用深度自编码器自动检测修正脑电噪声，减少人工干预。
EEG Artifact Detection and Correction with Deep Autoencoders
- 基于LSTM的自编码器捕捉脑电信号时序非线性特征。
- 在去噪和检测任务中优于现有卷积自编码器方法。
- 适合需要自动化脑电预处理的神经科学与临床研究者。
脑电信号能反映健康与病理状态下的大脑活动，但本身具有高噪声特性，给准确分析带来挑战。传统去噪方法虽有效，但常需大量专家参与。本研究提出LSTEEG，一种基于LSTM的自编码器，用于脑电信号中的伪迹检测与校正。通过深度学习，特别是LSTM层，模型能够捕捉序列脑电信号中的非线性依赖关系。实验表明，LSTEEG在伪迹检测与校正任务中均优于其他先进卷积自编码器。该方法提升了自编码器隐空间的可解释性，支持数据驱动的自动化伪迹去除，适用于下游分析任务。本研究推动了多通道脑电高效、精准预处理的发展，促进自动化脑电分析流程在脑健康应用中的部署与使用。
原文摘要 · Abstract (English)
EEG signals convey important information about brain activity both in healthy and pathological conditions. However, they are inherently noisy, which poses significant challenges for accurate analysis and interpretation. Traditional EEG artifact removal methods, while effective, often require extensive expert intervention. This study presents LSTEEG, a novel LSTM-based autoencoder designed for the detection and correction of artifacts in EEG signals. Leveraging deep learning, particularly LSTM layers, LSTEEG captures non-linear dependencies in sequential EEG data. LSTEEG demonstrates superior performance in both artifact detection and correction tasks compared to other state-of-the-art convolutional autoencoders. Our methodology enhances the interpretability and utility of the autoencoder's latent space, enabling data-driven automated artefact removal in EEG its application in downstream tasks. This research advances the field of efficient and accurate multi-channel EEG preprocessing, and promotes the implementation and usage of automated EEG analysis pipelines for brain health applications.
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