Citation

Al-Quraishi, Maged S. and Tan, Wooi Haw and Elamvazuthi, Irraivan and Ooi, Chee Pun and Saad, Naufal M. and Al-Hiyali, Mohammed Isam and Abdul Karim, Hezerul and Azhar Ali, Syed Saad (2024) Cortical signals analysis to recognize intralimb mobility using modified RNN and various EEG quantities. Heliyon, 10 (9). e30406. ISSN 2405-8440

Text Cortical signals analysis to recognize intralimb mobility using modified RNN and various EEG quantities.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

Electroencephalogram (EEG) signals are critical in interpreting sensorimotor activities for predicting body movements. However, their efficacy in identifying intralimb movements, such as the dorsiflexion and plantar flexion of the foot, remains suboptimal. This study aims to explore whether various EEG signal quantities can effectively recognize intralimb movements to facilitate the development of Brain-Computer Interface (BCI) devices for foot rehabilitation. This research involved twenty-two healthy, right-handed participants. EEG data were collected using 21 electrodes positioned over the motor cortex, while two electromyography (EMG) electrodes recorded the onset of ankle joint movements. The study focused on analyzing slow cortical potential (SCP) and sensorimotor rhythms (SMR) in alpha and beta bands from the EEG. Five key features—fourth-order Autoregressive feature, variance, waveform length, standard deviation, and permutation entropy—were extracted. A modified Recurrent Neural Network (RNN) including Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms was developed for movement recognition. These were compared against conventional machine learning algorithms, including nonlinear Support Vector Machine (SVM) and k Nearest Neighbourhood (kNN) classifiers. The performance of the proposed models was assessed using two data schemes: withinsubject and across-subjects. The findings demonstrated that the GRU and LSTM models significantly outperformed traditional machine learning algorithms in recognizing different EEG signal quantities for intralimb movement. The study indicates that deep learning models, particularly GRU and LSTM, hold superior potential over standard machine learning techniques in identifying intralimb movements using EEG signals. Where the accuracies of LSTM for within and across subjects were 98.87 ± 1.80 % and 87.38 ± 0.86 % respectively. Whereas the accuracy of GRU within and across subjects were 99.18 ± 1.28 % and 86.44 ± 0.69 % respectively. This advancement could significantly benefit the development of BCI devices aimed at foot rehabilitation, suggesting a new avenue for enhancing physical therapy outcomes.

Item Type:	Article
Uncontrolled Keywords:	EEG, Intralimb movement, Deep leaning, Machine learning, Rehabilitation
Subjects:	H Social Sciences > HV Social pathology. Social and public welfare. Criminology Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	28 May 2024 08:21
Last Modified:	28 May 2024 08:21
URII:	http://shdl.mmu.edu.my/id/eprint/12456

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