Detection of Lower Limb Movements using Sensorimotor Rhythms

Maged S. Al-Quraishi; Irraivan Elamvazuthi; Tong Boon Tang; Muhammad Al-Qurishi; S. Parasuraman; Alberto Borboni

doi:10.1109/ICIAS49414.2021.9642696

Detection of Lower Limb Movements using Sensorimotor Rhythms

Maged S. Al-Quraishi
, Irraivan Elamvazuthi^*
, Tong Boon Tang
, Muhammad Al-Qurishi
, S. Parasuraman
, Alberto Borboni

^*Corresponding author for this work

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

4 Scopus citations

Abstract

In contrast to other brain imaging methods, electroencephalography (EEG) has become a feasible method for investigating brain activity and is an interesting modality for brain-machine interfaces (BMIs) due to its portability and high temporal resolution. In this work, sensorimotor rhythms (SMR) signal was utilized to classify ankle joint movements. To achieve this goal the EEG signal in the motor cortex area was measured using 21 electrodes during the motor execution task of ankle joint movements. The event-related (de)synchronization (ERD/ ERS) technique was utilized to quantify the event-related in relation to EEG power changes. Inter and intralimb ankle movements were detected and classified. The results show interlimb movements can be recognized better than intralimb movements. Where the average classification accuracy of the interlimb movements was 89.44 ± 10.26% and 84.83 ± 13.65% for the intralimb movements.

Original language	English
Title of host publication	International Conference on Intelligent and Advanced Systems
Subtitle of host publication	Enhance the Present for a Sustainable Future, ICIAS 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728176666
DOIs	https://doi.org/10.1109/ICIAS49414.2021.9642696
State	Published - 2021
Externally published	Yes

Publication series

Name	International Conference on Intelligent and Advanced Systems: Enhance the Present for a Sustainable Future, ICIAS 2021

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

BMI
EEG
ERD
classification
movements

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials
Instrumentation

Access to Document

10.1109/ICIAS49414.2021.9642696

Cite this

Al-Quraishi, M. S., Elamvazuthi, I., Tang, T. B., Al-Qurishi, M., Parasuraman, S., & Borboni, A. (2021). Detection of Lower Limb Movements using Sensorimotor Rhythms. In International Conference on Intelligent and Advanced Systems: Enhance the Present for a Sustainable Future, ICIAS 2021 (International Conference on Intelligent and Advanced Systems: Enhance the Present for a Sustainable Future, ICIAS 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIAS49414.2021.9642696

Al-Quraishi, Maged S. ; Elamvazuthi, Irraivan ; Tang, Tong Boon et al. / Detection of Lower Limb Movements using Sensorimotor Rhythms. International Conference on Intelligent and Advanced Systems: Enhance the Present for a Sustainable Future, ICIAS 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (International Conference on Intelligent and Advanced Systems: Enhance the Present for a Sustainable Future, ICIAS 2021).

@inproceedings{66734bbf0eff4df193f2f6d77c237c2a,

title = "Detection of Lower Limb Movements using Sensorimotor Rhythms",

abstract = "In contrast to other brain imaging methods, electroencephalography (EEG) has become a feasible method for investigating brain activity and is an interesting modality for brain-machine interfaces (BMIs) due to its portability and high temporal resolution. In this work, sensorimotor rhythms (SMR) signal was utilized to classify ankle joint movements. To achieve this goal the EEG signal in the motor cortex area was measured using 21 electrodes during the motor execution task of ankle joint movements. The event-related (de)synchronization (ERD/ ERS) technique was utilized to quantify the event-related in relation to EEG power changes. Inter and intralimb ankle movements were detected and classified. The results show interlimb movements can be recognized better than intralimb movements. Where the average classification accuracy of the interlimb movements was 89.44 ± 10.26\% and 84.83 ± 13.65\% for the intralimb movements.",

keywords = "BMI, EEG, ERD, classification, movements",

author = "Al-Quraishi, \{Maged S.\} and Irraivan Elamvazuthi and Tang, \{Tong Boon\} and Muhammad Al-Qurishi and S. Parasuraman and Alberto Borboni",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.",

year = "2021",

doi = "10.1109/ICIAS49414.2021.9642696",

language = "English",

series = "International Conference on Intelligent and Advanced Systems: Enhance the Present for a Sustainable Future, ICIAS 2021",

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

booktitle = "International Conference on Intelligent and Advanced Systems",

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}

Al-Quraishi, MS, Elamvazuthi, I, Tang, TB, Al-Qurishi, M, Parasuraman, S & Borboni, A 2021, Detection of Lower Limb Movements using Sensorimotor Rhythms. in International Conference on Intelligent and Advanced Systems: Enhance the Present for a Sustainable Future, ICIAS 2021. International Conference on Intelligent and Advanced Systems: Enhance the Present for a Sustainable Future, ICIAS 2021, Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICIAS49414.2021.9642696

Detection of Lower Limb Movements using Sensorimotor Rhythms. / Al-Quraishi, Maged S.; Elamvazuthi, Irraivan; Tang, Tong Boon et al.
International Conference on Intelligent and Advanced Systems: Enhance the Present for a Sustainable Future, ICIAS 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (International Conference on Intelligent and Advanced Systems: Enhance the Present for a Sustainable Future, ICIAS 2021).

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

TY - GEN

T1 - Detection of Lower Limb Movements using Sensorimotor Rhythms

AU - Al-Quraishi, Maged S.

AU - Elamvazuthi, Irraivan

AU - Tang, Tong Boon

AU - Al-Qurishi, Muhammad

AU - Parasuraman, S.

AU - Borboni, Alberto

PY - 2021

Y1 - 2021

N2 - In contrast to other brain imaging methods, electroencephalography (EEG) has become a feasible method for investigating brain activity and is an interesting modality for brain-machine interfaces (BMIs) due to its portability and high temporal resolution. In this work, sensorimotor rhythms (SMR) signal was utilized to classify ankle joint movements. To achieve this goal the EEG signal in the motor cortex area was measured using 21 electrodes during the motor execution task of ankle joint movements. The event-related (de)synchronization (ERD/ ERS) technique was utilized to quantify the event-related in relation to EEG power changes. Inter and intralimb ankle movements were detected and classified. The results show interlimb movements can be recognized better than intralimb movements. Where the average classification accuracy of the interlimb movements was 89.44 ± 10.26% and 84.83 ± 13.65% for the intralimb movements.

AB - In contrast to other brain imaging methods, electroencephalography (EEG) has become a feasible method for investigating brain activity and is an interesting modality for brain-machine interfaces (BMIs) due to its portability and high temporal resolution. In this work, sensorimotor rhythms (SMR) signal was utilized to classify ankle joint movements. To achieve this goal the EEG signal in the motor cortex area was measured using 21 electrodes during the motor execution task of ankle joint movements. The event-related (de)synchronization (ERD/ ERS) technique was utilized to quantify the event-related in relation to EEG power changes. Inter and intralimb ankle movements were detected and classified. The results show interlimb movements can be recognized better than intralimb movements. Where the average classification accuracy of the interlimb movements was 89.44 ± 10.26% and 84.83 ± 13.65% for the intralimb movements.

KW - BMI

KW - EEG

KW - ERD

KW - classification

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DO - 10.1109/ICIAS49414.2021.9642696

M3 - Conference contribution

AN - SCOPUS:85124162289

T3 - International Conference on Intelligent and Advanced Systems: Enhance the Present for a Sustainable Future, ICIAS 2021

BT - International Conference on Intelligent and Advanced Systems

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Al-Quraishi MS, Elamvazuthi I, Tang TB, Al-Qurishi M, Parasuraman S, Borboni A. Detection of Lower Limb Movements using Sensorimotor Rhythms. In International Conference on Intelligent and Advanced Systems: Enhance the Present for a Sustainable Future, ICIAS 2021. Institute of Electrical and Electronics Engineers Inc. 2021. (International Conference on Intelligent and Advanced Systems: Enhance the Present for a Sustainable Future, ICIAS 2021). doi: 10.1109/ICIAS49414.2021.9642696

Detection of Lower Limb Movements using Sensorimotor Rhythms

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this