Emotion Recognition Based on Physiological Signals Using Convolution Neural Networks

Abstract

Physiological signal which consists of electrocardiogram (EEG) and peripheral signal is becoming increasingly important in affective computing, because of its intimacy with nerves. In this paper, two models are proposed on the basis of Convolution Neural Network (CNN) to process EEG and peripheral signals respectively. Taking the extraction of traditional features into account, the first model is based on two-dimensional Convolutional Neural Network (2D-CNN) using original EEG data, where its kernel is one-dimensional to extract same kinds of features for every channel. In the second model, we apply one-dimensional Convolution Neural Network (1D-CNN) to every channel of peripheral signals and then concatenate results for classification. Experiments have been done to evaluate our models on the MAHNOB-HCI database. As a result, in the three-category model, the classification accuracies in arousal dimension of the two models using CNN are 61.5%, 58.01% and 58%, 56.28% in valence. Compared with the classical methods, the accuracy using CNN is increased by 9.1% in arousal for EEG and 11.81% for peripheral signals, achieving state-of-the-art performance.