Meta-Learning for Fast and Privacy-Preserving Source Knowledge Transfer of EEG-Based BCIs
Siyang Li, Huanyu Wu, Lieyun Ding, Dongrui Wu
Abstract
Electroencephalogram (EEG) based brain-computer interfaces (BCIs) are used in many applications, due to their low-risk, low-cost, and convenience. Because of EEG’s high variations across subjects and sessions, a long calibration session is usually needed to adjust the system before each use, which is time-consuming and user-unfriendly. Though various machine learning approaches have been proposed to cope with this problem, none of them considered individual differences, data scarcity and data privacy simultaneously. In this paper, a Multi-Domain Model-Agnostic Meta-Learning (MDMAML) approach is proposed to address challenging cross-subject, few-shot and source-free (privacy protection) classification tasks in EEG-based BCIs. Experiments on four datasets from two different BCI paradigms demonstrated that MDMAML outperformed several classical and state-of-the-art approaches in both online and offline applications.