Exploring Deep Learning Models for EEG Neural Decoding

Neural decoding is an important method in cognitive neuroscience that aims to decode brain representations from recorded neural
activity using a multivariate machine learning model. The THINGS initiative provides a large EEG dataset of 46 subjects watching rapidly
shown images. Here, we test the feasibility of using this method for decoding high-level object features using recent deep learning models. We create a derivative dataset from this of living vs non-living entities test 15 different deep learning models with 5 different architectures and compare to a SOTA linear model. We show that the linear model is not able to solve the decoding task, while almost all the deep learning models are successful, suggesting that in some cases non-linear models are needed to decode neural representations. We also run a comparative study of the models’ performance on individual object categories, and suggest how
artificial neural networks can be used to study brain activity.