Clustering Linear Discriminant Analysis for MEG-Based Brain Computer Interfaces

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 automatic...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering Vol. 19; no. 3; pp. 221 - 231
Main Authors: Zhang, Jinyin, Sudre, Gustavo, Li, Xin, Wang, Wei, Weber, Douglas J., Bagic, Anto
Format: Journal Article
Language:English
Published: United States IEEE 01.06.2011
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Algorithms
Brain - physiology
Brain-computer interface (BCI)
Cluster Analysis
Clustering algorithms
Correlation
Covariance matrix
Data Interpretation, Statistical
Decoding
Discriminant Analysis
Electroencephalography
Humans
Laplace equations
linear discriminant analysis (LDA)
Linear Models
magnetoencephalography (MEG)
Magnetoencephalography - methods
Magnetoencephalography - statistics & numerical data
Movement - physiology
Psychomotor Performance - physiology
spectral clustering
Studies
Training data
User-Computer Interface
ISSN:1534-4320, 1558-0210, 1558-0210
Online Access:Get full text
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Summary: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).
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ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2011.2116125