An EEG-based framework for exploring adaptive rhythmic human-machine interaction

Objective.Understanding rhythmic human-human interaction and its underlying mechanisms can enhance experiential value and enjoyment by providing a tailored experience and supporting applications in medical human-machine contexts. Existing experimental paradigms often lack a unified and holistic analysis, characterised by limited ecological validity in partner realism, active engagement, and visual interaction. These can produce hidebound insights due to variable partner behaviour, inflexible design, or insufficient user experience analysis. The study presents and validates a multimodal paradigm that addresses these limitations and enables controlled evaluation of human-human rhythm interaction and its extension to virtual AI agents.Approach.Participants completed a tapping paradigm with an audio-visual drum animation driven by either a human or AI-based partner under simple and complex (polyrhythmic) conditions. Portable electroencephalography (EEG) recordings and post-trial questionnaires assessed neural and subjective responses.Main results.The framework improves ecological validity relative to existing approaches and effectively masks partner identity (human vs AI) without reducing experienced flow, arousal, or enjoyment, which remained positive overall. Notably, the AI-based partner considered a first attempt to create a virtual AI-driven interacting drummer, suitable for future consideration of alternative algorithms. Additionally, the design supports unobtrusive, portable EEG measurement of neural modulation and temporal alignment with both performed and presented stimuli.Significance.This paradigm offers a flexible foundation for studying rhythmic interaction in human-machine systems, balancing ecological realism with experimental partner control while supporting future adaptive or biofeedback-driven systems that optimise rhythm interaction in real-time.