EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIs

TL;DR

EEG-Reptile is an automated meta-learning library that enhances EEG classifier adaptation to new subjects with minimal data, simplifying meta-learning application in BCIs.

Contribution

It introduces an automated, user-friendly library implementing Reptile meta-learning for EEG classification, including hyperparameter tuning and data management.

Findings

Improved zero-shot and few-shot learning performance on benchmark datasets.

Effective domain adaptation across subjects with minimal data.

Automated hyperparameter tuning enhances usability and results.

Abstract

Meta-learning, i.e., "learning to learn", is a promising approach to enable efficient BCI classifier training with limited amounts of data. It can effectively use collections of in some way similar classification tasks, with rapid adaptation to new tasks where only minimal data are available. However, applying meta-learning to existing classifiers and BCI tasks requires significant effort. To address this issue, we propose EEG-Reptile, an automated library that leverages meta-learning to improve classification accuracy of neural networks in BCIs and other EEG-based applications. It utilizes the Reptile meta-learning algorithm to adapt neural network classifiers of EEG data to the inter-subject domain, allowing for more efficient fine-tuning for a new subject on a small amount of data. The proposed library incorporates an automated hyperparameter tuning module, a data management pipeline, and an implementation of the Reptile meta-learning algorithm. EEG-Reptile automation level allows using it without deep understanding of meta-learning. We demonstrate the effectiveness of EEG-Reptile on two benchmark datasets (BCI IV 2a, Lee2019 MI) and three neural network architectures (EEGNet, FBCNet, EEG-Inception). Our library achieved improvement in both zero-shot and few-shot learning scenarios compared to traditional transfer learning approaches.