At-Home Stroke Neurorehabilitation: Early Findings with the NeuroExo BCI System
Background: Democratized access to safe and effective robotic neurorehabilitation for stroke survivors requires innovative, affordable solutions that can be used not only in clinics but also at home. This requires the high usability of the devices involved to minimize costs associated with support f...
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MDPI AG
2025-02-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/5/1322 |
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| author | Juan José González-España Lianne Sánchez-Rodríguez Maxine Annel Pacheco-Ramírez Jeff Feng Kathryn Nedley Shuo-Hsiu Chang Gerard E. Francisco Jose L. Contreras-Vidal |
| author_facet | Juan José González-España Lianne Sánchez-Rodríguez Maxine Annel Pacheco-Ramírez Jeff Feng Kathryn Nedley Shuo-Hsiu Chang Gerard E. Francisco Jose L. Contreras-Vidal |
| author_sort | Juan José González-España |
| collection | DOAJ |
| description | Background: Democratized access to safe and effective robotic neurorehabilitation for stroke survivors requires innovative, affordable solutions that can be used not only in clinics but also at home. This requires the high usability of the devices involved to minimize costs associated with support from physical therapists or technicians. Methods: This paper describes the early findings of the NeuroExo brain–machine interface (BMI) with an upper-limb robotic exoskeleton for stroke neurorehabilitation. This early feasibility study consisted of a six-week protocol, with an initial training and BMI calibration phase at the clinic followed by 60 sessions of neuromotor therapy at the homes of the participants. Pre- and post-assessments were used to assess users’ compliance and system performance. Results: Participants achieved a compliance rate between <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>21</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>100</mn><mo>%</mo></mrow></semantics></math></inline-formula>, with an average of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>69</mn><mo>%</mo></mrow></semantics></math></inline-formula>, while maintaining adequate signal quality and a positive perceived BMI performance during home usage with an average Likert scale score of four out of five. Moreover, adequate signal quality was maintained for four out of five participants throughout the protocol. These findings provide valuable insights into essential components for comprehensive rehabilitation therapy for stroke survivors. Furthermore, linear mixed-effects statistical models showed a significant reduction in trial duration (<i>p</i>-value < 0.02) and concomitant changes in brain patterns (<i>p</i>-value < 0.02). Conclusions: the analysis of these findings suggests that a low-cost, safe, simple-to-use BMI system for at-home stroke rehabilitation is feasible. |
| format | Article |
| id | doaj-art-7fded45d7e68449a8dacea8b1f9dff36 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-7fded45d7e68449a8dacea8b1f9dff362025-08-20T02:59:01ZengMDPI AGSensors1424-82202025-02-01255132210.3390/s25051322At-Home Stroke Neurorehabilitation: Early Findings with the NeuroExo BCI SystemJuan José González-España0Lianne Sánchez-Rodríguez1Maxine Annel Pacheco-Ramírez2Jeff Feng3Kathryn Nedley4Shuo-Hsiu Chang5Gerard E. Francisco6Jose L. Contreras-Vidal7Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USADepartment of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USADepartment of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USANoninvasive Brain-Machine Interface Systems Laboratory, NSF Industry—University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USADepartment of Physical Medicine & Rehabilitation, University of Texas Health McGovern Medical School, Houston, TX 77030, USADepartment of Physical Medicine & Rehabilitation, University of Texas Health McGovern Medical School, Houston, TX 77030, USADepartment of Physical Medicine & Rehabilitation, University of Texas Health McGovern Medical School, Houston, TX 77030, USADepartment of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USABackground: Democratized access to safe and effective robotic neurorehabilitation for stroke survivors requires innovative, affordable solutions that can be used not only in clinics but also at home. This requires the high usability of the devices involved to minimize costs associated with support from physical therapists or technicians. Methods: This paper describes the early findings of the NeuroExo brain–machine interface (BMI) with an upper-limb robotic exoskeleton for stroke neurorehabilitation. This early feasibility study consisted of a six-week protocol, with an initial training and BMI calibration phase at the clinic followed by 60 sessions of neuromotor therapy at the homes of the participants. Pre- and post-assessments were used to assess users’ compliance and system performance. Results: Participants achieved a compliance rate between <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>21</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>100</mn><mo>%</mo></mrow></semantics></math></inline-formula>, with an average of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>69</mn><mo>%</mo></mrow></semantics></math></inline-formula>, while maintaining adequate signal quality and a positive perceived BMI performance during home usage with an average Likert scale score of four out of five. Moreover, adequate signal quality was maintained for four out of five participants throughout the protocol. These findings provide valuable insights into essential components for comprehensive rehabilitation therapy for stroke survivors. Furthermore, linear mixed-effects statistical models showed a significant reduction in trial duration (<i>p</i>-value < 0.02) and concomitant changes in brain patterns (<i>p</i>-value < 0.02). Conclusions: the analysis of these findings suggests that a low-cost, safe, simple-to-use BMI system for at-home stroke rehabilitation is feasible.https://www.mdpi.com/1424-8220/25/5/1322brain–computer interfaceselectroencephalographystroke rehabilitationmovement intent detectionhome therapywearables |
| spellingShingle | Juan José González-España Lianne Sánchez-Rodríguez Maxine Annel Pacheco-Ramírez Jeff Feng Kathryn Nedley Shuo-Hsiu Chang Gerard E. Francisco Jose L. Contreras-Vidal At-Home Stroke Neurorehabilitation: Early Findings with the NeuroExo BCI System Sensors brain–computer interfaces electroencephalography stroke rehabilitation movement intent detection home therapy wearables |
| title | At-Home Stroke Neurorehabilitation: Early Findings with the NeuroExo BCI System |
| title_full | At-Home Stroke Neurorehabilitation: Early Findings with the NeuroExo BCI System |
| title_fullStr | At-Home Stroke Neurorehabilitation: Early Findings with the NeuroExo BCI System |
| title_full_unstemmed | At-Home Stroke Neurorehabilitation: Early Findings with the NeuroExo BCI System |
| title_short | At-Home Stroke Neurorehabilitation: Early Findings with the NeuroExo BCI System |
| title_sort | at home stroke neurorehabilitation early findings with the neuroexo bci system |
| topic | brain–computer interfaces electroencephalography stroke rehabilitation movement intent detection home therapy wearables |
| url | https://www.mdpi.com/1424-8220/25/5/1322 |
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