Transformed common spatial pattern for motor imagery-based brain-computer interfaces
ObjectiveThe motor imagery (MI)-based brain–computer interface (BCI) is one of the most popular BCI paradigms. Common spatial pattern (CSP) is an effective algorithm for decoding MI-related electroencephalogram (EEG) patterns. However, it highly depends on the selection of EEG frequency bands. To ad...
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| Format: | Article |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1116721/full |
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| author | Zhen Ma Kun Wang Minpeng Xu Minpeng Xu Minpeng Xu Weibo Yi Fangzhou Xu Dong Ming Dong Ming |
| author_facet | Zhen Ma Kun Wang Minpeng Xu Minpeng Xu Minpeng Xu Weibo Yi Fangzhou Xu Dong Ming Dong Ming |
| author_sort | Zhen Ma |
| collection | DOAJ |
| description | ObjectiveThe motor imagery (MI)-based brain–computer interface (BCI) is one of the most popular BCI paradigms. Common spatial pattern (CSP) is an effective algorithm for decoding MI-related electroencephalogram (EEG) patterns. However, it highly depends on the selection of EEG frequency bands. To address this problem, previous researchers often used a filter bank to decompose EEG signals into multiple frequency bands before applying the traditional CSP.ApproachThis study proposed a novel method, i.e., transformed common spatial pattern (tCSP), to extract the discriminant EEG features from multiple frequency bands after but not before CSP. To verify its effectiveness, we tested tCSP on a dataset collected by our team and a public dataset from BCI competition III. We also performed an online evaluation of the proposed method.Main resultsAs a result, for the dataset collected by our team, the classification accuracy of tCSP was significantly higher than CSP by about 8% and filter bank CSP (FBCSP) by about 4.5%. The combination of tCSP and CSP further improved the system performance with an average accuracy of 84.77% and a peak accuracy of 100%. For dataset IVa in BCI competition III, the combination method got an average accuracy of 94.55%, which performed best among all the presented CSP-based methods. In the online evaluation, tCSP and the combination method achieved an average accuracy of 80.00 and 84.00%, respectively.SignificanceThe results demonstrate that the frequency band selection after CSP is better than before for MI-based BCIs. This study provides a promising approach for decoding MI EEG patterns, which is significant for the development of BCIs. |
| format | Article |
| id | doaj-art-894b93beaadb43d582df56fc7b0e25af |
| institution | DOAJ |
| issn | 1662-453X |
| language | English |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-894b93beaadb43d582df56fc7b0e25af2025-08-20T03:16:22ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-03-011710.3389/fnins.2023.11167211116721Transformed common spatial pattern for motor imagery-based brain-computer interfacesZhen Ma0Kun Wang1Minpeng Xu2Minpeng Xu3Minpeng Xu4Weibo Yi5Fangzhou Xu6Dong Ming7Dong Ming8School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, ChinaAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, ChinaSchool of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, ChinaAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, ChinaInternational School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaBeijing Machine and Equipment Institute, Beijing, ChinaInternational School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaSchool of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, ChinaAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, ChinaObjectiveThe motor imagery (MI)-based brain–computer interface (BCI) is one of the most popular BCI paradigms. Common spatial pattern (CSP) is an effective algorithm for decoding MI-related electroencephalogram (EEG) patterns. However, it highly depends on the selection of EEG frequency bands. To address this problem, previous researchers often used a filter bank to decompose EEG signals into multiple frequency bands before applying the traditional CSP.ApproachThis study proposed a novel method, i.e., transformed common spatial pattern (tCSP), to extract the discriminant EEG features from multiple frequency bands after but not before CSP. To verify its effectiveness, we tested tCSP on a dataset collected by our team and a public dataset from BCI competition III. We also performed an online evaluation of the proposed method.Main resultsAs a result, for the dataset collected by our team, the classification accuracy of tCSP was significantly higher than CSP by about 8% and filter bank CSP (FBCSP) by about 4.5%. The combination of tCSP and CSP further improved the system performance with an average accuracy of 84.77% and a peak accuracy of 100%. For dataset IVa in BCI competition III, the combination method got an average accuracy of 94.55%, which performed best among all the presented CSP-based methods. In the online evaluation, tCSP and the combination method achieved an average accuracy of 80.00 and 84.00%, respectively.SignificanceThe results demonstrate that the frequency band selection after CSP is better than before for MI-based BCIs. This study provides a promising approach for decoding MI EEG patterns, which is significant for the development of BCIs.https://www.frontiersin.org/articles/10.3389/fnins.2023.1116721/fullbrain–computer interface (BCI)electroencephalography (EEG)motor imagery (MI)common spatial pattern (CSP)transformed common spatial pattern (tCSP) |
| spellingShingle | Zhen Ma Kun Wang Minpeng Xu Minpeng Xu Minpeng Xu Weibo Yi Fangzhou Xu Dong Ming Dong Ming Transformed common spatial pattern for motor imagery-based brain-computer interfaces Frontiers in Neuroscience brain–computer interface (BCI) electroencephalography (EEG) motor imagery (MI) common spatial pattern (CSP) transformed common spatial pattern (tCSP) |
| title | Transformed common spatial pattern for motor imagery-based brain-computer interfaces |
| title_full | Transformed common spatial pattern for motor imagery-based brain-computer interfaces |
| title_fullStr | Transformed common spatial pattern for motor imagery-based brain-computer interfaces |
| title_full_unstemmed | Transformed common spatial pattern for motor imagery-based brain-computer interfaces |
| title_short | Transformed common spatial pattern for motor imagery-based brain-computer interfaces |
| title_sort | transformed common spatial pattern for motor imagery based brain computer interfaces |
| topic | brain–computer interface (BCI) electroencephalography (EEG) motor imagery (MI) common spatial pattern (CSP) transformed common spatial pattern (tCSP) |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1116721/full |
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