4 papers · 1 filter
A Statistical Mixture-of-Experts Framework for EMG Artifact Removal in EEG: Empirical Insights and a Proof-of-Concept Application
Benjamin J. Choi, Griffin Milsap, Clara A. Scholl +2
Effective control of neural interfaces is limited by poor signal quality. While neural network-based electroencephalography (EEG) denoising methods for electromyogenic (EMG) artifa…
Geometric Machine Learning on EEG Signals
Benjamin J. Choi
Brain-computer interfaces (BCIs) offer transformative potential, but decoding neural signals presents significant challenges. The core premise of this paper is built around demonst…
Removing Neural Signal Artifacts with Autoencoder-Targeted Adversarial Transformers (AT-AT)
Benjamin J. Choi
Electromyogenic (EMG) noise is a major contamination source in EEG data that can impede accurate analysis of brain-specific neural activity. Recent literature on EMG artifact remov…
Targeted Adversarial Denoising Autoencoders (TADA) for Neural Time Series Filtration
Benjamin J. Choi, Griffin Milsap, Clara A. Scholl +2
Current machine learning (ML)-based algorithms for filtering electroencephalography (EEG) time series data face challenges related to cumbersome training times, regularization, and…