Fixed distance neighbour classifiers in brain computer interface systems

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

[1]  Helge J. Ritter,et al.  BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm , 2004, IEEE Transactions on Biomedical Engineering.

[2]  Gary E. Birch,et al.  A brain-controlled switch for asynchronous control applications , 2000, IEEE Trans. Biomed. Eng..

[3]  Bin He,et al.  Classifying EEG-based motor imagery tasks by means of time–frequency synthesized spatial patterns , 2004, Clinical Neurophysiology.

[4]  Dimitrios Gunopulos,et al.  Locally Adaptive Metric Nearest-Neighbor Classification , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  G Pfurtscheller,et al.  Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[6]  N. Birbaumer.  slow Cortical Potentials: Plasticity, Operant Control, and Behavioral Effects , 1999 .

[7]  George D. Magoulas,et al.  Extensions of the k Nearest Neighbour methods for classification problems , 2008 .

[8]  M J Stokes,et al.  EEG-based communication: a pattern recognition approach. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[9]  M Congedo,et al.  Classification of movement intention by spatially filtered electromagnetic inverse solutions , 2006, Physics in medicine and biology.

[10]  G. Pfurtscheller,et al.  EEG-based discrimination between imagination of right and left hand movement. , 1997, Electroencephalography and clinical neurophysiology.

[11]  J. Mourino,et al.  Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[12]  Samy Bengio,et al.  HMM and IOHMM modeling of EEG rhythms for asynchronous BCI systems , 2004, ESANN.

[13]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[14]  B. Kamousi,et al.  Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  Clemens Brunner,et al.  Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.

[16]  Lei Ding,et al.  Motor imagery classification by means of source analysis for brain–computer interface applications , 2004, Journal of neural engineering.

[17]  Z. Keirn,et al.  A new mode of communication between man and his surroundings , 1990, IEEE Transactions on Biomedical Engineering.

[18]  Mikko Sams,et al.  Online Classification of Single EEG Trials During Finger Movements , 2008, IEEE Transactions on Biomedical Engineering.