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NFBLab-A Versatile Software for Neurofeedback and Brain-Computer Interface Research.

Nikolai Smetanin1, Ksenia Volkova1, Stanislav Zabodaev2

  • 1Center for Bioelectric Interfaces, National Research University Higher School of Economics, Moscow, Russia.

Frontiers in Neuroinformatics
|January 9, 2019
PubMed
Summary
This summary is machine-generated.

Neurofeedback (NFB) training can be optimized with NFBLab, a new open-source software. NFBLab enables reproducible, low-latency brain-computer interface experiments for improved neurofeedback efficacy.

Keywords:
LSL-protocolbrain-computer interfaceflexible experiment designlow-latencyneurofeedbackreal-time EEGsoftware

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computer Science

Background:

  • Neurofeedback (NFB) is a technique for volitionally modulating brain activity using real-time feedback from EEG or MEG.
  • Despite decades of research, NFB's efficacy remains debated, partly due to suboptimal training conditions.
  • There is a need for advanced experimental tools to optimize NFB paradigms and facilitate reproducible research.

Purpose of the Study:

  • To introduce NFBLab, an open-source, versatile software for conducting reproducible NFB experiments.
  • To enable low-latency feedback presentation and individualized signal processing for enhanced NFB training.
  • To support complex experimental protocols, source-space NFB, and integration with standard BCI paradigms.

Main Methods:

  • Developed NFBLab, a Python-based software with a GUI for configuring complex NFB protocols.
  • Implemented individualized EEG/MEG signal processing using spatial/temporal filters and artifact removal.
  • Integrated NFBLab with MNE-Python for source-space NFB and utilized Lab Streaming Layer for device interfacing.

Main Results:

  • NFBLab allows for completely reproducible NFB paradigms and low-latency feedback presentation.
  • The software supports individualized signal processing tracts and novel algorithms for reduced feedback latency.
  • NFBLab facilitates interfacing with EEG/MEG devices and supports standard BCI paradigms.

Conclusions:

  • NFBLab provides an optimized environment for NFB research, addressing limitations of previous approaches.
  • The software's features facilitate exploration of NFB paradigms and comparison of training effects.
  • NFBLab has the potential to improve NFB training efficacy through optimized experimental conditions and feedback delivery.