A multi-target brain-computer interface based on code modulated visual evoked potentials

Area of Science:

• Neuroscience
• Biomedical Engineering
• Human-Computer Interaction

Background:

• Brain-computer interface (BCI) performance is often limited by the number of selectable targets.
• Existing code-modulated visual evoked potential (c-VEP) BCIs face limitations in target selection due to modulation code constraints.

Purpose of the Study:

• To propose and validate a novel c-VEP BCI paradigm that substantially increases the number of selectable targets.
• To develop and test a c-VEP BCI system capable of distinguishing between a large number of stimulus targets.

Main Methods:

• A new c-VEP BCI paradigm was developed, grouping 64 stimulus targets into four distinct sets, each modulated by unique pseudorandom binary codes and their shifts.
• An experiment was conducted with eight subjects using the developed four-group c-VEP BCI system.
• Signal characteristics were analyzed using auto- and cross-correlation, frequency spectrum, signal-to-noise ratio, and correlation coefficients.

Main Results:

• The developed c-VEP BCI system demonstrated high classification accuracy, averaging 88.36% across subjects for single-trial data.
• An impressive information transfer rate of 184.6 bit/min was achieved.
• Analysis confirmed the feasibility and effectiveness of the proposed c-VEP BCI paradigm.

Conclusions:

• The novel c-VEP BCI paradigm effectively increases the number of selectable targets.
• This approach offers a significant advancement for BCI research, enabling more complex and user-friendly interfaces.
• The study validates a new solution for enhancing BCI capabilities through innovative target modulation strategies.