A study of motor imagery EEG classification based on feature fusion and attentional mechanisms
IntroductionMotor imagery EEG-based action recognition is an emerging field arising from the intersection of brain science and information science, which has promising applications in the fields of neurorehabilitation and human-computer collaboration. However, existing methods face challenges includ...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Human Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2025.1611229/full |
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| author | Tingting Zhu Hailin Tang Lei Jiang Yijia Li Shijun Li Shijun Li Zhijian Wu Zhijian Wu |
| author_facet | Tingting Zhu Hailin Tang Lei Jiang Yijia Li Shijun Li Shijun Li Zhijian Wu Zhijian Wu |
| author_sort | Tingting Zhu |
| collection | DOAJ |
| description | IntroductionMotor imagery EEG-based action recognition is an emerging field arising from the intersection of brain science and information science, which has promising applications in the fields of neurorehabilitation and human-computer collaboration. However, existing methods face challenges including the low signal-to-noise ratio of EEG signals, inter-subject variability, and model overfitting.MethodsWe propose HA-FuseNet, an end-to-end motor imagery action classification network. This model integrates feature fusion and attention mechanisms to classify left hand, right hand, foot, and tongue movements. Its innovations include: (1) multi-scale dense connectivity, (2) hybrid attention mechanism, (3) global self-attention module, and (4) lightweight design for reduced computational overhead.ResultsOn BCI Competition IV Dataset 2A, HA-FuseNet achieved 77.89% average within-subject accuracy (8.42% higher than EEGNet) and 68.53% cross-subject accuracy.ConclusionThe model demonstrates robustness to spatial resolution variations and individual differences, effectively mitigating key challenges in motor imagery EEG classification. |
| format | Article |
| id | doaj-art-8a4a8508e76a419aa1b460e3343f4549 |
| institution | Kabale University |
| issn | 1662-5161 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj-art-8a4a8508e76a419aa1b460e3343f45492025-08-20T03:27:52ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612025-07-011910.3389/fnhum.2025.16112291611229A study of motor imagery EEG classification based on feature fusion and attentional mechanismsTingting Zhu0Hailin Tang1Lei Jiang2Yijia Li3Shijun Li4Shijun Li5Zhijian Wu6Zhijian Wu7School of Big Data and Computing, Guangdong Baiyun University, Guangzhou, ChinaSchool of Big Data and Computing, Guangdong Baiyun University, Guangzhou, ChinaSchool of Big Data and Computing, Guangdong Baiyun University, Guangzhou, ChinaDropbox Inc., San Francisco, CA, United StatesSchool of Big Data and Computing, Guangdong Baiyun University, Guangzhou, ChinaSchool of Computer Science, Wuhan University, Wuhan, ChinaSchool of Big Data and Computing, Guangdong Baiyun University, Guangzhou, ChinaSchool of Computer Science, Wuhan University, Wuhan, ChinaIntroductionMotor imagery EEG-based action recognition is an emerging field arising from the intersection of brain science and information science, which has promising applications in the fields of neurorehabilitation and human-computer collaboration. However, existing methods face challenges including the low signal-to-noise ratio of EEG signals, inter-subject variability, and model overfitting.MethodsWe propose HA-FuseNet, an end-to-end motor imagery action classification network. This model integrates feature fusion and attention mechanisms to classify left hand, right hand, foot, and tongue movements. Its innovations include: (1) multi-scale dense connectivity, (2) hybrid attention mechanism, (3) global self-attention module, and (4) lightweight design for reduced computational overhead.ResultsOn BCI Competition IV Dataset 2A, HA-FuseNet achieved 77.89% average within-subject accuracy (8.42% higher than EEGNet) and 68.53% cross-subject accuracy.ConclusionThe model demonstrates robustness to spatial resolution variations and individual differences, effectively mitigating key challenges in motor imagery EEG classification.https://www.frontiersin.org/articles/10.3389/fnhum.2025.1611229/fullbrain-computer interfacemotor imageryEEGattention mechanismfeature fusion |
| spellingShingle | Tingting Zhu Hailin Tang Lei Jiang Yijia Li Shijun Li Shijun Li Zhijian Wu Zhijian Wu A study of motor imagery EEG classification based on feature fusion and attentional mechanisms Frontiers in Human Neuroscience brain-computer interface motor imagery EEG attention mechanism feature fusion |
| title | A study of motor imagery EEG classification based on feature fusion and attentional mechanisms |
| title_full | A study of motor imagery EEG classification based on feature fusion and attentional mechanisms |
| title_fullStr | A study of motor imagery EEG classification based on feature fusion and attentional mechanisms |
| title_full_unstemmed | A study of motor imagery EEG classification based on feature fusion and attentional mechanisms |
| title_short | A study of motor imagery EEG classification based on feature fusion and attentional mechanisms |
| title_sort | study of motor imagery eeg classification based on feature fusion and attentional mechanisms |
| topic | brain-computer interface motor imagery EEG attention mechanism feature fusion |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2025.1611229/full |
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