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A Hybrid Brain-Computer Interface Based on Visual Evoked Potential and Pupillary Response.

Lu Jiang^1,2, Xiaoyang Li³, Weihua Pei^1,4

¹State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China.

Frontiers in Human Neuroscience

|February 21, 2022

Summary

This study introduces a hybrid brain-computer interface (h-BCI) using low-frequency visual evoked potential (VEP) and pupillary response (PR) to improve comfort and performance. The novel h-BCI system demonstrates higher accuracy and information transfer rates compared to traditional methods.

Keywords:

BCI illiteracy canonical correlation analysis electroencephalogram hybrid brain-computer interface pupillary response task-related component analysis visual evoked potential

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Area of Science:

Neuroscience
Biomedical Engineering
Human-Computer Interaction

Background:

Steady-state visual evoked potential (SSVEP) based Brain-Computer Interfaces (BCIs) offer high information transfer rates but cause visual discomfort and "BCI illiteracy."
Traditional SSVEP BCIs often utilize the alpha frequency range, which can be uncomfortable for users.

Purpose of the Study:

To develop a more comfortable and effective hybrid BCI (h-BCI) system.
To investigate the use of low-frequency stimulations (0.8-2.12 Hz) to elicit both visual evoked potential (VEP) and pupillary response (PR) simultaneously.
To enhance BCI performance by fusing VEP and PR information.

Main Methods:

A novel h-BCI system was constructed using low-frequency stimulations (12 classes, 0.8-2.12 Hz).
Visual evoked potential (VEP) and pupillary response (PR) signals were simultaneously elicited and analyzed using supervised and unsupervised classification.
A decision fusion method was employed to combine VEP and PR data for hybrid accuracy calculation.

Main Results:

The supervised method achieved an average accuracy of 94.90% (1.5s data length) with an ITR of 64.35 bits/min.
The unsupervised method achieved an average accuracy of 91.88% (4s data length) with an ITR of 33.19 bits/min.
The hybrid method demonstrated superior accuracy and ITR compared to individual VEP or PR methods for most subjects, particularly with shorter data lengths.

Conclusions:

The proposed low-frequency stimulation paradigm for h-BCI is more comfortable and user-friendly than traditional SSVEP-BCI.
This hybrid approach offers a promising alternative for BCI development, addressing limitations of existing SSVEP systems.
The simultaneous elicitation of VEP and PR provides a robust foundation for advanced BCI applications.