Computational Intelligence and Neuroscience

Computational Intelligence and Neuroscience / 2007 / Article
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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 054925 | https://doi.org/10.1155/2007/54925

Jakub Štastný, Pavel Sovka, "High-Resolution Movement EEG Classification", Computational Intelligence and Neuroscience, vol. 2007, Article ID 054925, 12 pages, 2007. https://doi.org/10.1155/2007/54925

High-Resolution Movement EEG Classification

Academic Editor: Andrzej Cichocki
Received17 Feb 2007
Accepted23 Sep 2007
Published15 Jan 2008

Abstract

The aim of the contribution is to analyze possibilities of high-resolution movement classification using human EEG. For this purpose, a database of the EEG recorded during right-thumb and little-finger fast flexion movements of the experimental subjects was created. The statistical analysis of the EEG was done on the subject's basis instead of the commonly used grand averaging. Statistically significant differences between the EEG accompanying movements of both fingers were found, extending the results of other so far published works. The classifier based on hidden Markov models was able to distinguish between movement and resting states (classification score of 94–100%), but it was unable to recognize the type of the movement. This is caused by the large fraction of other (nonmovement related) EEG activities in the recorded signals. A classification method based on advanced EEG signal denoising is being currently developed to overcome this problem.

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Copyright © 2007 Jakub Štastný and Pavel Sovka. 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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