Advancing EEG-Based Approaches to Dementia Research
As part of the OCC Neuroscience Colloquia, we were pleased to welcome Asst. Prof. Dr. Mesut Seker from Dicle University, Diyarbakir, Turkey, who visited us here in Münster on 17. December to deliver an insightful and inspirational guest lecture. This speaker was an invited guest of our TReND doctoral researcher Dilşah Gençaslan.
In his talk, Mesut Seker took us through multiple integrative computational frameworks for the classification of Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) using resting-state EEG data. The approach combines classical signal analysis methods, such as complexity measures, spectral rhythms, and synchrony features, with state-of-the-art deep learning techniques (incl. EEGNet, ResNet architectures, and Vision Transformers). This dual perspective enables both the identification of neuromarkers and the automatic discovery of hidden patterns associated with cognitive decline.
The presentation highlighted how resting-state EEG can serve as a practical and powerful diagnostic tool, supporting the early detection of AD, and demonstrated the potential of computational neuroscience to support clinical decisions.
We sincerely thank Dr. Mesut Seker for his visit and for sharing his expertise and valuable insights with our research community. His contribution sparked stimulating discussions and provided important perspectives on the future of resting-state EEG research analysis.