Fractal Spiking Neural Network Scheme for EEG-Based Emotion Recognition
Electroencephalogram (EEG)-based emotion recognition is of great significance for aiding in clinical diagnosis, treatment, nursing and rehabilitation. Current research on this issue mainly focuses on utilizing various network architectures with different types of neurons to exploit the temporal, spe...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10266337/ |
| _version_ | 1797394895429697536 |
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| author | Wei Li Cheng Fang Zhihao Zhu Chuyi Chen Aiguo Song |
| author_facet | Wei Li Cheng Fang Zhihao Zhu Chuyi Chen Aiguo Song |
| author_sort | Wei Li |
| collection | DOAJ |
| description | Electroencephalogram (EEG)-based emotion recognition is of great significance for aiding in clinical diagnosis, treatment, nursing and rehabilitation. Current research on this issue mainly focuses on utilizing various network architectures with different types of neurons to exploit the temporal, spectral, or spatial information from EEG for classification. However, most studies fail to take full advantage of the useful Temporal-Spectral-Spatial (TSS) information of EEG signals. In this paper, we propose a novel and effective Fractal Spike Neural Network (Fractal-SNN) scheme, which can exploit the multi-scale TSS information from EEG, for emotion recognition. Our designed Fractal-SNN block in the proposed scheme approximately simulates the biological neural connection structures based on spiking neurons and a new fractal rule, allowing for the extraction of discriminative multi-scale TSS features from the signals. Our designed training technique, inverted drop-path, can enhance the generalization ability of the Fractal-SNN scheme. Sufficient experiments on four public benchmark databases, DREAMER, DEAP, SEED-IV and MPED, under the subject-dependent protocols demonstrate the superiority of the proposed scheme over the related advanced methods. In summary, the proposed scheme provides a promising solution for EEG-based emotion recognition. |
| first_indexed | 2024-03-09T00:27:28Z |
| format | Article |
| id | doaj.art-803db8dfbb354a2f8ce2073435f26e5d |
| institution | Directory Open Access Journal |
| issn | 2168-2372 |
| language | English |
| last_indexed | 2024-03-09T00:27:28Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj.art-803db8dfbb354a2f8ce2073435f26e5d2023-12-12T00:00:25ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722024-01-011210611810.1109/JTEHM.2023.332013210266337Fractal Spiking Neural Network Scheme for EEG-Based Emotion RecognitionWei Li0https://orcid.org/0000-0002-9235-9429Cheng Fang1https://orcid.org/0000-0003-1660-4848Zhihao Zhu2https://orcid.org/0000-0002-4063-6009Chuyi Chen3https://orcid.org/0000-0002-9806-8737Aiguo Song4https://orcid.org/0000-0002-1982-6780School of Instrument Science and Engineering, Southeast University, Nanjing, ChinaSchool of Instrument Science and Engineering, Southeast University, Nanjing, ChinaSchool of Instrument Science and Engineering, Southeast University, Nanjing, ChinaSchool of Instrument Science and Engineering, Southeast University, Nanjing, ChinaSchool of Instrument Science and Engineering, Southeast University, Nanjing, ChinaElectroencephalogram (EEG)-based emotion recognition is of great significance for aiding in clinical diagnosis, treatment, nursing and rehabilitation. Current research on this issue mainly focuses on utilizing various network architectures with different types of neurons to exploit the temporal, spectral, or spatial information from EEG for classification. However, most studies fail to take full advantage of the useful Temporal-Spectral-Spatial (TSS) information of EEG signals. In this paper, we propose a novel and effective Fractal Spike Neural Network (Fractal-SNN) scheme, which can exploit the multi-scale TSS information from EEG, for emotion recognition. Our designed Fractal-SNN block in the proposed scheme approximately simulates the biological neural connection structures based on spiking neurons and a new fractal rule, allowing for the extraction of discriminative multi-scale TSS features from the signals. Our designed training technique, inverted drop-path, can enhance the generalization ability of the Fractal-SNN scheme. Sufficient experiments on four public benchmark databases, DREAMER, DEAP, SEED-IV and MPED, under the subject-dependent protocols demonstrate the superiority of the proposed scheme over the related advanced methods. In summary, the proposed scheme provides a promising solution for EEG-based emotion recognition.https://ieeexplore.ieee.org/document/10266337/Electroencephalogramfractal spiking neural networkinverted drop-pathemotion recognition |
| spellingShingle | Wei Li Cheng Fang Zhihao Zhu Chuyi Chen Aiguo Song Fractal Spiking Neural Network Scheme for EEG-Based Emotion Recognition IEEE Journal of Translational Engineering in Health and Medicine Electroencephalogram fractal spiking neural network inverted drop-path emotion recognition |
| title | Fractal Spiking Neural Network Scheme for EEG-Based Emotion Recognition |
| title_full | Fractal Spiking Neural Network Scheme for EEG-Based Emotion Recognition |
| title_fullStr | Fractal Spiking Neural Network Scheme for EEG-Based Emotion Recognition |
| title_full_unstemmed | Fractal Spiking Neural Network Scheme for EEG-Based Emotion Recognition |
| title_short | Fractal Spiking Neural Network Scheme for EEG-Based Emotion Recognition |
| title_sort | fractal spiking neural network scheme for eeg based emotion recognition |
| topic | Electroencephalogram fractal spiking neural network inverted drop-path emotion recognition |
| url | https://ieeexplore.ieee.org/document/10266337/ |
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