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

TL;DR

This paper introduces TADA, a targeted adversarial autoencoder that effectively filters EEG signals by removing EMG noise, achieving superior performance with less computational cost compared to traditional and other deep learning methods.

Contribution

The paper presents TADA, a novel correlation-driven autoencoder with adversarial training, demonstrating improved EEG denoising efficiency and reduced model size over existing algorithms.

Findings

TADA outperforms conventional filtering algorithms in key metrics.

TADA achieves competitive results with fewer than 400,000 parameters.

TADA reduces computational requirements while maintaining high denoising quality.

Abstract

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.