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Patterns of brain activity are associated with different brain processes andcan be used to identify different brain states and make behavioral predictions.However, the relevant features are not readily apparent and accessible. To mineinformative latent representations from multichannel recordings of ongoing EEGactivity, we propose a novel differentiable decoding pipeline consisting oflearnable filters and a pre-determined feature extraction module. Specifically,we introduce filters parameterized by generalized Gaussian functions that offera smooth derivative for stable end-to-end model training and allow for learninginterpretable features. For the feature module, we use signal magnitude andfunctional connectivity estimates. We demonstrate the utility of our modeltowards emotion recognition from EEG signals on the SEED dataset, as well as ona new EEG dataset of unprecedented size (i.e., 761 subjects), where we identifyconsistent trends of music perception and related individual differences. Thediscovered features align with previous neuroscience studies and offer newinsights, such as marked differences in the functional connectivity profilebetween left and right temporal areas during music listening. This agrees withthe respective specialisation of the temporal lobes regarding music perceptionproposed in the literature.

EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters