RESHARE: REGION-STRUCTURED LEARNING FOR SHARED NEURAL VISUAL DECODING

Neural visual decoding holds promise for brain-computer interfaces, but collecting neural recordings paired with visual stimuli is time-consuming. Many highperforming EEG and MEG decoding methods rely on participant-specific models, while existing shared decoders remain challenged by differences in neural response distributions across participants. These differences hinder the reuse of existing recordings when only limited training data are available from a new participant. We propose ReShare, a region-structured framework for shared visual decoding from EEG and MEG. ReShare uses EEG and MEG sensor layouts to define regional groups for spatial and temporal encoding. It then calibrates the encoded responses using learned, participant-specific scaling and offsets within each region. A network shared across participants integrates these calibrated responses and aligns its outputs with intermediate visual features from a frozen CLIP ViT-L/14 encoder. Fine-tuning the pretrained decoder with 10% of a new EEG participant’s training pairs achieves 78.0% Top-1 accuracy. This exceeds the 75.0% achieved by a prior state-of-the-art participant-specific model trained on the same participant’s full training set. With one shared model per dataset, ReShare also achieves state-of-the-art 200-way brain-to-image retrieval of unseen images from participants included in training. Top-1 accuracies reach 95.70% on THINGS-EEG2 and 61.88% on THINGS-MEG. These results show that region-structured shared learning supports accurate visual decoding while reducing the paired training data needed to adapt to a new participant.