Toward Open-World Electroencephalogram Decoding Via Deep Learning: A comprehensive survey-Reference-Cited by-同舟云学术

Toward Open-World Electroencephalogram Decoding Via Deep Learning: A comprehensive survey

Published:2022-03 Issue:2 Volume:39 Page:117-134
ISSN:1053-5888
Container-title:IEEE Signal Processing Magazine
language:
Short-container-title:IEEE Signal Process. Mag.

Author:

Chen Xun¹,Li Chang²,Liu Aiping³,McKeown Martin J.⁴,Qian Ruobing⁵,Wang Z. Jane⁶

Affiliation:

1. Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC), Hefei, China

2. Department of Biomedical Engineering, Hefei University of Technology, Hefei, China

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

4. Pacific Parkinson’s Research Center, Vancouver, British Columbia, Canada

5. Department of Neurosurgery, The First Affiliated Hospital of University of Science and Technology of China, Hefei, China

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

Funder

National Natural Science Foundation of China

University of Science and Technology of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/79/9721176/09721187.pdf?arnumber=9721187

Reference88 articles.

1. A Semi-Supervised Wasserstein Generative Adversarial Network for Classifying Driving Fatigue from EEG signals

2. A survey on semi-supervised learning

3. EZSL-GAN: EEG-based Zero-Shot Learning approach using a Generative Adversarial Network

4. EEG-Based Emotion Recognition Using Regularized Graph Neural Networks

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