Journal
COGNITIVE COMPUTATION
Volume 10, Issue 2, Pages 368-380Publisher
SPRINGER
DOI: 10.1007/s12559-017-9533-x
Keywords
Affective brain-computer interface; Emotion recognition; Brain wave; Deep learning; EEG
Funding
- National Natural Science Foundation of China [91520202, 81671651]
- CAS Scientific Equipment Development Project [YJKYYQ20170050]
- Youth Innovation Promotion Association CAS
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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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