An Interpretable Deep Learning Model for Speech Activity Detection Using Electrocorticographic Signals-Reference-Cited by-同舟云学术

An Interpretable Deep Learning Model for Speech Activity Detection Using Electrocorticographic Signals

Published:2022 Issue: Volume:30 Page:2783-2792
ISSN:1534-4320
Container-title:IEEE Transactions on Neural Systems and Rehabilitation Engineering
language:
Short-container-title:IEEE Trans. Neural Syst. Rehabil. Eng.

Author:

Stuart Morgan¹^ORCID,Lesaja Srdjan²^ORCID,Shih Jerry J.³^ORCID,Schultz Tanja⁴^ORCID,Manic Milos¹^ORCID,Krusienski Dean J.²^ORCID

Affiliation:

1. Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA

2. Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA, USA

3. Neurology Department, UCSD Health, San Diego, CA, USA

4. Cognitive Systems Laboratory, University of Bremen, Bremen, Germany

Funder

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation

Link

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5. PyTorch: An imperative style, high-performance deep learning library;paszke;Advances in neural information processing systems,2019

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