Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A
  One-Year Demonstration of Seamless Brain-to-Text Communication
Chaofei Fan Nick Hahn Foram Kamdar Donald Avansino Guy H. Wilson Leigh Hochberg Krishna V. Shenoy Jaimie M. Henderson Francis R. Willett
Intracortical brain-computer interfaces (iBCIs) have shown promise for
restoring rapid communication to people with neurological disorders such as
amyotrophic lateral sclerosis (ALS). However, to maintain high performance over
time, iBCIs typically need frequent recalibration to combat changes in the
neural recordings that accrue over days. This requires iBCI users to stop using
the iBCI and engage in supervised data collection, making the iBCI system hard
to use. In this paper, we propose a method that enables self-recalibration of
communication iBCIs without interrupting the user. Our method leverages large
language models (LMs) to automatically correct errors in iBCI outputs. The
self-recalibration process uses these corrected outputs ("pseudo-labels") to
continually update the iBCI decoder online. Over a period of more than one year
(403 days), we evaluated our Continual Online Recalibration with Pseudo-labels
(CORP) framework with one clinical trial participant. CORP achieved a stable
decoding accuracy of 93.84% in an online handwriting iBCI task, significantly
outperforming other baseline methods. Notably, this is the longest-running iBCI
stability demonstration involving a human participant. Our results provide the
first evidence for long-term stabilization of a plug-and-play, high-performance
communication iBCI, addressing a major barrier for the clinical translation of
iBCIs.