Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/152723
Title: | EEG-based emotion recognition using regularized graph neural networks | Authors: | Zhong, Peixiang Wang, Di Miao, Chunyan |
Keywords: | Engineering::Computer science and engineering | Issue Date: | 2020 | Source: | Zhong, 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.2020.2994159 | Project: | Alibaba-NTU-AIR2019B1 | Journal: | IEEE Transactions on Affective Computing | Abstract: | Electroencephalography (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. | URI: | https://hdl.handle.net/10356/152723 | ISSN: | 1949-3045 | DOI: | 10.1109/TAFFC.2020.2994159 | Schools: | School of Computer Science and Engineering | Research Centres: | Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) Alibaba-NTU Singapore Joint Research Institute |
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. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Journal Articles |
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EEG-Based_Emotion_Recognition_Using_Regularized_Graph_Neural_Networks.pdf | 2.4 MB | Adobe PDF | View/Open |
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