EEGdenoiseNet: a benchmark dataset for deep learning solutions of EEG denoising

OBJECTIVE 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. APPROACH 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. MAIN RESULTS 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 results suggested that deep learning methods have great potential for EEG denoising even under high noise contamination. SIGNIFICANCE Through EEGdenoiseNet, we hope to accelerate the development of the emerging field of deep learning-based EEG denoising. The dataset and code are available at https://github.com/ncclabsustech/EEGdenoiseNet.