S
Stephen M. Gordon
Researcher at United States Army Research Laboratory
Publications - 51
Citations - 2994
Stephen M. Gordon is an academic researcher from United States Army Research Laboratory. The author has contributed to research in topics: Feature extraction & Cognition. The author has an hindex of 14, co-authored 49 publications receiving 1625 citations. Previous affiliations of Stephen M. Gordon include Vanderbilt 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.
Journal ArticleDOI
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.
Journal ArticleDOI
Real-World Neuroimaging Technologies
Kaleb McDowell,Chin-Teng Lin,Kelvin S. Oie,Tzyy-Ping Jung,Stephen M. Gordon,Keith W. Whitaker,Shih-Yu Li,Shao-Wei Lu,W. David Hairston +8 more
TL;DR: The efforts of a joint government-academic-industry team to take an integrative, interdisciplinary, and multi-aspect approach to translate current technologies into devices that are truly fieldable across a range of environments are discussed.
Journal ArticleDOI
EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features
TL;DR: A new feature extraction approach for electroencephalogram (EEG)-based BCI regression problems is proposed: a spatial filter is first used to increase the signal quality of the EEG trials and also to reduce the dimensionality of the covariance matrices, and then Riemannian tangent space features are extracted.