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Inter- and Intra-subject Template-Based Multivariate Synchronization Index Using an Adaptive Threshold for

Haoran Wang1, Yaoru Sun1, Yunxia Li2

  • 1Department of Computer Science and Technolgy, College of Electronic and Information Engineering, Tongji University, Shanghai, China.

Frontiers in Neuroscience
|October 5, 2020
PubMed
Summary

This study introduces an enhanced Multivariate Synchronization Index (MSI) for brain-computer interfaces (BCIs) using template transfer and adaptive thresholds. The new method improves recognition accuracy and information transfer rates for steady-state visually evoked potential (SSVEP) detection.

Keywords:
adaptive thresholdbrain-computer interface (BCI)inter- and intra-subject template-based multivariate synchronization indexsteady-state visually evoked potentials (SSVEP)transfer learning

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Steady-state visually evoked potential (SSVEP) is crucial for brain-computer interfaces (BCIs).
  • Multivariate Synchronization Index (MSI) is effective for SSVEP frequency recognition, but accuracy is limited by simplified templates.
  • Individual calibration data has improved SSVEP recognition, yet further enhancements are needed.

Purpose of the Study:

  • To introduce a novel extension of the MSI method for improved SSVEP recognition in BCIs.
  • To enhance SSVEP recognition robustness by employing inter- and intra-subject template signals.
  • To increase the information transfer rate (ITR) by integrating an adaptive threshold strategy for dynamic windowing.

Main Methods:

  • An extended MSI method incorporating inter- and intra-subject template transfer was developed.
  • A novel adaptive threshold strategy with a dynamic window was integrated for temporal feature extraction.
  • The method was evaluated on a 12-class SSVEP dataset from 10 subjects.

Main Results:

  • The proposed method demonstrated higher recognition accuracy compared to CCA, MSI, Multi-set CCA, and Individual Template-based CCA.
  • The extended MSI achieved a superior information transfer rate (ITR) over existing methods.
  • The adaptive threshold strategy effectively identified stimulus frequencies and filtered invalid trials.

Conclusions:

  • The developed extended MSI method offers a promising approach for robust SSVEP recognition.
  • The integration of template transfer and adaptive thresholding significantly enhances BCI performance.
  • This method holds potential for the development of high-speed and accurate BCIs.