  The recorded electroencephalography (EEG) signals are usually contaminated by
many artifacts. In recent years, deep learning models have been used for
denoising of electroencephalography (EEG) data and provided comparable
performance with that of traditional techniques. However, the performance of
the existing networks in electromyograph (EMG) artifact removal was limited and
suffered from the over-fitting problem. Here we introduce a novel convolutional
neural network (CNN) with gradually ascending feature dimensions and
downsampling in time series for removing muscle artifacts in EEG data. Compared
with other types of convolutional networks, this model largely eliminates the
over-fitting and significantly outperforms four benchmark networks in
EEGdenoiseNet. Our study suggested that the deep network architecture might
help avoid overfitting and better remove EMG artifacts in EEG.
