Analysis of EEG Features to Control Multiple Devices

Brain-Computer Interface (BCI) or Human-Machine Interface (HMI) now becoming vital engineering and technology field which applying EEG technologies to provide Assistive Technology (AT) to humans. This paper presents the analysis of EEG signals from various human cognitive or mental states to determi...

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Main Authors:	Rashid, Mamunur, Norizam, Sulaiman, Mahfuzah, Mustafa, M. S., Jadin, M. S., Najib
Format:	Conference or Workshop Item
Language:	English
Published:	Universiti Malaysia Pahang 2018
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/22385/ http://umpir.ump.edu.my/id/eprint/22385/1/25.%20Analysis%20of%20EEG%20Features%20to%20Control%20Multiple%20Devices.pdf

id	ump-22385
recordtype	eprints
spelling	ump-223852019-10-09T03:10:48Z http://umpir.ump.edu.my/id/eprint/22385/ Analysis of EEG Features to Control Multiple Devices Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa M. S., Jadin M. S., Najib TK Electrical engineering. Electronics Nuclear engineering Brain-Computer Interface (BCI) or Human-Machine Interface (HMI) now becoming vital engineering and technology field which applying EEG technologies 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 to control multiple devices. Here, EEG features in term of average of power spectrum, standard deviation of power spectrum and spectral centroid of power spectrum are selected to recognize human mental or cognitive state from 3 difference exercises; i) solving math problem, ii) Playing game and iii) do nothing (relax). We have calculated average power spectrum, average standard deviation of power spectrum and average spectral centroid of power spectrum of alpha and beta band for three mental exercises. Universiti Malaysia Pahang 2018-07 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/22385/1/25.%20Analysis%20of%20EEG%20Features%20to%20Control%20Multiple%20Devices.pdf Rashid, Mamunur and Norizam, Sulaiman and Mahfuzah, Mustafa and M. S., Jadin and M. S., Najib (2018) Analysis of EEG Features to Control Multiple Devices. In: National Conference for Postgraduate Research (NCON-PGR 2018), 28-29 August 2018 , Universiti Malaysia Pahang, Gambang, Pahang. pp. 1-7.. (Unpublished)
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 Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa M. S., Jadin M. S., Najib Analysis of EEG Features to Control Multiple Devices
description	Brain-Computer Interface (BCI) or Human-Machine Interface (HMI) now becoming vital engineering and technology field which applying EEG technologies 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 to control multiple devices. Here, EEG features in term of average of power spectrum, standard deviation of power spectrum and spectral centroid of power spectrum are selected to recognize human mental or cognitive state from 3 difference exercises; i) solving math problem, ii) Playing game and iii) do nothing (relax). We have calculated average power spectrum, average standard deviation of power spectrum and average spectral centroid of power spectrum of alpha and beta band for three mental exercises.
format	Conference or Workshop Item
author	Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa M. S., Jadin M. S., Najib
author_facet	Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa M. S., Jadin M. S., Najib
author_sort	Rashid, Mamunur
title	Analysis of EEG Features to Control Multiple Devices
title_short	Analysis of EEG Features to Control Multiple Devices
title_full	Analysis of EEG Features to Control Multiple Devices
title_fullStr	Analysis of EEG Features to Control Multiple Devices
title_full_unstemmed	Analysis of EEG Features to Control Multiple Devices
title_sort	analysis of eeg features to control multiple devices
publisher	Universiti Malaysia Pahang
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/22385/ http://umpir.ump.edu.my/id/eprint/22385/1/25.%20Analysis%20of%20EEG%20Features%20to%20Control%20Multiple%20Devices.pdf
first_indexed	2023-09-18T22:33:18Z
last_indexed	2023-09-18T22:33:18Z
_version_	1777416429499318272

Analysis of EEG Features to Control Multiple Devices

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