A Cross-Scale Transformer and Triple-View Attention Based Domain-Rectified Transfer Learning for EEG Classification in RSVP Tasks-Reference-Cited by-同舟云学术

A Cross-Scale Transformer and Triple-View Attention Based Domain-Rectified Transfer Learning for EEG Classification in RSVP Tasks

Published:2024 Issue: Volume:32 Page:672-683
ISSN:1534-4320
Container-title:IEEE Transactions on Neural Systems and Rehabilitation Engineering
language:
Short-container-title:IEEE Trans. Neural Syst. Rehabil. Eng.

Author:

Luo Jie¹^ORCID,Cui Weigang²^ORCID,Xu Song³,Wang Lina³,Chen Huiling⁴^ORCID,Li Yang⁵^ORCID

Affiliation:

1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

2. School of Engineering Medicine, Beihang University, Beijing, China

3. National Key Laboratory of Science and Technology on Aerospace Intelligence Control, Beijing Aerospace Automatic Control Institute, Beijing, China

4. College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China

5. Department of Automation Science and Electrical Engineering and the State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Beijing Natural Science Foundation

Zhejiang Provincial Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7333/10398877/10415444.pdf?arnumber=10415444

Reference44 articles.

1. EEG-Based Driver Fatigue Detection Using FAWT and Multiboosting Approaches

2. A Dual-Branch Spatio-Temporal-Spectral Transformer Feature Fusion Network for EEG-Based Visual Recognition

3. Advances in P300 brain–computer interface spellers: toward paradigm design and performance evaluation

4. ERP prototypical matching net: a meta-learning method for zero-calibration RSVP-based image retrieval

5. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces

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