META-EEG: Meta-learning-based class-relevant EEG representation learning for zero-calibration brain-computer interfaces

• Published in: Expert systems with applications Vol. 238; p. 121986
• Main Authors: Han, Ji-Wung, Bak, Soyeon, Kim, Jun-Mo, Choi, WooHyeok, Shin, Dong-Hee, Son, Young-Han, Kam, Tae-Eui
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.03.2024
• Subjects: Brain-computer interface; Electroencephalography; Inter-subject variability; Meta-learning; Motor imagery; Zero-calibration; Brain-computer interface; Electroencephalography; Motor imagery; Zero-calibration; Inter-subject variability; Meta-learning
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.121986

Transfer learning for motor imagery-based brain-computer interfaces (MI-BCIs) struggles with inter-subject variability, hindering its generalization to new users. This paper proposes an advanced implicit transfer learning framework, META-EEG, designed to overcome the challenge arising from inter-subject variability. By incorporating gradient-based meta-learning with an intermittent freezing strategy, META-EEG ensures efficient feature representation learning, providing a robust zero-calibration solution. A comparative analysis reveals that META-EEG significantly outperforms all the baseline methods and competing methods on three different public datasets. Moreover, we demonstrate the efficiency of the proposed model through a neurophysiological and feature-representational analysis. With its robustness and superior performance on challenging datasets, META-EEG provides an effective solution for calibration-free MI-EEG classification, facilitating broader usability. •We propose a meta-learning-based zero-calibration EEG feature learning framework.•We construct meta-tasks robust to unseen subjects in meta-training.•We design intermittent freezing to learn class-relevant EEG features efficiently.•It shows effectiveness in motor imagery EEG classification for new stroke patients.•It provides an effective solution for zero-calibration BCI with broader usability.