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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/)
---
## 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
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