A Relation Feature Comparison Network for Cross-Domain Recognition of Motion Intention-Reference-Cited by-同舟云学术

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A Relation Feature Comparison Network for Cross-Domain Recognition of Motion Intention

Published:2024 Issue: Volume:73 Page:1-13
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Xu Jiacan¹^ORCID,Li Donglin²^ORCID,Zhou Peng¹^ORCID,Zhang Yuxian²^ORCID,Wang Zinan¹^ORCID,Ma Dazhong³^ORCID

Affiliation:

1. School of Engineering Training and Innovation, Shenyang Jianzhu University, Shenyang, China

2. College of Electrical Engineering, Shenyang University of Technology, Shenyang, China

3. College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China

Funder

National Natural Science Foundation of China

Applied Basic Research of Liaoning Province

Natural Science Foundation of Liaoning Province

Liaoning Revitalization Talents Program

Doctor Research Project of Liaoning Provincial Science and Technology Department

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/19/10367905/10599327.pdf?arnumber=10599327

Reference40 articles.

1. Brain–computer interfaces for communication and control

2. A Complex Network-Based Broad Learning System for Detecting Driver Fatigue From EEG Signals

3. EEG-Based Emotion Recognition for Hearing Impaired and Normal Individuals With Residual Feature Pyramids Network Based on Time–Frequency–Spatial Features

4. A Multi-Scale Fusion Convolutional Neural Network Based on Attention Mechanism for the Visualization Analysis of EEG Signals Decoding

5. EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation

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