Abstract
Human Computer Interaction (HCI) enables people to transfer and exchange information with computers. For the purpose of friendliness, integrating HCI with emotional factors has been intensively investigated. In this paper, an effective method is proposed to recognize human emotions by electroencephalogram (EEG) signals, which record electrical activities of the brain. First of all, the EEG signals are converted to the multispectral image that preserves the local distance between any two nearby electrodes. Notably, our method preserves the features of EEG signals in frequency and spatial dimensions, unlike standard EEG analysis techniques inaccurately interpreting the location of electrodes. And then a Convolutional Neural Network (CNN) model is performed to identify human emotions by virtue of the image containing EEG feature, for the reason of CNN’s significant effect in image recognition. A publicly available dataset, DEAP dataset, is used to validate our algorithm. The results show that the mean classification accuracy is 81.64% for valence (low and high) and 80.25% for arousal (low and high) across 32 subjects.
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Li, C., Sun, X., Dong, Y., Ren, F. (2019). Convolutional Neural Networks on EEG-Based Emotion Recognition. In: Jin, H., Lin, X., Cheng, X., Shi, X., Xiao, N., Huang, Y. (eds) Big Data. BigData 2019. Communications in Computer and Information Science, vol 1120. Springer, Singapore. https://doi.org/10.1007/978-981-15-1899-7_11
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