Efficacy evaluation of neurofeedback applied for treatment of central neuropathic pain using machine learning

Saif, M. G. M., Hassan, M. A. and Vuckovic, A. (2021) Efficacy evaluation of neurofeedback applied for treatment of central neuropathic pain using machine learning. SN Applied Sciences, 3(1), 58. (doi: 10.1007/s42452-020-04035-9)

Abstract

Brain-computer interface (BCI) is believed to be the translator of brain signals into actions based on the model, built on the machine learning (ML) algorithms, incorporated in it. This study reports on the performance of various ML algorithms in evaluating efficacy of neurofeedback applied for treatment of central neuropathic pain (CNP). In the first phase of this study, we applied different ML algorithms for classification of electroencephalography (EEG) patterns, associated with CNP, obtained from three groups of participants, during imagined movement of their limbs, named as able-bodied (AB), paraplegic patients with (PWP) and without (PNP) neuropathic pain. In the second phase, we tested the accuracy of BCI-classifier by applying new EEG data obtained from PWP participants who have completed neurofeedback training provided for the management of pain. Support vector Machine (SVM) algorithm gained higher accuracy, with all groups, than the other classifiers. However, the highest classification accuracy of 99 ± 0.49% was obtained with the right hand motor imagery of (AB vs PWP) group and 61 electrodes. In Conclusion, SVM based BCI-classifier achieved high accuracy in evaluating efficacy of neurofeedback applied for treatment of CNP. Results of this study show that the accuracy of BCI changes with ML algorithm, electrodes combinations, and training data set.

• Item Type: Articles
• Keywords: Electroencephalography, neuropathic pain, neurofeedback training, motor imagery, EEG classification, paraplegic patients, BCI.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Vuckovic, Dr Aleksandra
• Authors: Saif, M. G. M., Hassan, M. A., and Vuckovic, A.
• College/School: College of Science and Engineering > School of Engineering > Biomedical Engineering
• Journal Name: SN Applied Sciences
• Publisher: Springer
• ISSN: 2523-3963
• ISSN (Online): 2523-3971
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in SN Applied Sciences 3(1):58
• Publisher Policy: Reproduced under a Creative Commons licence