Dynamic Cognitive Prior Generation for Robust EEG-to-Image Decoding

Abstract

One of the key challenges in brain computer interfaces (BCIs) is to understand the visual perceptual content (VPC) of non-invasive electroencephalography (EEG) signals with high accuracy, without the aid of brain mapping techniques, which is hindered by high inter-subject variations and the non-stationary nature of neural responses. Current methods, including NeuroBridge, use handcrafted, fixed, subject-agnostic transformations of perceptual variance called Cognitive Prior Augmentation (CPA). These static priors, however, have little capability for modelling the dynamic changes of cognition states and individual brain properties, which severely constrain across-subjects generalization. We introduced Dynamic Cognitive Prior Generation (DCPG) a new framework, which can be learned and is prototype based to adaptively generate priors instead of heuristic augmentations. Our approach distills the subject-specific cognitive priors by modelling the attention to a common bank of prototype representations, based on a given EEG trial and based on a learnable subject embedding. Using feature modulation, DCPG can adaptively calibrate the representation of EEG before semantic projection, thus reducing the domain shift between different subjects effectively. It was shown that DCPG can be used to significantly increase the accuracy of inter-subject retrieval on the THINGS-EEG dataset with an accuracy improvement of +3.0% while adding 4.9% more parameters compared to NeuroBridge. This framework is the new state-of-the-art in the field of robust EEG-image decoding, with results in a variety of populations.

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