Advancing BCI with a transformer-based model for motor imagery classification
Abstract Brain-computer interfaces (BCIs) harness electroencephalographic signals for direct neural control of devices, offering significant benefits for individuals with motor impairments. Traditional machine learning methods for EEG-based motor imagery (MI) classification encounter challenges such...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-06364-4 |
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| author | Wangdan Liao Hongyun Liu Weidong Wang |
| author_facet | Wangdan Liao Hongyun Liu Weidong Wang |
| author_sort | Wangdan Liao |
| collection | DOAJ |
| description | Abstract Brain-computer interfaces (BCIs) harness electroencephalographic signals for direct neural control of devices, offering significant benefits for individuals with motor impairments. Traditional machine learning methods for EEG-based motor imagery (MI) classification encounter challenges such as manual feature extraction and susceptibility to noise. This paper introduces EEGEncoder, a deep learning framework that employs modified transformers and Temporal Convolutional Networks (TCNs) to surmount these limitations. We propose a novel fusion architecture, named Dual-Stream Temporal-Spatial Block (DSTS), to capture temporal and spatial features, improving the accuracy of Motor Imagery classification task. Additionally, we use multiple parallel structures to enhance the model’s performance. When tested on the BCI Competition IV-2a dataset, our proposed model achieved an average accuracy of 86.46% for subject dependent and average 74.48% for subject independent. |
| format | Article |
| id | doaj-art-ad091aafb6e643eba04d2f738898302e |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ad091aafb6e643eba04d2f738898302e2025-08-20T03:03:27ZengNature PortfolioScientific Reports2045-23222025-07-0115111210.1038/s41598-025-06364-4Advancing BCI with a transformer-based model for motor imagery classificationWangdan Liao0Hongyun Liu1Weidong Wang2School of Biological Science and Medical Engineering, Beihang UniversityMedical Innovation Research Division, Chinese PLA General HospitalSchool of Biological Science and Medical Engineering, Beihang UniversityAbstract Brain-computer interfaces (BCIs) harness electroencephalographic signals for direct neural control of devices, offering significant benefits for individuals with motor impairments. Traditional machine learning methods for EEG-based motor imagery (MI) classification encounter challenges such as manual feature extraction and susceptibility to noise. This paper introduces EEGEncoder, a deep learning framework that employs modified transformers and Temporal Convolutional Networks (TCNs) to surmount these limitations. We propose a novel fusion architecture, named Dual-Stream Temporal-Spatial Block (DSTS), to capture temporal and spatial features, improving the accuracy of Motor Imagery classification task. Additionally, we use multiple parallel structures to enhance the model’s performance. When tested on the BCI Competition IV-2a dataset, our proposed model achieved an average accuracy of 86.46% for subject dependent and average 74.48% for subject independent.https://doi.org/10.1038/s41598-025-06364-4Motor imagery (MI)Electroencephalography (EEG)ClassificationTransformerTemporal Convolutional Networks (TCNs) |
| spellingShingle | Wangdan Liao Hongyun Liu Weidong Wang Advancing BCI with a transformer-based model for motor imagery classification Scientific Reports Motor imagery (MI) Electroencephalography (EEG) Classification Transformer Temporal Convolutional Networks (TCNs) |
| title | Advancing BCI with a transformer-based model for motor imagery classification |
| title_full | Advancing BCI with a transformer-based model for motor imagery classification |
| title_fullStr | Advancing BCI with a transformer-based model for motor imagery classification |
| title_full_unstemmed | Advancing BCI with a transformer-based model for motor imagery classification |
| title_short | Advancing BCI with a transformer-based model for motor imagery classification |
| title_sort | advancing bci with a transformer based model for motor imagery classification |
| topic | Motor imagery (MI) Electroencephalography (EEG) Classification Transformer Temporal Convolutional Networks (TCNs) |
| url | https://doi.org/10.1038/s41598-025-06364-4 |
| work_keys_str_mv | AT wangdanliao advancingbciwithatransformerbasedmodelformotorimageryclassification AT hongyunliu advancingbciwithatransformerbasedmodelformotorimageryclassification AT weidongwang advancingbciwithatransformerbasedmodelformotorimageryclassification |