1 citations · 2 across the 4 of their papers we have counts for
8 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…
Large Language Models are Highly Aligned with Human Ratings of Emotional Stimuli
Mattson Ogg, Chace Ashcraft, Ritwik Bose +2
Emotions exert an immense influence over human behavior and cognition in both commonplace and high-stress tasks. Discussions of whether or how to integrate large language models (L…
EEG Foundation Models for BCI Learn Diverse Features of Electrophysiology
Mattson Ogg, Rahul Hingorani, Diego Luna +3
Brain computer interface (BCI) research, as well as increasing portions of the field of neuroscience, have found success deploying large-scale artificial intelligence (AI) pre-trai…
Self-Supervised Speech Quality Assessment (S3QA): Leveraging Speech Foundation Models for a Scalable Speech Quality Metric
Mattson Ogg, Caitlyn Bishop, Han Yi +1
Methods for automatically assessing speech quality in real world environments are critical for developing robust human language technologies and assistive devices. Behavioral ratin…
Getting More from Less: Transfer Learning Improves Sleep Stage Decoding Accuracy in Peripheral Wearable Devices
William G Coon, Diego Luna, Akshita Panagrahi +2
Transfer learning, a technique commonly used in generative artificial intelligence, allows neural network models to bring prior knowledge to bear when learning a new task. This stu…
Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study
Mattson Ogg
Self-supervised learning (SSL) algorithms have emerged as powerful tools that can leverage large quantities of unlabeled audio data to pre-train robust representations that support…
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…
A Flexible Method for Behaviorally Measuring Alignment Between Human and Artificial Intelligence Using Representational Similarity Analysis
Mattson Ogg, Ritwik Bose, Jamie Scharf +2
As we consider entrusting Large Language Models (LLMs) with key societal and decision-making roles, measuring their alignment with human cognition becomes critical. This requires m…