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Amelia J. Solon
Researcher at United States Army Research Laboratory
Publications - 9
Citations - 2316
Amelia J. Solon 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 5, co-authored 9 publications receiving 1046 citations.
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
Decoding P300 Variability Using Convolutional Neural Networks.
Amelia J. Solon,Vernon J. Lawhern,Jonathan Touryan,Jonathan R. McDaniel,Anthony J. Ries,Stephen M. Gordon +5 more
TL;DR: This article trains a CNN model using data from prior experiments in order to later decode the P300 evoked response from an unseen, hold-out experiment and demonstrates that the CNN output is sensitive to the experiment-induced changes in the neural response.
Proceedings ArticleDOI
Real World BCI: Cross-Domain Learning and Practical Applications
TL;DR: This concept paper describes the initial investigation into Deep Learning tools to create generalized models for both cross-subject and cross-domain learning, and demonstrates the approach using two different, laboratory grade data sets to train a learning model that is applied to a third more complex scenario.
Proceedings ArticleDOI
Analyzing P300 Distractors for Target Reconstruction
TL;DR: The performance of the generalized model equals that of the user-specific models, without any user specific data, when combined with other intelligent agents, such as computer vision systems.