Design-development of an at-home modular brain–computer interface (BCI) platform in a case study of cervical spinal cord injury
Abstract Objective The objective of this study was to develop a portable and modular brain–computer interface (BCI) software platform independent of input and output devices. We implemented this platform in a case study of a subject with cervical spinal cord injury (C5 ASIA A). Background BCIs can r...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-06-01
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| Series: | Journal of NeuroEngineering and Rehabilitation |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12984-022-01026-2 |
| _version_ | 1828777595453308928 |
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| author | Kevin C. Davis Benyamin Meschede-Krasa Iahn Cajigas Noeline W. Prins Charles Alver Sebastian Gallo Shovan Bhatia John H. Abel Jasim A. Naeem Letitia Fisher Fouzia Raza Wesley R. Rifai Matthew Morrison Michael E. Ivan Emery N. Brown Jonathan R. Jagid Abhishek Prasad |
| author_facet | Kevin C. Davis Benyamin Meschede-Krasa Iahn Cajigas Noeline W. Prins Charles Alver Sebastian Gallo Shovan Bhatia John H. Abel Jasim A. Naeem Letitia Fisher Fouzia Raza Wesley R. Rifai Matthew Morrison Michael E. Ivan Emery N. Brown Jonathan R. Jagid Abhishek Prasad |
| author_sort | Kevin C. Davis |
| collection | DOAJ |
| description | Abstract Objective The objective of this study was to develop a portable and modular brain–computer interface (BCI) software platform independent of input and output devices. We implemented this platform in a case study of a subject with cervical spinal cord injury (C5 ASIA A). Background BCIs can restore independence for individuals with paralysis by using brain signals to control prosthetics or trigger functional electrical stimulation. Though several studies have successfully implemented this technology in the laboratory and the home, portability, device configuration, and caregiver setup remain challenges that limit deployment to the home environment. Portability is essential for transitioning BCI from the laboratory to the home. Methods The BCI platform implementation consisted of an Activa PC + S generator with two subdural four-contact electrodes implanted over the dominant left hand-arm region of the sensorimotor cortex, a minicomputer fixed to the back of the subject’s wheelchair, a custom mobile phone application, and a mechanical glove as the end effector. To quantify the performance for this at-home implementation of the BCI, we quantified system setup time at home, chronic (14-month) decoding accuracy, hardware and software profiling, and Bluetooth communication latency between the App and the minicomputer. We created a dataset of motor-imagery labeled signals to train a binary motor imagery classifier on a remote computer for online, at-home use. Results Average bluetooth data transmission delay between the minicomputer and mobile App was 23 ± 0.014 ms. The average setup time for the subject’s caregiver was 5.6 ± 0.83 min. The average times to acquire and decode neural signals and to send those decoded signals to the end-effector were respectively 404.1 ms and 1.02 ms. The 14-month median accuracy of the trained motor imagery classifier was 87.5 ± 4.71% without retraining. Conclusions The study presents the feasibility of an at-home BCI system that subjects can seamlessly operate using a friendly mobile user interface, which does not require daily calibration nor the presence of a technical person for at-home setup. The study also describes the portability of the BCI system and the ability to plug-and-play multiple end effectors, providing the end-user the flexibility to choose the end effector to accomplish specific motor tasks for daily needs. Trial registration ClinicalTrials.gov: NCT02564419. First posted on 9/30/2015 |
| first_indexed | 2024-12-11T16:25:11Z |
| format | Article |
| id | doaj.art-320ce1f9057e419aa453b99ea7e8b351 |
| institution | Directory Open Access Journal |
| issn | 1743-0003 |
| language | English |
| last_indexed | 2024-12-11T16:25:11Z |
| publishDate | 2022-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of NeuroEngineering and Rehabilitation |
| spelling | doaj.art-320ce1f9057e419aa453b99ea7e8b3512022-12-22T00:58:45ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032022-06-0119111410.1186/s12984-022-01026-2Design-development of an at-home modular brain–computer interface (BCI) platform in a case study of cervical spinal cord injuryKevin C. Davis0Benyamin Meschede-Krasa1Iahn Cajigas2Noeline W. Prins3Charles Alver4Sebastian Gallo5Shovan Bhatia6John H. Abel7Jasim A. Naeem8Letitia Fisher9Fouzia Raza10Wesley R. Rifai11Matthew Morrison12Michael E. Ivan13Emery N. Brown14Jonathan R. Jagid15Abhishek Prasad16Department of Biomedical Engineering, University of MiamiDepartment of Brain and Cognitive Science, Massachusetts Institute of TechnologyDepartment of Neurological Surgery, University of MiamiDepartment of Biomedical Engineering, University of MiamiDepartment of Biomedical Engineering, University of MiamiDepartment of Biomedical Engineering, University of MiamiDepartment of Biomedical Engineering, Georgia Institute of TechnologyDepartment of Anesthesia, Critical Care and Pain Medicine, Massachusetts General HospitalDepartment of Biomedical Engineering, University of MiamiMiami Project to Cure Paralysis, University of MiamiHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard UniversityDepartment of Biomedical Engineering, University of MiamiDepartment of Biomedical Engineering, University of MiamiDepartment of Neurological Surgery, University of MiamiDepartment of Brain and Cognitive Science, Massachusetts Institute of TechnologyDepartment of Neurological Surgery, University of MiamiDepartment of Biomedical Engineering, University of MiamiAbstract Objective The objective of this study was to develop a portable and modular brain–computer interface (BCI) software platform independent of input and output devices. We implemented this platform in a case study of a subject with cervical spinal cord injury (C5 ASIA A). Background BCIs can restore independence for individuals with paralysis by using brain signals to control prosthetics or trigger functional electrical stimulation. Though several studies have successfully implemented this technology in the laboratory and the home, portability, device configuration, and caregiver setup remain challenges that limit deployment to the home environment. Portability is essential for transitioning BCI from the laboratory to the home. Methods The BCI platform implementation consisted of an Activa PC + S generator with two subdural four-contact electrodes implanted over the dominant left hand-arm region of the sensorimotor cortex, a minicomputer fixed to the back of the subject’s wheelchair, a custom mobile phone application, and a mechanical glove as the end effector. To quantify the performance for this at-home implementation of the BCI, we quantified system setup time at home, chronic (14-month) decoding accuracy, hardware and software profiling, and Bluetooth communication latency between the App and the minicomputer. We created a dataset of motor-imagery labeled signals to train a binary motor imagery classifier on a remote computer for online, at-home use. Results Average bluetooth data transmission delay between the minicomputer and mobile App was 23 ± 0.014 ms. The average setup time for the subject’s caregiver was 5.6 ± 0.83 min. The average times to acquire and decode neural signals and to send those decoded signals to the end-effector were respectively 404.1 ms and 1.02 ms. The 14-month median accuracy of the trained motor imagery classifier was 87.5 ± 4.71% without retraining. Conclusions The study presents the feasibility of an at-home BCI system that subjects can seamlessly operate using a friendly mobile user interface, which does not require daily calibration nor the presence of a technical person for at-home setup. The study also describes the portability of the BCI system and the ability to plug-and-play multiple end effectors, providing the end-user the flexibility to choose the end effector to accomplish specific motor tasks for daily needs. Trial registration ClinicalTrials.gov: NCT02564419. First posted on 9/30/2015https://doi.org/10.1186/s12984-022-01026-2NeuroscienceRehabilitationSignal processing systems |
| spellingShingle | Kevin C. Davis Benyamin Meschede-Krasa Iahn Cajigas Noeline W. Prins Charles Alver Sebastian Gallo Shovan Bhatia John H. Abel Jasim A. Naeem Letitia Fisher Fouzia Raza Wesley R. Rifai Matthew Morrison Michael E. Ivan Emery N. Brown Jonathan R. Jagid Abhishek Prasad Design-development of an at-home modular brain–computer interface (BCI) platform in a case study of cervical spinal cord injury Journal of NeuroEngineering and Rehabilitation Neuroscience Rehabilitation Signal processing systems |
| title | Design-development of an at-home modular brain–computer interface (BCI) platform in a case study of cervical spinal cord injury |
| title_full | Design-development of an at-home modular brain–computer interface (BCI) platform in a case study of cervical spinal cord injury |
| title_fullStr | Design-development of an at-home modular brain–computer interface (BCI) platform in a case study of cervical spinal cord injury |
| title_full_unstemmed | Design-development of an at-home modular brain–computer interface (BCI) platform in a case study of cervical spinal cord injury |
| title_short | Design-development of an at-home modular brain–computer interface (BCI) platform in a case study of cervical spinal cord injury |
| title_sort | design development of an at home modular brain computer interface bci platform in a case study of cervical spinal cord injury |
| topic | Neuroscience Rehabilitation Signal processing systems |
| url | https://doi.org/10.1186/s12984-022-01026-2 |
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