Interpretable Emotion Classification Using Multidomain Feature of EEG Signals
Author:
Affiliation:
1. Department of Informatics, Yunnan University, Kunming, China
2. Information and Technology Center, Yunnan University, Kunming, China
Funder
National Natural Science Foundation of China
Yunnan Ten Thousand Talents Program and Yunling Scholars Special Project
joint fund of Yunnan Provincial Science and Technology Department-Yunnan University “Double First Class” Construction
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/10139852/10102828.pdf?arnumber=10102828
Reference43 articles.
1. WeDea: A new EEG-based framework for emotion recognition;sunhee;IEEE J Biomed Health Inform,2021
2. Optimal Feature Selection and Deep Learning Ensembles Method for Emotion Recognition From Human Brain EEG Sensors
3. DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices
4. A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction
5. EEG-based emotion recognition using tunable Q wavelet transform and rotation forest ensemble classifier
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1. Assessing Feature Importance in Eye-Tracking Data within Virtual Reality Using Explainable Artificial Intelligence Techniques;Applied Sciences;2024-07-11
2. Spectral Graph Wavelet Transform-Based Feature Representation for Automated Classification of Emotions From EEG Signal;IEEE Sensors Journal;2023-12-15
3. Stress Detection via Multimodal Multitemporal-Scale Fusion: A Hybrid of Deep Learning and Handcrafted Feature Approach;IEEE Sensors Journal;2023-11-15
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