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An open-source human-in-the-loop BCI research framework: method and design.

Martin Gemborn Nilsson1, Pex Tufvesson1,2, Frida Heskebeck1

  • 1Department of Automatic Control, Lund University, Lund, Sweden.

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|July 13, 2023
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Summary
This summary is machine-generated.

This study introduces an open-source framework for next-generation brain-computer interfaces (BCIs). It enables faster online classification of brain activity (EEG) for improved cognitive neuroscience research and medical applications.

Keywords:
EEGbrain-computer interfaceonlinereal-timeresearch framework

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Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Current brain-computer interfaces (BCIs) offer limited online classification of mental states.
  • Sophisticated decoding of mental states requires time-consuming offline data analysis.
  • There is a need for advanced online BCIs for research and applications.

Purpose of the Study:

  • To address the limitations of current BCIs by improving the analytical pipeline.
  • To introduce a novel open-source research framework for next-generation online BCIs.
  • To facilitate more sophisticated and rapid decoding of mental states.

Main Methods:

  • Developed a modular, hardware-independent, open-source research framework.
  • Integrated human-in-the-loop (HIL) model training and retraining.
  • Utilized Timeflux Python package for real-time signal processing, classification, and machine learning model training.
  • Employed web browser technologies for stimuli presentation and diagnostics, and Lab Streaming Layer with websockets for communication.

Main Results:

  • The framework supports real-time processing and online classification of electroencephalography (EEG) data.
  • It enables transfer learning and cloud computing for enhanced classification capabilities.
  • The system is compatible with Linux, MacOS, and Windows operating systems.
  • Facilitates both online analysis and offline analysis using various scientific software packages.

Conclusions:

  • The developed BCI-HIL framework significantly advances the capabilities of online BCIs.
  • It provides a versatile platform for human-in-the-loop BCI research, accelerating cognitive neuroscience studies.
  • The open-source nature and modular design promote accessibility and further development in the field.