Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)

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An Experimental Study: ICA-Based Sensorimotor Rhythms Detection in ALS Patients for BCI Applications

Authors
Vahid Gerami Oskouei1, Ali Naderi Saatlo1, *, Sobhan Sheykhivand2, Ali Farzamnia3, *
1Department of Electrical-Electronics Engineering Urmia Branch, Islamic Azad University Urmia, Urmia, Iran
2Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
3Faculty of Engineering, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
*Corresponding author. Email: a.naderi@iaurmia.ac.ir
*Corresponding author. Email: alifarzamnia@ums.edu.my
Corresponding Authors
Ali Naderi Saatlo, Ali Farzamnia
Available Online 27 December 2022.
DOI
10.2991/978-94-6463-094-7_12How to use a DOI?
Keywords
Sensorimotor Rhythm; EEG; ALS patients; independent component analysis; EEGLAB; MATLAB; BCI
Abstract

Independent Component Analysis (ICA) is used in this paper to study the brain signals of patients with Amyotrophic Lateral Sclerosis (ALS) in the EEGLAB toolbox. Electroencephalography (EEG) signals are recorded in unipolar mode, wherein the Cz electrode is selected as the reference electrode. Therefore, it is expected that the independent components of brain signals can be analyzed while the patient moves his/her hands, and the event-related potential of the process can be separated as an independent component using the maximum value of its variance. The results show that by using ICA in analyzing the brain signals during hand movement, different brain activities that are related to the moving process can be separated. One of the major problems in analyzing brain activity is feature extraction. In this study, the absolute value of the amplitude, variance, θ (3–8 Hz) and α (8–13 Hz) band average power and the power of frequency components inα and θ bands are considered as the features. The results show that the features of the independent components present more accurate diagnosis compared to the brain signals characteristics. These features can be used in brain-computer interface systems to determine SMR in patients with ALS.

Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)
Series
Atlantis Highlights in Computer Sciences
Publication Date
27 December 2022
ISBN
10.2991/978-94-6463-094-7_12
ISSN
2589-4900
DOI
10.2991/978-94-6463-094-7_12How to use a DOI?
Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Vahid Gerami Oskouei
AU  - Ali Naderi Saatlo
AU  - Sobhan Sheykhivand
AU  - Ali Farzamnia
PY  - 2022
DA  - 2022/12/27
TI  - An Experimental Study: ICA-Based Sensorimotor Rhythms Detection in ALS Patients for BCI Applications
BT  - Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)
PB  - Atlantis Press
SP  - 145
EP  - 155
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-094-7_12
DO  - 10.2991/978-94-6463-094-7_12
ID  - Oskouei2022
ER  -