用对抗性自编码器精准去除脑电图中的肌电噪声，模型更小更快。
Targeted Adversarial Denoising Autoencoders (TADA) for Neural Time Series Filtration
- 基于协方差目标的对抗性去噪自编码器，聚焦信号相关性建模。
- 在67人数据集上，相关系数与误差指标均优于传统方法和深度模型。
- 模型参数少于40万，适合资源受限场景部署，适合神经信号处理研究者。
当前基于机器学习的脑电图（EEG）时间序列滤波算法存在训练耗时长、正则化困难及重建精度不足等问题。为此，本文提出一种基于逻辑协方差目标的对抗性去噪自编码器（TADA），假设具有相关性驱动的卷积结构能有效实现时间序列滤波，同时降低计算开销。通过在包含67名受试者数据的EEGdenoiseNet数据集上进行实验，验证了该方法可有效去除肌电（EMG）噪声。TADA在相关系数、时间域和频域相对均方根误差等定量指标上均优于传统滤波方法，并在模型参数少于40万的情况下，性能媲美其他深度学习架构。未来需进一步评估其在更多应用场景的可行性。
原文摘要 · Abstract (English)
Current machine learning (ML)-based algorithms for filtering electroencephalography (EEG) time series data face challenges related to cumbersome training times, regularization, and accurate reconstruction. To address these shortcomings, we present an ML filtration algorithm driven by a logistic covariance-targeted adversarial denoising autoencoder (TADA). We hypothesize that the expressivity of a targeted, correlation-driven convolutional autoencoder will enable effective time series filtration while minimizing compute requirements (e.g., runtime, model size). Furthermore, we expect that adversarial training with covariance rescaling will minimize signal degradation. To test this hypothesis, a TADA system prototype was trained and evaluated on the task of removing electromyographic (EMG) noise from EEG data in the EEGdenoiseNet dataset, which includes EMG and EEG data from 67 subjects. The TADA filter surpasses conventional signal filtration algorithms across quantitative metrics (Correlation Coefficient, Temporal RRMSE, Spectral RRMSE), and performs competitively against other deep learning architectures at a reduced model size of less than 400,000 trainable parameters. Further experimentation will be necessary to assess the viability of TADA on a wider range of deployment cases.
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