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Space-time filter for SSVEP brain-computer interface based on the minimum variance distortionless response.

Sarah Negreiros de Carvalho1,2, Guilherme Vettorazzi Vargas3, Thiago Bulhões da Silva Costa4,3

  • 1Institute of Exact and Applied Sciences, Federal University of Ouro Preto, UFOP, Ouro Preto, Brazil. sarah@ufop.edu.br.

Medical & Biological Engineering & Computing
|April 28, 2021
PubMed
Summary

The Minimum Variance Distortionless Response (MVDR) filter enhances brain-computer interface (BCI) accuracy for steady-state visually evoked potentials (SSVEP). This advanced filtering method maintains high performance even with more visual stimuli, outperforming traditional techniques.

Keywords:
Brain-computer interfaceMinimum variance distortionless responseSpatial filteringSteady-state visually evoked potentialTemporal filtering

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Steady-state visually evoked potentials (SSVEP) are key for brain-computer interfaces (BCI).
  • Effective filtering is essential for accurate SSVEP detection and BCI performance.
  • Existing methods like Common Average Reference (CAR) and Canonical Correlation Analysis (CCA) have limitations.

Purpose of the Study:

  • To analyze a novel space-time filter, the Minimum Variance Distortionless Response (MVDR), for SSVEP-based BCIs.
  • To compare the MVDR filter's performance against CAR and CCA techniques.
  • To evaluate the impact of combining CAR and MVDR filters.

Main Methods:

  • Implemented and compared four filtering scenarios: CAR, CCA, MVDR, and CAR+MVDR.
  • Utilized Welch periodogram, Fast Fourier Transform, and CCA for feature extraction.
  • Employed forward wrappers for feature selection and a linear classifier for discrimination.
  • Conducted experiments with ten volunteers using four and six visual stimuli.

Main Results:

  • The MVDR filter demonstrated superior performance compared to CAR and CCA.
  • BCI accuracy with the MVDR filter remained stable as the number of visual stimuli increased.
  • Other techniques showed performance degradation with a higher number of stimuli.

Conclusions:

  • The MVDR filter offers a robust and accurate solution for SSVEP-based BCIs.
  • MVDR provides a significant advantage in maintaining system accuracy, especially in multi-stimulus environments.
  • This finding has implications for advancing BCI applications in various fields.