Decoding Workload and Agreement From EEG During Spoken Dialogue With Conversational AI
This work addresses the challenge of integrating passive brain-computer interfaces into conversational AI systems for implicit feedback, though it is incremental as it builds on existing EEG classifiers.
The paper tackled the problem of transferring EEG classifiers for mental workload and implicit agreement from controlled tasks to spoken human-AI dialogue, and found that workload decoding showed interpretable trends and agreement decoding could be aligned to conversational events, establishing feasibility with identified limitations.
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.