V
Vernon J. Lawhern
Researcher at United States Army Research Laboratory
Publications - 65
Citations - 3952
Vernon J. Lawhern is an academic researcher from United States Army Research Laboratory. The author has contributed to research in topics: Deep learning & Convolutional neural network. The author has an hindex of 21, co-authored 62 publications receiving 2274 citations. Previous affiliations of Vernon J. Lawhern include University of Texas at San Antonio & Florida State University.
Papers
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Journal ArticleDOI
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces
Vernon J. Lawhern,Amelia J. Solon,Nicholas R. Waytowich,Nicholas R. Waytowich,Stephen M. Gordon,Chou P. Hung,Chou P. Hung,Brent J. Lance +7 more
TL;DR: This work introduces EEGNet, a compact convolutional neural network for EEG-based BCIs, and introduces the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI.
Journal ArticleDOI
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
Vernon J. Lawhern,Amelia J. Solon,Nicholas R. Waytowich,Stephen M. Gordon,Chou P. Hung,Brent J. Lance +5 more
TL;DR: In this paper, a compact convolutional network for EEG-based brain computer interfaces (BCI) is proposed, which can learn a wide variety of interpretable features over a range of BCI tasks.
Posted Content
Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces
TL;DR: Deep TAMER is proposed, an extension of the TAMER framework that leverages the representational power of deep neural networks in order to learn complex tasks in just a short amount of time with a human trainer and demonstrated by using it and just 15 minutes of human-provided feedback to train an agent that performs better than humans on the Atari game of Bowling.
Journal ArticleDOI
Detection and classification of subject-generated artifacts in EEG signals using autoregressive models.
TL;DR: Autoregressive (AR) models are used for feature extraction and characterization of EEG signals containing several kinds of subject-generated artifacts, suggesting that AR modeling can be a useful tool for discriminating among artifact signals both within and across individuals.
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Driver Drowsiness Estimation From EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)
TL;DR: This paper proposes a novel online weighted adaptation regularization for regression (OwARR) algorithm to reduce the amount of subject-specific calibration data, and also a source domain selection (SDS) approach to save about half of the computational cost of OwARR.