EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals (1806.01875v1)

Abstract: Generative adversarial networks (GANs) are recently highly successful in generative applications involving images and start being applied to time series data. Here we describe EEG-GAN as a framework to generate electroencephalographic (EEG) brain signals. We introduce a modification to the improved training of Wasserstein GANs to stabilize training and investigate a range of architectural choices critical for time series generation (most notably up- and down-sampling). For evaluation we consider and compare different metrics such as Inception score, Frechet inception distance and sliced Wasserstein distance, together showing that our EEG-GAN framework generated naturalistic EEG examples. It thus opens up a range of new generative application scenarios in the neuroscientific and neurological context, such as data augmentation in brain-computer interfacing tasks, EEG super-sampling, or restoration of corrupted data segments. The possibility to generate signals of a certain class and/or with specific properties may also open a new avenue for research into the underlying structure of brain signals.

• The paper introduces an EEG-GAN framework that employs dynamic gradient penalty scaling to stabilize training and generate high-quality EEG signals.
• It leverages CNN-based architecture adaptations and progressive resolution training to effectively capture the temporal and spectral nuances of EEG data.
• Empirical results using IS, FID, and SWD metrics demonstrate that EEG-GAN outperforms traditional GAN approaches, enabling improved data augmentation and signal restoration in neuroscience.

EEG-GAN: Generative Adversarial Networks for EEG Brain Signals

The paper introduces a novel approach for generating electroencephalographic (EEG) data using Generative Adversarial Networks (GANs), termed EEG-GAN. By integrating GAN frameworks, primarily utilized in image synthesis, this paper extends their applicability to time-series data such as EEG signals. This advancement holds potential to transform various aspects of neuroscience and neurology by offering new avenues for data augmentation, restoration, and feature exploration in EEG datasets.

Theoretical Contributions

The authors propose significant modifications to the traditional Wasserstein GAN (WGAN) framework to stabilize training specifically for EEG signal generation. By crafting the framework to progressively grow the training of GANs to higher resolutions, they address inherent challenges related to mode collapse and instability observed in traditional GAN training. Unlike previous methodologies that clip gradients, the proposed method scales the gradient penalty dynamically based on current critic estimates, thereby enhancing stability and facilitating the generation of realistic time-series data.

Methodological Insights

1. Architecture Adaptations: The paper leverages convolutional neural networks (CNNs) over autoregressive models, which are typically used for sequential data tasks. This choice is argued to provide superior interpretability, which is critical when handling complex neurophysiological signals. Key architectural elements include the incorporation of techniques such as linear or cubic interpolation methods for upsampling, avoiding the high-frequency artifacts introduced by methods like nearest-neighbor interpolation.
2. Evaluation Metrics: The paper evaluates the generated EEG signals using metrics such as Inception Score (IS), Frechet Inception Distance (FID), and Sliced Wasserstein Distance (SWD). These metrics assess the qualitative aspects of the generated data and ensure that the generated signals align closely with the statistical properties of real EEG signals.
3. Training Paradigms: The authors employ a progressive training technique across resolutions, initiated with smaller time sample sequences and scaled up to detailed high-resolution sequences. Training involves optimizing the critic networks to robustly distinguish between real and synthesized signal distributions, thereby improving the quality of generated data progressively.

Empirical Results

The empirical evaluation demonstrates that the proposed EEG-GAN framework can generate EEG data that reflects both temporal and spectral properties consistent with real-world EEG signals. The methodology outperforms traditional GAN approaches that suffer from training instabilities, illustrated by the superior performance across selected metric benchmarks.

Implications and Future Directions

The practical implications of EEG-GAN are manifold:

• Data Augmentation: The generation of synthetic EEG data can enhance machine learning model training by supplementing limited real-world datasets without the need for labor-intensive data collection.
• Signal Restoration and Super-sampling: The framework can potentially aid in filling gaps in corrupted datasets and improving the spatial or temporal resolution of existing EEG data without additional data acquisition.
• Scientific Exploration: By enabling the generative simulation of brain signals with specified properties, the research opens novel paths for exploring the neurological basis of EEG and understanding brain function through synthesized scenarios.

Looking forward, the research lays the groundwork for developing more generalized multi-channel EEG generative models and extending the framework's applicability to diverse neuroscientific tasks. Future work could focus on refining model hyperparameters through extensive search strategies, implementing multi-channel EEG synthesis, and drawing insights from expert evaluation studies to further validate the efficacy of EEG-GANs in real-world applications.