International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 8, No. 12, 8 Dec. 2016
Cover page and Table of Contents: PDF (size: 760KB)
A Survey on Emotion Classification from Eeg Signal Using Various Techniques and Performance Analysis
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Author(s)
M. Sreeshakthy
1,*
J. Preethi
1
A. Dhilipan
1
Index Terms
Feature Extraction, Classification, Valence and Arousal, Neural Networks
Abstract
In this paper, the human emotions are analyzed from EEG Signal (Electroencephalogram) with different kinds of situation. Emotions are very important in different activity and decision making. Various feature extraction techniques like discrete wavelet transform, Higher Order crossings, Independent component analysis is used to extract the particular features. Those features are used to classify the emotions with different groups like arousal and valence level using different classification techniques like Neural networks, Support vector machine etc.. Based on these emotion groups analyze the performance and accuracy for the classification are determined.
Cite This Paper
M. Sreeshakthy, J. Preethi, A. Dhilipan, "A Survey On Emotion Classification From Eeg Signal Using Various Techniques and Performance Analysis", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.12, pp.19-26, 2016. DOI:10.5815/ijitcs.2016.12.03
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