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Deep learning networks are increasingly attracting attention in variousfields, including electroencephalography (EEG) signal processing. These modelsprovided comparable performance with that of traditional techniques. Atpresent, however, lacks of well-structured and standardized datasets withspecific benchmark limit the development of deep learning solutions for EEGdenoising. Here, we present EEGdenoiseNet, a benchmark EEG dataset that issuited for training and testing deep learning-based denoising models, as wellas for performance comparisons across models. EEGdenoiseNet contains 4514 cleanEEG segments, 3400 ocular artifact segments and 5598 muscular artifactsegments, allowing users to synthesize contaminated EEG segments with theground-truth clean EEG. We used EEGdenoiseNet to evaluate denoising performanceof four classical networks (a fully-connected network, a simple and a complexconvolution network, and a recurrent neural network). Our analysis suggestedthat deep learning methods have great potential for EEG denoising even underhigh noise contamination. Through EEGdenoiseNet, we hope to accelerate thedevelopment of the emerging field of deep learning-based EEG denoising.

EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising