CNS neuroscience & therapeutics · 2025
LRR-UNet: A Deep Unfolding Network With Low-Rank Recovery for EEG Signal Denoising.
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Yue Xiaoxiong, Lu Liangfu, Liu Haipeng, Zang Yunliang
Аннотация
BACKGROUND: Electroencephalogram (EEG) signals are crucial for brain-computer interface research but are highly susceptible to noise contamination, necessitating effective denoising. While deep learning has been widely applied, its "black-box" nature limits interpretability. In contrast, traditional model-based methods like Low-Rank Recovery (LRR) offer strong interpretability by decomposing signals into low-rank and sparse components.
OBJECTIVE: This paper aims to develop an interpretable deep-learning model for EEG denoising that combines the performance of deep learning with the interpretability of traditional LRR methods.
METHODS: We propose LRR-Unet, a deep unfolding network that transforms the traditional iterative LRR algorithm into a neural network architecture. Specifically, the time-consuming Singular Value Decomposition (SVD) and sparse optimization processes in LRR are replaced with learnable neural network modules.
RESULTS: Extensive experiments demonstrate that LRR-Unet outperforms other state-of-the-art models in removing ocular and electromyographic artifacts, achieving superior performance on both quantitative and qualitative metrics. Furthermore, in downstream classification tasks, EEG signals preprocessed with LRR-Unet yield better results across various evaluation indicators.
CONCLUSION: The proposed LRR-Unet provides an effective and interpretable solution for EEG denoising. Its superiority in denoising performance and practical utility in enhancing downstream application performance is validated through comprehensive experiments.
OBJECTIVE: This paper aims to develop an interpretable deep-learning model for EEG denoising that combines the performance of deep learning with the interpretability of traditional LRR methods.
METHODS: We propose LRR-Unet, a deep unfolding network that transforms the traditional iterative LRR algorithm into a neural network architecture. Specifically, the time-consuming Singular Value Decomposition (SVD) and sparse optimization processes in LRR are replaced with learnable neural network modules.
RESULTS: Extensive experiments demonstrate that LRR-Unet outperforms other state-of-the-art models in removing ocular and electromyographic artifacts, achieving superior performance on both quantitative and qualitative metrics. Furthermore, in downstream classification tasks, EEG signals preprocessed with LRR-Unet yield better results across various evaluation indicators.
CONCLUSION: The proposed LRR-Unet provides an effective and interpretable solution for EEG denoising. Its superiority in denoising performance and practical utility in enhancing downstream application performance is validated through comprehensive experiments.