Controlling Wheelchair Using EEG Signal and Machine Learning

M. Alseddiqi; M. Almahmood; U. Baroudi; A. Alzaydi

doi:10.1109/SSD58187.2023.10411202

Controlling Wheelchair Using EEG Signal and Machine Learning

M. Alseddiqi^*
, M. Almahmood
, U. Baroudi
, A. Alzaydi

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

With technology evolving these days, paralyzed patients have found solutions that eased their live a lot. Electrical wheelchairs are commercialized which help patients to move around effortlessly without any assistance. Robotic arms are being commercialized as well and are being connected to the human brain directly for commands. However, extremely paralyzed patients that cannot move any part of their bodies need to feel self-dependent and find something that can ease their lives. This work aims to deliver a wheelchair that can move based on the brain signals of the patient. The brain signals are recorded by an EEG sensor from EMOTIV. There will be a Brain-Computer interface to analyze the brain signals and translate them to commands between the patient's brain and the computer. The goal of this study is to move the wheelchair freely based on the patient's intention (i.e. forward, backward, right or left) by building an algorithm that compares a trained set of data from the patient's brain with the live data that is recorded by the EEG sensor. The outcomes indicated the possibility of BCI-controlled systems being used in complex daily tasks and showed that the suggested BCI could offer satisfactory control accuracy for the wheelchair.

Original language	English
Title of host publication	2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	898-905
Number of pages	8
ISBN (Electronic)	9798350332568
DOIs	https://doi.org/10.1109/SSD58187.2023.10411202
State	Published - 2023
Event	20th International Multi-Conference on Systems, Signals and Devices, SSD 2023 - Mahdia, Tunisia Duration: 20 Feb 2023 → 23 Feb 2023

Publication series

Name	2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

Conference

Conference	20th International Multi-Conference on Systems, Signals and Devices, SSD 2023
Country/Territory	Tunisia
City	Mahdia
Period	20/02/23 → 23/02/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

BCI
EEG
KNN
Machine Learning

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Computer Networks and Communications
Information Systems
Signal Processing
Health Informatics
Instrumentation

Access to Document

10.1109/SSD58187.2023.10411202

Cite this

Alseddiqi, M., Almahmood, M., Baroudi, U., & Alzaydi, A. (2023). Controlling Wheelchair Using EEG Signal and Machine Learning. In 2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023 (pp. 898-905). (2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSD58187.2023.10411202

@inproceedings{23eb11d1295c4a72987476d3793ebb9c,

title = "Controlling Wheelchair Using EEG Signal and Machine Learning",

abstract = "With technology evolving these days, paralyzed patients have found solutions that eased their live a lot. Electrical wheelchairs are commercialized which help patients to move around effortlessly without any assistance. Robotic arms are being commercialized as well and are being connected to the human brain directly for commands. However, extremely paralyzed patients that cannot move any part of their bodies need to feel self-dependent and find something that can ease their lives. This work aims to deliver a wheelchair that can move based on the brain signals of the patient. The brain signals are recorded by an EEG sensor from EMOTIV. There will be a Brain-Computer interface to analyze the brain signals and translate them to commands between the patient's brain and the computer. The goal of this study is to move the wheelchair freely based on the patient's intention (i.e. forward, backward, right or left) by building an algorithm that compares a trained set of data from the patient's brain with the live data that is recorded by the EEG sensor. The outcomes indicated the possibility of BCI-controlled systems being used in complex daily tasks and showed that the suggested BCI could offer satisfactory control accuracy for the wheelchair.",

keywords = "BCI, EEG, KNN, Machine Learning",

author = "M. Alseddiqi and M. Almahmood and U. Baroudi and A. Alzaydi",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023 ; Conference date: 20-02-2023 Through 23-02-2023",

year = "2023",

doi = "10.1109/SSD58187.2023.10411202",

language = "English",

series = "2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "898--905",

booktitle = "2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023",

address = "United States",

}

Alseddiqi, M, Almahmood, M, Baroudi, U & Alzaydi, A 2023, Controlling Wheelchair Using EEG Signal and Machine Learning. in 2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023. 2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023, Institute of Electrical and Electronics Engineers Inc., pp. 898-905, 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023, Mahdia, Tunisia, 20/02/23. https://doi.org/10.1109/SSD58187.2023.10411202

Controlling Wheelchair Using EEG Signal and Machine Learning. / Alseddiqi, M.; Almahmood, M.; Baroudi, U. et al.
2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 898-905 (2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Controlling Wheelchair Using EEG Signal and Machine Learning

AU - Alseddiqi, M.

AU - Almahmood, M.

AU - Baroudi, U.

AU - Alzaydi, A.

PY - 2023

Y1 - 2023

N2 - With technology evolving these days, paralyzed patients have found solutions that eased their live a lot. Electrical wheelchairs are commercialized which help patients to move around effortlessly without any assistance. Robotic arms are being commercialized as well and are being connected to the human brain directly for commands. However, extremely paralyzed patients that cannot move any part of their bodies need to feel self-dependent and find something that can ease their lives. This work aims to deliver a wheelchair that can move based on the brain signals of the patient. The brain signals are recorded by an EEG sensor from EMOTIV. There will be a Brain-Computer interface to analyze the brain signals and translate them to commands between the patient's brain and the computer. The goal of this study is to move the wheelchair freely based on the patient's intention (i.e. forward, backward, right or left) by building an algorithm that compares a trained set of data from the patient's brain with the live data that is recorded by the EEG sensor. The outcomes indicated the possibility of BCI-controlled systems being used in complex daily tasks and showed that the suggested BCI could offer satisfactory control accuracy for the wheelchair.

AB - With technology evolving these days, paralyzed patients have found solutions that eased their live a lot. Electrical wheelchairs are commercialized which help patients to move around effortlessly without any assistance. Robotic arms are being commercialized as well and are being connected to the human brain directly for commands. However, extremely paralyzed patients that cannot move any part of their bodies need to feel self-dependent and find something that can ease their lives. This work aims to deliver a wheelchair that can move based on the brain signals of the patient. The brain signals are recorded by an EEG sensor from EMOTIV. There will be a Brain-Computer interface to analyze the brain signals and translate them to commands between the patient's brain and the computer. The goal of this study is to move the wheelchair freely based on the patient's intention (i.e. forward, backward, right or left) by building an algorithm that compares a trained set of data from the patient's brain with the live data that is recorded by the EEG sensor. The outcomes indicated the possibility of BCI-controlled systems being used in complex daily tasks and showed that the suggested BCI could offer satisfactory control accuracy for the wheelchair.

KW - BCI

KW - EEG

KW - KNN

KW - Machine Learning

UR - https://www.scopus.com/pages/publications/85185838563

U2 - 10.1109/SSD58187.2023.10411202

DO - 10.1109/SSD58187.2023.10411202

M3 - Conference contribution

AN - SCOPUS:85185838563

T3 - 2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

SP - 898

EP - 905

BT - 2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

Y2 - 20 February 2023 through 23 February 2023

ER -

Alseddiqi M, Almahmood M, Baroudi U , Alzaydi A. Controlling Wheelchair Using EEG Signal and Machine Learning. In 2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 898-905. (2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023). doi: 10.1109/SSD58187.2023.10411202

Controlling Wheelchair Using EEG Signal and Machine Learning

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this