Research output per year
Research output per year
Personal profile
Research Interests
Network neuroscience - signal processing - machine learning - graph inference
- Functional connectivity models
- Graph classification and embedding
- Graph neural network
Master's Project
Energy landscape of Alzheimer's disease EEG - prediction
PhD Project
Joint research project of Coventry University and A*STAR, Singapore: Network Inference And Machine Learning: Identification Of Neurodegenerative Diseases
Supervised by Dr Fei He (Coventry University) and Dr Min Wu (A*STAR)
The human brain is a complex system comprised of many interacting components. In this project, the tools from graph theory and graph deep learning are leveraged to model the structure and function of the brain captured via electroencephalography (EEG) signals. The developed models can then be used to predict the presence of neurodegenerative disease and interpret the changes in the brain networks caused by the disease.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Education/Academic qualification
Data science and computational intelligence, MSc, Coventry University
15 Sept 2019 → 11 Sept 2020
Award Date: 11 Sept 2020
Cognitive Science, Degree, Aarhus University
1 Sept 2016 → 30 May 2019
Award Date: 30 May 2019
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Collaborations and top research areas from the last five years
External collaboration on country/territory level for the past five years. Dive into details by clicking on the dots or
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Characterising Alzheimer's Disease with EEG-based Energy Landscape Analysis
Klepl, D., He, F., Wu, M., Blackburn, D. J., Marco, M. D. & Sarrigiannis, P., 19 Feb 2021, arXiv, 10 p.Research output: Working paper/Preprint › Preprint
File77 Downloads (Pure) -
Graph Neural Network-based EEG Classification: A Survey
Klepl, D., Wu, M. & He, F., 18 Jan 2024, In: IEEE Transactions on Neural Systems and Rehabilitation Engineering. 32, p. 493-503 11 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile2 Citations (Scopus)58 Downloads (Pure) -
Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer’s Disease using EEG Data
Klepl, D., He, F., Wu, M., Blackburn, D. J. & Sarrigiannis, P. G., 4 Oct 2023, In: IEEE Transactions on Neural Systems and Rehabilitation Engineering. 31, p. 3978-3987 10 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile2 Citations (Scopus)54 Downloads (Pure) -
Cross-Frequency Multilayer Network Analysis with Bispectrum-based Functional Connectivity: A Study of Alzheimer's Disease
Klepl, D., He, F., Wu, M., Blackburn, D. J. & Sarrigiannis, P. G., 15 Jun 2023, In: Neuroscience. 521, p. 77-88 12 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile7 Citations (Scopus)64 Downloads (Pure) -
Gene Regulatory Network Inference through Link Prediction using Graph Neural Network
Ganeshamoorthy, S., Roden, L., Klepl, D. & He, F., 19 Jan 2023, 2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE, p. 1-5 5 p. 10014835. (IEEE Signal Processing in Medicine and Biology Symposium (SPMB)).Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review
1 Citation (Scopus)
Thesis
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Network Inference And Graph Learning in Characterising Alzheimer’s Disease
Author: Klepl, D., May 2024Supervisor: He, F. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy
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