Meta-Learning Enhanced Multi-Source Domain Adaptation for zero-calibration motor imagery EEG decoding

Motor imagery (MI) based brain-computer interface (BCI) holds promising application prospects for closed-loop neurorehabilitation in stroke recovery. Despite substantial progress, challenges such as inter-subject variability, lack of training data for specific subject, and the need for time-consuming calibration still hinder the practical deployment of MI-BCI systems.

In this work, aiming to address these issues, we propose a novel Meta-Learning Enhanced Multi-Source Domain Adaptation (MLEMSDA) framework that unifies cross-task, cross-dataset, and cross-subject domain adaptation with gradient-based meta-learning to enable calibration-free MI-EEG decoding. Specifically, two large public ME and MI EEG datasets are firstly used for pre-training to facilitate cross-task and cross-dataset knowledge transfer. Afterward, to further reduce the differences in feature distribution among different individuals, meta-learning based fine-tuning is performed using data from all subjects in the target dataset except the unseen subject. Finally, the obtained decoding model is tested on the unseen subject.

The proposed MLEMSDA framework was validated on a public stroke MI EEG dataset (CBCIC), our own collected MI EEG dataset, and BCI Competition IV dataset 2b using leave-one-out cross-validation method. DeepConvNet achieved the highest average accuracy of 77.87% on CBCIC dataset, EEGNet yielded the highest average accuracy of 75.54% on our own collected dataset, and ShallowConvNet obtained the highest average accuracy of 72.72% on BCI Competition IV dataset 2b.

With respect to classification accuracy in the zero-calibration scenario, our method outperforms all the competing methods.

These results clearly demonstrate the effectiveness and generalizability of our method, paving the way for more practical MI-BCI applications.