Generative Adversarial Network (GAN) for Simulating Electroencephalography

Abstract

Electroencephalographs record the electrical activity of your brain through the scalp. Electroencephalography is difficult to obtain due to its sensitivity and variability. Applications of electroencephalography such as for diagnosis, education, brain-computer interfaces require large samples of electroencephalography recording, however, it is often difficult to obtain the required datasets. Generative adversarial networks are robust deep learning framework which have proven themselves to be capable of synthesizing data. The robust nature of a generative adversarial network was used to generate multi-channel electroencephalography data in order to see if generative adversarial networks could reconstruct the spatio-temporal aspects of multi-channel electroencephalography signals. We were able to find that the synthetic electroencephalography data was able to replicate fine details of electroencephalography data and could potentially help us to generate large sample synthetic resting-state electroencephalography data for use in simulation testing of neuroimaging analyses. Generative adversarial networks (GANs) are robust deep-learning frameworks that can be trained to be convincing replicants of real data GANs were capable of generating "fake" EEG data that replicated fine details and topographies of "real" resting-state EEG data.

Keywords: Electroencephalography; Generative adversarial networks; Machine learning.