Brain–computer interfaces (BCIs) enable control of assistive devices in individuals with severe motor impairments. A limitation of BCIs that has hindered real-world adoption is poor long-term reliability and lengthy daily recalibration times. To develop methods that allow stable performance without recalibration, we used a 128-channel chronic electrocorticography (ECoG) implant in a paralyzed individual, which allowed stable monitoring of signals. We show that long-term closed-loop decoder adaptation, in which decoder weights are carried across sessions over multiple days, results in consolidation of a neural map and ‘plug-and-play’ control. In contrast, daily reinitialization led to degradation of performance with variable relearning. Consolidation also allowed the addition of control features over days, that is, long-term stacking of dimensions. Our results offer an approach for reliable, stable BCI control by leveraging the stability of ECoG interfaces and neural plasticity.
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The data that support the findings of this study are available on reasonable request from the corresponding author. The data are not publicly available because they contain information that might compromise the privacy of the research participant.
The custom MATLAB code used for analyses is available upon request from the corresponding author.
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This work was funded by the National Institutes of Health (NIH) through the NIH Director’s New Innovator Award Program (grant no. 1 DP2 HD087955]. The development of the signal processing and decoding approach used in this study was supported by the Doris Duke Charitable Foundation (grant no. 2013101). The authors especially thank our participant ‘Bravo-1’ for his unwavering commitment to this project. We also thank C.C. Rodriguez for assisting with data collection and D.A. Moses, J.R. Liu and M. Dougherty for assistance with the clinical trial.
E.F.C., K.G., N.N. and A.T.C. receive some salary support from Facebook Reality Labs for a separate project on speech decoding. The funders had no role in study design, data collection, data analysis and the contents of this paper.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figs. 1–12, Supplementary Table 1, Supplementary Video Legends 1 and 2 and Supplementary Note
Example of BCI cursor control during center-out task. This video shows performance during a fixed block after ltCLDA. The 8 targets are shown.
Example of point-and-click task using ECoG-based BCI. Neural control of the cursor during reach to an instructed target in a 4x4 grid, clicking on that target was a correct selection. The instructed target was illuminated green to indicate that it was the instructed target. After each trial, the next instructed target was randomly selected from the grid and immediately illuminated. Overall, for this example of block, the bitrate was calculated as 0.92.
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Silversmith, D.B., Abiri, R., Hardy, N.F. et al. Plug-and-play control of a brain–computer interface through neural map stabilization. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0662-5