论文标题
一种新型的卷积神经网络模型，以去除脑电图中的肌肉伪像
A novel convolutional neural network model to remove muscle artifacts from EEG
论文作者
论文摘要
记录的脑电图（EEG）信号通常被许多人工制品污染。近年来，深度学习模型已用于降解脑电图（EEG）数据，并提供了与传统技术相当的性能。但是，现有网络在肌电图（EMG）中的脱毛的性能受到限制，并且遭受了过度拟合的问题。在这里，我们介绍了一个新型的卷积神经网络（CNN），其特征尺寸逐渐上升，并在时间序列中进行下采样，以消除EEG数据中的肌肉伪像。与其他类型的卷积网络相比，该模型在很大程度上消除了过度拟合，并且在Eegdenoisenet中胜过四个基准网络。我们的研究表明，深层网络体系结构可能有助于避免过度拟合并更好地去除脑电图中的EMG工件。
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.
