Robust EEG channel selection across sessions in brain-computer interface involving stroke patients
Brain-computer interface (BCI) technology has shown the capability of improving the quality of life for people with severe motor disabilities. To improve the portability and practicability of BCI systems, it is crucial to reduce the number of EEG channels as well as to have a good reliability. Howev...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/98313 http://hdl.handle.net/10220/12419 |
| _version_ | 1826122346473193472 |
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| author | Arvaneh, Mahnaz Guan, Cuntai Ang, Kai Keng Quek, Chai |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Arvaneh, Mahnaz Guan, Cuntai Ang, Kai Keng Quek, Chai |
| author_sort | Arvaneh, Mahnaz |
| collection | NTU |
| description | Brain-computer interface (BCI) technology has shown the capability of improving the quality of life for people with severe motor disabilities. To improve the portability and practicability of BCI systems, it is crucial to reduce the number of EEG channels as well as to have a good reliability. However, a relatively neglected issue in the EEG channel selection studies is the robustness of selected channels across sessions. This paper investigates whether the selected channels from first session is also useful for subsequent sessions on other days for a stroke patient. For this purpose, a new robust sparse common spatial pattern (RSCSP) algorithm is proposed for optimal EEG channel selection. Thereafter, the robustness of the proposed algorithm as well as 5 existing channel selection algorithms is investigated across 12 sessions data from 11 stroke patients who performed motor imagery based-BCI rehabilitation. The experimental results show that the proposed RSCSP channel selection algorithm significantly outperforms the other channel selection algorithms, when the 8 channels selected from the first session are evaluated on the 11 subsequent sessions. Moreover, there is no significant difference between the classification results of 8 channels selected by the proposed RSCSP algorithm from the first session and the classification results of 8 optimal channels selected from the same session as the test session. |
| first_indexed | 2024-10-01T05:47:00Z |
| format | Conference Paper |
| id | ntu-10356/98313 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:47:00Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/983132020-05-28T07:18:50Z Robust EEG channel selection across sessions in brain-computer interface involving stroke patients Arvaneh, Mahnaz Guan, Cuntai Ang, Kai Keng Quek, Chai School of Computer Engineering International Joint Conference on Neural Networks (2012 : Brisbane, Australia) DRNTU::Engineering::Computer science and engineering Brain-computer interface (BCI) technology has shown the capability of improving the quality of life for people with severe motor disabilities. To improve the portability and practicability of BCI systems, it is crucial to reduce the number of EEG channels as well as to have a good reliability. However, a relatively neglected issue in the EEG channel selection studies is the robustness of selected channels across sessions. This paper investigates whether the selected channels from first session is also useful for subsequent sessions on other days for a stroke patient. For this purpose, a new robust sparse common spatial pattern (RSCSP) algorithm is proposed for optimal EEG channel selection. Thereafter, the robustness of the proposed algorithm as well as 5 existing channel selection algorithms is investigated across 12 sessions data from 11 stroke patients who performed motor imagery based-BCI rehabilitation. The experimental results show that the proposed RSCSP channel selection algorithm significantly outperforms the other channel selection algorithms, when the 8 channels selected from the first session are evaluated on the 11 subsequent sessions. Moreover, there is no significant difference between the classification results of 8 channels selected by the proposed RSCSP algorithm from the first session and the classification results of 8 optimal channels selected from the same session as the test session. 2013-07-29T03:15:15Z 2019-12-06T19:53:26Z 2013-07-29T03:15:15Z 2019-12-06T19:53:26Z 2012 2012 Conference Paper Arvaneh, M., Guan, C., Ang, K. K., & Quek, C. (2012). Robust EEG channel selection across sessions in brain-computer interface involving stroke patients. The 2012 International Joint Conference on Neural Networks (IJCNN). https://hdl.handle.net/10356/98313 http://hdl.handle.net/10220/12419 10.1109/IJCNN.2012.6252687 en © 2012 IEEE. |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Arvaneh, Mahnaz Guan, Cuntai Ang, Kai Keng Quek, Chai Robust EEG channel selection across sessions in brain-computer interface involving stroke patients |
| title | Robust EEG channel selection across sessions in brain-computer interface involving stroke patients |
| title_full | Robust EEG channel selection across sessions in brain-computer interface involving stroke patients |
| title_fullStr | Robust EEG channel selection across sessions in brain-computer interface involving stroke patients |
| title_full_unstemmed | Robust EEG channel selection across sessions in brain-computer interface involving stroke patients |
| title_short | Robust EEG channel selection across sessions in brain-computer interface involving stroke patients |
| title_sort | robust eeg channel selection across sessions in brain computer interface involving stroke patients |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/98313 http://hdl.handle.net/10220/12419 |
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