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

Neuroinformatics. 2021 Apr;19(2):251-266. doi: 10.1007/s12021-020-09479-3.

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.

Keywords: Electroencephalography; Head model; Neural activity; Online processing; Source localization.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Artifacts
  • Brain / diagnostic imaging
  • Brain / physiology*
  • Brain Mapping / methods*
  • Brain-Computer Interfaces*
  • Computer Systems*
  • Electroencephalography / methods*
  • Humans
  • Magnetic Resonance Imaging / methods