Emotion recognition from multi-channel EEG data through Convolutional Recurrent Neural Network

作者：Xiang Li, Dawei Song, Peng Zhang, Guangliang Yu, Yuexian Hou, Bin Hu · 年份：2016 · DOI：10.1109/bibm.2016.7822545 · 被引用次数：284 · 研究领域：EEG and Brain-Computer Interfaces、Emotion and Mood Recognition、ECG Monitoring and Analysis

Automatic emotion recognition based on multi-channel neurophysiological signals, as a challenging pattern recognition task, is becoming an important computer-aided method for emotional disorder diagnoses in neurology and psychiatry. Traditional approaches require designing and extracting a range of features from single or multiple channel signals based on extensive domain knowledge. This may be an obstacle for non-domain experts. Moreover, traditional feature fusion method can not fully utilize correlation information between different channels. In this paper, we propose a preprocessing method that encapsulates the multi-channel neurophysiological signals into grid-like frames through wavelet and scalogram transform. We further design a hybrid deep learning model that combines the `Convolutional Neural Network (CNN)' and `Recurrent Neural Network (RNN)', for extracting task-related features, mining inter-channel correlation and incorporating contextual information from those frames. Experiments are carried out, in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Our results demonstrate the effectiveness of the proposed methods, with respect to the emotional dimensions of Valence and Arousal.