EEG Emotion Recognition Based on Dynamical Graph Attention Network-Reference-Cited by-同舟云学术

EEG Emotion Recognition Based on Dynamical Graph Attention Network

Published:2024-04-14 Issue: Volume:1050 Page:1921-1925
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Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Guo Yi¹,Tang Chao¹,Wu Hao²,Chen Badong¹

Affiliation:

1. Xi’an Jiaotong University,National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics,Xi’an,China,710049

2. Xi’an University of Technology,School of Electrical Engineering,Xi’an,China,710048

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10447925.pdf?arnumber=10447925

Reference21 articles.

1. The James-Lange Theory of Emotions: A Critical Examination and an Alternative Theory

2. Integrating Facial Expression and Body Gesture in Videos for Emotion Recognition

3. Prosody-based automatic detection of annoyance and frustration in human-computer dialog

4. A real-time automated system for the recognition of human facial expressions

5. Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks

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1. Advancing EEG-Based Emotion Recognition: Unleashing the Power of Graph Neural Networks for Dynamic and Topology-Aware Models;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30