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Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition
Li, Jinpeng1,2; Zhang, Zhaoxiang1,2,3; He, Huiguang1,2,3
Source PublicationCOGNITIVE COMPUTATION
2018-04-01
Volume10Issue:2Pages:368-380
SubtypeArticle
AbstractTraditional 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.
KeywordAffective Brain-computer Interface Emotion Recognition Brain Wave Deep Learning Eeg
WOS HeadingsScience & Technology ; Technology ; Life Sciences & Biomedicine
DOI10.1007/s12559-017-9533-x
WOS KeywordBRAIN-COMPUTER INTERFACES ; VIGILANCE ESTIMATION ; CLASSIFICATION ; RESPONSES
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(91520202 ; CAS Scientific Equipment Development Project(YJKYYQ20170050) ; Youth Innovation Promotion Association CAS ; 81671651)
WOS Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS IDWOS:000430190600016
Citation statistics
Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21582
Collection类脑智能研究中心
Affiliation1.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing, Peoples R China
2.UCAS, Beijing, Peoples R China
3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China
First Author Affilication类脑智能研究中心
Recommended Citation
GB/T 7714
Li, Jinpeng,Zhang, Zhaoxiang,He, Huiguang. Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition[J]. COGNITIVE COMPUTATION,2018,10(2):368-380.
APA Li, Jinpeng,Zhang, Zhaoxiang,&He, Huiguang.(2018).Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition.COGNITIVE COMPUTATION,10(2),368-380.
MLA Li, Jinpeng,et al."Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition".COGNITIVE COMPUTATION 10.2(2018):368-380.
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