Classification of electroencephalography signal using statistical features and regression classifier

Enormous digital electroencephalography (EEG) acquisition systems available nowadays for researchers due to the high demand in the brain signal research. Using EEG-based emotion recognition, the computer can look inside a user head to observe their mental state of sad and happy emotion. Thus, there...

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Main Author:	Sabri, Nurbaity
Format:	Thesis
Language:	English
Published:	2014
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/48054/25/NurbaitySabriMFC2014.pdf
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id	my-utm-ep.48054
record_format	uketd_dc
spelling	my-utm-ep.480542017-07-29T01:46:49Z Classification of electroencephalography signal using statistical features and regression classifier 2014-07 Sabri, Nurbaity QA75 Electronic computers. Computer science Enormous digital electroencephalography (EEG) acquisition systems available nowadays for researchers due to the high demand in the brain signal research. Using EEG-based emotion recognition, the computer can look inside a user head to observe their mental state of sad and happy emotion. Thus, there is a need for efficient mechanism to detect those emotions accurately along with computation complexity. The current algorithms available are excessively complex with higher computational time. In this study, 14 channels of EEG signals acquired from emotive device with 128 Hz sample rate. These raw signals undergo preprocess stage using band pass and ICA filter. This research focuses two components which is feature extraction and classification. A combination of statistical features has been carrying out to extract important signal. To classify the EEG signal into sad and happy classes, Support Vector Machine (SVM) and Linear Regression has been applied. Waikato Environment for Knowledge Analysis (WEKA) as training tools is employ to train the dataset and test the accuracy of the classifier. Results presented that Linear Regression has better detection accuracy with 95% compared to SVM with 80% average accuracy. In conclusion this research suggests using Linear Regression for future work on predicting between sad and happy emotion from the EEG signal. 2014-07 Thesis http://eprints.utm.my/id/eprint/48054/ http://eprints.utm.my/id/eprint/48054/25/NurbaitySabriMFC2014.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Sabri, Nurbaity Classification of electroencephalography signal using statistical features and regression classifier
description	Enormous digital electroencephalography (EEG) acquisition systems available nowadays for researchers due to the high demand in the brain signal research. Using EEG-based emotion recognition, the computer can look inside a user head to observe their mental state of sad and happy emotion. Thus, there is a need for efficient mechanism to detect those emotions accurately along with computation complexity. The current algorithms available are excessively complex with higher computational time. In this study, 14 channels of EEG signals acquired from emotive device with 128 Hz sample rate. These raw signals undergo preprocess stage using band pass and ICA filter. This research focuses two components which is feature extraction and classification. A combination of statistical features has been carrying out to extract important signal. To classify the EEG signal into sad and happy classes, Support Vector Machine (SVM) and Linear Regression has been applied. Waikato Environment for Knowledge Analysis (WEKA) as training tools is employ to train the dataset and test the accuracy of the classifier. Results presented that Linear Regression has better detection accuracy with 95% compared to SVM with 80% average accuracy. In conclusion this research suggests using Linear Regression for future work on predicting between sad and happy emotion from the EEG signal.
format	Thesis
qualification_level	Master's degree
author	Sabri, Nurbaity
author_facet	Sabri, Nurbaity
author_sort	Sabri, Nurbaity
title	Classification of electroencephalography signal using statistical features and regression classifier
title_short	Classification of electroencephalography signal using statistical features and regression classifier
title_full	Classification of electroencephalography signal using statistical features and regression classifier
title_fullStr	Classification of electroencephalography signal using statistical features and regression classifier
title_full_unstemmed	Classification of electroencephalography signal using statistical features and regression classifier
title_sort	classification of electroencephalography signal using statistical features and regression classifier
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
url	http://eprints.utm.my/id/eprint/48054/25/NurbaitySabriMFC2014.pdf
_version_	1747817296580050944

Classification of electroencephalography signal using statistical features and regression classifier

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