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A user-friendly visual brain-computer interface based on high-frequency steady-state visual evoked fields recorded by

Dengpei Ji1,2, Xiaolin Xiao1,2, Jieyu Wu1,2

  • 1Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China.

Journal of Neural Engineering
|May 30, 2024
PubMed
Summary

This study introduces a novel brain-computer interface (BCI) using high-frequency steady-state visual evoked fields (SSVEFs) with optically pumped magnetometer magnetoencephalography (OPM-MEG). The OPM-MEG BCI achieved high accuracy and information transfer rates, demonstrating its potential for advanced brain-computer interfaces.

Keywords:
brain-computer interface (BCI)high frequencymagnetoencephalography (MEG)optically pumped magnetometer (OPM)steady-state visual evoked field (SSVEF)

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Magnetoencephalography (MEG) offers superior spatial resolution compared to electroencephalography (EEG) for brain-computer interfaces (BCIs).
  • Optically Pumped Magnetometer (OPM) based MEG (OPM-MEG) provides enhanced sensitivity and portability, making it ideal for advanced BCI applications.
  • High-frequency steady-state visual evoked fields (SSVEFs) offer a promising, flickering-imperceptible stimulus for accurate BCI control.

Purpose of the Study:

  • To develop and evaluate a novel OPM-MEG based BCI system utilizing high-frequency SSVEFs.
  • To assess the accuracy and information transfer rate of the proposed BCI system.
  • To explore the feasibility of using multi-dimensional MEG signals for improved BCI performance.

Main Methods:

  • Constructed a nine-command BCI system using high-frequency SSVEFs (58-62 Hz, 0.5 Hz interval).
  • Collected six-channel multi-dimensional MEG signals (Z and Y axes) from five participants during offline experiments.
  • Employed the ensemble task-related component analysis algorithm for SSVEF identification and performance evaluation.

Main Results:

  • Achieved an average offline accuracy of 92.98% using conjoint analysis of Z and Y axis MEG data.
  • Attained a theoretical average information transfer rate (ITR) of 58.36 bits/min with a 0.7s data length.
  • Reached a peak individual ITR of 63.75 bits/min, demonstrating high system efficacy.

Conclusions:

  • This study represents the first investigation into high-frequency SSVEF-BCI using OPM-MEG.
  • The results highlight the significant potential of OPM-MEG for detecting subtle brain signals for BCI applications.
  • The developed system offers both theoretical insights and practical value for advancing MEG-based BCIs.