期刊
COGNITIVE COMPUTATION
卷 10, 期 2, 页码 368-380出版社
SPRINGER
DOI: 10.1007/s12559-017-9533-x
关键词
Affective brain-computer interface; Emotion recognition; Brain wave; Deep learning; EEG
资金
- National Natural Science Foundation of China [91520202, 81671651]
- CAS Scientific Equipment Development Project [YJKYYQ20170050]
- Youth Innovation Promotion Association CAS
Traditional machine learning methods suffer from severe overfitting in EEG-based emotion reading. In this paper, we use hierarchical convolutional neural network (HCNN) to classify the positive, neutral, and negative emotion states. We organize differential entropy features from different channels as two-dimensional maps to train the HCNNs. This approach maintains information in the spatial topology of electrodes. We use stacked autoencoder (SAE), SVM, and KNN as competing methods. HCNN yields the highest accuracy, and SAE is slightly inferior. Both of them show absolute advantage over traditional shallow models including SVM and KNN. We confirm that the high-frequency wave bands Beta and Gamma are the most suitable bands for emotion reading. We visualize the hidden layers of HCNNs to investigate the feature transformation flow along the hierarchical structure. Benefiting from the strong representational learning capacity in the two-dimensional space, HCNN is efficient in emotion recognition especially on Beta and Gamma waves.
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