EmotioNet: A 3-D Convolutional Neural Network for EEG-based Emotion Recognition

In this paper, an emotional EEG-specific three-dimensional Convolutional Neural Network, EmotioNet, is proposed and implemented to accurately recognize emotion states. For the first time, raw data in the benchmark emotional EEG database, i.e., DEAP, are used as the input to a CNN architecture. In order to investigate the spatio-temporal character of emotional features, the effectiveness of 2-D and 3-D convolution kernels, which extract spatial and temporal features separately and simultaneously, are compared in detail. Furthermore, two major problems of EEG-based emotion recognition, namely, covariance shift and the unreliability of emotional ground truth, are described, and the effectiveness of batch normalization and dense prediction, which alleviate these problems respectively, are also investigated. Experimental results show that 3-D kernels, batch normalization, and dense prediction are all essential techniques for the emotional EEG-specific CNN architecture. The proposed EmotioNet, namely, a 3-D covariance shift adaptation-based CNN with a dense prediction layer, achieves classification rates of 73.3% and 72.1% for arousal and valence, equivalent to the best performance of several previous studies. Importantly, our results are based on automatic feature extraction, which is in contrast to previous handcrafted features. Therefore, EmotioNet provides a new method for EEG-based emotion recognition.