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
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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