The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI)-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial
Open accessAbstract
Repeated use of brain-computer interfaces (BCIs) providing contingent sensory feedback of brain activity were recently proposed as a rehabilitation approach to restore motor function after stroke or spinal cord lesions. However, there are only a few clinical studies that investigate feasibility and effectiveness of such an approach. Here we report on a placebo-controlled, multicenter clinical trial that investigated whether stroke survivors with severe upper limb (UL) paralysis benefit from 10 BCI training sessions each lasting up to 40 minutes. A total of 74 patients participated: median time since stroke is 8 months, 25% and 75% quartiles [3.0; 13.0]; median severity of UL paralysis is 4.5 points [0.0; 30.0] as measured by the Action Research Arm Test , ARAT, and 19.5 points [11.0; 40.0] as measured by the Fugl-Meyer Motor Assessment, FMMA. Patients in the BCI group (n=55) performed motor imagery of opening their affected hand. Motor imagery-related brain electric activity was translated into contingent hand exoskeleton-driven opening movements of the affected hand. In a control group (n=19), hand exoskeleton-driven opening movements of the affected hand were independent of brain electric activity. Evaluation of the UL clinical assessments indicated that both groups improved, but only the BCI group showed an improvement in the ARAT’s grasp score from 0 [0.0; 14.0] to 3.0 [0.0; 15.0] points (p<0.001) and pinch scores from 0.0 [0.0; 7.0] to 1.0 [0.0; 12.0] points (p<0.001). Upon training completion, 21.8% (36.4%) of the patients in the BCI group improved their ARAT (FMMA) scores. The corresponding numbers for the control group were 5.3% (ARAT) and 15.8% (FMMA). These results suggests that adding BCI control to exoskeleton-assisted physical therapy can improve post-stroke rehabilitation outcomes. Both maximum and mean values of the percentage of successfully decoded imagery-related EEG activity, were higher than chance level. A correlation between the classification accuracy and the improvement in the upper extremity function was found. An improvement of motor function was found for all patients independently of duration, severity and location of the stroke. Clinical trial registration number: NCT02325947.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The assessment and analysis of handedness: The Edinburgh inventory
Oldfield, R. C. (1971) · Neuropsychologia · doi:10.1016/0028-3932(71)90067-4 · Cited by 35,900
Interrater Reliability of a Modified Ashworth Scale of Muscle Spasticity
Bohannon, R. W., & Smith, M. B. (1987) · Physical Therapy · doi:10.1093/ptj/67.2.206 · Cited by 5,424
Winstein, C. J., Stein, J., Arena, R., Bates, B., Cherney, L. R., Cramer, S. C., Deruyter, F., Eng, J. J., Fisher, B., Harvey, R. L., et al. (2016) · Stroke · doi:10.1161/str.0000000000000098 · Cited by 3,293
Langhorne, P., Bernhardt, J., & Kwakkel, G. (2011) · The Lancet · doi:10.1016/s0140-6736(11)60325-5 · Cited by 2,726
Brain Computer Interfaces, a Review
Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012) · Sensors · doi:10.3390/s120201211 · Cited by 2,143
A survey on robotic devices for upper limb rehabilitation
Maciejasz, P., Eschweiler, J., Gerlach-Hahn, K., Jansen-Troy, A., & Leonhardt, S. (2014) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-11-3 · Cited by 1,210
Brain–machine interface in chronic stroke rehabilitation: A controlled study
Ramos‐Murguialday, A., Broetz, D., Rea, M., Läer, L., Yilmaz, Ö., Brasil, F. L., Liberati, G., Curado, M. R., Garcia‐Cossio, E., Vyziotis, A., et al. (2013) · Annals of Neurology · doi:10.1002/ana.23879 · Cited by 994
Hatem, S. M., Saussez, G., della Faille, M., Prist, V., Zhang, X., Dispa, D., & Bleyenheuft, Y. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00442 · Cited by 918
Pfurtscheller, G., & Aranibar, A. (1979) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(79)90063-4 · Cited by 758 · cited 2×
“As motor imagery results in specific modulations of brain electroencephalographic (EEG) signals, e.g., sensorimotor rhythms (SMR) (Pfurtscheller and Aranibar, 1979), it can be used to voluntarily control an external device, e.g., a robot or exoskeleton using a brain-computer interface (BCI) (Nicolas-Alonso and Gomez-G…”
MethodReliability of the Fugl-Meyer Assessment for Testing Motor Performance in Patients Following Stroke
Sanford, J., Moreland, J., Swanson, L. R., Stratford, P. W., & Gowland, C. (1993) · Physical Therapy · doi:10.1093/ptj/73.7.447 · Cited by 732
Cited by
Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke
Biasiucci, A., Leeb, R., Iturrate, I., Perdikis, S., Al-Khodairy, A., Corbet, T., Schnider, A., Schmidlin, T., Zhang, H., Bassolino, M., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04673-z · Cited by 649
BCI for stroke rehabilitation: motor and beyond
Mane, R., Chouhan, T., & Guan, C. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/aba162 · Cited by 466
Benabid, A. L., Costecalde, T., Eliseyev, A., Charvet, G., Verney, A., Karakas, S., Foerster, M., Lambert, A., Morinière, B., Abroug, N., et al. (2019) · The Lancet Neurology · doi:10.1016/s1474-4422(19)30321-7 · Cited by 404
Benefits of deep learning classification of continuous noninvasive brain–computer interface control
Stieger, J. R., Engel, S. A., Suma, D., & He, B. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac0584 · Cited by 55
Ribeiro, T. F., Carriello, M. A., de Paula, E. P., Garcia, A. C., Rocha, G. L. D., & Teive, H. A. G. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1195066 · Cited by 13
Metadata
- Authors
- Frolov, A. A., Mokienko, O., Lyukmanov, R., Biryukova, E., Kotov, S., Turbina, L., Nadareyshvily, G., & Bushkova, Y.
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2017-07-20
- Volume
- 11
- Pages
- 400-400
- ISSN
- 1662-453X (ISSN-L)
- OpenAlex
- W2739229484
- Cited by
- 396
- References
- 39
- FWCI
- 23.95
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI)-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial. https://app.cochat.ai/papers/10.3389/fnins.2017.00400
- The paper
- Frolov, A. A., Mokienko, O., Lyukmanov, R., Biryukova, E., Kotov, S., Turbina, L., Nadareyshvily, G., & Bushkova, Y. (2017). Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI)-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial. Frontiers in Neuroscience. https://doi.org/10.3389/fnins.2017.00400
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.