An Algorithm for Idle-State Detection in Motor-Imagery-Based Brain-Computer Interface

Zhang, Dan; Wang, Yijun; Gao, Xiaorong; Hong, Bo; Gao, Shangkai

doi:https://doi.org/10.1155/2007/39714

Computational Intelligence and Neuroscience

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Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications

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Research Article | Open Access

Volume 2007 | Article ID 039714 | https://doi.org/10.1155/2007/39714

An Algorithm for Idle-State Detection in Motor-Imagery-Based Brain-Computer Interface

Dan Zhang,¹Yijun Wang,¹Xiaorong Gao,¹Bo Hong,¹and Shangkai Gao¹

Academic Editor: Andrzej Cichocki

Received28 Feb 2007

Accepted27 May 2007

Published12 Jul 2007

Abstract

For a robust brain-computer interface (BCI) system based on motor imagery (MI), it should be able to tell when the subject is not concentrating on MI tasks (the “idle state”) so that real MI tasks could be extracted accurately. Moreover, because of the diversity of idle state, detecting idle state without training samples is as important as classifying MI tasks. In this paper, we propose an algorithm for solving this problem. A three-class classifier was constructed by combining two two-class classifiers, one specified for idle-state detection and the other for these two MI tasks. Common spatial subspace decomposition (CSSD) was used to extract the features of event-related desynchronization (ERD) in two motor imagery tasks. Then Fisher discriminant analysis (FDA) was employed in the design of two two-class classifiers for completion of detecting each task, respectively. The algorithm successfully provided a way to solve the problem of “idle-state detection without training samples.” The algorithm was applied to the dataset IVc from BCI competition III. A final result with mean square error of 0.30 was obtained on the testing set. This is the winning algorithm in BCI competition III. In addition, the algorithm was also validated by applying to the EEG data of an MI experiment including “idle” task.

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Copyright

Copyright © 2007 Dan Zhang 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.

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