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Electroencephalography (EEG) power spectrum estimation is one of the common methods to analysis the work situation of brain. This paper adopts a numerical analysis method named the Autoregressive (AR) model with Butterworth filter to improve the effectiveness of EEG power spectrum estimation. Based on the AR model and time series analysis of T4 region of EEG signal, the comparison results indicate that the Butterworth filter is better for the power spectrum estimation and analysis of EEG signal, where the change-points of frequency of EEG signal are obvious. The numerical analysis method adopted in this paper may be benefits for the fatigue state judgment of flight crews.

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Electroencephalography Power Spectrum Estimation Based on the AR Model with Butterworth Filter

  • 会议论文

  • 发表日期：

    2026年

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  • Cui Yingjie,
  • Li Yan,
  • Li Xu,
  • Chen Zhongxian

摘要

Electroencephalography (EEG) power spectrum estimation is one of the common methods to analysis the work situation of brain. This paper adopts a numerical analysis method named the Autoregressive (AR) model with Butterworth filter to improve the effectiveness of EEG power spectrum estimation. Based on the AR model and time series analysis of T4 region of EEG signal, the comparison results indicate that the Butterworth filter is better for the power spectrum estimation and analysis of EEG signal, where the change-points of frequency of EEG signal are obvious. The numerical analysis method adopted in this paper may be benefits for the fatigue state judgment of flight crews.