A Dynamic Multi-Scale Network for EEG Signal Classification
Accurate and automatic classification of the speech imagery electroencephalography (EEG) signals from a Brain-Computer Interface (BCI) system is highly demanded in clinical diagnosis. The key factor in designing an automatic classification system is to extract essential features from the original in...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-01-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2020.578255/full |
| _version_ | 1818407851899486208 |
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| author | Guokai Zhang Jihao Luo Letong Han Zhuyin Lu Rong Hua Jianqing Chen Wenliang Che |
| author_facet | Guokai Zhang Jihao Luo Letong Han Zhuyin Lu Rong Hua Jianqing Chen Wenliang Che |
| author_sort | Guokai Zhang |
| collection | DOAJ |
| description | Accurate and automatic classification of the speech imagery electroencephalography (EEG) signals from a Brain-Computer Interface (BCI) system is highly demanded in clinical diagnosis. The key factor in designing an automatic classification system is to extract essential features from the original input; though many methods have achieved great success in this domain, they may fail to process the multi-scale representations from different receptive fields and thus hinder the model from achieving a higher performance. To address this challenge, in this paper, we propose a novel dynamic multi-scale network to achieve the EEG signal classification. The whole classification network is based on ResNet, and the input signal first encodes the features by the Short-time Fourier Transform (STFT); then, to further improve the multi-scale feature extraction ability, we incorporate a dynamic multi-scale (DMS) layer, which allows the network to learn multi-scale features from different receptive fields at a more granular level. To validate the effectiveness of our designed network, we conduct extensive experiments on public dataset III of BCI competition II, and the experimental results demonstrate that our proposed dynamic multi-scale network could achieve promising classification performance in this task. |
| first_indexed | 2024-12-14T09:34:25Z |
| format | Article |
| id | doaj.art-9f2f22f095224234a25cf810c573b2fc |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-14T09:34:25Z |
| publishDate | 2021-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-9f2f22f095224234a25cf810c573b2fc2022-12-21T23:07:58ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2021-01-011410.3389/fnins.2020.578255578255A Dynamic Multi-Scale Network for EEG Signal ClassificationGuokai Zhang0Jihao Luo1Letong Han2Zhuyin Lu3Rong Hua4Jianqing Chen5Wenliang Che6School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaSchool of Software Engineering, Tongji University, Shanghai, ChinaSchool of Software Engineering, Tongji University, Shanghai, ChinaSchool of Software Engineering, Tongji University, Shanghai, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaDepartment of Otolaryngology, Head & Neck Surgery, Shanghai Ninth People's Hospital, Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, ChinaDepartment of Cardiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, ChinaAccurate and automatic classification of the speech imagery electroencephalography (EEG) signals from a Brain-Computer Interface (BCI) system is highly demanded in clinical diagnosis. The key factor in designing an automatic classification system is to extract essential features from the original input; though many methods have achieved great success in this domain, they may fail to process the multi-scale representations from different receptive fields and thus hinder the model from achieving a higher performance. To address this challenge, in this paper, we propose a novel dynamic multi-scale network to achieve the EEG signal classification. The whole classification network is based on ResNet, and the input signal first encodes the features by the Short-time Fourier Transform (STFT); then, to further improve the multi-scale feature extraction ability, we incorporate a dynamic multi-scale (DMS) layer, which allows the network to learn multi-scale features from different receptive fields at a more granular level. To validate the effectiveness of our designed network, we conduct extensive experiments on public dataset III of BCI competition II, and the experimental results demonstrate that our proposed dynamic multi-scale network could achieve promising classification performance in this task.https://www.frontiersin.org/articles/10.3389/fnins.2020.578255/fullbrain-computer interfaceelectroencephalographymulti-scaleFourier transformdynamic learning |
| spellingShingle | Guokai Zhang Jihao Luo Letong Han Zhuyin Lu Rong Hua Jianqing Chen Wenliang Che A Dynamic Multi-Scale Network for EEG Signal Classification Frontiers in Neuroscience brain-computer interface electroencephalography multi-scale Fourier transform dynamic learning |
| title | A Dynamic Multi-Scale Network for EEG Signal Classification |
| title_full | A Dynamic Multi-Scale Network for EEG Signal Classification |
| title_fullStr | A Dynamic Multi-Scale Network for EEG Signal Classification |
| title_full_unstemmed | A Dynamic Multi-Scale Network for EEG Signal Classification |
| title_short | A Dynamic Multi-Scale Network for EEG Signal Classification |
| title_sort | dynamic multi scale network for eeg signal classification |
| topic | brain-computer interface electroencephalography multi-scale Fourier transform dynamic learning |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2020.578255/full |
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