Literature DB >> 25012465

Performance assessment of a brain-computer interface driven hand orthosis.

Christine E King1, Kunal R Dave, Po T Wang, Masato Mizuta, David J Reinkensmeyer, An H Do, Shunji Moromugi, Zoran Nenadic.   

Abstract

Stroke survivors are typically affected by hand motor impairment. Despite intensive rehabilitation and spontaneous recovery, improvements typically plateau a year after a stroke. Therefore, novel approaches capable of restoring or augmenting lost motor behaviors are needed. Brain-computer interfaces (BCIs) may offer one such approach by using neurophysiological activity underlying hand movements to control an upper extremity orthosis. To test the performance of such a system, we developed an electroencephalogram-based BCI controlled electrically actuated hand orthosis. Six able-bodied participants voluntarily grasped/relaxed one hand to elicit BCI-mediated closing/opening of the orthosis mounted on the opposite hand. Following a short training/calibration procedure, participants demonstrated real-time, online control of the orthosis by following computer cues. Their performances resulted in an average of 1.15 (standard deviation: 0.85) false alarms and 0.22 (0.36) omissions per minute. Analysis of signals from electrogoniometers mounted on both hands revealed an average correlation between voluntary and BCI-mediated movements of 0.58 (0.13), with all but one online performance being statistically significant. This suggests that a BCI driven hand orthosis is feasible, and therefore should be tested in stroke individuals with hand weakness. If proven viable, this technology may provide a novel approach to the neuro-rehabilitation of hand function after stroke.

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Year:  2014        PMID: 25012465     DOI: 10.1007/s10439-014-1066-9

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  9 in total

1.  Brain-Computer Interfaces in Neurorecovery and Neurorehabilitation.

Authors:  Michael J Young; David J Lin; Leigh R Hochberg
Journal:  Semin Neurol       Date:  2021-03-19       Impact factor: 3.212

Review 2.  A structured overview of trends and technologies used in dynamic hand orthoses.

Authors:  Ronald A Bos; Claudia J W Haarman; Teun Stortelder; Kostas Nizamis; Just L Herder; Arno H A Stienen; Dick H Plettenburg
Journal:  J Neuroeng Rehabil       Date:  2016-06-29       Impact factor: 4.262

3.  Motor Imagery-Based Brain-Computer Interface Coupled to a Robotic Hand Orthosis Aimed for Neurorehabilitation of Stroke Patients.

Authors:  Jessica Cantillo-Negrete; Ruben I Carino-Escobar; Paul Carrillo-Mora; David Elias-Vinas; Josefina Gutierrez-Martinez
Journal:  J Healthc Eng       Date:  2018-04-03       Impact factor: 2.682

Review 4.  Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation.

Authors:  Jessica Gantenbein; Jan Dittli; Jan Thomas Meyer; Roger Gassert; Olivier Lambercy
Journal:  Front Neurorobot       Date:  2022-02-21       Impact factor: 2.650

5.  A benchtop system to assess the feasibility of a fully independent and implantable brain-machine interface.

Authors:  Po T Wang; Everardo Camacho; Ming Wang; Yongcheng Li; Susan J Shaw; Michelle Armacost; Hui Gong; Daniel Kramer; Brian Lee; Richard A Andersen; Charles Y Liu; Payam Heydari; Zoran Nenadic; An H Do
Journal:  J Neural Eng       Date:  2019-11-12       Impact factor: 5.379

Review 6.  Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review.

Authors:  Daniela Camargo-Vargas; Mauro Callejas-Cuervo; Stefano Mazzoleni
Journal:  Sensors (Basel)       Date:  2021-06-24       Impact factor: 3.576

7.  The feasibility of a brain-computer interface functional electrical stimulation system for the restoration of overground walking after paraplegia.

Authors:  Christine E King; Po T Wang; Colin M McCrimmon; Cathy C Y Chou; An H Do; Zoran Nenadic
Journal:  J Neuroeng Rehabil       Date:  2015-09-24       Impact factor: 4.262

8.  Factors of Influence on the Performance of a Short-Latency Non-Invasive Brain Switch: Evidence in Healthy Individuals and Implication for Motor Function Rehabilitation.

Authors:  Ren Xu; Ning Jiang; Natalie Mrachacz-Kersting; Kim Dremstrup; Dario Farina
Journal:  Front Neurosci       Date:  2016-01-21       Impact factor: 4.677

9.  Enhancing Performance and Bit Rates in a Brain-Computer Interface System With Phase-to-Amplitude Cross-Frequency Coupling: Evidences From Traditional c-VEP, Fast c-VEP, and SSVEP Designs.

Authors:  Stavros I Dimitriadis; Avraam D Marimpis
Journal:  Front Neuroinform       Date:  2018-05-08       Impact factor: 4.081

  9 in total

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