Analysis of EEG features for brain computer interface application

Brain-Computer Interface (BCI) or Human-Machine Interface (HMI) is now becoming vital engineering and technology field which applies electroencephalography (EEG) signal to provide Assistive Technology (AT) to humans. This paper presents the analysis of EEG signals from various human cognitive or men...

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Main Authors:	Rashid, Mamunur, Norizam, Sulaiman, Mahfuzah, Mustafa, M. S., Jadin, M. S., Najib, Sabira, Khatun, Bari, Bifta Sama
Format:	Conference or Workshop Item
Language:	English English
Published:	Universiti Malaysia Pahang 2019
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/26561/ http://umpir.ump.edu.my/id/eprint/26561/1/9.%20Analysis%20of%20EEG%20features%20for%20brain%20computer.pdf http://umpir.ump.edu.my/id/eprint/26561/2/9.1%20Analysis%20of%20EEG%20features%20for%20brain%20computer.pdf

id	ump-26561
recordtype	eprints
spelling	ump-265612020-01-20T02:28:42Z http://umpir.ump.edu.my/id/eprint/26561/ Analysis of EEG features for brain computer interface application Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa M. S., Jadin M. S., Najib Sabira, Khatun Bari, Bifta Sama TK Electrical engineering. Electronics Nuclear engineering Brain-Computer Interface (BCI) or Human-Machine Interface (HMI) is now becoming vital engineering and technology field which applies electroencephalography (EEG) signal to provide Assistive Technology (AT) to humans. This paper presents the analysis of EEG signals from various human cognitive or mental states to determine the suitable EEG features that can be employed in BCI field. Here, EEG features in term of power spectral density, log energy entropy and spectral centroid are selected to recognize human men- tal or cognitive state from 3 different exercises; i) solving math problem, ii) playing game and iii) do nothing (relax). The average power spectral density, average log energy entropy and average spectral centroid of EEG Alpha and Beta band for three mental exercises are calculated in order to determine the best features that can be used for BCI application. The results of the research shows that the EEG features in term of power spectral density, log energy en- tropy and spectral centroid can be used to indicate the change in cognitive states after exposing human to several cognitive exercises. Universiti Malaysia Pahang 2019 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/26561/1/9.%20Analysis%20of%20EEG%20features%20for%20brain%20computer.pdf pdf en http://umpir.ump.edu.my/id/eprint/26561/2/9.1%20Analysis%20of%20EEG%20features%20for%20brain%20computer.pdf Rashid, Mamunur and Norizam, Sulaiman and Mahfuzah, Mustafa and M. S., Jadin and M. S., Najib and Sabira, Khatun and Bari, Bifta Sama (2019) Analysis of EEG features for brain computer interface application. In: 5th International Conference on Electrical, Control and Computer Engineering (INECCE 2019), 29-30 July 2019 , Swiss Garden Kuantan. pp. 1-12.. (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa M. S., Jadin M. S., Najib Sabira, Khatun Bari, Bifta Sama Analysis of EEG features for brain computer interface application
description	Brain-Computer Interface (BCI) or Human-Machine Interface (HMI) is now becoming vital engineering and technology field which applies electroencephalography (EEG) signal to provide Assistive Technology (AT) to humans. This paper presents the analysis of EEG signals from various human cognitive or mental states to determine the suitable EEG features that can be employed in BCI field. Here, EEG features in term of power spectral density, log energy entropy and spectral centroid are selected to recognize human men- tal or cognitive state from 3 different exercises; i) solving math problem, ii) playing game and iii) do nothing (relax). The average power spectral density, average log energy entropy and average spectral centroid of EEG Alpha and Beta band for three mental exercises are calculated in order to determine the best features that can be used for BCI application. The results of the research shows that the EEG features in term of power spectral density, log energy en- tropy and spectral centroid can be used to indicate the change in cognitive states after exposing human to several cognitive exercises.
format	Conference or Workshop Item
author	Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa M. S., Jadin M. S., Najib Sabira, Khatun Bari, Bifta Sama
author_facet	Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa M. S., Jadin M. S., Najib Sabira, Khatun Bari, Bifta Sama
author_sort	Rashid, Mamunur
title	Analysis of EEG features for brain computer interface application
title_short	Analysis of EEG features for brain computer interface application
title_full	Analysis of EEG features for brain computer interface application
title_fullStr	Analysis of EEG features for brain computer interface application
title_full_unstemmed	Analysis of EEG features for brain computer interface application
title_sort	analysis of eeg features for brain computer interface application
publisher	Universiti Malaysia Pahang
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/26561/ http://umpir.ump.edu.my/id/eprint/26561/1/9.%20Analysis%20of%20EEG%20features%20for%20brain%20computer.pdf http://umpir.ump.edu.my/id/eprint/26561/2/9.1%20Analysis%20of%20EEG%20features%20for%20brain%20computer.pdf
first_indexed	2023-09-18T22:41:25Z
last_indexed	2023-09-18T22:41:25Z
_version_	1777416940783927296

Analysis of EEG features for brain computer interface application

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