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Nicholas R. Waytowich
Researcher at United States Army Research Laboratory
Publications - 83
Citations - 3356
Nicholas R. Waytowich is an academic researcher from United States Army Research Laboratory. The author has contributed to research in topics: Reinforcement learning & Computer science. The author has an hindex of 14, co-authored 66 publications receiving 1709 citations. Previous affiliations of Nicholas R. Waytowich include University of North Florida & Columbia 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
Discriminative Feature Extraction via Multivariate Linear Regression for SSVEP-Based BCI
Haiqiang Wang,Yu Zhang,Nicholas R. Waytowich,Dean J. Krusienski,Guoxu Zhou,Jing Jin,Xingyu Wang,Andrzej Cichocki +7 more
TL;DR: Experimental results show that the proposedMLR method significantly outperforms CCA as well as several other competing methods for SSVEP detection, especially for time windows shorter than 1 second, demonstrating that the MLR method is a promising new approach for achieving improved real-time performance of SSVEp-BCIs.
Journal ArticleDOI
Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials
Nicholas R. Waytowich,Vernon J. Lawhern,Javier O. Garcia,Javier O. Garcia,Jennifer Cummings,Josef Faller,Paul Sajda,Jean M. Vettel,Jean M. Vettel,Jean M. Vettel +9 more
TL;DR: This paper shows how a compact convolutional neural network (Compact-CNN), which only requires raw EEG signals for automatic feature extraction, can be used to decode signals from a 12-class SSVEP dataset without the need for user-specific calibration.