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Evaluation of Neurofeedback Therapy for Treatment of Central Neuropathic Pain in Paraplegic Patients Using Deep Learning

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Abstract

Neurofeedback training was proven to be applicable for treatment of various chronic pains including central neuropathic pain (CNP) owing to the ability of the brain to self-regulate its activities. This study reports on the performance of deep learning features-based brain–computer interface (BCI) in evaluating the efficacy of neurofeedback training carried out for treating paraplegic patients for CNP. Motor-imagery EEG (MI-EEG) data, in this study, was obtained from able-bodied (AB) participants, paraplegic patients with CNP (PPW), and paraplegic patients without CNP (PP). ResNet50 network was applied for obtaining deep learning features from MI-EEG; support vector machine (SVM) used for classifying obtained deep learning features. Following the SVM-based BCI was developed, new MI-EEG data obtained from the paraplegic patients who have completed neurofeedback sessions in order to evaluate the performance of SVM-based BCI. Results of this study demonstrate that the prediction accuracy of the developed BCI changes with MI task, groups of participants from which MI-EEG was obtained, and EEG channels combination. The highest efficiency of the BCI, with an accuracy of 99.94 ± 0.05 %, was gained with MI-EEG of the feet, AB vs PP group, and combination C4 over the sensorimotor cortex. In conclusion, deep learning features-based BCI can be applied efficiently to evaluate the efficacy of neurofeedback training applied for treating paraplegic patients for CNP. This study provides significant implications for improving the efficiency of MI-EEG-based BCIs.

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Availability of Data and Materials

The data that support the findings of this study are available on request from the corresponding author M.G.S. The data are not publicly available due to restrictions written down in informed consent obtained from the participants.

Code Availability

The code used to analyze the data of this study is available on request from the corresponding author M.G.S.

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Acknowledgements

We thank Dr. Purcell and Dr. Mclean, Southern General Hospital, Glasgow, for choosing participants of the study and to all participants for taking part. We thank Dr. Muhammad Abul Hasan and Dr. A. Vuckovic as well for their efforts in data processing. This work has been partially supported by the MRC grant G0902257/1, the Glasgow Research Partnership in Engineering, NED University of Pakistan PhD scholarship, and Ministry of Higher Education and Scientific Research of the Republic of Yemen.

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Authors and Affiliations

  1. Department of Electronics, Instrumentation and Biotechnical Systems, Vladimir State University named after Alexander and Nikolay Stoletovs, Vladimir, Russia

    Mohammed Gamil Mohammed Saif & Lyudmila Sushkova

  2. Queen Elizabeth National Spinal Injuries Unit, Southern General Hospital, Glasgow, UK

    Matthew Fraser

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  1. Mohammed Gamil Mohammed Saif
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  2. Lyudmila Sushkova
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Correspondence to Mohammed Gamil Mohammed Saif.

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This research study is related to the master’s thesis of the corresponding author, and all authors declare that there is no conflict of interests regarding publication of this paper.

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Informed consent was obtained from all participants, and the ethical approval to the trials was granted from the University of Strathclyde Ethical Committee and from National Health Service Ethical Committee for Greater Glasgow and Clyde.

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Saif, M.G.M., Sushkova, L. & Fraser, M. Evaluation of Neurofeedback Therapy for Treatment of Central Neuropathic Pain in Paraplegic Patients Using Deep Learning. SN COMPUT. SCI. 4, 598 (2023). https://doi.org/10.1007/s42979-023-02183-4

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