data for each individual. In order to construct user-dependent model merely based on a small amount of training EEG data, we incorporate transfer learning framework and propose a individual similarity guided transfer modeling method for EEG-based emotion recognition. We first measure the similarities between individuals using maximum mean discrepancy (MMD), then utilize pre-existing EEG data of similar individuals to assist construction of user-dependent model for the target individual using an instance-based transfer learning algorithm named TrAdaBoost. We compared this method with traditional user-independent and user-dependent methods on DEAP dataset. Experimental results showed that our method could transfer useful knowledge from other individuals for user-dependent emotion recognition, which achieved classification accuracies of 66.1% and 66.7% on arousal and valence dimentions, respectively.