  Deep learning networks are increasingly attracting attention in various
fields, including electroencephalography (EEG) signal processing. These models
provided comparable performance with that of traditional techniques. At
present, however, lacks of well-structured and standardized datasets with
specific benchmark limit the development of deep learning solutions for EEG
denoising. Here, we present EEGdenoiseNet, a benchmark EEG dataset that is
suited for training and testing deep learning-based denoising models, as well
as for performance comparisons across models. EEGdenoiseNet contains 4514 clean
EEG segments, 3400 ocular artifact segments and 5598 muscular artifact
segments, allowing users to synthesize contaminated EEG segments with the
ground-truth clean EEG. We used EEGdenoiseNet to evaluate denoising performance
of four classical networks (a fully-connected network, a simple and a complex
convolution network, and a recurrent neural network). Our analysis suggested
that deep learning methods have great potential for EEG denoising even under
high noise contamination. Through EEGdenoiseNet, we hope to accelerate the
development of the emerging field of deep learning-based EEG denoising.
