Individual Similarity Guided Transfer Modeling for EEGbased Emotion Recognition Intelligent recognition of electroencephalogram (EEG) signals has been an important means to recognize emotions. Traditional userindependent method, which treats each individualu0027s 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 userdependent method could alleviate abovementioned problem, it faces difficulty in collection of sufficient training EEG data for each individual. In order to construct userdependent 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 EEGbased emotion recognition. We first measure the similarities between individuals using maximum mean discrepancy (MMD), then utilize preexisting EEG data of similar individuals to assist construction of userdependent model for the target individual using an instancebased transfer learning algorithm named TrAdaBoost. We compared this method with traditional userindependent and userdependent methods on DEAP dataset. Experimental results showed that our method could transfer useful knowledge from other individuals for userdependent emotion recognition, which achieved classification accuracies of 66.1% and 66.7% on arousal and valence dimentions, respectively.

Variational Autoencoder based Latent Factor Decoding of Multichannel EEG for Emotion Recognition Robust crosssubject emotion recognition based on multichannel EEG has always been a hard work. In this work, we hypothesize there exists default brain variables across subjects in emotional processes. Hence, the states of the latent variables that related to emotional processing must contribute to building robust recognition models. We propose to utilize variational autoencoder (VAE) to determine the latent factors from the multichannel EEG. Through sequence modeling method, we examine the emotion recognition performance based on the learnt latent factors. The performance of the proposed methodology is verified on two public datasets (DEAP and SEED), and compared with traditional matrix factorization based (ICA) and autoencoder based (AE) approaches. Experimental results demonstrate that neural network is suitable for unsupervised EEG modeling and our proposed emotion recognition framework achieves the stateoftheart performance. As far as we know, it is the first work that introduces VAE into multichannel EEG decoding for emotion recognition.