Denoising-Based Domain Adaptation Network for EEG Source Imaging
Abstract
Electrophysiological source imaging (ESI) is a widespread and no-invasive technique in neuroscientific research and clinical diagnostics. It provides a well-established and high temporal resolution of source activity and gives the brain signal by analyzing the corresponding EEG signal.
However, it is still a major challenge to deal with the domain shift problem between the datasets of different subjects or sessions in ESI problem. Furthermore, the variable noise included in the EEG signals inevitably influence the accuracy of localization of source activity.
In this paper, we propose a novel denoising autoencoder-based unsupervised domain adaptation (DAE-UDA) algorithm to tackle these problems. To the best of our knowledge, it is the first to solve the domain shift in the ESI problem by using UDA to narrow the discrepancy between different domains. Moreover, we innovatively combine our model with denoising autoencoder (DAE) to remove noise and learn a robust mapping from the noisy EEG signal to the brain activity.
Extensive numerical experiments and the analysis of real EEG datasets demonstrate that DAE-UDA can effectively remove noise and mitigates the domain shift of low-SNR EEG signal. Our model outperforms other classical ESI methods in robustly imaging source activities under a variety of source settings.