快慢脑机接口：防止AI介导神经接口中的神经适应性过拟合
Slow-Fast Brain-Computer Interfaces: Preventing Neuroadaptive Overfitting in AI-Mediated Neural Interfaces
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中文总结 AI 辅助
该研究针对AI介导BCI的神经适应性过拟合问题，提出快慢BCI框架，按情境调整AI辅助节奏，多维度评估BCI性能以保障用户自主性与临床价值。
中文摘要 AI 辅助
人工智能（AI）正将脑机接口（BCI）从特定任务的神经解码器转变为能完成语言处理、流畅运动控制、调节康复支持及调整刺激的自适应系统。这些能力可提升速度、流畅度、可用性和临床应用范围，但传统性能指标可能忽略意图保真度、作者身份、自主性、治疗挑战及持久临床获益的损失。我将神经适应性过拟合定义为一种闭环失效模式：AI介导的BCI过度优化短期成功代理指标，包括减少努力、快速接受度、低工作负荷或流畅任务完成，同时偏离用户的持久目标。随后我提出快慢BCI框架，该框架根据解码器证据、不确定性、情境与临床风险、疲劳以及用户或临床医生定义的目标来调整AI辅助节奏。该框架区分以下三种辅助模式：意图清晰且风险低时的快速辅助、存在不确定性时的谨慎辅助、以及错位可能损害安全性、自主性、作者身份、运动学习或治疗价值时的慢速辅助。针对通信、运动控制、神经康复和闭环神经调节应用，我概述了相应的保障措施和评估指标。本观点认为，AI介导的BCI不仅应通过解码准确率和任务性能进行评估，还应通过AI辅助的部署方式进行评估：系统何时自主行动、何时寻求确认、何时保留用户努力或何时将控制权交还给用户。
英文摘要
Artificial intelligence (AI) is transforming brain-computer interfaces (BCIs) from task-specific neural decoders into adaptive systems that complete language, smooth movement, regulate rehabilitation support and adjust stimulation. These capabilities can increase speed, fluency, usability and clinical reach, yet conventional performance metrics may overlook losses in intent fidelity, authorship, agency, therapeutic challenge and durable clinical benefit. I define neuroadaptive overfitting as a closed-loop failure mode in which an AI-mediated BCI becomes over-optimized to short-term proxies of success, including reduced effort, rapid acceptance, lower workload or smooth task completion, while drifting from the user's durable goals. I then propose Slow-Fast BCI, a framework for pacing AI assistance according to decoder evidence, uncertainty, contextual and clinical stakes, fatigue, and user- or clinician-defined goals. The framework distinguishes fast assistance when intent is clear and stakes are low, guarded assistance under uncertainty and slow assistance when misalignment could compromise safety, agency, authorship, motor learning or therapeutic value. Across communication, motor-control, neurorehabilitation and closed-loop neuromodulation applications, I outline corresponding safeguards and evaluation measures. This Perspective argues that AI-mediated BCIs should be evaluated not only by decoding accuracy and task performance, but also by how AI assistance is deployed: when systems act autonomously, seek confirmation, preserve user effort or return control to the user.
发表机构
- University of Notre Dame（圣母大学）
机构由 AI 辅助整理，请以论文原文为准。
