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

Authors
  • Guarnieri, Roberto; 112315;
  • Zhao, Mingqi;
  • Taberna, Gaia Amaranta; 114283;
  • Ganzetti, Marco;
  • Swinnen, Stephan P; 15707;
  • Mantini, Dante; 64085;
Publication Date
Jul 28, 2020
Source
Lirias
Keywords
License
Unknown
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Abstract

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. / status: published

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