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.
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 strokepatients. 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.
Entities:
Keywords:
Brain–computer interface; EEG; Motor imagery; Rehabilitation; Robotics; Stroke
Authors: T S Grummett; R E Leibbrandt; T W Lewis; D DeLosAngeles; D M W Powers; J O Willoughby; K J Pope; S P Fitzgibbon Journal: Physiol Meas Date: 2015-05-28 Impact factor: 2.833
Authors: Charles Damian Holmes; Mark Wronkiewicz; Thane Somers; Jenny Liu; Elizabeth Russell; DoHyun Kim; Colleen Rhoades; Jason Dunkley; David Bundy; Elad Galboa; Eric Leuthardt Journal: Conf Proc IEEE Eng Med Biol Soc Date: 2012
Authors: Leigh R Hochberg; Mijail D Serruya; Gerhard M Friehs; Jon A Mukand; Maryam Saleh; Abraham H Caplan; Almut Branner; David Chen; Richard D Penn; John P Donoghue Journal: Nature Date: 2006-07-13 Impact factor: 49.962
Authors: Jack Brookes; Faisal Mushtaq; Earle Jamieson; Aaron J Fath; Geoffrey Bingham; Peter Culmer; Richard M Wilkie; Mark Mon-Williams Journal: PLoS One Date: 2020-05-20 Impact factor: 3.240
Authors: María A Cervera; Surjo R Soekadar; Junichi Ushiba; José Del R Millán; Meigen Liu; Niels Birbaumer; Gangadhar Garipelli Journal: Ann Clin Transl Neurol Date: 2018-03-25 Impact factor: 4.511
Authors: Alexander B Remsik; Peter L E van Kan; Shawna Gloe; Klevest Gjini; Leroy Williams; Veena Nair; Kristin Caldera; Justin C Williams; Vivek Prabhakaran Journal: Front Hum Neurosci Date: 2022-07-06 Impact factor: 3.473
Authors: Emília M G S Silva; Ledycnarf J Holanda; Gustavo K B Coutinho; Fernanda S Andrade; Gabriel I S Nascimento; Danilo A P Nagem; Ricardo A de M Valentim; Ana Raquel Lindquist Journal: Front Neurosci Date: 2021-06-24 Impact factor: 4.677
Authors: Elena V Bobrova; Varvara V Reshetnikova; Elena A Vershinina; Alexander A Grishin; Pavel D Bobrov; Alexander A Frolov; Yury P Gerasimenko Journal: Brain Sci Date: 2021-06-25
Authors: Ahmed Mohammed Balkhoyor; Muhammad Awais; Shekhar Biyani; Alexandre Schaefer; Matt Craddock; Olivia Jones; Michael Manogue; Mark A Mon-Williams; Faisal Mushtaq Journal: BMJ Surg Interv Health Technol Date: 2020-11-09