Decoding Workload and Agreement From EEG During Spoken Dialogue With Conversational AI

TL;DR

This study explores the use of EEG-based classifiers to decode mental workload and agreement during spoken dialogue with AI, demonstrating potential and limitations for integrating passive brain signals into conversational systems.

Contribution

It introduces a pipeline for aligning EEG data with conversational events and evaluates transferability of workload and agreement classifiers in natural dialogue settings.

Findings

Workload decoding shows interpretable trends during spoken interaction.

Implicit agreement classifiers can be applied continuously and aligned with conversation.

Limitations exist in transferability and asynchronous application of classifiers.

Abstract

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.