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--- |
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license: other |
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license_name: commercial |
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license_link: LICENSE |
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datasets: |
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- DBbun/EEG-250Hz_v1.0 |
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language: |
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- en |
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pipeline_tag: feature-extraction |
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--- |
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# DBbun EEG Encoder — Pretrained Encoder Evaluation and Demo |
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## Overview |
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This repository provides a pretrained **EEG encoder** and two demonstration scripts developed by **DBbun LLC**. |
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The model converts short segments of multi-channel EEG into **128-dimensional embeddings** that summarize the temporal and spectral structure of the signal. |
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It was trained self-supervised on DBbun’s synthetic multi-patient EEG corpus sampled at **250 Hz** using the **10–20 montage (38 channels)**. |
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All data are fully synthetic and privacy-safe. |
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## Key Features |
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- **2-second EEG encoder** trained at 250 Hz (38 channels). |
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- Produces **128-D embeddings** suitable for: |
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- Seizure vs. non-seizure discrimination |
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- EEG morphology clustering and visualization |
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- Similarity search and retrieval |
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- Anomaly and quality detection |
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- Downstream feature extraction for ML models |
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- Includes demonstration scripts for embedding extraction and PCA-based visualization. |
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--- |
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## Related Dataset |
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The encoder was trained and evaluated using **[DBbun/EEG-250Hz_v1.0](https://huggingface.co/datasets/DBbun/EEG-250Hz_v1.0)**. |
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Each file represents one synthetic patient with 38-channel EEG sampled at 250 Hz. |
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When available, `labels_sec` (0 = non-seizure, 1 = seizure) allows computing a **seizure fraction** or training evaluation probes. |
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--- |
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## Repository Contents |
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| File | Description | |
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|------|--------------| |
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| `encoder_state.pt` | PyTorch weights (state dictionary). | |
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| `encoder_traced.pt` | TorchScript version for deployment. | |
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| `model_def.json` | Model configuration (architecture, channels, latent dimension, dropout, etc.). | |
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| **`DBbun_EEG_Encoder_Eval_Demo_v1.py`** | Baseline script: loads EEG files, runs the pretrained encoder, and exports embeddings. | |
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| **`DBbun_EEG_Encoder_Eval_Demo_v2.py`** | Extended demo: includes **PCA visualization** that colors seizure vs. non-seizure embeddings for interpretability. | |
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## Intended Use |
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This model and accompanying scripts are intended for **research, education, and development** purposes. |
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They support reproducible EEG feature learning, visualization, and benchmarking without access to real patient data. |
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They are **not intended for clinical diagnosis or medical use**. |
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## Suggested Applications |
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Evaluate representation quality on labeled synthetic EEG. |
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Visualize clustering patterns of seizure vs. non-seizure embeddings using PCA. |
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Train simple classifiers (e.g., logistic regression, SVM) on 128-D features for benchmarking. |
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Apply the encoder as a fixed feature extractor in other time-series tasks. |
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--- |
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## What Users Can Do with the Model |
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The **DBbun EEG Encoder (250 Hz)** acts as a **feature extractor** — it converts raw EEG windows into compact **128-dimensional embeddings** that summarize the shape, rhythm, and energy distribution of brain signals. |
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### ✅ Typical Use Cases |
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| Goal | What the user does | |
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|------|--------------------| |
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| **Feature extraction** | Feed EEG windows (2 s × 38 channels × 250 Hz) into the encoder → obtain 128-D embeddings for each window. | |
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| **Classification** | Use the embeddings to train a simple model (e.g., logistic regression, random forest, MLP) for tasks such as seizure vs. non-seizure or artifact vs. clean. | |
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| **Visualization** | Reduce embeddings to 2-D (PCA or UMAP) to explore clusters or signal structure. | |
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| **Similarity search** | Build a FAISS or Annoy index to find EEG segments that resemble each other in latent space. | |
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| **Anomaly detection** | Identify rare or abnormal patterns by computing distances to nearest neighbors. | |
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| **Patient-level summaries** | Average embeddings across all windows from one patient to form a stable EEG “signature.” | |
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--- |
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### 💾 Use of Precomputed Embeddings |
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Precomputed embeddings are optional and depend on the user’s objective: |
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| Scenario | Use precomputed embeddings? | Reason | |
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|-----------|-----------------------------|---------| |
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| **Quick exploration of results** | ✅ Yes | The file `demo_embeddings.npy` already contains 128-D features ready for clustering, visualization, or linear probes. | |
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| **Custom EEG data (real or synthetic)** | ❌ No | The pretrained encoder can be applied directly to new EEG windows to generate embeddings. | |
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| **Cross-model or cross-dataset comparison** | Optional | Both the provided embeddings and newly generated ones can be used for benchmarking and evaluation. | |
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--- |
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## License |
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Licensed for non-clinical research and educational use. |
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For commercial licensing inquiries, please contact **DBbun LLC**. |
