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Reseach Article

Linking and Familiarity Rating Method Classifies the Music, Video Assessment Responses of EEG-Signal

by B.senthil Kumar, S.santhosh Baboo
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 10
Year of Publication: 2015
Authors: B.senthil Kumar, S.santhosh Baboo

B.senthil Kumar, S.santhosh Baboo . Linking and Familiarity Rating Method Classifies the Music, Video Assessment Responses of EEG-Signal. International Journal of Computer Applications. 116, 10 ( April 2015), 6-11. DOI=10.5120/20370-2577

@article{ 10.5120/20370-2577,
author = { B.senthil Kumar, S.santhosh Baboo },
title = { Linking and Familiarity Rating Method Classifies the Music, Video Assessment Responses of EEG-Signal },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 10 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 6-11 },
numpages = {9},
url = { },
doi = { 10.5120/20370-2577 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:57:16.706321+05:30
%A B.senthil Kumar
%A S.santhosh Baboo
%T Linking and Familiarity Rating Method Classifies the Music, Video Assessment Responses of EEG-Signal
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 10
%P 6-11
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

The most renowned strategy utilized for perusing mind movement is electroencephalography (EEG). Electroencephalography is the neurophysiologic estimation of the electrical action of the cerebrum by recording from anodes put on the scalp, or in the exceptional cases on the cortex. The ensuing follows are known as an electroencephalogram (EEG) and speak to alleged brainwaves. This system is picking up prevalence as it is a non-intrusive interface and is giving a methodology to controlling machines through contemplations. The proposed linking and familiarity rating method classifies the music, video assessment responses of EEG-Signal. The metrics namely true positive, true negative, false positive, false negative, sensitivity, specificity and classification accuracy are chosen for evaluating the performance of the proposed classifier. The simulation result shows that the proposed classifier achieves 95. 4 % accuracy which is better than other methods.

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Index Terms

Computer Science
Information Sciences


Usability Electroencephalography EEG Familiarity rating Bio feedback User experience Music video assessment responsive signals BCI Brain Compute Interface.