Create README.md

README.md

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+---
+license: mit
+tags:
+  - eeg
+  - gan
+  - wgan-gp
+  - brain-computer-interface
+  - data-synthesis
+  - pytorch
+language: en
+library_name: pytorch
+datasets:
+  - physionet/eegmmidb
+model-index:
+  - name: EEG Data Synthesis with WGAN-GP
+    results: []
+---
+
+# EEG Data Synthesis with WGAN-GP
+
+**Model Type:** Conditional WGAN-GP  
+**Dataset:** [EEG Motor Movement/Imagery Dataset (PhysioNet)](https://physionet.org/content/eegmmidb/1.0.0/)
+
+---
+
+## Model Summary
+
+This model implements a **Wasserstein GAN with Gradient Penalty (WGAN-GP)** for generating **subject-conditioned synthetic EEG signals** based on the PhysioNet EEG Motor Movement/Imagery dataset.
+
+It generates realistic EEG segments that mimic real recordings in both **time** and **frequency** domains.
+
+**Applications:**
+- EEG data augmentation  
+- Privacy-preserving EEG synthesis  
+- Adversarial and spoofing research in BCI security
+
+---
+
+## Model Architecture
+
+### Generator (G)
+- **Input:** Latent vector *z ∈ ℝ¹²⁸* + subject embedding (128-dim)
+- **Layers:**
+  - Fully connected → reshape to (64, 120)
+  - Dilated ConvTranspose1D stack → (64, 480)
+  - Gaussian noise injection
+  - **Activation:** `tanh`
+- **Output:** EEG segment (64 channels × 480 samples)
+
+### Discriminator / Critic (D)
+- **Input:** EEG + embedded label
+- Conv2D + LeakyReLU + global pooling + linear critic output
+- **Regularization:** Dropout + drift penalty + gradient penalty
+
+---
+
+## Training Configuration
+
+| Parameter | Value |
+|------------|--------|
+| Optimizer | Adam (β₁=0.0, β₂=0.9) |
+| Learning Rate (G/D) | 1e-4 / 5e-5 |
+| Gradient Penalty λ | 10 |
+| Drift Regularization | 1e-3 × D(real)² |
+| n_critic | 3 |
+| Epochs | 300 |
+| Mixed Precision | Enabled (GradScaler) |
+
+---
+
+## Dataset
+
+**Source:** PhysioNet EEG Motor Movement/Imagery Dataset  
+**Subjects:** 109  
+**Channels:** 64  
+**Sampling Rate:** 160 Hz  
+**Segment Length:** 480 samples (~3 seconds)  
+**Tasks:** Motor imagery (fists, feet) + baseline (eyes open/closed)
+
+**Preprocessing:**
+- Downsampled to smallest subject class  
+- Per-channel normalization to [-1, 1]  
+- Integer-encoded subject IDs (0–108)
+
+---
+
+## Training Behavior
+
+Training remained stable across all epochs — no mode collapse or exploding gradients.
+
+| Metric | Mean | Std |
+|---------|------|-----|
+| D(real) | −0.43 | ±0.06 |
+| D(fake) | 0.38 | ±0.04 |
+| GP term | 0.96 | ±0.08 |
+| G loss | −0.41 | ±0.05 |
+
+---
+
+## Evaluation & Results
+
+### Visual & Spectral Fidelity
+- Synthetic EEG signals preserve **oscillatory structure** and amplitude range (±100 µV normalized).  
+- **Channel correlations** match real EEG patterns.  
+- Spectral energy distribution consistent with 1–40 Hz range.
+
+### Quantitative Similarity
+
+| Metric | Mean | Std | Interpretation |
+|---------|------|-----|----------------|
+| MSE | 2.279 | 2.806 | Low reconstruction error |
+| MAE | 0.870 | 0.487 | Normalized amplitude deviation |
+| Correlation | −0.0014 | 0.075 | Low linear correlation due to stochastic nature |
+| MMD | 0.0129 | — | Good distribution alignment |
+| Fréchet PCA (32-D) | 40092.3 | — | Baseline EEG-FID |
+| Covariance Similarity | 0.673 | ±0.182 | Preserves inter-channel dependencies |
+
+### Bandpower Fidelity
+
+| Band | Δ Mean | Δ Std | Interpretation |
+|------|---------|--------|----------------|
+| Delta (1–4 Hz) | 0.391 | 0.675 | Slight underfit |
+| Theta (4–8 Hz) | 0.082 | 0.131 | Close match |
+| Alpha (8–13 Hz) | 0.032 | 0.038 | Excellent fidelity |
+| Beta (13–30 Hz) | 0.012 | 0.016 | Excellent fidelity |
+| Gamma (30–40 Hz) | 0.0007 | 0.0014 | Negligible difference |
+
+---
+
+## Discussion
+
+This WGAN-GP model effectively learns to reproduce **subject-specific EEG morphology** and maintains **stable training** across complex, high-dimensional signals.
+
+It provides a promising basis for **privacy-preserving EEG synthesis** and **data augmentation** in BCI research.
+
+---
+
+## References
+
+- Arjovsky, M., Chintala, S., & Bottou, L. (2017). *Wasserstein GAN.* arXiv:1701.07875  
+- Gulrajani, I. et al. (2017). *Improved Training of Wasserstein GANs.* NIPS  
+- Lawhern, V. J. et al. (2018). *EEGNet: A Compact CNN for EEG-based BCI.* *J. Neural Eng.*, 15(5), 056013  
+- Goldberger, A. L. et al. (2000). *PhysioBank, PhysioToolkit, and PhysioNet.* *Circulation*, 101(23), e215–e220  
+
+---