ArticleJournal of neuroengineering and rehabilitation2022
Design-development of an at-home modular brain-computer interface (BCI) platform in a case study of cervical spinal cord injury.
Article in Journal of neuroengineering and rehabilitation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02564419 (Early Feasibility Study of a Medtronic Activa PC+S System for Persons Living With Spinal Cord Injury), which is not on this map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Early Feasibility Study of a Medtronic Activa PC+S System for Persons Living With Spinal Cord Injury
Who cites it
10 citing papers in PubMed, 24 citations in OpenAlex.
- Brain-Computer Interfaces: The Dawn of a New Era in Disease Treatment.Exploration (Beijing, China) · 2026Review
- Reconnecting Minds to the World: Patient Perspectives on Brain-Computer Interface After High Cervical Spinal Cord Injury.Annals of rehabilitation medicine · 2026Article
- [Research progress on flexible electrode technology in brain computer interface applications].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- Brain-computer interface: an update for the clinicians.Frontiers in human neuroscience · 2026Review
- Current status and future prospects of brain-computer interfaces in the field of neurological disease rehabilitation.Frontiers in rehabilitation sciences · 2026Review
- 5-year follow-up of a fully implanted brain-computer interface in a spinal cord injury patient.Journal of neural engineering · 2025Article
- A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks.Frontiers in neuroscience · 2024Article
- Brain-Computer Interfaces with Intracortical Implants for Motor and Communication Functions Compensation: Review of Recent Developments.Sovremennye tekhnologii v meditsine · 2024Review
- Effectiveness of the Combined Use of a Brain-Machine Interface System and Virtual Reality as a Therapeutic Approach in Patients with Spinal Cord Injury: A Systematic Review.Healthcare (Basel, Switzerland) · 2023Review
- Spinal cord injury: molecular mechanisms and therapeutic interventions.Signal transduction and targeted therapy · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 5 institutions in 2 countries.
Funding
Abstract
objectiveThe 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).
backgroundBCIs 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.
methodsThe 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.
resultsAverage 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.
conclusionsThe 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.