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