5-year follow-up of a fully implanted brain–computer interface in a spinal cord injury patient

Spinal cord injury (SCI) affects over 250 000 individuals in the US. Brain-computer interfaces (BCIs) may improve quality of life by controlling external devices. Invasive intracortical BCIs have shown promise in clinical trials but degrade in the chronic period and tether patients to acquisition hardware. Alternatively, electrocorticography (ECoG) records data from electrodes on the cortex,<i>and studies evaluating fully implanted BCI-ECoG systems are scarce. Objective. We seek to address this need using a fully implanted ECoG-based BCI that allows for home use in SCI.</i><i>Approach.</i>The patient used a long-term BCI system, initially controlling an functional electrical stimulation orthosis in the lab and later using an external mechanical orthosis at home. To evaluate its long-term viability, electrode contact impedance, signal quality, and decoder performance were measured. Signal quality was assessed using signal-to-noise ratio and maximum bandwidth of the signal. Decoder performance was monitored using the area under the receiver operator characteristic curve (AUROC).<i>Main results.</i>The study analyzed data from the patient's home environment over 54 months, revealing that the device was used at home for 38 ± 24 min on average daily. After six months, we observed stable event-related desynchronization that aided in determining the onset of motor intention. The decoder's average AUROC across months was 0.959. Importantly, 40 months of the data collected was gather from the subject's home or community environment. The results indicate long-term ECoG recordings were stable for motor-imagery classification and motor control in the community environment in a case of an individual with SCI.<i>Significance.</i>This study presents the long-term feasibility and viability of an ECoG-based BCI system that persists in the home environment in a case of SCI. Future research should explore larger electrode counts with more participants to confirm this stability. Understanding these trends is crucial for clinical utility and chronic viability in broader patient populations.

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Publication Details

Journal
Journal of Neural Engineering
Published
2025-03-24
DOI
https://doi.org/10.1088/1741-2552/adc48c
Citations
10
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
14.31
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article

5-year follow-up of a fully implanted brain–computer interface in a spinal cord injury patient

Benyamin Meschede-Krasa, Noeline W. Prins, Emery N. Brown, Abhishek Prasad et al.
10 citations
Journal of Neural Engineering
EEG and Brain-Computer Interfaces
14.31
article

5-year follow-up of a fully implanted brain–computer interface in a spinal cord injury patient

Benyamin Meschede-Krasa, Noeline W. Prins, Emery N. Brown, Abhishek Prasad, Jonathan Jagid, Letitia Fisher, Michael E. Ivan, Iahn Cajigas, Kimberley Wyse‐Sookoo, Kevin Davis, Fouzia Raza
article en
10 citations

Abstract

Spinal cord injury (SCI) affects over 250 000 individuals in the US. Brain-computer interfaces (BCIs) may improve quality of life by controlling external devices. Invasive intracortical BCIs have shown promise in clinical trials but degrade in the chronic period and tether patients to acquisition hardware. Alternatively, electrocorticography (ECoG) records data from electrodes on the cortex,<i>and studies evaluating fully implanted BCI-ECoG systems are scarce. Objective. We seek to address this need using a fully implanted ECoG-based BCI that allows for home use in SCI.</i><i>Approach.</i>The patient used a long-term BCI system, initially controlling an functional electrical stimulation orthosis in the lab and later using an external mechanical orthosis at home. To evaluate its long-term viability, electrode contact impedance, signal quality, and decoder performance were measured. Signal quality was assessed using signal-to-noise ratio and maximum bandwidth of the signal. Decoder performance was monitored using the area under the receiver operator characteristic curve (AUROC).<i>Main results.</i>The study analyzed data from the patient's home environment over 54 months, revealing that the device was used at home for 38 ± 24 min on average daily. After six months, we observed stable event-related desynchronization that aided in determining the onset of motor intention. The decoder's average AUROC across months was 0.959. Importantly, 40 months of the data collected was gather from the subject's home or community environment. The results indicate long-term ECoG recordings were stable for motor-imagery classification and motor control in the community environment in a case of an individual with SCI.<i>Significance.</i>This study presents the long-term feasibility and viability of an ECoG-based BCI system that persists in the home environment in a case of SCI. Future research should explore larger electrode counts with more participants to confirm this stability. Understanding these trends is crucial for clinical utility and chronic viability in broader patient populations.

Journal of Neural EngineeringVol. 22(2)
University of Minnesota (US), Harvard University (US), Johns Hopkins University (US), University of Miami (US), Harvard University Press (US), Massachusetts General Hospital (US), Institute of Cognitive and Brain Sciences (US), IIT@MIT (US), Center for Pain and the Brain (US), Massachusetts Institute of Technology (US), University of Pennsylvania (US)
Openalex Percentile: Top 1%
EEG and Brain-Computer Interfaces
14.31
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