On the use of generative deep neural networks to synthesize artificial multichannel EEG signals

Ozan Özdenizci, Deniz Erdogmus

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

Abstract

Recent promises of generative deep learning lately brought interest to its potential uses in neural engineering. In this paper we firstly review recently emerging studies on generating artificial electroencephalography (EEG) signals with deep neural networks. Subsequently, we present our feasibility experiments on generating condition-specific multichannel EEG signals using conditional variational autoencoders. By manipulating real resting-state EEG epochs, we present an approach to synthetically generate time-series multichannel signals that show spectro-temporal EEG patterns which are expected to be observed during distinct motor imagery conditions.
Originalspracheenglisch
Titel2021 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
Seiten427-430
Seitenumfang4
ISBN (elektronisch)9781728143378
DOIs
PublikationsstatusVeröffentlicht - 2021
Veranstaltung10th International IEEE/EMBS Conference on Neural Engineering - Virtuell
Dauer: 4 Mai 20216 Mai 2021

Konferenz

Konferenz10th International IEEE/EMBS Conference on Neural Engineering
KurztitelNER '21
OrtVirtuell
Zeitraum4/05/216/05/21

ASJC Scopus subject areas

  • Artificial intelligence
  • !!Mechanical Engineering

Fields of Expertise

  • Human- & Biotechnology
  • Information, Communication & Computing

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