K-NN classification of brain dominance

The brain dominance is referred to right brain and left brain. The brain dominance can be observed with an Electroencephalogram (EEG) signal to identify different types of electrical pattern in the brain and will form the foundation of one’s personality. The objective of this project is to analyze b...

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Main Authors:	Khairul Amrizal, Abu Nawas, Mahfuzah, Mustafa, Rosdiyana, Samad, Pebrianti, Dwi, Nor Rul Hasma, Abdullah
Format:	Article
Language:	English
Published:	Institute of Advanced Engineering and Science (IAES) 2017
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/21013/ http://umpir.ump.edu.my/id/eprint/21013/ http://umpir.ump.edu.my/id/eprint/21013/ http://umpir.ump.edu.my/id/eprint/21013/1/K-NN%20classification%20of%20brain%20dominance.pdf

id	ump-21013
recordtype	eprints
spelling	ump-210132019-10-07T08:08:41Z http://umpir.ump.edu.my/id/eprint/21013/ K-NN classification of brain dominance Khairul Amrizal, Abu Nawas Mahfuzah, Mustafa Rosdiyana, Samad Pebrianti, Dwi Nor Rul Hasma, Abdullah TK Electrical engineering. Electronics Nuclear engineering The brain dominance is referred to right brain and left brain. The brain dominance can be observed with an Electroencephalogram (EEG) signal to identify different types of electrical pattern in the brain and will form the foundation of one’s personality. The objective of this project is to analyze brain dominance by using Wavelet analysis. The Wavelet analysis is done in 2-D Gabor Wavelet and the result of 2-D Gabor Wavelet is validated with an establish brain dominance questionnaire. Twenty one samples from University Malaysia Pahang (UMP) student are required to answer the establish brain dominance questionnaire has been collected in this experiment. Then, brainwave signal will record using Emotiv device. The threshold value is used to remove the artifact and noise from data collected to acquire a smoother signal. Next, the Band-pass filter is applied to the signal to extract the sub-band frequency components from Delta, Theta, Alpha, and Beta. After that, it will extract the energy of the signal from image feature extraction process. Next the features were classified by using K-Nearest Neighbor (K-NN) in two ratios which 70:30 and 80:20 that are training set and testing set (training: testing). The ratio of 70:30 gave the highest percentage of 83% accuracy while a ratio of 80:20 gave 100% accuracy. The result shows that 2-D Gabor Wavelet was able to classify brain dominance with accuracy 83% to 100%. Institute of Advanced Engineering and Science (IAES) 2017 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/21013/1/K-NN%20classification%20of%20brain%20dominance.pdf Khairul Amrizal, Abu Nawas and Mahfuzah, Mustafa and Rosdiyana, Samad and Pebrianti, Dwi and Nor Rul Hasma, Abdullah (2017) K-NN classification of brain dominance. International Journal of Electrical and Computer Engineering (IJECE), 8 (4). pp. 2494-2502. ISSN 2088-8708 http://ijece.iaescore.com/index.php/IJECE/article/view/11846 http://doi.org/10.11591/ijece.v8i4.pp2494-2502
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Khairul Amrizal, Abu Nawas Mahfuzah, Mustafa Rosdiyana, Samad Pebrianti, Dwi Nor Rul Hasma, Abdullah K-NN classification of brain dominance
description	The brain dominance is referred to right brain and left brain. The brain dominance can be observed with an Electroencephalogram (EEG) signal to identify different types of electrical pattern in the brain and will form the foundation of one’s personality. The objective of this project is to analyze brain dominance by using Wavelet analysis. The Wavelet analysis is done in 2-D Gabor Wavelet and the result of 2-D Gabor Wavelet is validated with an establish brain dominance questionnaire. Twenty one samples from University Malaysia Pahang (UMP) student are required to answer the establish brain dominance questionnaire has been collected in this experiment. Then, brainwave signal will record using Emotiv device. The threshold value is used to remove the artifact and noise from data collected to acquire a smoother signal. Next, the Band-pass filter is applied to the signal to extract the sub-band frequency components from Delta, Theta, Alpha, and Beta. After that, it will extract the energy of the signal from image feature extraction process. Next the features were classified by using K-Nearest Neighbor (K-NN) in two ratios which 70:30 and 80:20 that are training set and testing set (training: testing). The ratio of 70:30 gave the highest percentage of 83% accuracy while a ratio of 80:20 gave 100% accuracy. The result shows that 2-D Gabor Wavelet was able to classify brain dominance with accuracy 83% to 100%.
format	Article
author	Khairul Amrizal, Abu Nawas Mahfuzah, Mustafa Rosdiyana, Samad Pebrianti, Dwi Nor Rul Hasma, Abdullah
author_facet	Khairul Amrizal, Abu Nawas Mahfuzah, Mustafa Rosdiyana, Samad Pebrianti, Dwi Nor Rul Hasma, Abdullah
author_sort	Khairul Amrizal, Abu Nawas
title	K-NN classification of brain dominance
title_short	K-NN classification of brain dominance
title_full	K-NN classification of brain dominance
title_fullStr	K-NN classification of brain dominance
title_full_unstemmed	K-NN classification of brain dominance
title_sort	k-nn classification of brain dominance
publisher	Institute of Advanced Engineering and Science (IAES)
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/21013/ http://umpir.ump.edu.my/id/eprint/21013/ http://umpir.ump.edu.my/id/eprint/21013/ http://umpir.ump.edu.my/id/eprint/21013/1/K-NN%20classification%20of%20brain%20dominance.pdf
first_indexed	2023-09-18T22:30:39Z
last_indexed	2023-09-18T22:30:39Z
_version_	1777416263257030656

K-NN classification of brain dominance

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