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An Explainable Deep Learning Approach for Multimodal Electrophysiology Classification
Charles A. Ellis, Darwin A. Carbajal, Rongen Zhang, View ORCID ProfileRobyn L. Miller, View ORCID ProfileVince D. Calhoun, May D. Wang
doi: https://doi.org/10.1101/2021.05.12.443594
Charles A. Ellis
1Tri-institutional Center for, Translational Research in, Neuroimaging and Data Science, Georgia State University, Georgia, Institute of Technology, and Emory, University, Atlanta, USA
Darwin A. Carbajal
2Wallace H. Coulter Department of, Biomedical Engineering, Georgia Institute of Technology, Atlanta, USA
Rongen Zhang
3Department of Computer Information, Systems, Georgia State University, Atlanta, USA
Robyn L. Miller
2Wallace H. Coulter Department of, Biomedical Engineering, Georgia Institute of Technology, Atlanta, USA
Vince D. Calhoun
1Tri-institutional Center for, Translational Research in, Neuroimaging and Data Science, Georgia State University, Georgia, Institute of Technology, and Emory, University, Atlanta, USA
May D. Wang
4Wallace H. Coulter Department of, Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, USA
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Posted June 08, 2021.
An Explainable Deep Learning Approach for Multimodal Electrophysiology Classification
Charles A. Ellis, Darwin A. Carbajal, Rongen Zhang, Robyn L. Miller, Vince D. Calhoun, May D. Wang
bioRxiv 2021.05.12.443594; doi: https://doi.org/10.1101/2021.05.12.443594
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