Evidence map›Paper›PMID 35874158›Full record

ArticleFrontiers in human neuroscience2022

BCI-FES With Multimodal Feedback for Motor Recovery Poststroke.

Alexander B Remsik, Peter L E van Kan, Shawna Gloe, Klevest Gjini, Leroy Williams, Veena Nair, Kristin Caldera, Justin C Williams, Vivek Prabhakaran

Open access · goldAbstract read
In one paragraph

Article in Frontiers in human neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 3 pooled it
2.3field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 3 syntheses or guidelines pooled it, 26 citations in OpenAlex.

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  11. [A review of functional electrical stimulation based on brain-computer interface].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Alexander B RemsikDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, United States.
Peter L E van KanDepartment of Kinesiology, University of Wisconsin-Madison, Madison, WI, United States.
Shawna GloeDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, United States.
Klevest GjiniDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, United States.
Leroy WilliamsDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, United States.
Veena NairDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, United States.
Kristin CalderaDepartment of Orthopedics and Rehabilitation, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States.
Justin C WilliamsDepartment of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States.
Vivek PrabhakaranDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, United States.
University of Wisconsin–Madison · USNeurological Surgery · US

Funding

PET/MR Correlates of Accelerated Aging in Chronic EpilepsyR01NS117568 · NINDS · UNIVERSITY OF WISCONSIN-MADISON · PI Alan Blair McMillan, Vivek Prabhakaran · 2021 to 2026
$3.0M
Stroke Rehabilitation utilizing BCI technologyR01NS105646 · NINDS · UNIVERSITY OF WISCONSIN-MADISON · PI PRABHAKARAN, VIVEK · 2018 to 2022
$2.3M
NINDS NIH HHS R01 NS105646NINDS NIH HHS R01 NS117568
6 · The paper itself

Abstract

An increasing number of research teams are investigating the efficacy of brain-computer interface (BCI)-mediated interventions for promoting motor recovery following stroke. A growing body of evidence suggests that of the various BCI designs, most effective are those that deliver functional electrical stimulation (FES) of upper extremity (UE) muscles contingent on movement intent. More specifically, BCI-FES interventions utilize algorithms that isolate motor signals-user-generated intent-to-move neural activity recorded from cerebral cortical motor areas-to drive electrical stimulation of individual muscles or muscle synergies. BCI-FES interventions aim to recover sensorimotor function of an impaired extremity by facilitating and/or inducing long-term motor learning-related neuroplastic changes in appropriate control circuitry. We developed a non-invasive, electroencephalogram (EEG)-based BCI-FES system that delivers closed-loop neural activity-triggered electrical stimulation of targeted distal muscles while providing the user with multimodal sensory feedback. This BCI-FES system consists of three components: (1) EEG acquisition and signal processing to extract real-time volitional and task-dependent neural command signals from cerebral cortical motor areas, (2) FES of muscles of the impaired hand contingent on the motor cortical neural command signals, and (3) multimodal sensory feedback associated with performance of the behavioral task, including visual information, linked activation of somatosensory afferents through intact sensorimotor circuits, and electro-tactile stimulation of the tongue. In this report, we describe device parameters and intervention protocols of our BCI-FES system which, combined with standard physical rehabilitation approaches, has proven efficacious in treating UE motor impairment in stroke survivors, regardless of level of impairment and chronicity.

Indexed as

brain-computer interfaceclosed-loop systemfunctional electrical stimulationmotor functional recoverymotor recoveryneurorehabilitationopen-loop systemstroke

Identifiers

PMID35874158
PMCPMC9296822
OpenAlexW4283834355

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.