  Neurophysiological time series recordings like the electroencephalogram (EEG)
or local field potentials are obtained from multiple sensors. They can be
decoded by machine learning models in order to estimate the ongoing brain state
of a patient or healthy user. In a brain-computer interface (BCI), this decoded
brain state information can be used with minimal time delay to either control
an application, e.g., for communication or for rehabilitation after stroke, or
to passively monitor the ongoing brain state of the subject, e.g., in a
demanding work environment. A specific challenge in such decoding tasks is
posed by the small dataset sizes in BCI compared to other domains of machine
learning like computer vision or natural language processing. A possibility to
tackle classification or regression problems in BCI despite small training data
sets is through transfer learning, which utilizes data from other sessions,
subjects or even datasets to train a model. In this exploratory study, we
propose novel domain-specific embeddings for neurophysiological data. Our
approach is based on metric learning and builds upon the recently proposed
ladder loss. Using embeddings allowed us to benefit, both from the good
generalisation abilities and robustness of deep learning and from the fast
training of classical machine learning models for subject-specific calibration.
In offline analyses using EEG data of 14 subjects, we tested the embeddings'
feasibility and compared their efficiency with state-of-the-art deep learning
models and conventional machine learning pipelines. In summary, we propose the
use of metric learning to obtain pre-trained embeddings of EEG-BCI data as a
means to incorporate domain knowledge and to reach competitive performance on
novel subjects with minimal calibration requirements.
