2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDBIO-10.5
Paper Title ENHANCING MULTI-CHANNEL EEG CLASSIFICATION WITH GRAMIAN TEMPORAL GENERATIVE ADVERSARIAL NETWORKS
Authors Chi Nok Enoch Kan, Richard Povinelli, Dong Hye Ye, Marquette University, United States
SessionBIO-10: Deep Learning for EEG Analysis
LocationGather.Town
Session Time:Thursday, 10 June, 13:00 - 13:45
Presentation Time:Thursday, 10 June, 13:00 - 13:45
Presentation Poster
Topic Biomedical Imaging and Signal Processing: [BIO] Biomedical signal processing
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Virtual Presentation  Click here to watch in the Virtual Conference
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.