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Related Experiment Video

Updated: May 7, 2026

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

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A 240-target VEP-based BCI system employing narrow-band random sequences.

Yida Dong^1,2, Li Zheng¹, Weihua Pei^1,2

¹Key Laboratory of Solid-State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, People's Republic of China.

Journal of Neural Engineering

|March 12, 2025

Summary

This study introduces a novel brain-computer interface (BCI) using code-modulated visual evoked potentials (c-VEP) to achieve high information transfer rates (ITR) with a large number of commands. The system demonstrates strong performance in both offline and online experiments, paving the way for more practical BCI applications.

Keywords:

brain–computer interface code-modulated visual evoked potential (c-VEP)deep learning narrow-band random sequences

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Last Updated: May 7, 2026

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Area of Science:

Neuroscience
Biomedical Engineering
Computer Science

Background:

Brain-computer interfaces (BCI) are crucial for assistive technologies.
High information transfer rates (ITR) and large instruction sets are key challenges in current BCI systems.
Code-modulated visual evoked potential (c-VEP) BCIs offer a promising avenue for improved performance.

Purpose of the Study:

To develop a novel c-VEP BCI system capable of handling an extensive instruction set.
To maintain high performance metrics, including information transfer rates (ITR).
To address the challenge of achieving high ITR with a large number of targets in BCI.

Main Methods:

A novel c-VEP BCI system utilizing narrow-band random sequences as visual stimuli.
A convolutional neural network (CNN)-based EEG2Code decoding algorithm for stimulus sequence prediction.
Both offline (sequential paradigm) and online (cued spelling task) experiments were conducted.

Main Results:

Offline experiments achieved an average accuracy of 87.66% and a simulated ITR of 260.14 bits/min.
Online experiments demonstrated an accuracy of 76.27% and an ITR of 213.80 bits/min.
The system supported one of the largest known instruction sets for VEP-based BCIs.

Conclusions:

The developed c-VEP BCI system represents an advancement in the field.
The system exhibits robust performance, offering high accuracy and ITR.
This work has the potential for more practical and efficient brain-computer interface applications.