Decoding Movement Imagination and Execution From Eeg Signals Using Bci-Transfer Learning Method Based on Relation Network

• Published in: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 1354 - 1358
• Main Authors: Lee, Do-Yeun, Jeong, Ji-Hoon, Shim, Kyung-Hwan, Lee, Seong-Whan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2020
• Subjects: Brain-computer interface (BCI); Brain-computer interfaces; Decoding; Electroencephalogram (EEG); Electroencephalography; Learning systems; Movement imagination and execution; Performance evaluation; Three-dimensional displays; Transfer learning
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP40776.2020.9052997

A brain-computer interface (BCI) is used to control external devices for healthy people as well as 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.