Please use this identifier to cite or link to this item: http://13.232.72.61:8080/jspui/handle/123456789/301
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dc.contributor.authorDeepa, T. P.-
dc.contributor.authorVinay, H. S.-
dc.date.accessioned2018-09-25T10:01:17Z-
dc.date.available2018-09-25T10:01:17Z-
dc.date.issued2014-10-
dc.identifier.citationDeepa,T. P., & Vinay, H. S. (2014). Classification of Motion and Speech Based Brain EEG Signals using Bilayer Bayesian Classifier with Association Rule (BBC-AR). International Journal of Computer Science and Mobile Computing. 3(10), 777-782.en_US
dc.identifier.issn2320–088X-
dc.identifier.urihttp://13.232.72.61:8080/jspui/handle/123456789/301-
dc.description.abstractThis paper aims at developing Brain Computer Interface (BCI) system which uses EEG signals. In this, user thinking is extracted from brain activity of healthy person. Features are extracted from pre-processed signals and classify them into their respective alpha, beta, delta and gamma signal classes. Main intension of this work is to provide better communication interface to a person using brain signals. This is helpful to patients who have severe motor impairments and also unable to speak. This system can be used as an alternative form of communication by using mental activity.en_US
dc.language.isoenen_US
dc.publisherIJCSMCen_US
dc.subjectComputer scienceen_US
dc.subjectComputer Interfaceen_US
dc.titleClassification of Motion and Speech Based Brain EEG Signals using Bilayer Bayesian Classifier with Association Rule (BBC-AR)en_US
dc.typeArticleen_US
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