Model-agnostic meta-learning for EEG motor imagery decoding in brain-computer-interfacing
File(s)
Accepted version
Author(s)
Denghao, Li
Ortega San Miguel, Pablo
Wei, Xiaoxi
Faisal, A Aldo
Type
Conference Paper
Abstract
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.
Date Acceptance
2021-02-01
Publisher
IEEE
Copyright Statement
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Identifier
https://ieeexplore.ieee.org/document/9441077
Source
10th International IEEE EMBS Conference on Neural Engineering (NER 21)
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Theory & Methods
Engineering, Biomedical
Neurosciences
Computer Science
Engineering
Neurosciences & Neurology
Publication Status
Published
Start Date
2021-05-04
Finish Date
2021-05-06
Coverage Spatial
Vitual