Cross-dataset transfer learning for Motor Imagery signal classification via multi-task learning and pre-training.

Researchers

Journal

Modalities

Models

Abstract

Deep Learning (DL) models have been proven to be effective in decoding Motor Imagery (MI) signals in Electroencephalogram (EEG) data. However, DL models’ success relies heavily on large amounts of training data, whereas EEG data collection is laborious and time-consuming. Recently, cross-dataset transfer learning has emerged as a promising approach to meet the data requirements of DL models. Nevertheless, transferring knowledge across datasets involving different MI tasks remains a significant challenge in cross-dataset transfer learning, limiting the full utilization of valuable data resources. This study proposes a pre-training-based cross-dataset transfer learning method inspired by Hard Parameter Sharing in multi-task learning. The method enables knowledge transfer across datasets with distinct MI tasks. We also designed four fine-tuning schemes and conducted extensive experiments on them. The results showed that compared to models without pre-training, models with pre-training achieved a maximum increase in accuracy of 7.76%. Moreover, when limited training data were available, the pre-training method significantly improved DL model’s accuracy by 27.34% at most. The experiments also revealed that pre-trained models exhibit faster convergence and remarkable robustness. The training time per subject could be reduced by up to 102.83 seconds, and the variance of classification accuracy decreased by 75.22% at best. This study represents the first comprehensive investigation of the cross-dataset transfer learning method between two datasets with different MI tasks. The proposed pre-training method requires only minimal fine-tuning data when applying DL models to new MI paradigms, making MI-BCI more practical and user-friendly.© 2023 IOP Publishing Ltd.