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Comparison of different wavelet features from EEG signals for classifying human emotions

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Main Authors: Murugappan, Muthusamy, Dr., Nagarajan, Ramachandran, Prof. Dr., Sazali, Yaacob, Prof. Dr.
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineering (IEEE) 2010
Subjects:
EEG
Emotions
KNN
LDA
Surface laplacian filtering
Wavelet transform
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/8450
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spelling my.unimap-84502014-09-02T06:38:37Z Comparison of different wavelet features from EEG signals for classifying human emotions Murugappan, Muthusamy, Dr. Nagarajan, Ramachandran, Prof. Dr. Sazali, Yaacob, Prof. Dr. EEG Emotions KNN LDA Surface laplacian filtering Wavelet transform Link to publisher's hompage at http://ieeexplore.ieee.org/ In recent years, estimation of human emotions from Electroencephalogram (EEG) signals plays a vital role on developing intellectual Brain Computer Interface (BCI) devices. In this work, we have collected the EEG signals using 64 channels from 20 subjects in the age group of 21∼39 years for determining discrete emotions (happy, surprise, fear, disgust, and neutral) under audio-visual induction (video/film clips) stimuli. Surface Laplacian filtering is used to preprocess the EEG signals and decomposed into five different EEG frequency bands (delta, theta, alpha, beta, and gamma) using Wavelet Transform (WT). The statistical features are derived from all these five frequency bands are considered for classifying the emotions using two linear classifiers (K Nearest Neighbor (KNN) & Linear Discriminant Analysis (LDA)). The main objective of this work is to consider a selected number of 24 channels for assessing emotions from the original EEG channels. There are three different wavelet functions ("db8", "sym8", and "coif5") are used to derive the linear and non linear features for emotion classification. The validation of statistical features is performed using 5 fold cross validation. In this work, KNN outperforms LDA by offering a maximum average classification rate of 79.174 %. Finally we present the average and individual classification rate of emotions over various statistical features on three different wavelet functions for justifying the performance of our emotion recognition system. 2010-08-04T03:47:28Z 2010-08-04T03:47:28Z 2009-10-04 Working Paper Vol.2, p.836-841 978-142444682-7 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5356339&tag=1 http://hdl.handle.net/123456789/8450 en Proceedings of the Symposium on Industrial Electronics and Applications (ISIEA) 2009 Institute of Electrical and Electronics Engineering (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic EEG
Emotions
KNN
LDA
Surface laplacian filtering
Wavelet transform
spellingShingle EEG
Emotions
KNN
LDA
Surface laplacian filtering
Wavelet transform
Murugappan, Muthusamy, Dr.
Nagarajan, Ramachandran, Prof. Dr.
Sazali, Yaacob, Prof. Dr.
Comparison of different wavelet features from EEG signals for classifying human emotions
description Link to publisher's hompage at http://ieeexplore.ieee.org/
format Working Paper
author Murugappan, Muthusamy, Dr.
Nagarajan, Ramachandran, Prof. Dr.
Sazali, Yaacob, Prof. Dr.
author_facet Murugappan, Muthusamy, Dr.
Nagarajan, Ramachandran, Prof. Dr.
Sazali, Yaacob, Prof. Dr.
author_sort Murugappan, Muthusamy, Dr.
title Comparison of different wavelet features from EEG signals for classifying human emotions
title_short Comparison of different wavelet features from EEG signals for classifying human emotions
title_full Comparison of different wavelet features from EEG signals for classifying human emotions
title_fullStr Comparison of different wavelet features from EEG signals for classifying human emotions
title_full_unstemmed Comparison of different wavelet features from EEG signals for classifying human emotions
title_sort comparison of different wavelet features from eeg signals for classifying human emotions
publisher Institute of Electrical and Electronics Engineering (IEEE)
publishDate 2010
url http://dspace.unimap.edu.my/xmlui/handle/123456789/8450
_version_ 1643789180690497536
score 13.222552

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