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

Published

Journal Article

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).

Full Text

Duke Authors

Cited Authors

  • Zhang, J; Sudre, G; Li, X; Wang, W; Weber, DJ; Bagic, A

Published Date

  • June 2011

Published In

Volume / Issue

  • 19 / 3

Start / End Page

  • 221 - 231

PubMed ID

  • 21342856

Pubmed Central ID

  • 21342856

Electronic International Standard Serial Number (EISSN)

  • 1558-0210

International Standard Serial Number (ISSN)

  • 1534-4320

Digital Object Identifier (DOI)

  • 10.1109/tnsre.2011.2116125

Language

  • eng