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Reseach Article

Statistical Feature based Activity Classification

by Supriya Shete, Revathi Shriram
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 96 - Number 15
Year of Publication: 2014
Authors: Supriya Shete, Revathi Shriram

Supriya Shete, Revathi Shriram . Statistical Feature based Activity Classification. International Journal of Computer Applications. 96, 15 ( June 2014), 49-52. DOI=10.5120/16874-6776

@article{ 10.5120/16874-6776,
author = { Supriya Shete, Revathi Shriram },
title = { Statistical Feature based Activity Classification },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 96 },
number = { 15 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 49-52 },
numpages = {9},
url = { },
doi = { 10.5120/16874-6776 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:21:52.193913+05:30
%A Supriya Shete
%A Revathi Shriram
%T Statistical Feature based Activity Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 96
%N 15
%P 49-52
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

Activity refers two activities and in this work to classification between two activities is achieved with the help of statistical feature extraction technique. [5] The term silent activity refers to the two processes in which we are proposing a method to predict Cognitive and Non cognitive tasks performed by the human brain. Electroencephalogram (EEG) is the electrical signal of brain which contains valuable information. In this work EEG and its frequency sub-bands have been analyzed to detect silent activity signal. The electroencephogram (EEG) contains information about brain hence the sub band decomposition of EEG is used for analyzing many brain diseases. [1] The sub-band decomposition means to extract various brain waves with different frequency bands such as alpha, beta, delta, theta and gamma from EEG signal to get more information from it. The work was carried out to extract various brain waves using discrete wavelet transform. The EEG signal is decompose into five sub bands alpha, beta, gamma, theta, and delta. [2] A wavelet transform has been applied to decompose the EEG into its sub bands. Statistical features Standard deviation, Covariance is calculated for each sub-band. The effective classification of EEG used for brain computer interface and can be used for silent communication or for recognizing different mental tasks. [5]

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Index Terms

Computer Science
Information Sciences


Sub-band Decomposition EEG Wavelet Statistical features Standard Deviation Variance