4.8k citations · 5.3k across the 23 of their papers we have counts for
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Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time
Vinicius G. Goecks, Gregory M. Gremillion, Vernon J. Lawhern +2
This paper investigates how to utilize different forms of human interaction to safely train autonomous systems in real-time by learning from both human demonstrations and intervent…
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…
Cycle-of-Learning for Autonomous Systems from Human Interaction
Nicholas R. Waytowich, Vinicius G. Goecks, Vernon J. Lawhern
We discuss different types of human-robot interaction paradigms in the context of training end-to-end reinforcement learning algorithms. We provide a taxonomy to categorize the typ…
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…
Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials
Nicholas R. Waytowich, Vernon Lawhern, Javier O. Garcia +4
Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli. SSVEPs a…