EmotioNet: A 3-D Convolutional Neural Network for EEG-based Emotion Recognition
| Autor: | Yi Wang, Brendan McCane, Zhiyi Huang, Phoebe S.-H. Neo |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Normalization (statistics)
Ground truth medicine.diagnostic_test Computer science business.industry Feature extraction Normalization (image processing) Pattern recognition 02 engineering and technology Covariance Electroencephalography Convolutional neural network Support vector machine 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn.2018.8489715 |
| Popis: | In this paper, an emotional EEG-specific threedimensional Convolutional Neural Network, EmotioNet, is proposed and implemented to accurately recognize emotion states. For the first time, raw data in the benchmark emotional EEG database, i.e., DEAP, are used as the input to a CNN architecture. In order to investigate the spatio-temporal character of emotional features, the effectiveness of 2-D and 3-D convolution kernels, which extract spatial and temporal features separately and simultaneously, are compared in detail. Furthermore, two major problems of EEG-based emotion recognition, namely, covariance shift and the unreliability of emotional ground truth, are described, and the effectiveness of batch normalization and dense prediction, which alleviate these problems respectively, are also investigated. Experimental results show that 3-D kernels, batch normalization, and dense prediction are all essential techniques for the emotional EEG-specific CNN architecture. The proposed EmotioNet, namely, a 3-D covariance shift adaptation-based CNN with a dense prediction layer, achieves classification rates of 73.3% and 72.1% for arousal and valence, equivalent to the best performance of several previous studies. Importantly, our results are based on automatic feature extraction, which is in contrast to previous handcrafted features. Therefore, EmotioNet provides a new method for EEG-based emotion recognition. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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