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Event Evoked Signal Classification in Frequency Domain for Brain Computer Interface

by G. V. Sridhar, P. Mallikarjuna Rao
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 45 - Number 14
Year of Publication: 2012
Authors: G. V. Sridhar, P. Mallikarjuna Rao
10.5120/6852-9434

G. V. Sridhar, P. Mallikarjuna Rao . Event Evoked Signal Classification in Frequency Domain for Brain Computer Interface. International Journal of Computer Applications. 45, 14 ( May 2012), 38-42. DOI=10.5120/6852-9434

@article{ 10.5120/6852-9434,
author = { G. V. Sridhar, P. Mallikarjuna Rao },
title = { Event Evoked Signal Classification in Frequency Domain for Brain Computer Interface },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 45 },
number = { 14 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 38-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume45/number14/6852-9434/ },
doi = { 10.5120/6852-9434 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:37:37.890341+05:30
%A G. V. Sridhar
%A P. Mallikarjuna Rao
%T Event Evoked Signal Classification in Frequency Domain for Brain Computer Interface
%J International Journal of Computer Applications
%@ 0975-8887
%V 45
%N 14
%P 38-42
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Brain Computer Interface (BCI) is an emerging research area which tries to capture the motor imagery thought process from brain using Electro-encephalogram (EEG) and process the data using signal processing techniques to classify the motor imagery thought process. Physically impaired people without any muscular activity can carry on their day to day operation with the use of BCI as it can be used to control devices including computers using the thoughts of the person. Devices such as wheelchair have been successfully connected to BCI system and these devices can be controlled using thought. In this paper, it is proposed to investigate EEG signals, extract features of motor imagery in the frequency domain using Hilbert transform, compute the maximum and minimum energies and classify the brain signal activity using pattern recognition techniques.

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Index Terms

Computer Science
Information Sciences

Keywords

Brain Computer Interface (bci) Fast Hilbert Transform Support Vector Machine (svm) Pattern Recognition

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