Novel Approach Explains Spatio-Spectral Interactions In Raw Electroencephalogram Deep Learning Classifiers
Author:
Affiliation:
1. Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University,Atlanta,Georgia,United States
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10192576/10192577/10193605.pdf?arnumber=10193605
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1. EEG in schizophrenic patients: mutual information analysis
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4. A wavelet-based technique to predict treatment outcome for Major Depressive Disorder
5. Classification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network;phang,0
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1. Cross-Sampling Rate Transfer Learning for Enhanced Raw EEG Deep Learning Classifier Performance in Major Depressive Disorder Diagnosis;2024 IEEE International Symposium on Biomedical Imaging (ISBI);2024-05-27
2. Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data*;2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2023-12-05
3. Improving Explainability for Single-Channel EEG Deep Learning Classifiers via Interpretable Filters and Activation Analysis*;2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2023-12-05
4. An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-Based Schizophrenia Diagnosis;2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE);2023-12-04
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