Why we should systematically assess, control and report somatosensory impairments in BCI-based motor rehabilitation after stroke studies
- PMID: 33039972
- PMCID: PMC7551360
- DOI: 10.1016/j.nicl.2020.102417
Why we should systematically assess, control and report somatosensory impairments in BCI-based motor rehabilitation after stroke studies
Abstract
The neuronal loss resulting from stroke forces 80% of the patients to undergo motor rehabilitation, for which Brain-Computer Interfaces (BCIs) and NeuroFeedback (NF) can be used. During the rehabilitation, when patients attempt or imagine performing a movement, BCIs/NF provide them with a synchronized sensory (e.g., tactile) feedback based on their sensorimotor-related brain activity that aims at fostering brain plasticity and motor recovery. The co-activation of ascending (i.e., somatosensory) and descending (i.e., motor) networks indeed enables significant functional motor improvement, together with significant sensorimotor-related neurophysiological changes. Somatosensory abilities are essential for patients to perceive the feedback provided by the BCI system. Thus, somatosensory impairments may significantly alter the efficiency of BCI-based motor rehabilitation. In order to precisely understand and assess the impact of somatosensory impairments, we first review the literature on post-stroke BCI-based motor rehabilitation (14 randomized clinical trials). We show that despite the central role that somatosensory abilities play on BCI-based motor rehabilitation post-stroke, the latter are rarely reported and used as inclusion/exclusion criteria in the literature on the matter. We then argue that somatosensory abilities have repeatedly been shown to influence the motor rehabilitation outcome, in general. This stresses the importance of also considering them and reporting them in the literature in BCI-based rehabilitation after stroke, especially since half of post-stroke patients suffer from somatosensory impairments. We argue that somatosensory abilities should systematically be assessed, controlled and reported if we want to precisely assess the influence they have on BCI efficiency. Not doing so could result in the misinterpretation of reported results, while doing so could improve (1) our understanding of the mechanisms underlying motor recovery (2) our ability to adapt the therapy to the patients' impairments and (3) our comprehension of the between-subject and between-study variability of therapeutic outcomes mentioned in the literature.
Keywords: Brain-computer interfaces; Motor recovery; Neurofeedback; Somatosensory impairments; Stroke rehabilitation.
Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.
Conflict of interest statement
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Figures

References
-
- Andersen G., Vestergaard K., Ingeman-Nielsen M., Jensen T.S. Incidence of central post-stroke pain. Pain. 1995;61:187–193. - PubMed
-
- Ang K.K., Guan C., Chua K.S.G., Ang B.T., Kuah C., Wang C., Phua K.S., Chin Z.Y., Zhang H. Engineering in Medicine and Biology Society. EMBC 2009. Annual International Conference of the IEEE. IEEE; 2009. A clinical study of motor imagery-based brain-computer interface for upper limb robotic rehabilitation; pp. 5981–5984. - PubMed
-
- Ang K.K., Guan C., Chua K.S.G., Ang B.T., Kuah C., Wang C., Phua K.S., Chin Z.Y., Zhang H. Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE. IEEE; 2010. Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback; pp. 5549–5552. - PubMed
-
- Ang K.K., Guan C., Phua K.S., Wang C., Zhou L., Tang K.Y., Joseph E., Gopal J., Kuah C.W.K., Chua K.S.G. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke. Front. Neuroeng. 2014;7:30. - PMC - PubMed
-
- Ang K.K., Chua K.S.G., Phua K.S., Wang C., Chin Z.Y., Kuah C.W.K., Low W., Guan C. A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke. Clinical EEG Neurosci. 2015;46:310–320. - PubMed
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Medical