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Intracortical brain-computer interfaces (iBCIs) have shown promise forrestoring rapid communication to people with neurological disorders such asamyotrophic lateral sclerosis (ALS). However, to maintain high performance overtime, iBCIs typically need frequent recalibration to combat changes in theneural recordings that accrue over days. This requires iBCI users to stop usingthe iBCI and engage in supervised data collection, making the iBCI system hardto use. In this paper, we propose a method that enables self-recalibration ofcommunication iBCIs without interrupting the user. Our method leverages largelanguage models (LMs) to automatically correct errors in iBCI outputs. Theself-recalibration process uses these corrected outputs ("pseudo-labels") tocontinually 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 stabledecoding accuracy of 93.84% in an online handwriting iBCI task, significantlyoutperforming other baseline methods. Notably, this is the longest-running iBCIstability demonstration involving a human participant. Our results provide thefirst evidence for long-term stabilization of a plug-and-play, high-performancecommunication iBCI, addressing a major barrier for the clinical translation ofiBCIs.

Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication