Single-channel attention classification algorithm based on robust Kalman filtering and norm-constrained ELM.

Journal: Frontiers in human neuroscience
Published Date: Jan 9, 2025

Abstract

INTRODUCTION: Attention classification based on EEG signals is crucial for brain-computer interface (BCI) applications. However, noise interference and real-time signal fluctuations hinder accuracy, especially in portable single-channel devices. This study proposes a robust Kalman filtering method combined with a norm-constrained extreme learning machine (ELM) to address these challenges.

Authors

  • Jing He
    School of Management, Guilin University of Aerospace Technology, Guilin, China.
  • Zijun Huang
    School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China.
  • Yunde Li
    Biomedical and Artificial Intelligence Laboratory, Guilin University of Aerospace Technology, Guilin, China.
  • Jiangfeng Shi
    School of Automation Science and Engineering, South China University of Technology, Guangzhou, China.
  • Yehang Chen
    Biomedical and Artificial Intelligence Laboratory, Guilin University of Aerospace Technology, Guilin, China.
  • Chengliang Jiang
    Biomedical and Artificial Intelligence Laboratory, Guilin University of Aerospace Technology, Guilin, China.
  • Jin Feng
    Student Affairs Office, Guilin Normal College, Guilin, China.

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