- Sleep
- 2026
EEG-based Alzheimer’s detection: abstract selected for Recent Advances in Machine Learning for Healthcare event.
Estimated reading time: 2 minutes
Selected poster on Alzheimer’s EEG foundation models
Our abstract, titled “Generalizable EEG-Based Alzheimer’s Detection Using Foundation Models: Validation on Independent Clinical Cohorts” has been selected for a poster presentation at the “Recent Advances in Machine Learning for Healthcare” event, organized by PariSanté Campus and Inria on January 27, 2026. Let us continue to advance AI-assisted neurological diagnosis through innovative EEG research and clinically validated machine learning approaches.
The role of BIOSerenity-E1 in the detection Alzheimer’s disease
During the poster presentation we will address the challenges of costly and limited-access Alzheimer’s diagnosis by exploring the use of electroencephalography combined with artificial intelligence.
We tuned BIOSerenity-E1, our EEG foundation model, to detect Alzheimer’s disease from EEG signals, using one of the largest Alzheimer’s EEG cohorts available to date. The model was then evaluated on a fully independent external dataset, demonstrating strong generalization and its ability to learn transferable neurophysiological signatures.
This work illustrates the potential of foundation models to overcome limitations related to EEG variability and opens the way toward more accessible diagnostic tools.
Discover Neurophy, our EEG diagnostic service for neurological care and the Large Brain Model, our artificial intelligence for electrophysiological diagnostics.