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Benchmarking Brain-Computer Interfaces Outside the Laboratory: The Cybathlon 2016.

Domen Novak1,2, Roland Sigrist1, Nicolas J Gerig1

  • 1Sensory-Motor Systems Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.

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|January 30, 2018
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Summary
This summary is machine-generated.

This study introduces a novel method for evaluating brain-computer interfaces (BCIs) outside laboratory settings using a game. The Cybathlon 2016 competition demonstrated this approach for assessing assistive BCIs in real-world conditions.

Keywords:
benchmark testingbrain-computer interfacescompetitionelectroencephalographymachine learning

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Area of Science:

  • Neuroscience
  • Rehabilitation Engineering
  • Human-Computer Interaction

Background:

  • Brain-computer interfaces (BCIs) offer potential for assistive technology for individuals with disabilities.
  • Evaluating BCIs in real-world, less structured environments presents significant challenges compared to laboratory settings.
  • Standardized benchmarking is crucial for advancing BCI development and application.

Purpose of the Study:

  • To develop and validate a novel, game-based benchmarking approach for evaluating brain-computer interfaces (BCIs) in non-laboratory conditions.
  • To assess the feasibility and utility of this benchmarking method within the context of the Cybathlon 2016 competition.
  • To analyze the performance of various BCI systems and teams under realistic assistive technology use scenarios.

Main Methods:

  • Development of a computer game simulating assistive BCI applications, with game completion time as the primary performance metric.
  • Establishment of rules for hardware, software, and human pilot inclusion in the benchmarking competition.
  • Application of the benchmarking game at the Cybathlon 2016, involving 11 participating BCI teams.

Main Results:

  • The developed benchmarking approach was successfully implemented at the Cybathlon 2016, providing insights into BCI performance in a real-world assistive technology competition.
  • Data from 11 teams' BCI approaches and results were collected and presented.
  • While limitations exist, the procedure proved effective for analyzing BCI performance in realistic, less structured conditions, despite challenges in identifying specific performance-driving factors.

Conclusions:

  • The game-based benchmarking method offers a viable approach for assessing brain-computer interfaces (BCIs) outside controlled laboratory settings.
  • The Cybathlon competition serves as a valuable platform for showcasing and evaluating assistive BCIs.
  • Future modifications to the game parameters could enhance its applicability to diverse BCI-driven applications and further refine performance analysis.