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Advances in Human-Computer Interaction
Volume 2012 (2012), Article ID 185320, 10 pages
http://dx.doi.org/10.1155/2012/185320
Research Article

A Combination of Pre- and Postprocessing Techniques to Enhance Self-Paced BCIs

1Department of Biomedical Engineering, Tarbiat Modares University, Tehran 14115194, Iran
2Intelligent Systems Research Centre, University of Ulster, Derry BT48 7JL, UK

Received 5 July 2012; Revised 29 October 2012; Accepted 1 December 2012

Academic Editor: Christoph Braun

Copyright © 2012 Raheleh Mohammadi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Mental task onset detection from the continuous electroencephalogram (EEG) in real time is a critical issue in self-paced brain computer interface (BCI) design. The paper shows that self-paced BCI performance can be significantly improved by combining a range of simple techniques including (1) constant-Q filters with varying bandwidth size depending on the center frequency, instead of constant bandwidth filters for frequency decomposition of the EEG signal in the 6 to 36 Hz band; (2) subject-specific postprocessing parameter optimization consisting of dwell time and threshold, and (3) debiasing before postprocessing by readjusting the classification output based on the current and previous brain states, to reduce the number of false detections. This debiasing block is shown to be optimal when activated only in special cases which are predetermined during the training phase. Analysis of the data recorded from seven subjects executing foot movement shows a statistically significant 10% ( ) average improvement in true positive rate (TPR) and a 1% reduction in false positive rate (FPR) detections compared with previous work on the same data.