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Streamlining cVEP Paradigms: Effects of a Minimized Electrode Montage on Brain-Computer Interface Performance.

Milán András Fodor1, Atilla Cantürk1, Gernot Heisenberg2

  • 1Faculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Kleve, Germany.

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|June 26, 2025
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Summary
This summary is machine-generated.

Reducing electrodes in brain-computer interfaces (BCIs) lowers performance, but retraining can restore accuracy for some users. Individual differences impact system functionality, suggesting a need for flexible classification methods in minimal electrode setups.

Keywords:
BCI spellerEEG-based BCIbrain–computer interface (BCI)code-modulated visual evoked potential (cVEP)electrode reductionvisual evoked potential (VEP)

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Area of Science:

  • Neuroscience
  • Biomedical Engineering

Background:

  • Brain-computer interfaces (BCIs) utilize electroencephalography (EEG) signals for direct brain-device communication.
  • Code-modulated visual evoked potential (cVEP)-based BCIs use visual stimuli for neural response classification.
  • Increasing EEG electrodes improves accuracy but reduces user comfort and increases system complexity.

Purpose of the Study:

  • To investigate the impact of reducing EEG electrode count from 16 to 6 on cVEP-BCI performance.
  • To evaluate the effectiveness of retraining in a reduced electrode setup.
  • To identify challenges and individual differences affecting cVEP-BCI functionality with fewer electrodes.

Main Methods:

  • An online BCI study with 38 able-bodied participants.
  • Comparison of a 16-electrode baseline with 6-electrode setups (with and without retraining).
  • Assessment of Information Transfer Rate (ITR) and classification accuracy.

Main Results:

  • Average performance decreased with fewer electrodes.
  • Retraining restored near-baseline performance for some participants where the system remained functional.
  • Classification pipeline failure occurred in a substantial number of participants after electrode reduction, indicating individual variability.

Conclusions:

  • Minimal electrode setups for cVEP-BCIs may require flexible classification methods to accommodate individual differences.
  • Current cVEP paradigms face limitations with reduced electrode counts, highlighting the need for further research.
  • Findings provide insights into current cVEP-BCI capabilities and guide future development towards user-friendly systems.