Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP Events

Abstract

Electroencephalography (EEG) data are difficult to obtain due to complex experimental setups and reduced comfort with prolonged wearing. This poses challenges to train powerful deep learning model with the limited EEG data. Being able to generate EEG data computationally could address this limitation. We propose a novel Wasserstein Generative Adversarial Network with gradient penalty (WGAN-GP) to synthesize EEG data. This network addresses several modeling challenges of simulating time-series EEG data including frequency artifacts and training instability. We further extended this network to a class-conditioned variant that also includes a classification branch to perform event-related classification. We trained the proposed networks to generate one and 64-channel data resembling EEG signals routinely seen in a rapid serial visual presentation (RSVP) experiment and demonstrated the validity of the generated samples. We also tested intra-subject cross-session classification performance for classifying the RSVP target events and showed that class-conditioned WGAN-GP can achieve improved event-classification performance over EEGNet.

References in corpus (16)

• Conditional Generative Adversarial Nets
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
• Unsupervised Data Augmentation for Consistency Training
• Improved Training of Wasserstein GANs
• Improved Techniques for Training GANs
• Semi-Supervised Learning with Generative Adversarial Networks
• Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
• Good Semi-supervised Learning that Requires a Bad GAN
• Adversarial Training Methods for Semi-Supervised Text Classification
• Data Augmentation Using GANs
• Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference
• EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals
• Attribute-Guided Face Generation Using Conditional CycleGAN
• Evolutionary Generative Adversarial Networks