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

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ID: 35015
2008
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Abstract
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.
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geng2008acomputational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Geng, Tao;Gan, John Q.;Dyson, Matthew;Tsui, Chun SL;Sepulveda, Francisco;
Journal Computational Intelligence and Neuroscience
Year 2008
DOI
DOI not found
URL
http://dx.doi.org/10.1155/2008/437306
Keywords
computer applications to medicine medical informaticsneurosciences neuropsychiatry biological psychiatry

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