50 citations · 52 across the 5 of their papers we have counts for
5 papers
Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer's Disease using EEG Data
Dominik Klepl, Fei He, Min Wu +2
Graph neural network (GNN) models are increasingly being used for the classification of electroencephalography (EEG) data. However, GNN-based diagnosis of neurological disorders, s…
Bispectrum-based Cross-frequency Functional Connectivity: Classification of Alzheimer's disease
Dominik Klepl, Fei He, Min Wu +2
Alzheimer's disease (AD) is a neurodegenerative disease known to affect brain functional connectivity (FC). Linear FC measures have been applied to study the differences in AD by s…
Characterising Alzheimer's Disease with EEG-based Energy Landscape Analysis
Dominik Klepl, Fei He, Min Wu +3
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, with around 50 million patients worldwide. Accessible and non-invasive methods of diagnosing and char…
Data augmentation using generative networks to identify dementia
Bahman Mirheidari, Yilin Pan, Daniel Blackburn +5
Data limitation is one of the most common issues in training machine learning classifiers for medical applications. Due to ethical concerns and data privacy, the number of people t…
Detecting Alzheimer's Disease by estimating attention and elicitation path through the alignment of spoken picture descriptions with the picture prompt
Bahman Mirheidari, Yilin Pan, Traci Walker +4
Cognitive decline is a sign of Alzheimer's disease (AD), and there is evidence that tracking a person's eye movement, using eye tracking devices, can be used for the automatic iden…