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Computational Intelligence and Neuroscience
Volume 2008, Article ID 437306, 5 pages
Research Article

A Novel Design of 4-Class BCI Using Two Binary Classifiers and Parallel Mental Tasks

BCI Group, Department of Computing and Electronic Systems, University of Essex, Colchester, CO4 3SQ, UK

Received 4 December 2007; Revised 20 March 2008; Accepted 2 June 2008

Academic Editor: Yuanqing Li

Copyright © 2008 Tao Geng 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.


A novel 4-class single-trial brain computer interface (BCI) based on two (rather than four or more) binary linear discriminant analysis (LDA) classifiers is proposed, which is called a “parallel BCI.” Unlike other BCIs where mental tasks are executed and classified in a serial way one after another, the parallel BCI uses properly designed parallel mental tasks that are executed on both sides of the subject body simultaneously, which is the main novelty of the BCI paradigm used in our experiments. Each of the two binary classifiers only classifies the mental tasks executed on one side of the subject body, and the results of the two binary classifiers are combined to give the result of the 4-class BCI. Data was recorded in experiments with both real movement and motor imagery in 3 able-bodied subjects. Artifacts were not detected or removed. Offline analysis has shown that, in some subjects, the parallel BCI can generate a higher accuracy than a conventional 4-class BCI, although both of them have used the same feature selection and classification algorithms.