Restoring function in paralyzed limbs using EEG

Diab, Maha S.; Hussain, Zainab; Mahmoud, Soliman A.

doi:10.1109/mwscas.2016.7870163

Cited by 5 publications

(7 citation statements)

References 5 publications

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“…A final sample of 30 studies were included in the qualitative review. Among the 30 studies, 11 [75][76][77][78][79][80][81][82][83][84][85] were involved in testing the BCI-hand robot system on chronic and subacute stroke patients ( [75,80] are RCTs) while the rest involved testing on healthy participants [86][87][88][89][90][91][92][93][94][95][96][97][98][99][100][101][102][103][104]. Table 2 shows a summary of the relevant data fields extracted from these studies.…”

Section: Search Resultsmentioning

confidence: 99%

“…In addition to MI, other strategies for robotic control were reported. Diab et al [103] and King et al [104] both facilitated the movements of their respective orthoses by physical practice while Stan et al [94] utilised a P-300 evoked potential speller BCI, where the user visually focused on a single alphanumerical character situated in a grid. The chosen character then corresponded to a command for the hand orthosis thereby producing the desired stimulus for the patient.…”

Section: Discussionmentioning

confidence: 99%

“…In order to facilitate hand MI and account for significant time-points in the EEG data, all the studies employed a cue-feedback strategy during their trials. 19 of the studies presented a form of visual cue while the rest, except for two unspecified [84,102], involved cues in auditory ("bleep") [91,[95][96][97][98], textual [93,94,104] or verbal [103] forms. As for the provision of a matching sensory feedback, 16 studies presented a combination of kinaesthetic and visual feedback with some also providing auditory feedback during successful movement attempts.…”

Section: Discussionmentioning

confidence: 99%

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

Baniqued

Stanyer

Awais

et al. 2021

J NeuroEngineering Rehabil

115

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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.

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Section: Search Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Baniqued

Stanyer

Awais

et al. 2021

J NeuroEngineering Rehabil

115

View full text Add to dashboard Cite

show abstract

“…In order to facilitate hand MI and account for significant time-points in the EEG data, all the studies employed a cue-feedback strategy during their trials. 19 of the studies presented a form of visual cue while the rest, except for two unspecified [69,87], involved cues in auditory ("bleep") [76,[80][81][82][83], textual [78,79,89] or verbal [88] forms. As for the provision of a matching sensory feedback, 16 studies presented a combination of kinaesthetic and visual feedback with some also providing auditory feedback during successful movement attempts.…”

Section: Discussionmentioning

confidence: 99%

“…A final list with 30 studies was identified as eligible for qualitative review. Among the 30 studies, 11 [60][61][62][63][64][65][66][67][68][69][70] were involved in testing the BCI-hand robot system on chronic and subacute stroke patients ( [60,65] are RCTs) while the rest involved testing on healthy participants [71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89].…”

Section: Figure 1 Study Selection Flowchartmentioning

confidence: 99%

Brain-Computer Interface Robotics for Hand Rehabilitation After Stroke: A Systematic Review

Baniqued

Stanyer

Awais

et al. 2019

Preprint

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Electroencephalography-based brain-computer interfaces (BCI) that allow the control of robotic devices to support stroke patients during upper limb rehabilitation are increasingly popular. Hand rehabilitation is focused on improving dexterity and fine motor control and is a core approach for helping stroke survivors regain activities of daily living. This systematic review examines recent developments in BCI-robotic systems for hand rehabilitation and identifies evidence-based clinical studies on stroke patients. 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 in various development stages involving tests on healthy human subjects or stroke survivors. Data fields include those related to study design, participant characteristics, technical specifications of the system, and clinical outcome measures. 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 two 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. Most studies on BCI-robotic systems for hand rehabilitation report systems at prototype or pre-clinical stages of development. Some studies report statistically significant improvements in functional recovery after stroke, but there is a 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.

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Progressive fusion of multi-rate motor imagery classification for brain computer interfaces

Maloney

Kalantar

Mohammadi

2017

2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS)

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Restoring function in paralyzed limbs using EEG

Cited by 5 publications

References 5 publications

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

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

Brain-Computer Interface Robotics for Hand Rehabilitation After Stroke: A Systematic Review

Progressive fusion of multi-rate motor imagery classification for brain computer interfaces

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