{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/eeg-gan-generative-adversarial-networks-for","title":"EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals","arxiv_id":"1806.01875","date":"2018-06-05","proceeding":null,"authors":["Kay Gregor Hartmann","Robin Tibor Schirrmeister","Tonio Ball"],"abstract":"Generative adversarial networks (GANs) are recently highly successful in\ngenerative applications involving images and start being applied to time series\ndata. Here we describe EEG-GAN as a framework to generate\nelectroencephalographic (EEG) brain signals. We introduce a modification to the\nimproved training of Wasserstein GANs to stabilize training and investigate a\nrange of architectural choices critical for time series generation (most\nnotably up- and down-sampling). For evaluation we consider and compare\ndifferent metrics such as Inception score, Frechet inception distance and\nsliced Wasserstein distance, together showing that our EEG-GAN framework\ngenerated naturalistic EEG examples. It thus opens up a range of new generative\napplication scenarios in the neuroscientific and neurological context, such as\ndata augmentation in brain-computer interfacing tasks, EEG super-sampling, or\nrestoration of corrupted data segments. The possibility to generate signals of\na certain class and/or with specific properties may also open a new avenue for\nresearch into the underlying structure of brain signals.","url_abs":"http://arxiv.org/abs/1806.01875v1","url_pdf":"http://arxiv.org/pdf/1806.01875v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"eeg-gan-generative-adversarial-networks-for","repo_url":"https://github.com/MichaelMurashov/ecg-testing","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"eeg-gan-generative-adversarial-networks-for","repo_url":"https://github.com/ethorsrud/Master","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-generation","task_name":"Time Series Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1806.01875","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}