  Imagined speech is spotlighted as a new trend in the brain-machine interface
due to its application as an intuitive communication tool. However, previous
studies have shown low classification performance, therefore its use in
real-life is not feasible. In addition, no suitable method to analyze it has
been found. Recently, deep learning algorithms have been applied to this
paradigm. However, due to the small amount of data, the increase in
classification performance is limited. To tackle these issues, in this study,
we proposed an end-to-end framework using Siamese neural network encoder, which
learns the discriminant features by considering the distance between classes.
The imagined words (e.g., arriba (up), abajo (down), derecha (right), izquierda
(left), adelante (forward), and atr\'as (backward)) were classified using the
raw electroencephalography (EEG) signals. We obtained a 6-class classification
accuracy of 31.40% for imagined speech, which significantly outperformed other
methods. This was possible because the Siamese neural network, which increases
the distance between dissimilar samples while decreasing the distance between
similar samples, was used. In this regard, our method can learn discriminant
features from a small dataset. The proposed framework would help to increase
the classification performance of imagined speech for a small amount of data
and implement an intuitive communication system.
