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Investigation of electroencephalogram (EEG) sensor position for brain-controlled home automation

Electroencephalogram (EEG) signals are widely employed in Brain-Computer Interface (BCI) or Human–Machine Interface (HMI) technique to provide assistive technology that can be used by paralyzed or disabled people. BCI or HMI technique will let paralyzed people to operate home automation such as lamp...

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Main Authors: Norizam, Sulaiman, Ailis Aimylia, Hasim, Islam, Md Nahidul, Mahfuzah, Mustafa, Mohd Shawal, Jadin
Format: Conference or Workshop Item
Language:English
English
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/39460/1/Investigation%20of%20Electroencephalogram%20%28EEG%29%20Sensor%20Position%20for%20Brain.pdf
http://umpir.ump.edu.my/id/eprint/39460/2/Investigation%20of%20electroencephalogram%20%28EEG%29%20sensor%20position%20for%20brain-controlled%20home%20automation_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39460/
https://doi.org/10.1007/978-981-19-2095-0_40
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spelling my.ump.umpir.394602023-12-01T03:27:37Z http://umpir.ump.edu.my/id/eprint/39460/ Investigation of electroencephalogram (EEG) sensor position for brain-controlled home automation Norizam, Sulaiman Ailis Aimylia, Hasim Islam, Md Nahidul Mahfuzah, Mustafa Mohd Shawal, Jadin T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Electroencephalogram (EEG) signals are widely employed in Brain-Computer Interface (BCI) or Human–Machine Interface (HMI) technique to provide assistive technology that can be used by paralyzed or disabled people. BCI or HMI technique will let paralyzed people to operate home automation such as lamp, fan, television and other home appliances by using their brainwaves. Since BCI or HMI is constructed from various sensors for various measurement position, it is vital to know which EEG sensors are really contributed to the control of the device. Thus, this study is conducted to investigate the EEG sensors position in controlling device by analyzing public EEG datasets. The scopes of the study include the construction of Graphical User Interface (GUI) in MATLAB for each selected sensor position and the classification of the selected EEG features from each sensor position. To implement the investigation process, first, the public EEG signals from the specified sensor location are filtered using preprocessing technique to remove the noise or artifacts such eyes movement and power line noise. Next, the filtered EEG signals are split to Alpha and Beta power spectrum using Fast Fourier Transform (FFT) technique. Next, the unique features from EEG Alpha and Beta Power are extracted for classification process in term of average (mean), standard deviation and spectral centroid. Finally, the prototype model using Arduino microcontroller is developed to implement home appliance. The results of the study show that the selected EEG features from the EEG signal produced by EEG position at Frontal side of brain lobes (F7 and F8) and Parietal side of brain lobes (P3 and P4) able to be classified with 83% classification accuracy. The selected EEG features from the selected sensors position can be converted to machine code to control the home appliance successfully. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39460/1/Investigation%20of%20Electroencephalogram%20%28EEG%29%20Sensor%20Position%20for%20Brain.pdf pdf en http://umpir.ump.edu.my/id/eprint/39460/2/Investigation%20of%20electroencephalogram%20%28EEG%29%20sensor%20position%20for%20brain-controlled%20home%20automation_ABS.pdf Norizam, Sulaiman and Ailis Aimylia, Hasim and Islam, Md Nahidul and Mahfuzah, Mustafa and Mohd Shawal, Jadin (2022) Investigation of electroencephalogram (EEG) sensor position for brain-controlled home automation. In: Lecture Notes in Electrical Engineering; Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2021, 20 September 2021 , Gambang. pp. 471-484., 900 (277979). ISSN 1876-1100 ISBN 978-981192094-3 https://doi.org/10.1007/978-981-19-2095-0_40
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Norizam, Sulaiman
Ailis Aimylia, Hasim
Islam, Md Nahidul
Mahfuzah, Mustafa
Mohd Shawal, Jadin
Investigation of electroencephalogram (EEG) sensor position for brain-controlled home automation
description Electroencephalogram (EEG) signals are widely employed in Brain-Computer Interface (BCI) or Human–Machine Interface (HMI) technique to provide assistive technology that can be used by paralyzed or disabled people. BCI or HMI technique will let paralyzed people to operate home automation such as lamp, fan, television and other home appliances by using their brainwaves. Since BCI or HMI is constructed from various sensors for various measurement position, it is vital to know which EEG sensors are really contributed to the control of the device. Thus, this study is conducted to investigate the EEG sensors position in controlling device by analyzing public EEG datasets. The scopes of the study include the construction of Graphical User Interface (GUI) in MATLAB for each selected sensor position and the classification of the selected EEG features from each sensor position. To implement the investigation process, first, the public EEG signals from the specified sensor location are filtered using preprocessing technique to remove the noise or artifacts such eyes movement and power line noise. Next, the filtered EEG signals are split to Alpha and Beta power spectrum using Fast Fourier Transform (FFT) technique. Next, the unique features from EEG Alpha and Beta Power are extracted for classification process in term of average (mean), standard deviation and spectral centroid. Finally, the prototype model using Arduino microcontroller is developed to implement home appliance. The results of the study show that the selected EEG features from the EEG signal produced by EEG position at Frontal side of brain lobes (F7 and F8) and Parietal side of brain lobes (P3 and P4) able to be classified with 83% classification accuracy. The selected EEG features from the selected sensors position can be converted to machine code to control the home appliance successfully.
format Conference or Workshop Item
author Norizam, Sulaiman
Ailis Aimylia, Hasim
Islam, Md Nahidul
Mahfuzah, Mustafa
Mohd Shawal, Jadin
author_facet Norizam, Sulaiman
Ailis Aimylia, Hasim
Islam, Md Nahidul
Mahfuzah, Mustafa
Mohd Shawal, Jadin
author_sort Norizam, Sulaiman
title Investigation of electroencephalogram (EEG) sensor position for brain-controlled home automation
title_short Investigation of electroencephalogram (EEG) sensor position for brain-controlled home automation
title_full Investigation of electroencephalogram (EEG) sensor position for brain-controlled home automation
title_fullStr Investigation of electroencephalogram (EEG) sensor position for brain-controlled home automation
title_full_unstemmed Investigation of electroencephalogram (EEG) sensor position for brain-controlled home automation
title_sort investigation of electroencephalogram (eeg) sensor position for brain-controlled home automation
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/39460/1/Investigation%20of%20Electroencephalogram%20%28EEG%29%20Sensor%20Position%20for%20Brain.pdf
http://umpir.ump.edu.my/id/eprint/39460/2/Investigation%20of%20electroencephalogram%20%28EEG%29%20sensor%20position%20for%20brain-controlled%20home%20automation_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39460/
https://doi.org/10.1007/978-981-19-2095-0_40
_version_ 1822923895061086208
score 13.232414

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