  We introduce and compare several strategies for learning discriminative
features from electroencephalography (EEG) recordings using deep learning
techniques. EEG data are generally only available in small quantities, they are
high-dimensional with a poor signal-to-noise ratio, and there is considerable
variability between individual subjects and recording sessions. Our proposed
techniques specifically address these challenges for feature learning.
Cross-trial encoding forces auto-encoders to focus on features that are stable
across trials. Similarity-constraint encoders learn features that allow to
distinguish between classes by demanding that two trials from the same class
are more similar to each other than to trials from other classes. This
tuple-based training approach is especially suitable for small datasets.
Hydra-nets allow for separate processing pathways adapting to subsets of a
dataset and thus combine the advantages of individual feature learning (better
adaptation of early, low-level processing) with group model training (better
generalization of higher-level processing in deeper layers). This way, models
can, for instance, adapt to each subject individually to compensate for
differences in spatial patterns due to anatomical differences or variance in
electrode positions. The different techniques are evaluated using the publicly
available OpenMIIR dataset of EEG recordings taken while participants listened
to and imagined music.
