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A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

A 120-target brain-computer interface based on code-modulated visual evoked potentials.

Qingyu Sun¹, Li Zheng¹, Weihua Pei¹

¹State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China.

Journal of Neuroscience Methods

|April 15, 2022

Summary

This summary is machine-generated.

This study introduces a 120-target brain-computer interface (BCI) using code-modulated visual evoked potentials (c-VEPs), achieving high information transfer rates for practical applications.

Keywords:

Brain-computer interface (BCI)Code-modulated visual evoked potential (c-VEP)Electroencephalogram (EEG)

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Area of Science:

Neuroscience
Biomedical Engineering
Human-Computer Interaction

Background:

Existing brain-computer interface (BCI) studies often lack sufficient targets for practical use.
Code-modulated visual evoked potentials (c-VEPs) offer a promising avenue for BCI development.

Purpose of the Study:

To develop a high-target BCI system for efficient communication.
To enhance the information transfer rate (ITR) of c-VEP based BCIs.

Main Methods:

Proposed a 120-target BCI system utilizing code-modulated visual evoked potentials (c-VEPs).
Employed four 31-bit pseudorandom codes, generating 30 targets per code via cyclic shifts with a 1-bit lag.
Conducted online experiments to evaluate system performance.

Main Results:

Achieved an average information transfer rate (ITR) of 265.74 bits/min.
Enabled target selection within 1.04 seconds (0.52s stimulation, 0.52s gaze shift).
Demonstrated superior performance in terms of target number and ITR compared to recent c-VEP studies.

Conclusions:

The developed BCI system offers a high ITR with reduced stimulation time.
The c-VEP paradigm effectively shortens training duration, enhancing real-world applicability.
This approach significantly advances the practicality of BCI systems.