EEG-Channel-Temporal-Spectral-Attention Correlation for Motor Imagery EEG Classification
In brain-computer interface (BCI) work, how correctly identifying various features and their corresponding actions from complex Electroencephalography (EEG) signals is a challenging technology. However, most current methods do not consider EEG feature information in spatial, temporal and spectral do...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10065454/ |
| _version_ | 1827927036165881856 |
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| author | Wei-Yen Hsu Ya-Wen Cheng |
| author_facet | Wei-Yen Hsu Ya-Wen Cheng |
| author_sort | Wei-Yen Hsu |
| collection | DOAJ |
| description | In brain-computer interface (BCI) work, how correctly identifying various features and their corresponding actions from complex Electroencephalography (EEG) signals is a challenging technology. However, most current methods do not consider EEG feature information in spatial, temporal and spectral domains, and the structure of these models cannot effectively extract discriminative features, resulting in limited classification performance. To address this issue, we propose a novel text motor-imagery EEG discrimination method, namely wavelet-based temporal-spectral-attention correlation coefficient (WTS-CC), to simultaneously consider the features and their weighting in spatial, EEG-channel, temporal and spectral domains in this study. The initial Temporal Feature Extraction (iTFE) module extracts the initial important temporal features of MI EEG signals. The Deep EEG-Channel-attention (DEC) module is then proposed to automatically adjust the weight of each EEG channel according to its importance, thereby effectively enhancing more important EEG channels and suppressing less important EEG channels. Next, the Wavelet-based Temporal-Spectral-attention (WTS) module is proposed to obtain more significant discriminative features between different MI tasks by weighting features on two-dimensional time-frequency maps. Finally, a simple discrimination module is used for MI EEG discrimination. The experimental results indicate that the proposed text WTS-CC method can achieve promising discrimination performance that outperforms the state-of-the-art methods in terms of classification accuracy, Kappa coefficient, F1 score, and AUC on three public datasets. |
| first_indexed | 2024-03-13T05:46:19Z |
| format | Article |
| id | doaj.art-edd1c7e54547448fa392e87ec2b90d20 |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:46:19Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-edd1c7e54547448fa392e87ec2b90d202023-06-13T20:10:02ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102023-01-01311659166910.1109/TNSRE.2023.325523310065454EEG-Channel-Temporal-Spectral-Attention Correlation for Motor Imagery EEG ClassificationWei-Yen Hsu0https://orcid.org/0000-0002-4599-0744Ya-Wen Cheng1Department of Information Management, Center for Innovative Research on Aging Society (CIRAS), Advanced Institute of Manufacturing with High-Tech Innovations, National Chung Cheng University, Chiayi, TaiwanDepartment of Information Management, National Chung Cheng University, Chiayi, TaiwanIn brain-computer interface (BCI) work, how correctly identifying various features and their corresponding actions from complex Electroencephalography (EEG) signals is a challenging technology. However, most current methods do not consider EEG feature information in spatial, temporal and spectral domains, and the structure of these models cannot effectively extract discriminative features, resulting in limited classification performance. To address this issue, we propose a novel text motor-imagery EEG discrimination method, namely wavelet-based temporal-spectral-attention correlation coefficient (WTS-CC), to simultaneously consider the features and their weighting in spatial, EEG-channel, temporal and spectral domains in this study. The initial Temporal Feature Extraction (iTFE) module extracts the initial important temporal features of MI EEG signals. The Deep EEG-Channel-attention (DEC) module is then proposed to automatically adjust the weight of each EEG channel according to its importance, thereby effectively enhancing more important EEG channels and suppressing less important EEG channels. Next, the Wavelet-based Temporal-Spectral-attention (WTS) module is proposed to obtain more significant discriminative features between different MI tasks by weighting features on two-dimensional time-frequency maps. Finally, a simple discrimination module is used for MI EEG discrimination. The experimental results indicate that the proposed text WTS-CC method can achieve promising discrimination performance that outperforms the state-of-the-art methods in terms of classification accuracy, Kappa coefficient, F1 score, and AUC on three public datasets.https://ieeexplore.ieee.org/document/10065454/Brain--computer interface (BCI)motor-imagery electroencephalography (MI EEG)EEG-channel attentiontemporal-spectral attentionwavelet transformcorrelation coefficient |
| spellingShingle | Wei-Yen Hsu Ya-Wen Cheng EEG-Channel-Temporal-Spectral-Attention Correlation for Motor Imagery EEG Classification IEEE Transactions on Neural Systems and Rehabilitation Engineering Brain--computer interface (BCI) motor-imagery electroencephalography (MI EEG) EEG-channel attention temporal-spectral attention wavelet transform correlation coefficient |
| title | EEG-Channel-Temporal-Spectral-Attention Correlation for Motor Imagery EEG Classification |
| title_full | EEG-Channel-Temporal-Spectral-Attention Correlation for Motor Imagery EEG Classification |
| title_fullStr | EEG-Channel-Temporal-Spectral-Attention Correlation for Motor Imagery EEG Classification |
| title_full_unstemmed | EEG-Channel-Temporal-Spectral-Attention Correlation for Motor Imagery EEG Classification |
| title_short | EEG-Channel-Temporal-Spectral-Attention Correlation for Motor Imagery EEG Classification |
| title_sort | eeg channel temporal spectral attention correlation for motor imagery eeg classification |
| topic | Brain--computer interface (BCI) motor-imagery electroencephalography (MI EEG) EEG-channel attention temporal-spectral attention wavelet transform correlation coefficient |
| url | https://ieeexplore.ieee.org/document/10065454/ |
| work_keys_str_mv | AT weiyenhsu eegchanneltemporalspectralattentioncorrelationformotorimageryeegclassification AT yawencheng eegchanneltemporalspectralattentioncorrelationformotorimageryeegclassification |