# Targeted Adversarial Denoising Autoencoders (TADA) for Neural Time Series Filtration

arXiv: https://arxiv.org/abs/2501.04967 · HTML: https://mlsanity.com/paper/2501.04967/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 9 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Deep Learning for Audio Signal Processing](https://mlsanity.com/paper/1905.00078/) (2019)
- [Predicting the Computational Cost of Deep Learning Models](https://mlsanity.com/paper/1811.11880/) (2018)
- [Building Brain Invaders: EEG data of an experimental validation](https://mlsanity.com/paper/1905.05182/) (2019)

## Built on by (most cited first, 5 of 5)

- [Geometric Machine Learning on EEG Signals](https://mlsanity.com/paper/2502.05334/) (2025)
- [Removing Neural Signal Artifacts with Autoencoder-Targeted Adversarial Transformers (AT-AT)](https://mlsanity.com/paper/2502.05332/) (2025)
- [SAVeD: Learning to Denoise Low-SNR Video for Improved Downstream Performance](https://mlsanity.com/paper/2504.00161/) (2025)
- [A Statistical Mixture-of-Experts Framework for EMG Artifact Removal in EEG: Empirical Insights and a Proof-of-Concept Application](https://mlsanity.com/paper/2509.19385/) (2025)
- [Physics-Constrained Denoising Autoencoders for Data-Scarce Wildfire UAV Sensing](https://mlsanity.com/paper/2601.11794/) (2026)

## Abstract (arXiv)

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.