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:
Online Access:http://umpir.ump.edu.my/id/eprint/22385/1/25.%20Analysis%20of%20EEG%20Features%20to%20Control%20Multiple%20Devices.pdf
http://umpir.ump.edu.my/id/eprint/22385/
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spelling my.ump.umpir.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 (Unpublished)
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
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/22385/1/25.%20Analysis%20of%20EEG%20Features%20to%20Control%20Multiple%20Devices.pdf
http://umpir.ump.edu.my/id/eprint/22385/
_version_ 1648741135903883264
score 13.160551