# Model-Agnostic Meta-Learning for EEG Motor Imagery Decoding in Brain-Computer-Interfacing

arXiv: https://arxiv.org/abs/2103.08664 · HTML: https://mlsanity.com/paper/2103.08664/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 10 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Meta Networks](https://mlsanity.com/paper/1703.00837/) (2017)
- [Transfer Learning in Brain-Computer Interfaces](https://mlsanity.com/paper/1512.00296/) (2015)
- [Inter-subject Deep Transfer Learning for Motor Imagery EEG Decoding](https://mlsanity.com/paper/2103.05351/) (2021)
- [Learning Not to Learn in the Presence of Noisy Labels](https://mlsanity.com/paper/2002.06541/) (2020)
- [Compact Convolutional Neural Networks for Multi-Class, Personalised, Closed-Loop EEG-BCI](https://mlsanity.com/paper/1807.11752/) (2018)
- [HemCNN: Deep Learning enables decoding of fNIRS cortical signals in hand grip motor tasks](https://mlsanity.com/paper/2103.05338/) (2021)

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

- [A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface](https://mlsanity.com/paper/2209.03785/) (2022)
- [Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning](https://mlsanity.com/paper/2207.13801/) (2022)
- [Evaluating Fast Adaptability of Neural Networks for Brain-Computer Interface](https://mlsanity.com/paper/2404.15350/) (2024)
- [Robust Neural Stimulation Response Modeling Through Meta-Learning and Pretraining](https://mlsanity.com/paper/2608.26649/) (2026)

## Abstract (arXiv)

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.