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

arXiv: https://arxiv.org/abs/2002.00538 · HTML: https://mlsanity.com/paper/2002.00538/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 4 of 4)

- [Motor imagery classification using EEG spectrograms](https://mlsanity.com/paper/2211.08350/) (2022)
- [Neurophysiological Analysis in Motor and Sensory Cortices for Improving Motor Imagination](https://mlsanity.com/paper/2411.05811/) (2024)
- [Siamese Sleep Transformer For Robust Sleep Stage Scoring With Self-knowledge Distillation and Selective Batch Sampling](https://mlsanity.com/paper/2212.13919/) (2022)
- [EEG classifier cross-task transfer to avoid training sessions in robot-assisted rehabilitation](https://mlsanity.com/paper/2402.17790/) (2024)

## Abstract (arXiv)

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.