Overview
- Researchers report two EEG-based AI approaches that distinguish healthy individuals from patients with Alzheimer’s disease or frontotemporal dementia.
- An explainable framework combining temporal convolutional networks and LSTM classified Alzheimer’s, frontotemporal dementia and healthy controls with over 80% accuracy.
- A compact hybrid‑fusion EEGNetv4 model trained via federated learning kept patient data local, measured under one megabyte in size and achieved over 97% accuracy.
- Explainable AI highlights which EEG signal segments and frequency bands—such as alpha, beta and gamma—contributed to each output to aid clinical interpretation.
- The studies, published in Frontiers journals, were conducted with international partners, and the team plans larger, more diverse datasets and added dementia types before any deployment.