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.

Shorttimespan EEGbased personalized emotion recognition with deep convolutional neural network Emotion recognition can be useful in various applications such as in neurofeedback training for functional enhancement. A practically realizable emotion recognition system should rely on as little physiological signalsmodalities as possible. Also, emotionrelated neurological activities may be vastly different from person to person. Hence, this paper presents the singlemodal EEGbased personalized emotion recognition convolutional neural network (CNN) models working on the DEAP dataset. The valence and arousal level classification performance of our presented CNN classifiers have surpassed the other emotion classifiers working on the DEAP dataset based on our scope of literature reviewed. The models, which are deep CNN, rely on only plain EEG data and require no preextracted EEG features. The design and application of the CNN models is aimed at possible future work of identification of new emotionrelated EEG features, relying on the automated feature extraction capability of the CNN. The two CNN models presented have achieved the 3class valence classification test accuracy of 97.59% and 98.75% respectively, and the 3class arousal classification test accuracy of 98.48% and 97.58%.