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Load-frequency control01:28

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Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...

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Leveraging low-frequency components for enhanced high-frequency steady-state visual evoked potential based brain

Yixin Chen1,2, Ren Xu3, Andrew Ty Lau4

  • 1East China University of Science and Technology, Shanghai, 200237 China.

Cognitive Neurodynamics
|August 5, 2025
PubMed
Summary

This study introduces a transfer learning method to boost high-frequency steady-state visual evoked potential brain-computer interface (SSVEP-BCI) performance. Leveraging low-frequency data significantly enhances accuracy with minimal calibration.

Keywords:
Brain computer interface (BCI)High-frequencySteady-state visual evoked potential (SSVEP)Transfer learning

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computer Science

Background:

  • High-frequency steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) systems offer user comfort but have lower performance than low-frequency systems.
  • This performance gap limits the practical application of high-frequency SSVEP-BCI.

Purpose of the Study:

  • To enhance the performance of high-frequency SSVEP-BCI systems.
  • To address the limitations of reduced performance and extensive calibration in high-frequency SSVEP-BCI.

Main Methods:

  • A transfer learning approach is proposed, utilizing low-frequency SSVEP data to improve high-frequency SSVEP performance.
  • A filtering mechanism extracts informative components from low-frequency signals.
  • The least squares algorithm generates synthetic high-frequency data.

Main Results:

  • Significant performance improvements were observed on two public datasets using TDCA, eTRCA, and TRCA-based algorithms.
  • Accuracy increased by 9.03% and 14.49% for eTRCA and TDCA on Dataset 1, and 13.91% and 14.53% on Dataset 2, all within 1.5 seconds.
  • The method requires only two calibration trials, effectively addressing the issue of single calibration data.

Conclusions:

  • The proposed transfer learning method significantly improves high-frequency SSVEP-BCI performance.
  • Fast calibration and enhanced accuracy are achievable for real-world high-frequency BCI applications.
  • This approach makes high-frequency SSVEP-BCI systems more practical and user-friendly.