  A brain-computer interface (BCI) is used not only to control external devices
for healthy people but also to rehabilitate motor functions for motor-disabled
patients. Decoding movement intention is one of the most significant aspects
for performing arm movement tasks using brain signals. Decoding movement
execution (ME) from electroencephalogram (EEG) signals have shown high
performance in previous works, however movement imagination (MI) paradigm-based
intention decoding has so far failed to achieve sufficient accuracy. In this
study, we focused on a robust MI decoding method with transfer learning for the
ME and MI paradigm. We acquired EEG data related to arm reaching for 3D
directions. We proposed a BCI-transfer learning method based on a Relation
network (BTRN) architecture. Decoding performances showed the highest
performance compared to conventional works. We confirmed the possibility of the
BTRN architecture to contribute to continuous decoding of MI using ME datasets.
