Abstract
Emotion recognition plays a vital role in Brain-Computer Interaction. To extract and employ the inherent information implied by functional connections among EEG electrodes, we propose a multichannel EEG emotion recognition method using convolutional neural network (CNN) with functional connectivity as input. Specifically, the phase synchronization indices are employed to compute the EEG functional connectivity matrices. Then a CNN is proposed to effectively extract the classification information of these functional connections. The experimental results based on the DEAP and SEED datasets validate the superior performance of the proposed method, compared with the input of raw EEG data. The code of the proposed model is available at https://github.com/deep-bci/ERBCPSI.
This work was supported in part by the National Natural Science Foundation of China under Grants 61703065, 61906048 and 61901077. Chongqing Research Program of Application Foundation and Advanced Technology under Grant cstc2018jcyjAX0151, the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant KJQN201800612.
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Wang, H., Liu, K., Qi, F., Deng, X., Li, P. (2021). EEG-Based Emotion Recognition Using Convolutional Neural Network with Functional Connections. In: Sun, F., Liu, H., Fang, B. (eds) Cognitive Systems and Signal Processing. ICCSIP 2020. Communications in Computer and Information Science, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-2336-3_3
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DOI: https://doi.org/10.1007/978-981-16-2336-3_3
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