EEG-HealthLLM

EEG-HealthLLM provides a unified closed-set language interface for five heterogeneous EEG health assessment tasks: epileptic seizure detection, major depressive disorder assessment, anxiety-risk assessment, depressive-symptom risk assessment, and emotional-state assessment.

This repository contains the complete released model extension:

• the shared language-side adapter;
• five task-specific sequence/global projectors;
• task manifests and portable route configuration.

The frozen microsoft/Phi-3-mini-4k-instruct backbone is not redistributed here.

Code

The model uses custom EEG routing and language-adapter code. Use the official implementation at:

https://github.com/maruisaa/EEG-HealthLLM

This release is not intended to be loaded through the standard Hugging Face text-generation pipeline without the accompanying code.

Download

hf download maruisaa/EEG-HealthLLM \
  --local-dir "weights/EEG-HealthLLM"

Alternatively, use Python:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="maruisaa/EEG-HealthLLM",
    local_dir="weights/EEG-HealthLLM",
)

Files

EEG-HealthLLM/
|-- adapter_config.json
|-- adapter_model.bin
|-- config.json
|-- unified_eeg_meta.json
|-- unified_routes_5tasks.json
`-- eeg_adapters/
    |-- epilepsy/
    |-- mumtaz/
    |-- gad7/
    |-- phq9/
    `-- seed/

Each task directory contains manifest.json and projectors/model.safetensors, together with route-specific checkpoint files where applicable.

Quick evaluation

After cloning the code repository and downloading this snapshot into the directory shown above:

PHI=/path/to/Phi-3-mini-4k-instruct bash "weights/evaluate_example.sh" seed

Valid task names are epilepsy, mumtaz, gad7, phq9, and seed.

Reported test performance

Task	Balanced accuracy (%)
Epilepsy	72.78
Mumtaz	93.91
GAD-7	49.94
PHQ-9	70.62
SEED	60.67
Five-task average	69.58

Data

Raw EEG recordings are not included. Users must obtain the datasets from their official sources and follow the preprocessing and subject-independent split metadata distributed with the code repository.

Intended use and limitations

EEG-HealthLLM is released for research and reproducibility. Its outputs must not be interpreted as clinical diagnoses, and the model is not intended for autonomous medical decision-making. Performance may change across populations, recording devices, electrode layouts, preprocessing pipelines, and distribution shifts.

Citation

Please cite the accompanying EEG-HealthLLM paper when using these weights. Full bibliographic metadata will be added after publication.