Graph-based learning of nonlinear physiological interactions for classification of emotions
Author:
Funder
Burroughs Wellcome Fund
Publisher
Elsevier BV
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software
Reference40 articles.
1. Emotion recognition as pattern recognition: the relevance of perception;Newen;Mind Lang.,2015
2. Brainprint: EEG biometric identification based on analyzing brain connectivity graphs;Wang;Pattern Recognit.,2020
3. Graph variational auto-encoder for deriving EEG-based graph embedding;Behrouzi;Pattern Recognit.,2022
4. Learning EEG topographical representation for classification via convolutional neural network;Xu;Pattern Recognit.,2020
5. Coexisting forms of coupling and phase-transitions in physiological networks;Bartsch,2014
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Deep Representation Learning for Multimodal Emotion Recognition Using Physiological Signals;IEEE Access;2024
2. Feature Fusion Approach for Emotion Classification in EEG Signals;Advances in Intelligent Systems and Computing;2024
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