Classifying Routine Clinical Electroencephalograms With Multivariate Iterative Filtering and Convolutional Neural Networks-Reference-Cited by-同舟云学术

Classifying Routine Clinical Electroencephalograms With Multivariate Iterative Filtering and Convolutional Neural Networks

Published:2024 Issue: Volume:32 Page:2038-2048
ISSN:1534-4320
Container-title:IEEE Transactions on Neural Systems and Rehabilitation Engineering
language:
Short-container-title:IEEE Trans. Neural Syst. Rehabil. Eng.

Author:

Paliwal Vardhan¹^ORCID,Das Kritiprasanna¹^ORCID,Doesburg Sam M.²,Medvedev George³,Xi Pengcheng⁴^ORCID,Ribary Urs⁵,Pachori Ram Bilas¹^ORCID,Vakorin Vasily A.²^ORCID

Affiliation:

1. Department of Electrical Engineering, Indian Institute of Technology Indore, Indore, India

2. Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada

3. Division of Neurology, Fraser Health Authority, Surrey, BC, Canada

4. Digital Technologies Research Centre, National Research Council of Canada, Ottawa, ON, Canada

5. Department of Psychology, Behavioral and Cognitive Neuroscience Institute, Simon Fraser University, Burnaby, BC, Canada

Funder

Digital Research Alliance of Canada

Digital Health and Geospatial Analytics Program at the National Research Council of Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7333/10398877/10535301.pdf?arnumber=10535301

Reference36 articles.

1. Cross-validated Adaboost Classification of Emotion Regulation Strategies Identified by Spectral Coherence in Resting-State

2. Brain age from the electroencephalogram of sleep

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4. Electroencephalogram Based Motor Imagery Brain Computer Interface Using Multivariate Iterative Filtering and Spatial Filtering

5. A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform