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

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.