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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Multi-input and Multi-variable systems

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In the absence of...

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Related Experiment Video

Updated: Jun 22, 2026

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

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

Published on: November 24, 2015

An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method.

Guangyu Bin¹, Xiaorong Gao, Zheng Yan

¹Biomedical Engineering Department, Tsinghua University, Beijing, People's Republic of China. bgy06@mails.tsinghua.edu.cn

Journal of Neural Engineering

|June 5, 2009

Summary

This summary is machine-generated.

This study introduces an improved steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) system. The novel system achieves high accuracy and speed, demonstrating reduced user variation and enhanced ease of use for BCI applications.

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Area of Science:

Neuroscience
Biomedical Engineering
Human-Computer Interaction

Background:

Steady-state visual evoked potential (SSVEP) is increasingly used in brain-computer interface (BCI) systems.
Current SSVEP-BCI systems require improvements in speed, user variability, and ease of use.

Purpose of the Study:

To present an improved online multi-channel SSVEP-based BCI system.
To enhance the speed, reduce user variation, and increase the ease of use of SSVEP-BCI systems.

Main Methods:

A canonical correlation analysis (CCA) method was employed for SSVEP frequency extraction.
Key parameters (channel location, window length, harmonic number) were optimized using offline data.
An online system with six targets, nine channels, a 2s window, and the first harmonic was tested on 12 subjects.

Main Results:

The proposed SSVEP-BCI system achieved an average accuracy of 95.3%.
The system demonstrated a high information transfer rate of 58 ± 9.6 bit/min.
The system requires no channel selection or parameter optimization, showing low user variation and easy setup.

Conclusions:

The developed online multi-channel SSVEP-BCI system offers high performance and efficiency.
The system's design minimizes user-specific adjustments and setup complexity.
The findings suggest a promising advancement for practical SSVEP-BCI applications.