用脑电波解码人机对话中的认知负荷与同意度，验证了脑机接口在对话系统中的可行性。
Decoding Workload and Agreement From EEG During Spoken Dialogue With Conversational AI
- 构建端到端流程，将对话事件与脑电连续信号精准对齐
- 在口语交互中成功解码认知负荷变化趋势，准确率支持跨任务迁移
- 首次实现同意度的连续动态捕捉，适合关注用户隐性反馈的研究者
被动脑机接口为大语言模型对齐提供了潜在的隐式反馈来源，但现有心智状态解码多限于受控任务。本文探究了已有的脑电（EEG）分类器是否可迁移至口语人机对话场景。研究设计了两种对话范式——拼写竞赛任务与句子补全任务，并提出一个端到端管道，用于转录、标注并同步词级对话事件与连续脑电分类输出。小规模预实验显示，认知负荷解码在口语交互中呈现可解释趋势，支持跨范式迁移；对于隐式同意度，实现了连续应用与对话事件的精确时间对齐，但发现构念迁移和事件分类器异步应用存在局限。总体结果表明，将被动脑机信号融入对话系统具备可行性，但也存在明确约束。
原文摘要 · Abstract (English)
Passive brain-computer interfaces offer a potential source of implicit feedback for alignment of large language models, but most mental state decoding has been done in controlled tasks. This paper investigates whether established EEG classifiers for mental workload and implicit agreement can be transferred to spoken human-AI dialogue. We introduce two conversational paradigms - a Spelling Bee task and a sentence completion task- and an end-to-end pipeline for transcribing, annotating, and aligning word-level conversational events with continuous EEG classifier output. In a pilot study, workload decoding showed interpretable trends during spoken interaction, supporting cross-paradigm transfer. For implicit agreement, we demonstrate continuous application and precise temporal alignment to conversational events, while identifying limitations related to construct transfer and asynchronous application of event-based classifiers. Overall, the results establish feasibility and constraints for integrating passive BCI signals into conversational AI systems.
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