Research Articles
Fixed distance neighbour classifiers in brain computer interface systems
Authors:
Asiri Nanayakkara ,
Artificial Intelligence and Applied Electronics Research Unit, Institute of Fundamental Studies, Hanthana Road, Kandy., LK
Zahmeeth Sakkaff
Artificial Intelligence and Applied Electronics Research Unit, Institute of Fundamental Studies, Hanthana Road, Kandy., LK
Abstract
A new classification method, which is closely related to k nearest neighbour (kNN) classification method is introduced for identifying cognitive tasks in brain computer interface (BCI) systems. This new method is named fixed distance neighbour (FDN) classifier. Performance of the FDN method is tested with feature vectors derived from EEG datasets recorded for imagery motor movement mental tasks. For comparison purposes, performance of kNN classification method is also tested with the same feature vectors. It was found that FDN performed slightly better than kNN for most of the datasets used in this study, indicating that FDN is a viable classification method, which can be used in place of kNN in BCI systems.
DOI: http://dx.doi.org/10.4038/jnsfsr.v40i3.4693
J.Natn.Sci.Foundation Sri Lanka 2012 40(3):195-200
How to Cite:
Nanayakkara, A. and Sakkaff, Z., 2012. Fixed distance neighbour classifiers in brain computer interface systems. Journal of the National Science Foundation of Sri Lanka, 40(3), pp.195–200. DOI: http://doi.org/10.4038/jnsfsr.v40i3.4693
Published on
26 Sep 2012.
Peer Reviewed
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