Cross-Subject EEG Emotion Recognition With Self-Organized Graph Neural Network
As a physiological process and high-level cognitive behavior, emotion is an important subarea in neuroscience research. Emotion recognition across subjects based on brain signals has attracted much attention. Due to individual differences across subjects and the low signal-to-noise ratio of EEG sign...
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| Published in | Frontiers in neuroscience Vol. 15; p. 611653 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Lausanne
Frontiers Research Foundation
09.06.2021
Frontiers Media S.A |
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| Online Access | Get full text |
| ISSN | 1662-453X 1662-4548 1662-453X |
| DOI | 10.3389/fnins.2021.611653 |
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| Abstract | As a physiological process and high-level cognitive behavior, emotion is an important subarea in neuroscience research. Emotion recognition across subjects based on brain signals has attracted much attention. Due to individual differences across subjects and the low signal-to-noise ratio of EEG signals, the performance of conventional emotion recognition methods is relatively poor. In this paper, we propose a self-organized graph neural network (SOGNN) for cross-subject EEG emotion recognition. Unlike the previous studies based on pre-constructed and fixed graph structure, the graph structure of SOGNN are dynamically constructed by self-organized module for each signal. To evaluate the cross-subject EEG emotion recognition performance of our model, leave-one-subject-out experiments are conducted on two public emotion recognition datasets, SEED and SEED-IV. The SOGNN is able to achieve state-of-the-art emotion recognition performance. Moreover, we investigated the performance variances of the models with different graph construction techniques or features in different frequency bands. Furthermore, we visualized the graph structure learned by the proposed model and found that part of the structure coincided with previous neuroscience research. The experiments demonstrated the effectiveness of the proposed model for cross-subject EEG emotion recognition. |
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| AbstractList | As a physiological process and high-level cognitive behavior, emotion is an important subarea in neuroscience research. Emotion recognition across subjects based on brain signals has attracted much attention. Due to individual differences across subjects and the low signal-to-noise ratio of EEG signals, the performance of conventional emotion recognition methods is relatively poor. In this paper, we propose a self-organized graph neural network (SOGNN) for cross-subject EEG emotion recognition. Unlike the previous studies based on pre-constructed and fixed graph structure, the graph structure of SOGNN are dynamically constructed by self-organized module for each signal. To evaluate the cross-subject EEG emotion recognition performance of our model, leave-one-subject-out experiments are conducted on two public emotion recognition datasets, SEED and SEED-IV. The SOGNN is able to achieve state-of-the-art emotion recognition performance. Moreover, we investigated the performance variances of the models with different graph construction techniques or features in different frequency bands. Furthermore, we visualized the graph structure learned by the proposed model and found that part of the structure coincided with previous neuroscience research. The experiments demonstrated the effectiveness of the proposed model for cross-subject EEG emotion recognition. As a physiological process and high-level cognitive behavior, emotion is an important subarea in neuroscience research. Emotion recognition across subjects based on brain signals has attracted much attention. Due to individual differences across subjects and the low signal-to-noise ratio of EEG signals, the performance of conventional emotion recognition methods is relatively poor. In this paper, we propose a self-organized graph neural network (SOGNN) for cross-subject EEG emotion recognition. Unlike the previous studies based on pre-constructed and fixed graph structure, the graph structure of SOGNN are dynamically constructed by self-organized module for each signal. To evaluate the cross-subject EEG emotion recognition performance of our model, leave-one-subject-out experiments are conducted on two public emotion recognition datasets, SEED and SEED-IV. The SOGNN is able to achieve state-of-the-art emotion recognition performance. Moreover, we investigated the performance variances of the models with different graph construction techniques or features in different frequency bands. Furthermore, we visualized the graph structure learned by the proposed model and found that part of the structure coincided with previous neuroscience research. The experiments demonstrated the effectiveness of the proposed model for cross-subject EEG emotion recognition.As a physiological process and high-level cognitive behavior, emotion is an important subarea in neuroscience research. Emotion recognition across subjects based on brain signals has attracted much attention. Due to individual differences across subjects and the low signal-to-noise ratio of EEG signals, the performance of conventional emotion recognition methods is relatively poor. In this paper, we propose a self-organized graph neural network (SOGNN) for cross-subject EEG emotion recognition. Unlike the previous studies based on pre-constructed and fixed graph structure, the graph structure of SOGNN are dynamically constructed by self-organized module for each signal. To evaluate the cross-subject EEG emotion recognition performance of our model, leave-one-subject-out experiments are conducted on two public emotion recognition datasets, SEED and SEED-IV. The SOGNN is able to achieve state-of-the-art emotion recognition performance. Moreover, we investigated the performance variances of the models with different graph construction techniques or features in different frequency bands. Furthermore, we visualized the graph structure learned by the proposed model and found that part of the structure coincided with previous neuroscience research. The experiments demonstrated the effectiveness of the proposed model for cross-subject EEG emotion recognition. |
| Author | Pan, Jiahui Li, Jingcong Wang, Fei Li, Shuqi |
| AuthorAffiliation | 3 School of Computer Science, South China Normal University , Guangzhou , China 2 Pazhou Lab , Guangzhou , China 1 School of Software, South China Normal University , Guangzhou , China |
| AuthorAffiliation_xml | – name: 3 School of Computer Science, South China Normal University , Guangzhou , China – name: 1 School of Software, South China Normal University , Guangzhou , China – name: 2 Pazhou Lab , Guangzhou , China |
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| Cites_doi | 10.1016/j.biopsycho.2004.03.002 10.1109/TCYB.2019.2904052 10.1109/ICCV.2013.368 10.1109/TBME.2010.2048568 10.1017/CBO9781139015165 10.1109/ICME.2014.6890166 10.1109/ACCESS.2019.2891579 10.1109/TAFFC.2017.2714671 10.1109/TAFFC.2018.2817622 10.1016/j.neulet.2006.04.006 10.3389/fpsyg.2017.01454 10.1016/j.neuroimage.2017.12.052 10.1109/TCYB.2018.2797176 10.1109/TAMD.2015.2431497 10.1109/TAFFC.2020.2994159 10.1016/S0925-2312(99)00126-5 10.1109/TAFFC.2019.2937768 10.1109/79.911197 10.14569/IJACSA.2017.081046 10.1109/TCDS.2020.2999337 10.3390/s18072074 10.1371/journal.pone.0194444 10.1109/MSP.2012.2235192 10.1109/JBHI.2020.2967128 10.1007/BF01200757 10.3389/fnhum.2018.00070 10.1109/NER.2013.6695876 10.1109/TAFFC.2018.2885474 10.1186/1471-2202-10-101 10.1016/j.biopsycho.2009.10.007 10.1109/TNN.2010.2091281 10.1016/S0959-4388(99)80032-4 10.1109/BIBM.2018.8621147 10.1145/1027933.1027968 10.1109/TCDS.2016.2587290 10.1145/2647868.2654916 10.1109/TAFFC.2017.2712143 10.3389/fnins.2018.00162 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Edited by: Haider Raza, University of Essex, United Kingdom This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience Reviewed by: Archana Venkataraman, Johns Hopkins University, United States; Di Wang, Nanyang Technological University, Singapore |
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| SubjectTerms | Asymmetry Cognitive ability cross-subject Datasets EEG Electroencephalography emotion recognition Emotions Experiments graph construction graph neural network Graph representations Machine learning Nervous system Neural networks Neuroscience Neurosciences SEED dataset |
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| Title | Cross-Subject EEG Emotion Recognition With Self-Organized Graph Neural Network |
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