We introduce here the idea of Meta-Learning for training EEG BCI decoders. Meta-Learning is a way of training machine learning systems so they learn to learn. We apply here meta-learning to a simple Deep Learning BCI architecture and compare it to transfer learning on the same architecture. Our Meta-learning strategy operates by finding optimal parameters for the BCI decoder so that it can quickly generalise between different users and recording sessions -- thereby also generalising to new users or new sessions quickly. We tested our algorithm on the Physionet EEG motor imagery dataset. Our approach increased motor imagery classification accuracy between 60% to 80%, outperforming other algorithms under the little-data condition. We believe that establishing the meta-learning or learning-to-learn approach will help neural engineering and human interfacing with the challenges of quickly setting up decoders of neural signals to make them more suitable for daily-life.
@article{arxiv.2103.08664,
title = {Model-Agnostic Meta-Learning for EEG Motor Imagery Decoding in Brain-Computer-Interfacing},
author = {Denghao Li and Pablo Ortega and Xiaoxi Wei and Aldo Faisal},
journal= {arXiv preprint arXiv:2103.08664},
year = {2021}
}