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RT-NET: real-time reconstruction of neural activity using high-density electroencephalography by Roberto Guarnieri; Mingqi Zhao; Gaia Amaranta Taberna; Marco Ganzetti; Stephan P. Swinnen; Dante Mantini is a Neuroscience article available to read on EtoBox.

What is RT-NET: real-time reconstruction of neural activity using high-density electroencephalography about?

High-density electroencephalography (hdEEG) has been successfully used for large-scale investigations of neural activity in the healthy and diseased human brain. Because of their high computational demand, analyses of source-projected hdEEG data are typically performed offline. Here, we present a real-time noninvasive electrophysiology toolbox, RT-NET, which has been specifically developed for online reconstruction of neural activity using hdEEG. RT-NET relies on the Lab Streaming Layer for acquiring raw data from a large number of EEG amplifiers and for streaming the processed data to external applications. RT-NET estimates a spatial filter for artifact removal and source activity reconstruction using a calibration dataset. This spatial filter is then applied to the hdEEG data as they are acquired, thereby ensuring low latencies and computation times. Overall, our analyses show that RT-NET can estimate real-time neural activity with performance comparable to offline analysis methods. It may therefore enable the development of novel brain–computer interface applications such as source-based neurofeedback.

Who reads RT-NET: real-time reconstruction of neural activity using high-density electroencephalography?

Author

Roberto Guarnieri; Mingqi Zhao; Gaia Amaranta Taberna; Marco Ganzetti; Stephan P. Swinnen; Dante Mantini

Publisher

Springer Science and Business Media LLC

Published

2020

Language

EN

Field

Neuroscience (Life Sciences)