/GenerateEEG

Two Conditional GAN frameworks to perform synthetic EEG generation for dataset augmentation.

Primary LanguageJupyter Notebook

GenerateEEG

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Conditional GAN frameworks to perform synthetic EEG generation for dataset augmentation as 1-D signals or 2-D spectrograms.

Contains the code implementation for GenerateEEG paper.

Developed for AS.050.337: Reading the Mind: Computational Cognitive Neuroscience of Vision, Johns Hopkins University.

Authored by Rishi Chandra, rchand18@jhu.edu