EEG Data Analytics to Distinguish Happy and Sad Emotions Based on Statistical Features
Author:
Affiliation:
1. Institut Teknologi Sepuluh Nopember,Department of Electrical Engineering,Surabaya,Indonesia
2. Institut Teknologi Sepuluh Nopember,Department of Electrical Engineering, Department of Computer Engineering,Surabaya,Indonesia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9702758/9702759/09702766.pdf?arnumber=9702766
Reference27 articles.
1. SAE+LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG
2. EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder
3. Cross-Subject EEG-Based Emotion Recognition Through Neural Networks With Stratified Normalization
4. Explainable AI and Reinforcement Learning—A Systematic Review of Current Approaches and Trends
5. Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation
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3. Coherence Analysis of EEG Signal in Happy and Sad Emotions During Visual Stimulation;2023 IEEE International Biomedical Instrumentation and Technology Conference (IBITeC);2023-11-09
4. Comparison of Human Emotion Classification on Single-Channel and Multi-Channel EEG using Gate Recurrent Unit Algorithm;2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE);2023-02-16
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