Brain Decoding: Toward Real-Time Readout of Visual Imagery

Decoding visual mental imagery typically requires subject-specific calibration, raising the question of whether imagery can be decoded from representations shared across individuals. We show that a decoder trained on perception EEG from 21 participants transfers to imagery in a previously unseen participant, with no labelled imagery trials from the target participant. Per-participant standardisation is sufficient, while learned alignment provides no additional benefit. Transfer is confined to a pre-specified posterior-alpha readout, increases with the number of source participants, and survives leakage controls. On an independent dataset, cross-subject transfer replicates for perception, whereas perception-to-imagery transfer does not, highlighting state generalisation as an open problem. At the single-neuron level, the same perception-to-imagery transfer under the identity map emerges in human ventral temporal cortex. Optimal-transport analysis of EEG further suggests that the perception-to-imagery transformation is better described as translation and rescaling of a common representation than as a change in neural code. Finally, conditioning a frozen next-scale autoregressive image model on brain-predicted token prefixes enables real-time reconstruction of perceived images, while imagery reconstruction remains limited primarily by neural decoding accuracy. Together, these results show that cross-subject transfer can replace subject-specific calibration in imagery decoding and provide evidence for a common visual representation underlying perception and imagery.