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This paper is in the following e-collection/theme issue:

Cognitive and Neurorehabilitation (445) Design and Usability of Medical Devices (237) Emerging Technologies for Rehabilitation (525) Assistive Technologies (267) Design and Usability of Consumer Health Tech and Home Monitoring Devices (473) Stroke (327) Neurorehabilitation and Technology (59)

Published on in Vol 10 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49702, first published .
Tablet screen showing "Sit to Stand" task selection for physical therapy exercises.

A Brain Computer Interface Neuromodulatory Device for Stroke Rehabilitation: Iterative User-Centered Design Approach

A Brain Computer Interface Neuromodulatory Device for Stroke Rehabilitation: Iterative User-Centered Design Approach

Authors of this article:

Gemma Alder1 Author Orcid Image ;   Denise Taylor1 Author Orcid Image ;   Usman Rashid1 Author Orcid Image ;   Sharon Olsen1 Author Orcid Image ;   Thonia Brooks1 Author Orcid Image ;   Gareth Terry1 Author Orcid Image ;   Imran Khan Niazi1, 2, 3 Author Orcid Image ;   Nada Signal1 Author Orcid Image
Article Authors Cited by (14) Tweetations (3) Metrics

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  2. Oxley T, Deo D, Cernera S, Sawyer A, Putrino D, Ramsey N, Fry A. The ‘Brussels 4’: essential requirements for implantable brain–computer interface user autonomy. Journal of Neural Engineering 2025;22(1):013002 View
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  4. Tonin A, Semprini M, Kiper P, Mantini D. Brain-Computer Interfaces for Stroke Motor Rehabilitation. Bioengineering 2025;12(8):820 View
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  6. Gomes E, Alder G, Boardsworth K, Anderson K, Olsen S, Signal N. Innovative but Difficult to Replicate: A Systematic Review of the Reporting Quality of Robotic and Conventional Upper-Limb Interventions in Stroke Rehabilitation Randomized Controlled Trials Using the TIDieR-Rehab Checklist. Applied Sciences 2025;15(15):8487 View
  7. Huang X, Tang X, Yang X, Wang X, Song L, Lin M. Application of new media-based home nursing intervention for stroke-related depression patients: a retrospective cohort study. Frontiers in Public Health 2026;14 View
  8. Grevet E, Izac M, Py J, Amadieu F, Glize B, Gasq D, Jeunet-Kelway C. Bridging innovation and adoption: a mixed-methods investigation into stroke survivors’ acceptability of brain-computer interface-based rehabilitation interventions. Journal of Neural Engineering 2026;23(2):026037 View
  9. Anaya J, Navarro M, Sotelo D, Gutiérrez R. Áreas de investigación en la usabilidad y la experiencia del usuario de tecnologías de asistencia para discapacidad motriz: un análisis cienciométrico. Cadernos Brasileiros de Terapia Ocupacional 2026;34 View
  10. Anaya J, Navarro M, Sotelo D, Gutiérrez R. Research areas in the usability and user experience of assistive technologies for motor disability: a scientometric analysis. Cadernos Brasileiros de Terapia Ocupacional 2026;34 View
  11. Dai M, Jiao M, Wang Y. From Signal Stacking to Dynamic Coupling: A Critical Review of Wearable EEG–EMG Fusion Brain–Computer Interfaces for Stroke Rehabilitation. Micromachines 2026;17(9):1013 View
  12. Grevet E, Lupascu A, Enriquez-Geppert S. Acceptability of a Robot-Like Learning Companion for Neurofeedback Training: Perspectives from Health Professionals, Self-Reported Clinical Groups, and the General Public. International Journal of Human–Computer Interaction 2026:1 View
  13. Suhartono S, Yulianto B, Arafah B, Andajani K, Kholiq A, Latief M, Prastio B, Rohaedi D, Dewi D, Taqdir T. The SmartGlove BISINDO Wearable Sensor and Large Language Model–Based Sign Language-to-Speech System for Users With Communication Disorders: Design Science Research (Preprint). JMIR Rehabilitation and Assistive Technologies 2026 View
  14. Min G, Li Z. Intelligent Rehabilitation Systems Based on Big Data Analytics and Artificial Intelligence: A Systematic Review. Applied System Innovation 2026;9(10):201 View