Cortical signals analysis to recognize intralimb mobility using modified RNN and various EEG quantities

Maged S Al-Quraishi; Wooi Haw Tan; Irraivan Elamvazuthi; Chee Pun Ooi; Naufal M Saad; Mohammed Isam Al-Hiyali; H A Karim; Syed Saad Azhar Ali

doi:10.1016/j.heliyon.2024.e30406

Cortical signals analysis to recognize intralimb mobility using modified RNN and various EEG quantities

Heliyon. 2024 Apr 30;10(9):e30406. doi: 10.1016/j.heliyon.2024.e30406. eCollection 2024 May 15.

Authors

Maged S Al-Quraishi¹, Wooi Haw Tan², Irraivan Elamvazuthi³, Chee Pun Ooi², Naufal M Saad³, Mohammed Isam Al-Hiyali³, H A Karim², Syed Saad Azhar Ali^{1

4}

Affiliations

¹ Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, 31261, Saudi Arabia.
² Center of Digital Home, Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia.
³ Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 36210, Perak, Malaysia.
⁴ Aerospace Engineering Department, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, 31261, Saudi Arabia.

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: within-subject 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.

Keywords: Deep leaning; EEG; Intralimb movement; Machine learning; Rehabilitation.