Fan Chaofei, Hahn Nick, Kamdar Foram, Avansino Donald, Wilson Guy H, Hochberg Leigh, Shenoy Krishna V, Henderson Jaimie M, Willett Francis R
ArXiv. 2023 Nov 6:arXiv:2311.03611v1.
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.
皮层内脑机接口（iBCIs）已展现出有望为患有诸如肌萎缩侧索硬化症（ALS）等神经系统疾病的患者恢复快速通信的潜力。然而，为了长期保持高性能，iBCIs通常需要频繁重新校准，以应对数天内积累的神经记录变化。这就要求iBCI用户停止使用iBCI并参与有监督的数据收集，从而使得iBCI系统难以使用。在本文中，我们提出了一种方法，该方法能够在不中断用户的情况下实现通信iBCIs的自我重新校准。我们的方法利用大语言模型（LMs）自动纠正iBCI输出中的错误。自我重新校准过程使用这些纠正后的输出（“伪标签”）在线持续更新iBCI解码器。在一年多（403天）的时间里，我们对一名临床试验参与者评估了我们的带有伪标签的持续在线重新校准（CORP）框架。在一项在线手写iBCI任务中，CORP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，这是涉及人类参与者的运行时间最长的iBCI稳定性演示。我们的结果为即插即用、高性能通信iBCI的长期稳定提供了首个证据，解决了iBCIs临床转化的一个主要障碍。
