Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/100036
Title: EEG data space adaptation to reduce intersession nonstationarity in brain-computer interface
Authors: Arvaneh, Mahnaz
Guan, Cuntai
Ang, Kai Keng
Quek, Chai
Keywords: DRNTU::Engineering::Computer science and engineering::Computer applications
Issue Date: 2013
Source: Arvaneh, M., Guan, C., Ang, K. K., & Quek, C. (2013). EEG data space adaptation to reduce intersession nonstationarity in brain-computer interface. Neural computation, 25(8), 2146-2171.
Series/Report no.: Neural computation
Abstract: A major challenge in EEG-based brain-computer interfaces (BCIs) is the intersession nonstationarity in the EEG data that often leads to deteriorated BCI performances. To address this issue, this letter proposes a novel data space adaptation technique, EEG data space adaptation (EEG-DSA), to linearly transform the EEG data from the target space (evaluation session), such that the distribution difference to the source space (training session) is minimized. Using the Kullback-Leibler (KL) divergence criterion, we propose two versions of the EEG-DSA algorithm: the supervised version, when labeled data are available in the evaluation session, and the unsupervised version, when labeled data are not available. The performance of the proposed EEG-DSA algorithm is evaluated on the publicly available BCI Competition IV data set IIa and a data set recorded from 16 subjects performing motor imagery tasks on different days. The results show that the proposed EEG-DSA algorithm in both the supervised and unsupervised versions significantly outperforms the results without adaptation in terms of classification accuracy. The results also show that for subjects with poor BCI performances when no adaptation is applied, the proposed EEG-DSA algorithm in both the supervised and unsupervised versions significantly outperforms the unsupervised bias adaptation algorithm (PMean).
URI: https://hdl.handle.net/10356/100036
http://hdl.handle.net/10220/18440
ISSN: 0899-7667
DOI: http://dx.doi.org/10.1162/NECO_a_00474
Rights: © 2013 Massachusetts Institute of Technology. This paper was published in Neural Computation and is made available as an electronic reprint (preprint) with permission of Massachusetts Institute of Technology. The paper can be found at the following official DOI: [http://dx.doi.org/10.1162/NECO_a_00474]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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