Academic
Publications
Clustering Linear Discriminant Analysis for MEG-Based Brain Computer Interfaces

Clustering Linear Discriminant Analysis for MEG-Based Brain Computer Interfaces,10.1109/TNSRE.2011.2116125,IEEE Transactions on Neural Systems and Reh

Clustering Linear Discriminant Analysis for MEG-Based Brain Computer Interfaces  
BibTex | RIS | RefWorks Download
In this paper, we propose a clustering linear dis- criminant 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 re- quiredbymovementdecodingfromasmallsetoftrainingdata.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). Index Terms—Brain-computer interface (BCI), linear discrim- inant analysis (LDA), magnetoencephalography (MEG), spectral clustering.
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.