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Optimizing Computer-Brain Interface Parameters for Non-invasive Brain-to-Brain Interface.

John LaRocco1, Dong-Guk Paeng1

  • 1Laboratory of Biomedical Ultrasound, Department of Ocean System Engineering, Jeju National University, Jeju City, South Korea.

Frontiers in Neuroinformatics
|March 3, 2020
PubMed
Summary

This study optimized computer-brain interface (CBI) parameters for non-invasive brain-to-brain interfaces (BBIs). Optimal CBI latency and timeout thresholds improve information transfer rates, even with high stimulation failure rates.

Keywords:
computer–braindevice portabilityinterfaceneuromodulationnon-invasivetemporal resolution

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Non-invasive brain-to-brain interfaces (BBIs) require precise neuromodulation, high temporal resolution, and portability for accessibility.
  • A BBI integrates brain-computer interface (BCI) and computer-brain interface (CBI) components.
  • While BCI optimization is well-studied, CBI parameter optimization remains underexplored.

Purpose of the Study:

  • To simulate and assess a two-class medical monitoring BBI system.
  • To investigate the impact of varying CBI parameters (system latency, stimulation failure rate (SFR), timeout threshold) on BBI performance.
  • To determine optimal CBI parameters for maximizing the information transfer rate (ITR) of non-invasive BBIs.

Main Methods:

  • Simulated a two-class medical monitoring BBI system using parameters from BCI and CBI literature.
  • Assessed BBI function via information transfer rate (ITR) in bits per trial and bits per minute.
  • Kept BCI parameters (window length, update rate, classifier accuracy) constant to focus on CBI parameter effects.

Main Results:

  • Optimal CBI system latency was determined to be 100 ms or less, with a timeout threshold no more than twice the latency.
  • The BBI system maintained near-maximum efficiency even with a 25% stimulation failure rate (SFR) under optimal latency and timeout conditions.
  • A base ITR of 1 bit/trial was established based on passively monitored BCI parameters.

Conclusions:

  • Optimizing CBI system latency and timeout thresholds is crucial for maximizing the ITR of non-invasive BBIs.
  • Reflecting CBI parameters in BCI update rates can enhance the number of trials per minute, improving ITR, especially at high SFRs.
  • High latencies inherent in BCI protocols and CBI stimulation methods are primary constraints for current non-invasive BBI technology.