Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98313
Title: Robust EEG channel selection across sessions in brain-computer interface involving stroke patients
Authors: Arvaneh, Mahnaz
Guan, Cuntai
Ang, Kai Keng
Quek, Chai
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: 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).
Abstract: 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.
URI: https://hdl.handle.net/10356/98313
http://hdl.handle.net/10220/12419
DOI: 10.1109/IJCNN.2012.6252687
Rights: © 2012 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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