Research Article
EEG Signal Classification using Linear Predictive Cepstral Coefficient Features
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 73 - Issue 1 |
| Published: July 2013 |
| Authors: S. Pazhanirajan, P. Dhanalakshmi |
10.5120/12707-9508
|
S. Pazhanirajan, P. Dhanalakshmi . EEG Signal Classification using Linear Predictive Cepstral Coefficient Features. International Journal of Computer Applications. 73, 1 (July 2013), 28-31. DOI=10.5120/12707-9508
@article{ 10.5120/12707-9508,
author = { S. Pazhanirajan,P. Dhanalakshmi },
title = { EEG Signal Classification using Linear Predictive Cepstral Coefficient Features },
journal = { International Journal of Computer Applications },
year = { 2013 },
volume = { 73 },
number = { 1 },
pages = { 28-31 },
doi = { 10.5120/12707-9508 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2013
%A S. Pazhanirajan
%A P. Dhanalakshmi
%T EEG Signal Classification using Linear Predictive Cepstral Coefficient Features%T
%J International Journal of Computer Applications
%V 73
%N 1
%P 28-31
%R 10.5120/12707-9508
%I Foundation of Computer Science (FCS), NY, USA
Abstract
An electroencephalogram (EEG) is a procedure that records brain wave patterns, which are used to identify abnormalities related to the electrical activities of the brain. In this study an effective algorithm is proposed to automatically classify EEG clips into two different classes: normal and abnormal. For categorizing the EEG data, feature extraction techniques such as linear predictive coefficients (LPC) and linear predictive cepstral coefficients (LPCC) are used. Support vector machines (SVM) is used to classify the EEG clip into their respective classes by learning from training data.
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