EEG-WGAN-GP

Georgios-Ak/EEG-WGAN-GP

EEG-WGAN-GP is a machine learning model from Georgios-Ak. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

Model Type: Conditional WGAN-GP Dataset: EEG Motor Movement/Imagery Dataset (PhysioNet)

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Access: Public
Created: Oct 12, 2025
Updated: Oct 12, 2025
SHA: 9efc160d

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physionet/eegmmidb

Model card

From the Hugging Face model README

EEG Data Synthesis with WGAN-GP

Model Type: Conditional WGAN-GP
Dataset: EEG Motor Movement/Imagery Dataset (PhysioNet)

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

Languages: en
Created: Oct 12, 2025
Updated: Oct 12, 2025