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评估 SSVEP 频谱估计的最佳多窗口长度和数量。

Evaluation of the optimal lengths and number of multiple windows for spectrogram estimation of SSVEP.

机构信息

Mathematical Statistics, Centre for Mathematical Sciences, Lund University, SE-221 00 Lund, Sweden.

出版信息

Med Eng Phys. 2010 May;32(4):372-83. doi: 10.1016/j.medengphy.2010.01.009.

Abstract

The purpose of this paper is to present the optimal number of windows and window lengths using multiple window spectrogram for estimation of non-stationary processes with shorter or longer duration. Such processes could start in the EEG as a result of a stimuli, e.g., steady-state visual evoked potentials (SSVEP). In many applications, the Welch method is used with standard set-ups for window lengths and number of averaged spectra/spectrograms. This paper optimizes the window lengths and number of windows of the Welch method and other more recent, so called, multiple window or multitaper methods and compares the mean squared errors of these methods. Approximative formulas for the choice of optimal number of windows and window lengths are also given. Examples of spectrogram estimation of SSVEP are shown.

摘要

本文旨在通过使用多个窗口频谱图来展示最优的窗口数量和窗口长度,以便对具有较短或较长持续时间的非平稳过程进行估计。这种过程可能是由于刺激而在 EEG 中开始的,例如稳态视觉诱发电位(SSVEP)。在许多应用中,Welch 方法与标准设置一起用于窗口长度和平均频谱/频谱图的数量。本文优化了 Welch 方法以及其他最近的所谓多窗口或多谱线方法的窗口长度和窗口数量,并比较了这些方法的均方误差。还给出了最优窗口数量和窗口长度选择的近似公式。展示了 SSVEP 的频谱图估计示例。

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