Explainable cross-task adaptive transfer learning for motor imagery EEG classification

Abstract

Objective. In the field of motor imagery (MI) electroencephalography (EEG) based brain-computer interfaces (BCIs), deep transfer learning (TL) has proven to be an effective tool for solving the problem of limited availability of subject-specific data for training of robust deep learning (DL) models. Despite considerable progress has been made in cross-subject/session and cross-device scenarios, the more challenging problem of cross-task deep TL still remains largely unexplored. Approach. We propose a novel explainable cross-task adaptive TL method for MI EEG decoding. Firstly, similarity analysis and data alignment are performed for EEG data of motor execution (ME) and MI tasks. Afterward, the MI EEG decoding model is obtained via pre-training with extensive ME EEG data and fine-tuning with partial MI EEG data. Finally, expected gradients based post-hoc explainability analysis is conducted for visualization of important temporal-spatial features. Main results. Extensive experiments are conducted on one large ME EEG dataset (HGD) and two large MI EEG datasets (openBMI and GIST). The best average classification accuracy of our method reaches 80.00% and 72.73% for openBMI and GIST respectively, which outperforms several state-of-the-art algorithms. Besides, the results of explainability analysis further validate the correlation between ME and MI EEG data and effectiveness of ME/MI cross-task adaptation. Significance. This paper confirms that decoding of MI EEG can be well facilitated by pre-existing ME EEG data, which largely relaxes the constraint of training samples for MI EEG decoding and has important practical sense.