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Computational and Mathematical Methods in Medicine
Volume 2018, Article ID 9476432, 14 pages
https://doi.org/10.1155/2018/9476432
Research Article

An Adaptive Calibration Framework for mVEP-Based Brain-Computer Interface

1Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
2Department of Computer Science and Engineering, Henan Institute of Engineering, Zhengzhou 451191, China
3Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China
4School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China

Correspondence should be addressed to Yangsong Zhang; moc.liamg@ymedacasygnahz and Peng Xu; nc.ude.ctseu@gnepux

Received 29 July 2017; Revised 10 January 2018; Accepted 24 January 2018; Published 26 February 2018

Academic Editor: Giancarlo Ferrigno

Copyright © 2018 Teng Ma 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

Electroencephalogram signals and the states of subjects are nonstationary. To track changing states effectively, an adaptive calibration framework is proposed for the brain-computer interface (BCI) with the motion-onset visual evoked potential (mVEP) as the control signal. The core of this framework is to update the training set adaptively for classifier training. The updating procedure consists of two operations, that is, adding new samples to the training set and removing old samples from the training set. In the proposed framework, a support vector machine (SVM) and fuzzy C-mean clustering (fCM) are combined to select the reliable samples for the training set from the blocks close to the current blocks to be classified. Because of the complementary information provided by SVM and fCM, they can guarantee the reliability of information fed into classifier training. The removing procedure will aim to remove those old samples recorded a relatively long time before current new blocks. These two operations could yield a new training set, which could be used to calibrate the classifier to track the changing state of the subjects. Experimental results demonstrate that the adaptive calibration framework is effective and efficient and it could improve the performance of online BCI systems.