Distilling Invariant Representations With Domain Adversarial Learning for Cross-Subject Children Seizure Prediction-Reference-Cited by-同舟云学术

Distilling Invariant Representations With Domain Adversarial Learning for Cross-Subject Children Seizure Prediction

Published:2024-02 Issue:1 Volume:16 Page:202-211
ISSN:2379-8920
Container-title:IEEE Transactions on Cognitive and Developmental Systems
language:
Short-container-title:IEEE Trans. Cogn. Dev. Syst.

Author:

Zhang Ziye¹^ORCID,Liu Aiping¹^ORCID,Gao Yikai¹^ORCID,Cui Xinrui²^ORCID,Qian Ruobing³^ORCID,Chen Xun⁴^ORCID

Affiliation:

1. School of Information Science and Technology, University of Science and Technology of China, Hefei, China

2. Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada

3. Epilepsy Center, Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China

4. Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, and the Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China

Funder

National Natural Science Foundation of China

Research Project of Health Commission of Anhui Province

Fundamental Research Funds for the Central Universities

University Synergy Innovation Program of Anhui Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Software

Link

http://xplorestaging.ieee.org/ielx7/7274989/10419120/10068788.pdf?arnumber=10068788

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1. Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting;Sensors;2024-04-30

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