Brain–computer interface robotics for hand rehabilitation after stroke: A systematic review

Paul Dominick E. Baniqued, Emily C. Stanyer, Muhammad Awais, Ali Alazmani, Andrew E. Jackson, Mark A. Mon-Williams, Faisal Mushtaq, Raymond J. Holt

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Abstract

Background: Hand rehabilitation is core to helping stroke survivors regain activities of daily living. Recent studies have suggested that the use of electroencephalography-based brain-computer interfaces (BCI) can promote this process. Here, we report the first systematic examination of the literature on the use of BCI-robot systems for the rehabilitation of fine motor skills associated with hand movement and profile these systems from a technical and clinical perspective.

Methods: A search for January 2010–October 2019 articles using Ovid MEDLINE, Embase, PEDro, PsycINFO, IEEE Xplore and Cochrane Library databases was performed. The selection criteria included BCI-hand robotic systems for rehabilitation at different stages of development involving tests on healthy participants or people who have had a stroke. Data fields include those related to study design, participant characteristics, technical specifications of the system, and clinical outcome measures.

Results: 30 studies were identified as eligible for qualitative review and among these, 11 studies involved testing a BCI-hand robot on chronic and subacute stroke patients. Statistically significant improvements in motor assessment scores relative to controls were observed for three BCI-hand robot interventions. The degree of robot control for the majority of studies was limited to triggering the device to perform grasping or pinching movements using motor imagery. Most employed a combination of kinaesthetic and visual response via the robotic device and display screen, respectively, to match feedback to motor imagery.

Conclusion: 19 out of 30 studies on BCI-robotic systems for hand rehabilitation report systems at prototype or pre-clinical stages of development. We identified large heterogeneity in reporting and emphasise the need to develop a standard protocol for assessing technical and clinical outcomes so that the necessary evidence base on efficiency and efficacy can be developed.
Original languageEnglish
Article number15
JournalJournal of NeuroEngineering and Rehabilitation
Volume18
DOIs
Publication statusPublished - 23 Jan 2021

Keywords

  • Brain–computer interface
  • EEG
  • Motor imagery
  • Rehabilitation
  • Robotics
  • Stroke

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