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A brain-computer interface (BCI) is used not only to control external devicesfor healthy people but also to rehabilitate motor functions for motor-disabledpatients. Decoding movement intention is one of the most significant aspectsfor performing arm movement tasks using brain signals. Decoding movementexecution (ME) from electroencephalogram (EEG) signals have shown highperformance in previous works, however movement imagination (MI) paradigm-basedintention decoding has so far failed to achieve sufficient accuracy. In thisstudy, we focused on a robust MI decoding method with transfer learning for theME and MI paradigm. We acquired EEG data related to arm reaching for 3Ddirections. We proposed a BCI-transfer learning method based on a Relationnetwork (BTRN) architecture. Decoding performances showed the highestperformance compared to conventional works. We confirmed the possibility of theBTRN architecture to contribute to continuous decoding of MI using ME datasets.

Decoding Movement Imagination and Execution from EEG Signals using BCI-Transfer Learning Method based on Relation Network