Enhancing Multi-Channel Eeg Classification with Gramian Temporal Generative Adversarial Networks

Abstract

Deep learning's requirements for large amounts of training data remains a challenge for researchers and developers. Generative Adversarial Network (GAN) is commonly used in medical image analysis to generate novel training images to help resolve this issue. While deep learning has many clinical applications in radiology, its applications in medical time series data such as electroencephalogram (EEG) are usually constrained to 1 dimension. Hence, there are few available GAN architectures that effectively synthesize single and multi-channel EEG. In this paper, we propose a novel method to synthesize multi-channel EEG in the form of Gramian Angular Field (GAF) images with a Gramian Temporal Generative Adversarial Network (GT-GAN). The proposed network is capable of generating realistic GAF images and enhances EEG anomaly detection accuracy in residual learning frameworks.