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
4 papers · 1 filter
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…
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…