MetaEmotionNet: Spatial–Spectral–Temporal-Based Attention 3-D Dense Network With Meta-Learning for EEG Emotion Recognition-Reference-Cited by-同舟云学术

MetaEmotionNet: Spatial–Spectral–Temporal-Based Attention 3-D Dense Network With Meta-Learning for EEG Emotion Recognition

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:

Ning Xiaojun¹^ORCID,Wang Jing¹^ORCID,Lin Youfang¹^ORCID,Cai Xiyang²^ORCID,Chen Haobin¹^ORCID,Gou Haijun¹^ORCID,Li Xiaoli³^ORCID,Jia Ziyu⁴^ORCID

Affiliation:

1. Beijing Key Laboratory of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China

2. Samueli School of Engineering, University of California at Los Angeles, Los Angeles, CA, USA

3. Institute for Infocomm Research, A*STAR, Fusionopolis, Singapore

4. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10342627.pdf?arnumber=10342627

Reference68 articles.

1. Predicting tDCS treatment outcomes of patients with major depressive disorder using automated EEG classification

2. Depression and implicit emotion processing: An EEG study

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

4. Classification of Human Emotions from Electroencephalogram (EEG) Signal using Deep Neural Network

5. Electroencephalogram Signal Clustering with Convex Cooperative Games

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