Philosophers-Stone — Brain-Health Inference from Single-Channel Sleep EEG
Philosophers-Stone is a lightweight inference tool that converts a single-channel overnight sleep EEG into a quantitative index of brain health.
It applies a validated multi-cohort deep-learning model trained on 36,000 sleep recordings to estimate cognitive performance, disease likelihoods, and mortality-related physiological patterns.
The tool runs in seconds and outputs both a single Brain Health Score and a 1024-dimensional latent embedding suitable for research and biomarker discovery.
If you use or reference this tool, please cite the peer-reviewed study:
Ganglberger, W., Sun, H., Turley, N., et al. and Westover, M.B. (2026) "Brain Health from Sleep EEG: A Multicohort, Deep Learning Biomarker for Cognition, Disease, and Mortality", NEJM AI, 3(3), DOI: 10.1056/AIoa2500487.
This model is intended for research use in sleep science, neurology, aging, and biomarker discovery.
It is not intended for clinical diagnosis, treatment decisions, or emergency use.
Who is this for?
Sleep scientists
Neurologists and dementia researchers
Aging and cognitive-decline investigators
Psychiatry researchers
Data scientists working with physiological signals
Clinical-trial teams exploring EEG-based biomarkers
Predictions for cognition, disease risk, and mortality-related physiology
Optional outputs: spectrograms and per-recording JSON summaries
Model provenance
This checkpoint implements the multi-task deep-learning framework described in:
Ganglberger, W., Sun, H., Turley, N., et al. and Westover, M.B. (2026) "Brain Health from Sleep EEG: A Multicohort, Deep Learning Biomarker for Cognition, Disease, and Mortality", NEJM AI, 3(3), DOI: 10.1056/AIoa2500487.
This Hugging Face repository hosts the checkpoint file used by the Philosophers-Stone codebase.
The current GitHub project auto-downloads it from this model repo when the local file is missing.
Inputs
Manifest CSV
A CSV with columns:
filepath
age (years)
sex (0=female, 1=male)
EEG file requirements
Philosophers-Stone accepts single-channel overnight EEG in HDF5 (.h5) or EDF (.edf) format.
Preferred channel: C4-M1.
Format
Requirements
HDF5 (.h5)
Dataset: signals/c4-m1 (1-D float array, full night) Attributes: sampling_rate=200, unit_voltage="uV" Extra channels and annotations are ignored Manifest uses absolute paths
EDF (.edf)
Must contain a C4-M1 channel (label variants allowed) Any sampling rate accepted; auto-resampled to 200 Hz with anti-aliasing
Sample full-night EEG data is included in the GitHub repository under ./sample-data/.
