5 papers
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…
Machine Learning Estimation on the Trace of Inverse Dirac Operator using the Gradient Boosting Decision Tree Regression
Benjamin J. Choi, Hiroshi Ohno, Takayuki Sumimoto +1
We present our preliminary results on the machine learning estimation of from other observables with the gradient boosting decision tree regression, where …