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.

翻译：脑皮层内脑机接口在恢复神经系统疾病（如肌萎缩侧索硬化症）患者的快速沟通能力方面展现出潜力。然而，为长期保持高性能，脑皮层内脑机接口通常需要频繁重新校准以应对数天内神经信号记录的变化。这要求用户停止使用系统并参与有监督的数据采集，导致系统难以应用。本文提出一种方法，能够在无需中断用户的情况下实现通信型脑皮层内脑机接口的自我重新校准。该方法利用大型语言模型自动修正脑皮层内脑机接口输出中的错误，并基于校正后的输出（"伪标签"）持续在线更新解码器。我们与一名临床试验参与者在超过一年（403天）的时间内评估了所提出的连续在线伪标签重新校准框架。CORP在在线手写脑皮层内脑机接口任务中实现了93.84%的稳定解码准确率，显著优于其他基线方法。值得注意的是，这是涉及人类被试的持续时间最长的脑皮层内脑机接口稳定性演示。本研究首次证明了即插即用、高性能通信型脑皮层内脑机接口长期稳定的可行性，解决了该技术临床转化的主要障碍。