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Micromachines 2014, 5(4), 1082-1105;

Executed Movement Using EEG Signals through a Naive Bayes Classifier

Assistive Technology Laboratory, Federal Institute of Rio Grande do Sul (IFSul), General Balbão Street 81, Charqueadas 96745-000, Brazil
Biomedical Instrumentation Laboratory, Federal University of Rio Grande do Sul (UFRGS), Avenue Osvaldo Aranha 103, Porto Alegre 90035-190, Brazil
Author to whom correspondence should be addressed.
Received: 15 May 2014 / Revised: 15 October 2014 / Accepted: 30 October 2014 / Published: 13 November 2014
(This article belongs to the Special Issue Mind-Controlled Robotics)
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Recent years have witnessed a rapid development of brain-computer interface (BCI) technology. An independent BCI is a communication system for controlling a device by human intension, e.g., a computer, a wheelchair or a neuroprosthes is, not depending on the brain’s normal output pathways of peripheral nerves and muscles, but on detectable signals that represent responsive or intentional brain activities. This paper presents a comparative study of the usage of the linear discriminant analysis (LDA) and the naive Bayes (NB) classifiers on describing both right- and left-hand movement through electroencephalographic signal (EEG) acquisition. For the analysis, we considered the following input features: the energy of the segments of a band pass-filtered signal with the frequency band in sensorimotor rhythms and the components of the spectral energy obtained through the Welch method. We also used the common spatial pattern (CSP) filter, so as to increase the discriminatory activity among movement classes. By using the database generated by this experiment, we obtained hit rates up to 70%. The results are compatible with previous studies. View Full-Text
Keywords: naive Bayes (NB); linear discriminant analysis (LDA); Welch method; brain computer interface (BCI) naive Bayes (NB); linear discriminant analysis (LDA); Welch method; brain computer interface (BCI)

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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 (CC BY 4.0).

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Machado, J.; Balbinot, A. Executed Movement Using EEG Signals through a Naive Bayes Classifier. Micromachines 2014, 5, 1082-1105.

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