Diploma Thesis Success: AI & EEG Analysis for Neurodegenerative Disease Detection
The SIPPRE Research Group (Signal, Image Processing and Pattern Recognition Group) is pleased to announce the successful completion and public defense of the Diploma Thesis of Maria Karadimitropoulou, entitled:
“EEG Analysis for the Discrimination between Healthy Subjects and Patients with Alzheimer’s Disease and Frontotemporal Dementia: Machine Learning Techniques and Brain Connectivity Analysis.”
Research Focus & Contribution
The thesis addresses a critical challenge in modern neuroscience and biomedical engineering: the early and reliable differentiation of neurodegenerative diseases using non-invasive brain signals.
The work focuses on the processing and analysis of electroencephalographic (EEG) recordings from:
- Patients with Alzheimer’s Disease (AD)
- Patients with Frontotemporal Dementia (FTD)
- Healthy control subjects (CN)
Using advanced signal processing, machine learning techniques, and brain connectivity analysis, the study develops automated pipelines capable of distinguishing between these groups and extracting meaningful neurophysiological biomarkers associated with each condition.
Alignment with SIPPRE Research
This diploma thesis is fully aligned with SIPPRE’s research directions in:
- EEG and brain signal analysis
- Machine learning for biomedical applications
- Functional brain connectivity and network analysis
- Data-driven approaches for neurological disease characterization
The work contributes to SIPPRE’s broader vision of AI-enabled, interpretable, and clinically relevant neurotechnology.
Congratulations to Maria Karadimitropoulou on this excellent academic achievement, and best wishes for her continued academic and professional path.