{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T11:35:21Z","timestamp":1783164921798,"version":"3.54.6"},"reference-count":53,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2014,11,13]],"date-time":"2014-11-13T00:00:00Z","timestamp":1415836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Micromachines"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/mi5041082","type":"journal-article","created":{"date-parts":[[2014,11,13]],"date-time":"2014-11-13T11:22:30Z","timestamp":1415877750000},"page":"1082-1105","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Executed Movement Using EEG Signals through a Naive  Bayes Classifier"],"prefix":"10.3390","volume":"5","author":[{"given":"Juliano","family":"Machado","sequence":"first","affiliation":[{"name":"Assistive Technology Laboratory, Federal Institute of Rio Grande do Sul (IFSul), General Balb\u00e3o Street 81, Charqueadas 96745-000, Brazil"},{"name":"Biomedical Instrumentation Laboratory, Federal University of Rio Grande do Sul (UFRGS), Avenue Osvaldo Aranha 103, Porto Alegre 90035-190, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandre","family":"Balbinot","sequence":"additional","affiliation":[{"name":"Biomedical Instrumentation Laboratory, Federal University of Rio Grande do Sul (UFRGS), Avenue Osvaldo Aranha 103, Porto Alegre 90035-190, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2014,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/S1388-2457(02)00057-3","article-title":"Brain-computer interfaces for communication and control","volume":"113","author":"Wolpaw","year":"2002","journal-title":"Clinic. 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