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Implementing a calibration-free SSVEP-based BCI system with 160 targets.

Yonghao Chen1,2, Chen Yang1, Xiaochen Ye1

  • 1School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, People's Republic of China.

Journal of Neural Engineering
|June 16, 2021
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Summary

This study developed a new brain-computer interface (BCI) system using steady-state visual evoked potentials (SSVEP) with 160 targets, significantly expanding BCI capabilities without calibration. The system achieved high accuracy in online experiments, demonstrating its efficiency for practical applications.

Keywords:
brain–computer interface (BCI)calibration-freeelectroencephalogram (EEG)steady-state visual evoked potential (SSVEP)

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Steady-state visual evoked potential (SSVEP) is a key electroencephalogram (EEG) based brain-computer interface (BCI) paradigm.
  • Existing BCI research often prioritizes classification accuracy and reduced stimulus duration.
  • This study addresses the need to increase the number of available targets in SSVEP-BCI systems without requiring user-specific calibration.

Purpose of the Study:

  • To develop a calibration-free SSVEP-BCI system capable of handling a large number of targets.
  • To optimize stimulus sequences for maximizing response distinguishability.
  • To evaluate the system's performance using both offline and online experiments.

Main Methods:

  • A calibration-free SSVEP-BCI system was designed using multiple frequency sequential coding with 160 targets.
  • Stimuli consisted of four continuous sinusoidal signals totaling four seconds.
  • An optimized stimulus sequence arrangement and a filter bank canonical correlation analysis (FBCCA) classification algorithm were employed.
  • Offline and online experiments with 8 and 12 subjects, respectively, were conducted.

Main Results:

  • Offline experiments validated the proposed stimulation selection and detection algorithms.
  • Online experiments demonstrated an average accuracy of 87.16 ± 11.46% and an information transfer rate of 78.84 ± 15.59 bits min⁻¹.
  • Seven out of 12 subjects achieved online accuracy exceeding 90%.

Conclusions:

  • The study presents a practical solution for implementing numerous targets in SSVEP-based BCIs.
  • The developed system offers a calibration-free SSVEP-BCI speller with over 100 commands, expanding BCI application scenarios.
  • The design criteria and findings can potentially enhance overall BCI system performance.