English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Recent promises of generative deep learning lately brought interest to itspotential uses in neural engineering. In this paper we firstly review recentlyemerging studies on generating artificial electroencephalography (EEG) signalswith deep neural networks. Subsequently, we present our feasibility experimentson generating condition-specific multichannel EEG signals using conditionalvariational autoencoders. By manipulating real resting-state EEG epochs, wepresent an approach to synthetically generate time-series multichannel signalsthat show spectro-temporal EEG patterns which are expected to be observedduring distinct motor imagery conditions.

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