Fixed distance neighbour classifiers in brain computer interface systems

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 ofthe datasets used in this study, indicating that FDN is a viable classification method, which can be used in place of kNN in BCI systems.