Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152723
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZhong, Peixiangen_US
dc.contributor.authorWang, Dien_US
dc.contributor.authorMiao, Chunyanen_US
dc.date.accessioned2021-12-09T08:22:12Z-
dc.date.available2021-12-09T08:22:12Z-
dc.date.issued2020-
dc.identifier.citationZhong, P., Wang, D. & Miao, C. (2020). EEG-based emotion recognition using regularized graph neural networks. IEEE Transactions On Affective Computing. https://dx.doi.org/10.1109/TAFFC.2020.2994159en_US
dc.identifier.issn1949-3045en_US
dc.identifier.urihttps://hdl.handle.net/10356/152723-
dc.description.abstractElectroencephalography (EEG) measures the neuronal activities in different brain regions via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit the topology of EEG channels. In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition. RGNN considers the biological topology among different brain regions to capture both local and global relations among different EEG channels. Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in a graph neural network where the connection and sparseness of the adjacency matrix are inspired by neuroscience theories of human brain organization. In addition, we propose two regularizers, namely node-wise domain adversarial training (NodeDAT) and emotion-aware distribution learning (EmotionDL), to better handle cross-subject EEG variations and noisy labels, respectively. Extensive experiments on two public datasets, SEED and SEED-IV, demonstrate the superior performance of our model than state-of-the-art models in most experimental settings. Moreover, ablation studies show that the proposed adjacency matrix and two regularizers contribute consistent and significant gain to the performance of our RGNN model. Finally, investigations on the neuronal activities reveal important brain regions and inter-channel relations for EEG-based emotion recognition.en_US
dc.description.sponsorshipAI Singaporeen_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.language.isoenen_US
dc.relationAlibaba-NTU-AIR2019B1en_US
dc.relation.ispartofIEEE Transactions on Affective Computingen_US
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TAFFC.2020.2994159.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleEEG-based emotion recognition using regularized graph neural networksen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.researchJoint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY)en_US
dc.contributor.researchAlibaba-NTU Singapore Joint Research Instituteen_US
dc.identifier.doi10.1109/TAFFC.2020.2994159-
dc.description.versionAccepted versionen_US
dc.subject.keywordsAffective Computingen_US
dc.subject.keywordsElectroencephalographyen_US
dc.description.acknowledgementThis research is supported, in part, by the Alibaba-NTU Singapore Joint Research Institute (Alibaba-NTU-AIR2019B1), Nanyang Technological University, Singapore.en_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
Appears in Collections:SCSE Journal Articles
Files in This Item:
File Description SizeFormat 
EEG-Based_Emotion_Recognition_Using_Regularized_Graph_Neural_Networks.pdf2.4 MBAdobe PDFView/Open

SCOPUSTM   
Citations 5

68
Updated on Feb 7, 2023

Web of ScienceTM
Citations 5

86
Updated on Feb 1, 2023

Page view(s)

104
Updated on Feb 7, 2023

Download(s) 20

266
Updated on Feb 7, 2023

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.