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Design-development of an at-home modular brain–computer interface (BCI) platform in a case study of cervical spinal cord injury

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...

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Autores principales: Davis, Kevin C., Meschede-Krasa, Benyamin, Cajigas, Iahn, Prins, Noeline W., Alver, Charles, Gallo, Sebastian, Bhatia, Shovan, Abel, John H., Naeem, Jasim A., Fisher, Letitia, Raza, Fouzia, Rifai, Wesley R., Morrison, Matthew, Ivan, Michael E., Brown, Emery N., Jagid, Jonathan R., Prasad, Abhishek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9166490/
https://www.ncbi.nlm.nih.gov/pubmed/35659259
http://dx.doi.org/10.1186/s12984-022-01026-2
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author Davis, Kevin C.
Meschede-Krasa, Benyamin
Cajigas, Iahn
Prins, Noeline W.
Alver, Charles
Gallo, Sebastian
Bhatia, Shovan
Abel, John H.
Naeem, Jasim A.
Fisher, Letitia
Raza, Fouzia
Rifai, Wesley R.
Morrison, Matthew
Ivan, Michael E.
Brown, Emery N.
Jagid, Jonathan R.
Prasad, Abhishek
author_facet Davis, Kevin C.
Meschede-Krasa, Benyamin
Cajigas, Iahn
Prins, Noeline W.
Alver, Charles
Gallo, Sebastian
Bhatia, Shovan
Abel, John H.
Naeem, Jasim A.
Fisher, Letitia
Raza, Fouzia
Rifai, Wesley R.
Morrison, Matthew
Ivan, Michael E.
Brown, Emery N.
Jagid, Jonathan R.
Prasad, Abhishek
author_sort Davis, Kevin C.
collection PubMed
description 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
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spelling pubmed-91664902022-06-05 Design-development of an at-home modular brain–computer interface (BCI) platform in a case study of cervical spinal cord injury Davis, Kevin C. Meschede-Krasa, Benyamin Cajigas, Iahn Prins, Noeline W. Alver, Charles Gallo, Sebastian Bhatia, Shovan Abel, John H. Naeem, Jasim A. Fisher, Letitia Raza, Fouzia Rifai, Wesley R. Morrison, Matthew Ivan, Michael E. Brown, Emery N. Jagid, Jonathan R. Prasad, Abhishek J Neuroeng Rehabil Research 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 BioMed Central 2022-06-03 /pmc/articles/PMC9166490/ /pubmed/35659259 http://dx.doi.org/10.1186/s12984-022-01026-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Davis, Kevin C.
Meschede-Krasa, Benyamin
Cajigas, Iahn
Prins, Noeline W.
Alver, Charles
Gallo, Sebastian
Bhatia, Shovan
Abel, John H.
Naeem, Jasim A.
Fisher, Letitia
Raza, Fouzia
Rifai, Wesley R.
Morrison, Matthew
Ivan, Michael E.
Brown, Emery N.
Jagid, Jonathan R.
Prasad, Abhishek
Design-development of an at-home modular brain–computer interface (BCI) platform in a case study of cervical spinal cord injury
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 Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9166490/
https://www.ncbi.nlm.nih.gov/pubmed/35659259
http://dx.doi.org/10.1186/s12984-022-01026-2
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