皮层内脑-计算机接口的插即用稳定性:一年的无脑到文本通信演示
Chaofei Fan1, Nick Hahn2, Foram Kamdar2
1Department of Computer Science, Stanford University.
Advances in neural information processing systems
|May 13, 2024
概括
本研究引入了使用大型语言模型进行皮质内脑计算机接口 (iBCI) 的新自我重新校准方法. 这种方法保持了在没有用户中断的情况下恢复神经障碍患者的通信的高性能.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 皮层内脑-计算机接口 (iBCI) 为像ALS这样的神经系统疾病提供通信恢复.
- 目前的iBCI需要经常重新校准,破坏用户体验并限制临床翻译.
- 神经记录漂移需要重新校准,这对长期iBCI使用构成重大挑战.
研究的目的:
- 开发一种自我重新校准的iBCI系统,消除了对用户中断的需求.
- 利用大型语言模型 (LMs) 来自动纠错和解码器更新.
- 为了证明通信iBCI的长期稳定性和高性能.
主要方法:
- 建议使用伪标签 (CORP) 框架进行持续在线重新校准.
- 使用LMS生成伪标签来纠正iBCI输出错误.
- 使用这些伪标签在线不断更新iBCI解码器.
- 在403天内对1名临床试验参与者进行CORP评估.
主要成果:
- 在线手写iBCI任务中实现了93.84%的稳定解码精度.
- 与基线方法相比,表现明显优越.
- 这代表了与人类参与者进行的最长时间的iBCI稳定性演示.
- 在不打扰用户的情况下展示了持续的高性能.
结论:
- CORP框架可以长期稳定高性能通信iBCI.
- 自行重新校准解决了iBCI临床转换的一个主要障碍.
- 这种plug-and-play方法提高了严重运动障碍的人的可用性和可访问性.
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