脑电图数据的统计处理。

T Gasser
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引用次数: 1

摘要

这一贡献集中在EEG数据作为时间序列的处理上。与事件相关的电位相比,更强调自发活动。讨论了回归函数和密度的非参数估计的平滑技术。提出了一些有前景的新方法。回顾了时间序列分析的基本原理,特别是频谱分析。针对脑电时间序列参数化的难点,将参数化的理想特性与现有方法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Statistical handling of EEG-data.

This contribution focusses on the treatment of EEG data as time series. More emphasis is placed on spontaneous activity than on event-related potentials. Smoothing techniques are discussed, both for the nonparametric estimation of regression functions and of densities. Some new promising methods are presented. The fundamentals of time series analysis, and in particular of spectrum analysis, are reviewed. For the difficult area of parameterization of EEG time series, we compare desirable characteristica of parameters with the methods already in use.

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