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Clustering linear discriminant analysis for MEG-based brain computer interfaces.

Publication ,  Journal Article
Zhang, J; Sudre, G; Li, X; Wang, W; Weber, DJ; Bagic, A
Published in: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
June 2011

In this paper, we propose a clustering linear discriminant analysis algorithm (CLDA) to accurately decode hand movement directions from a small number of training trials for magnetoencephalography-based brain computer interfaces (BCIs). CLDA first applies a spectral clustering algorithm to automatically partition the BCI features into several groups where the within-group correlation is maximized and the between-group correlation is minimized. As such, the covariance matrix of all features can be approximated as a block diagonal matrix, thereby facilitating us to accurately extract the correlation information required by movement decoding from a small set of training data. The efficiency of the proposed CLDA algorithm is theoretically studied and an error bound is derived. Our experiment on movement decoding of five human subjects demonstrates that CLDA achieves superior decoding accuracy over other traditional approaches. The average accuracy of CLDA is 87% for single-trial movement decoding of four directions (i.e., up, down, left, and right).

Duke Scholars

Published In

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

DOI

EISSN

1558-0210

ISSN

1534-4320

Publication Date

June 2011

Volume

19

Issue

3

Start / End Page

221 / 231

Related Subject Headings

  • User-Computer Interface
  • Psychomotor Performance
  • Movement
  • Magnetoencephalography
  • Linear Models
  • Humans
  • Electroencephalography
  • Discriminant Analysis
  • Data Interpretation, Statistical
  • Cluster Analysis
 

Citation

APA
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ICMJE
MLA
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Zhang, J., Sudre, G., Li, X., Wang, W., Weber, D. J., & Bagic, A. (2011). Clustering linear discriminant analysis for MEG-based brain computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering : A Publication of the IEEE Engineering in Medicine and Biology Society, 19(3), 221–231. https://doi.org/10.1109/tnsre.2011.2116125
Zhang, Jinyin, Gustavo Sudre, Xin Li, Wei Wang, Douglas J. Weber, and Anto Bagic. “Clustering linear discriminant analysis for MEG-based brain computer interfaces.IEEE Transactions on Neural Systems and Rehabilitation Engineering : A Publication of the IEEE Engineering in Medicine and Biology Society 19, no. 3 (June 2011): 221–31. https://doi.org/10.1109/tnsre.2011.2116125.
Zhang J, Sudre G, Li X, Wang W, Weber DJ, Bagic A. Clustering linear discriminant analysis for MEG-based brain computer interfaces. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. 2011 Jun;19(3):221–31.
Zhang, Jinyin, et al. “Clustering linear discriminant analysis for MEG-based brain computer interfaces.IEEE Transactions on Neural Systems and Rehabilitation Engineering : A Publication of the IEEE Engineering in Medicine and Biology Society, vol. 19, no. 3, June 2011, pp. 221–31. Epmc, doi:10.1109/tnsre.2011.2116125.
Zhang J, Sudre G, Li X, Wang W, Weber DJ, Bagic A. Clustering linear discriminant analysis for MEG-based brain computer interfaces. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. 2011 Jun;19(3):221–231.

Published In

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

DOI

EISSN

1558-0210

ISSN

1534-4320

Publication Date

June 2011

Volume

19

Issue

3

Start / End Page

221 / 231

Related Subject Headings

  • User-Computer Interface
  • Psychomotor Performance
  • Movement
  • Magnetoencephalography
  • Linear Models
  • Humans
  • Electroencephalography
  • Discriminant Analysis
  • Data Interpretation, Statistical
  • Cluster Analysis