Turning the AI Microscope on the Brain

The dominant paradigm in cognitive neuroimaging uses trial-based and tightly controlled experiments, while subsequent analysis relies on mass-univariate linear models. This approach rests on faulty assumption about neural pro-cessing and brain organisation, that are now widely recognised. Artificial Neural Networks (ANN) are in comparison powerful computational models that excel at learning nonlinear associations between relevant features to solve optimise any given objective. In this thesis, we explore how ANNs can be used as scientific tools to study electroencephalography (EEG) signal. We argue that ANNs are particularly well suited to analyse EEG signal outside of traditional experimental setups, where high variability between trials is expected. Representation spaces in ANNs trained to decode cognitive states from EEG signal can then be scrutinised using explainability techniques. We dub this approach the "AI Microscope", using the sophisticated ANN decoders to see patterns a human analyst could not have seen.

The empirical work in the thesis is presented through eight papers. They form a progression from traditional approaches such event-related potentials and band power analysis, to an advancement in experimental design using naturalistic stimuli and games, and conclude with work on the necessity and analysis of ANNs trained on EEG signal. We situate the AI microscope framework in the current neuroimaging and ANN literature as an alternative methodology for cognitive neuroscience, that can applied in circumstances where traditional methods fail. Throughout, we see that cognitive neuroscience and artificial intelligence research share many ideas and approaches, and such overlaps are highlighted in present work. The aim is not to replace traditional methods in neuroscience, but to complement them using the latest advancements from AI.