A dual EEG hyperscanning dataset of natural French face-to-face conversation

Conversation is a fundamental human behaviour that requires rapid coordination between speaking, listening, and turn-taking, yet datasets capturing its neural dynamics in natural interaction remain scarce. Hyperscanning EEG is particularly valuable for this purpose because it records both interlocutors simultaneously, enabling the study of speaker–listener coupling, response timing, and dyadic coordination during live exchange. Here we present DUET ( D yadic U nderstanding, E EG and T urn-taking), a hyperscanning dataset for studying natural French face-to-face conversation. The dataset comprises recordings from 18 dyads, or 36 French-speaking adults, performing the Diapix collaborative spot-the-difference task across eight 4-minute face-to-face conversation blocks. For each participant, EEG was recorded from 36 participants; most recordings used 64-channel EEG, with one pilot dyad recorded using 32 electrodes. The public release includes raw EEG recordings, precomputed ICA decompositions for reuse in downstream preprocessing as well as various features derived from the audio and manually corrected transcripts.