Charles A. Ellis
According to our database1,
Charles A. Ellis
authored at least 25 papers
between 2019 and 2023.
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Bibliography
2023
Novel methods for elucidating modality importance in multimodal electrophysiology classifiers.
Frontiers Neuroinformatics, March, 2023
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis.
CoRR, 2023
Identifying Neuropsychiatric Disorder Subtypes and Subtype-Dependent Variation in Diagnostic Deep Learning Classifier Performance.
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023
Novel Approach Explains Spatio-Spectral Interactions In Raw Electroencephalogram Deep Learning Classifiers.
Proceedings of the IEEE International Conference on Acoustics, 2023
Neuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations.
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023
A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG Analysis.
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023
A Novel Explainable Fuzzy Clustering Approach for fMRI Dynamic Functional Network Connectivity Analysis.
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023
Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2023
Improving Explainability for Single-Channel EEG Deep Learning Classifiers via Interpretable Filters and Activation Analysis.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2023
An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-Based Schizophrenia Diagnosis.
Proceedings of the 23rd IEEE International Conference on Bioinformatics and Bioengineering, 2023
2022
A Systematic Approach for Explaining Time and Frequency Features Extracted by Convolutional Neural Networks From Raw Electroencephalography Data.
Frontiers Neuroinformatics, August, 2022
An Unsupervised Feature Learning Approach for Elucidating Hidden Dynamics in rs-fMRI Functional Network Connectivity.
Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022
A Model Visualization-based Approach for Insight into Waveforms and Spectra Learned by CNNs.
Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022
Exploring Relationships between Functional Network Connectivity and Cognition with an Explainable Clustering Approach.
Proceedings of the 22nd IEEE International Conference on Bioinformatics and Bioengineering, 2022
Examining Reproducibility of EEG Schizophrenia Biomarkers Across Explainable Machine Learning Models.
Proceedings of the 22nd IEEE International Conference on Bioinformatics and Bioengineering, 2022
Proceedings of the 22nd IEEE International Conference on Bioinformatics and Bioengineering, 2022
Proceedings of the 22nd IEEE International Conference on Bioinformatics and Bioengineering, 2022
2021
Hierarchical Neural Network with Layer-wise Relevance Propagation for Interpretable Multiclass Neural State Classification.
Proceedings of the 10th International IEEE/EMBS Conference on Neural Engineering, 2021
Explainable Sleep Stage Classification with Multimodal Electrophysiology Time-series<sup>*</sup>.
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021
A Novel Activation Maximization-based Approach for Insight into Electrophysiology Classifiers.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2021
Proceedings of the 21st IEEE International Conference on Bioinformatics and Bioengineering, 2021
Proceedings of the 21st IEEE International Conference on Bioinformatics and Bioengineering, 2021
A Novel Local Explainability Approach for Spectral Insight into Raw EEG-based Deep Learning Classifiers.
Proceedings of the 21st IEEE International Conference on Bioinformatics and Bioengineering, 2021
2019
A Cloud-based Framework for Implementing Portable Machine Learning Pipelines for Neural Data Analysis.
Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2019