Yu Junjie, Li Chenyi, Lou Kexin, Wei Chen, Liu Quanying
Shenzhen Key Laboratory of Smart Healthcare Engineering, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, People's Republic of China.
School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, People's Republic of China.
J Neural Eng. 2022 Apr 22;19(2). doi: 10.1088/1741-2552/ac63eb.
Electroencephalogram (EEG) recordings are often contaminated with artifacts. Various methods have been developed to eliminate or weaken the influence of artifacts. However, most of them rely on prior experience for analysis.Here, we propose an deep learning framework to separate neural signal and artifacts in the embedding space and reconstruct the denoised signal, which is called DeepSeparator. DeepSeparator employs an encoder to extract and amplify the features in the raw EEG, a module called decomposer to extract the trend, detect and suppress artifact and a decoder to reconstruct the denoised signal. Besides, DeepSeparator can extract the artifact, which largely increases the model interpretability.The proposed method is tested with a semi-synthetic EEG dataset and a real task-related EEG dataset, suggesting that DeepSeparator outperforms the conventional models in both EOG and EMG artifact removal.DeepSeparator can be extended to multi-channel EEG and data with any arbitrary length. It may motivate future developments and application of deep learning-based EEG denoising. The code for DeepSeparator is available athttps://github.com/ncclabsustech/DeepSeparator.
脑电图（EEG）记录常常受到伪迹的干扰。人们已经开发出各种方法来消除或减弱伪迹的影响。然而，其中大多数方法依赖于先验经验进行分析。在此，我们提出一种深度学习框架，用于在嵌入空间中分离神经信号和伪迹，并重建去噪后的信号，该框架称为深度分离器（DeepSeparator）。深度分离器使用一个编码器来提取并放大原始脑电图中的特征，一个名为分解器的模块来提取趋势、检测并抑制伪迹，以及一个解码器来重建去噪后的信号。此外，深度分离器能够提取伪迹，这在很大程度上提高了模型的可解释性。所提出的方法通过一个半合成脑电图数据集和一个与实际任务相关的脑电图数据集进行了测试，结果表明深度分离器在去除眼电（EOG）和肌电（EMG）伪迹方面均优于传统模型。深度分离器可以扩展到多通道脑电图以及任意长度的数据。它可能会推动基于深度学习的脑电图去噪技术的未来发展和应用。深度分离器的代码可在https://github.com/ncclabsustech/DeepSeparator获取。
