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Cross-subject spatial filter transfer method for SSVEP-EEG feature recognition.

Wenqiang Yan1, Yongcheng Wu1, Chenghang Du1

  • 1School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China.

Journal of Neural Engineering
|April 28, 2022
PubMed
Summary
This summary is machine-generated.

A new cross-subject spatial filter transfer (CSSFT) method enables brain-computer interface (BCI) systems to transfer models between users without new data collection. This improves steady-state visual evoked potential (SSVEP) recognition, reducing user fatigue.

Keywords:
cross-subjectelectroencephalogram (EEG)ensemble learningspatial filtersteady-state visual evoked potential (SSVEP)transfer learning

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Steady-state visual evoked potential (SSVEP) is crucial for brain-computer interface (BCI) control.
  • Existing SSVEP algorithms require user-specific training data, increasing mental fatigue and limiting BCI applicability.
  • Reducing interference from spontaneous electroencephalogram (EEG) activity is key to improving SSVEP recognition accuracy.

Purpose of the Study:

  • To develop a cross-subject spatial filter transfer (CSSFT) method for efficient SSVEP feature decoding.
  • To enable model transfer to new users without collecting their training data.
  • To enhance the practical applicability of SSVEP-based BCI systems.

Main Methods:

  • Proposed a cross-subject spatial filter transfer (CSSFT) method.
  • Transferred existing user models with strong SSVEP responses to new user test data.
  • Evaluated the method on public datasets using canonical correlation analysis (CCA) and filter bank CCA.

Main Results:

  • The CSSFT method improved the distinction between target and non-target features.
  • Accurate identification of incorrect targets was achieved.
  • Significantly enhanced recognition performance for CCA and filter bank CCA was observed.
  • Effective feature recognition was maintained even with a single data block for model calculation.

Conclusions:

  • The CSSFT method eliminates the need for tedious calibration for new users.
  • It offers an effective solution for cross-subject model transfer in BCI.
  • The method holds significant potential for promoting wider BCI system application.