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

Ozan Özdenizci, Deniz Erdogmus

Research output: Chapter in Book/Report/Conference proceedingConference paper

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.
Original languageEnglish
Title of host publication10th International IEEE/EMBS Conference on Neural Engineering (NER)
DOIs
Publication statusPublished - 2021
Event10th International IEEE/EMBS Conference on Neural Engineering - Virtuell
Duration: 4 May 20216 May 2021

Conference

Conference10th International IEEE/EMBS Conference on Neural Engineering
Abbreviated titleNER '21
CityVirtuell
Period4/05/216/05/21

Fields of Expertise

  • Human- & Biotechnology
  • Information, Communication & Computing

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