4.8k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- DEVCOM Army Research LaboratoryUS8 papers
- The University of Texas at San AntonioUS4 papers
- University of Technology SydneyAU4 papers
- National Yang Ming Chiao Tung UniversityTW3 papers
- United States Army Combat Capabilities Development CommandUS2 papers
- Aristotle University of ThessalonikiGR1 paper
- CEA GrenobleFR1 paper
- CEA Paris-SaclayFR1 paper
- Center for Neurologic StudyUS1 paper
- Centre Inria de SaclayFR1 paper
- ClinatecFR1 paper
- Columbia UniversityUS1 paper
10 papers
Decoding Neural Activity to Assess Individual Latent State in Ecologically Valid Contexts
Stephen M. Gordon, Jonathan R. McDaniel, Kevin W. King +2
There exist very few ways to isolate cognitive processes, historically defined via highly controlled laboratory studies, in more ecologically valid contexts. Specifically, it remai…
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets
Xiaoxi Wei, A. Aldo Faisal, Moritz Grosse-Wentrup +18
Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is bec…
Nonequilibrium plastic roughening of metallic glasses yields self-affine topographies with strain-rate and temperature-dependent scaling exponents
Wolfram G. Nöhring, Adam R. Hinkle, Lars Pastewka
We study nonequilibrium roughening during compressive plastic flow of initially flat CuZr metallic glass using large-scale molecular dynamics simulations. Roughness e…
Collaborative Brain-Computer Interface for Human Interest Detection in Complex and Dynamic Settings
Amelia J. Solon, Stephen M. Gordon, Jonathan R. McDaniel +1
Humans can fluidly adapt their interest in complex environments in ways that machines cannot. Here, we lay the groundwork for a real-world system that passively monitors and merges…
Analyzing P300 Distractors for Target Reconstruction
Jonathan R. McDaniel, Stephen M. Gordon, Amelia J. Solon +1
P300-based brain-computer interfaces (BCIs) are often trained per-user and per-application space. Training such models requires ground truth knowledge of target and non-target stim…
Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface
Dongrui Wu, Vernon J. Lawhern, Stephen Gordon +2
Ensemble learning is a powerful approach to construct a strong learner from multiple base learners. The most popular way to aggregate an ensemble of classifiers is majority voting,…
Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression
Dongrui Wu, Vernon J. Lawhern, Stephen Gordon +2
There are many important regression problems in real-world brain-computer interface (BCI) applications, e.g., driver drowsiness estimation from EEG signals. This paper considers of…
EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features
Dongrui Wu, Brent J. Lance, Vernon J. Lawhern +3
Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance. In this paper, for the first ti…
Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)
Dongrui Wu, Vernon J. Lawhern, Stephen Gordon +2
One big challenge that hinders the transition of brain-computer interfaces (BCIs) from laboratory settings to real-life applications is the availability of high-performance and rob…
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
Vernon J. Lawhern, Amelia J. Solon, Nicholas R. Waytowich +3
Brain computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of w…