Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding
Decoding motor imagery electroencephalography (MI-EEG) signals presents significant challenges due to the difficulty in capturing the complex functional connectivity between channels and the temporal dependencies of EEG signals across different periods. These challenges are exacerbated by the low sp...
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MDPI AG
2025-02-01
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| author | Yelan Wu Pugang Cao Meng Xu Yue Zhang Xiaoqin Lian Chongchong Yu |
| author_facet | Yelan Wu Pugang Cao Meng Xu Yue Zhang Xiaoqin Lian Chongchong Yu |
| author_sort | Yelan Wu |
| collection | DOAJ |
| description | Decoding motor imagery electroencephalography (MI-EEG) signals presents significant challenges due to the difficulty in capturing the complex functional connectivity between channels and the temporal dependencies of EEG signals across different periods. These challenges are exacerbated by the low spatial resolution and high signal redundancy inherent in EEG signals, which traditional linear models struggle to address. To overcome these issues, we propose a novel dual-branch framework that integrates an adaptive graph convolutional network (Adaptive GCN) and bidirectional gated recurrent units (Bi-GRUs) to enhance the decoding performance of MI-EEG signals by effectively modeling both channel correlations and temporal dependencies. The Chebyshev Type II filter decomposes the signal into multiple sub-bands giving the model frequency domain insights. The Adaptive GCN, specifically designed for the MI-EEG context, captures functional connectivity between channels more effectively than conventional GCN models, enabling accurate spatial–spectral feature extraction. Furthermore, combining Bi-GRU and Multi-Head Attention (MHA) captures the temporal dependencies across different time segments to extract deep time–spectral features. Finally, feature fusion is performed to generate the final prediction results. Experimental results demonstrate that our method achieves an average classification accuracy of 80.38% on the BCI-IV Dataset 2a and 87.49% on the BCI-I Dataset 3a, outperforming other state-of-the-art decoding approaches. This approach lays the foundation for future exploration of personalized and adaptive brain–computer interface (BCI) systems. |
| format | Article |
| id | doaj-art-829d9baded9d447aad32a169e7f7d5c6 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-829d9baded9d447aad32a169e7f7d5c62025-08-20T02:03:27ZengMDPI AGSensors1424-82202025-02-01254114710.3390/s25041147Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG DecodingYelan Wu0Pugang Cao1Meng Xu2Yue Zhang3Xiaoqin Lian4Chongchong Yu5School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, ChinaDecoding motor imagery electroencephalography (MI-EEG) signals presents significant challenges due to the difficulty in capturing the complex functional connectivity between channels and the temporal dependencies of EEG signals across different periods. These challenges are exacerbated by the low spatial resolution and high signal redundancy inherent in EEG signals, which traditional linear models struggle to address. To overcome these issues, we propose a novel dual-branch framework that integrates an adaptive graph convolutional network (Adaptive GCN) and bidirectional gated recurrent units (Bi-GRUs) to enhance the decoding performance of MI-EEG signals by effectively modeling both channel correlations and temporal dependencies. The Chebyshev Type II filter decomposes the signal into multiple sub-bands giving the model frequency domain insights. The Adaptive GCN, specifically designed for the MI-EEG context, captures functional connectivity between channels more effectively than conventional GCN models, enabling accurate spatial–spectral feature extraction. Furthermore, combining Bi-GRU and Multi-Head Attention (MHA) captures the temporal dependencies across different time segments to extract deep time–spectral features. Finally, feature fusion is performed to generate the final prediction results. Experimental results demonstrate that our method achieves an average classification accuracy of 80.38% on the BCI-IV Dataset 2a and 87.49% on the BCI-I Dataset 3a, outperforming other state-of-the-art decoding approaches. This approach lays the foundation for future exploration of personalized and adaptive brain–computer interface (BCI) systems.https://www.mdpi.com/1424-8220/25/4/1147brain–computer interface (BCI)motor imagery (MI)electroencephalography (EEG)adaptive graph convolutional network (Adaptive GCN)attention modulechannel correlation |
| spellingShingle | Yelan Wu Pugang Cao Meng Xu Yue Zhang Xiaoqin Lian Chongchong Yu Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding Sensors brain–computer interface (BCI) motor imagery (MI) electroencephalography (EEG) adaptive graph convolutional network (Adaptive GCN) attention module channel correlation |
| title | Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding |
| title_full | Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding |
| title_fullStr | Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding |
| title_full_unstemmed | Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding |
| title_short | Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding |
| title_sort | adaptive gcn and bi gru based dual branch for motor imagery eeg decoding |
| topic | brain–computer interface (BCI) motor imagery (MI) electroencephalography (EEG) adaptive graph convolutional network (Adaptive GCN) attention module channel correlation |
| url | https://www.mdpi.com/1424-8220/25/4/1147 |
| work_keys_str_mv | AT yelanwu adaptivegcnandbigrubaseddualbranchformotorimageryeegdecoding AT pugangcao adaptivegcnandbigrubaseddualbranchformotorimageryeegdecoding AT mengxu adaptivegcnandbigrubaseddualbranchformotorimageryeegdecoding AT yuezhang adaptivegcnandbigrubaseddualbranchformotorimageryeegdecoding AT xiaoqinlian adaptivegcnandbigrubaseddualbranchformotorimageryeegdecoding AT chongchongyu adaptivegcnandbigrubaseddualbranchformotorimageryeegdecoding |