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Human emotion classification using wavelet transform and KNN

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Main Author: Murugappan, Muthusamy, Dr.
Other Authors: murugappan@unimap.edu.my
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2011
Subjects:
Electroencephalogram (EEG)
Surface Laplacian filtering
Wavelet transform
KNN
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/15856
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spelling my.unimap-158562011-11-17T02:42:05Z Human emotion classification using wavelet transform and KNN Murugappan, Muthusamy, Dr. murugappan@unimap.edu.my Electroencephalogram (EEG) Surface Laplacian filtering Wavelet transform KNN Link to publisher's homepage at http://ieeexplore.ieee.org/ Emotion is one of the most important features of humans. Without the ability of emotions processing, computers and robots cannot communicate with human in natural way. In this paper we presented the classification of human emotions using Electroencephalogram (EEG) signals. EEG signals are collected from 20 subjects through 62 active electrodes, which are placed over the entire scalp based on International 10-10 system. An audio-visual (video clips) stimuli based protocol has been designed for evoking the discrete emotions. The raw EEG signals are preprocessed through Surface Laplacian filtering method and decomposed into five different EEG frequency bands (delta, theta, alpha, beta and gamma) using Wavelet Transform (WT). We have considered three different wavelet functions namely: "db4", "db8", "sym8" and "coif5" for extracting the statistical features from the preprocessed signal. In this work, we have investigated the efficacy of emotion classification for two different set of EEG channels (62 channels & 24 channels). The validation of statistical features is performed using 5 fold cross validation and classified by using linear non-linear (KNN K Nearest Neighbor) classifier. KNN gives a maximum average classification rate of 82.87 % on 62 channels and 78.57% on 24 channels, respectively. Finally we present the average classification accuracy and individual classification accuracy of KNN for justifying the performance of our emotion recognition system. 2011-11-17T02:42:05Z 2011-11-17T02:42:05Z 2011-06-28 Working Paper Vol. 1, p. 148-153 978-1-6128-4404-6 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5976886 http://hdl.handle.net/123456789/15856 en Proceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics (ICPAIR 2011) Institute of Electrical and Electronics Engineers (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 Electroencephalogram (EEG)
Surface Laplacian filtering
Wavelet transform
KNN
spellingShingle Electroencephalogram (EEG)
Surface Laplacian filtering
Wavelet transform
KNN
Murugappan, Muthusamy, Dr.
Human emotion classification using wavelet transform and KNN
description Link to publisher's homepage at http://ieeexplore.ieee.org/
author2 murugappan@unimap.edu.my
author_facet murugappan@unimap.edu.my
Murugappan, Muthusamy, Dr.
format Working Paper
author Murugappan, Muthusamy, Dr.
author_sort Murugappan, Muthusamy, Dr.
title Human emotion classification using wavelet transform and KNN
title_short Human emotion classification using wavelet transform and KNN
title_full Human emotion classification using wavelet transform and KNN
title_fullStr Human emotion classification using wavelet transform and KNN
title_full_unstemmed Human emotion classification using wavelet transform and KNN
title_sort human emotion classification using wavelet transform and knn
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2011
url http://dspace.unimap.edu.my/xmlui/handle/123456789/15856
_version_ 1643791480857296896
score 13.214268

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