Journal
COGNITIVE NEURODYNAMICS
Volume 14, Issue 6, Pages 815-828Publisher
SPRINGER
DOI: 10.1007/s11571-020-09634-1
Keywords
EEG; Emotion recognition; 4D data; Convolutional recurrent neural network
Categories
Funding
- National Key R&D Program of China [2017YFE0118200, 2017YFE0116800]
- NSFC [61633010]
- key Research and Development Project of Zhejiang Province [2020C04009]
- Fundamental Research Funds for the Provincial Universities of Zhejiang [GK209907299001-008]
- National International Joint Research Center for Brain-Machine Collaborative Intelligence [2017B01020]
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province [2020E10010]
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In this paper, we present a novel method, called four-dimensional convolutional recurrent neural network, which integrating frequency, spatial and temporal information of multichannel EEG signals explicitly to improve EEG-based emotion recognition accuracy. First, to maintain these three kinds of information of EEG, we transform the differential entropy features from different channels into 4D structures to train the deep model. Then, we introduce CRNN model, which is combined by convolutional neural network (CNN) and recurrent neural network with long short term memory (LSTM) cell. CNN is used to learn frequency and spatial information from each temporal slice of 4D inputs, and LSTM is used to extract temporal dependence from CNN outputs. The output of the last node of LSTM performs classification. Our model achieves state-of-the-art performance both on SEED and DEAP datasets under intra-subject splitting. The experimental results demonstrate the effectiveness of integrating frequency, spatial and temporal information of EEG for emotion recognition.
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