Author:

Keywords:

Science & Technology, Technology, Life Sciences & Biomedicine, Engineering, Biomedical, Neurosciences, Engineering, Neurosciences & Neurology, deep learning, neural network, EEG dataset, benchmark dataset, EEG artifact removal, EEG denoising, BRAIN-COMPUTER-INTERFACE, REMOVE MUSCLE ARTIFACTS, INDEPENDENT COMPONENTS, OCULAR ARTIFACTS, EOG ARTIFACTS, REGRESSION, TIME, ELECTROENCEPHALOGRAM, SELECTION, Benchmarking, Deep Learning, Electroencephalography, Neural Networks, Computer, Signal Processing, Computer-Assisted, 0903 Biomedical Engineering, 1103 Clinical Sciences, 1109 Neurosciences, Biomedical Engineering, 3209 Neurosciences, 4003 Biomedical engineering

Abstract:

Objective.Deep learning (DL) networks are increasingly attracting attention across various fields, including electroencephalography (EEG) signal processing. These models provide comparable performance to that of traditional techniques. At present, however, there is a lack of well-structured and standardized datasets with specific benchmark limit the development of DL solutions for EEG denoising.Approach.Here, we present EEGdenoiseNet, a benchmark EEG dataset that is suited for training and testing DL-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 DL 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 DL-based EEG denoising. The dataset and code are available athttps://github.com/ncclabsustech/EEGdenoiseNet.