an adaptive calibration framework for mvep-based brain-computer interface

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ID: 207718
2018
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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.
Reference Key
ma2018computationalan Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Teng Ma;Fali Li;Peiyang Li;Dezhong Yao;Yangsong Zhang;Peng Xu
Journal advanced functional materials
Year 2018
DOI
10.1155/2018/9476432
URL
http://dx.doi.org/10.1155/2018/9476432
Keywords
medical informatics computer applications to medicine

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