A new training approach for deep learning in EEG biometrics using triplet loss and EMG-driven additive data augmentation-Reference-Cited by-同舟云学术

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A new training approach for deep learning in EEG biometrics using triplet loss and EMG-driven additive data augmentation

Published:2022-06 Issue: Volume:488 Page:194-211
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Seha Sherif Nagib Abbas,Hatzinakos Dimitrios

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference51 articles.

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2. Motor imagery classification for brain computer interface using deep metric learning;Alwasiti;IEEE Access,2020

3. Brainprint: Assessing the uniqueness, collectability, and permanence of a novel method for ERP biometrics;Armstrong;Neurocomputing,2015

4. A canonical correlation approach to blind source separation. Report LiU-IMT-EX-0062 Department of Biomedical Engineering;Borga,2001

5. Fast wavelet estimation of weak biosignals;Causevic;IEEE Trans. Biomed. Eng.,2005

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