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

Ji-Wung Han, Soyeon Bak, Jun-Mo Kim, WooHyeok Choi, Dong-Hee Shin, Young-Han Son, Tae-Eui Kam

Abstract: Highlights•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.