---
tags:
  - eeg
  - gan
  - wgan-gp
  - brain-computer-interface
  - data-synthesis
  - pytorch
language: en
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/)

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## 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

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## 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 |

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## 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 |

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## 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.

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## 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  

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