  Brain-Computer Interfaces (BCI) based on Electroencephalography (EEG)
signals, in particular motor imagery (MI) data have received a lot of attention
and show the potential towards the design of key technologies both in
healthcare and other industries. MI data is generated when a subject imagines
movement of limbs and can be used to aid rehabilitation as well as in
autonomous driving scenarios. Thus, classification of MI signals is vital for
EEG-based BCI systems. Recently, MI EEG classification techniques using deep
learning have shown improved performance over conventional techniques. However,
due to inter-subject variability, the scarcity of unseen subject data, and low
signal-to-noise ratio, extracting robust features and improving accuracy is
still challenging. In this context, we propose a novel two-way few shot network
that is able to efficiently learn how to learn representative features of
unseen subject categories and how to classify them with limited MI EEG data.
The pipeline includes an embedding module that learns feature representations
from a set of samples, an attention mechanism for key signal feature discovery,
and a relation module for final classification based on relation scores between
a support set and a query signal. In addition to the unified learning of
feature similarity and a few shot classifier, our method leads to emphasize
informative features in support data relevant to the query data, which
generalizes better on unseen subjects. For evaluation, we used the BCI
competition IV 2b dataset and achieved an 9.3% accuracy improvement in the
20-shot classification task with state-of-the-art performance. Experimental
results demonstrate the effectiveness of employing attention and the overall
generality of our method.
