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Analyzing and reconstructing visual stimuli from brain signals effectivelyadvances understanding of the human visual system. However, the EEG signals arecomplex and contain a amount of noise. This leads to substantial limitations inexisting works of visual stimuli reconstruction from EEG, such as difficultiesin aligning EEG embeddings with the fine-grained semantic information and aheavy reliance on additional large self-collected dataset for training. Toaddress these challenges, we propose a novel approach called BrainVis. Firstly,we divide the EEG signals into various units and apply a self-supervisedapproach on them to obtain EEG time-domain features, in an attempt to ease thetraining difficulty. Additionally, we also propose to utilize thefrequency-domain features to enhance the EEG representations. Then, wesimultaneously align EEG time-frequency embeddings with the interpolation ofthe coarse and fine-grained semantics in the CLIP space, to highlight theprimary visual components and reduce the cross-modal alignment difficulty.Finally, we adopt the cascaded diffusion models to reconstruct images. Ourproposed BrainVis outperforms state of the arts in both semantic fidelityreconstruction and generation quality. Notably, we reduce the training datascale to 10% of the previous work.

BrainVis: Exploring the Bridge between Brain and Visual Signals via Image Reconstruction