Gasser T, Molinari L
Abt. Biostatistik, ISPM, Universität Zürich, Switzerland.
Stat Methods Med Res. 1996 Mar;5(1):67-99. doi: 10.1177/096228029600500105.
The quantitative analysis of the electroencephalogram (EEG) relies heavily on methods of time series analysis. A quantitative approach seems indispensable for research (be it clinical or basic neurophysical research), but it can also be a useful information for purely clinical purposes. Apart from the ongoing spontaneous EEG, evoked potentials (EPs) also play an important role. They can be elicited by simple sensory stimuli or more complex stimuli. Their analysis requires methods which are different from those for the spontaneous EEG. Those methods operate usually in the time domain and offer many challenging problems to statisticians. Methods for analysing the spontaneous EEG usually work in the frequency domain in terms of spectra and coherences. Biomedical engineers who take care of the equipment are usually also trained in time series analysis. Thus, they have contributed much more to methodological progress for analysing EEGs and EPs, compared with statisticians. However, the availability of a sample of subjects, and the associated problems in modelling followed by an inferential analysis could make a larger influence from the statistical side quite profitable. This paper tries to give an overview of a fascinating area. In doing so we treat more extensively problems with some statistical appeal. This leads inevitably to some overlap with our own work.
脑电图（EEG）的定量分析在很大程度上依赖于时间序列分析方法。定量方法对于研究（无论是临床研究还是基础神经物理学研究）似乎是不可或缺的，而且对于纯粹的临床目的而言，它也可能是有用的信息。除了持续的自发脑电图外，诱发电位（EPs）也起着重要作用。它们可以由简单的感觉刺激或更复杂的刺激诱发。对它们的分析需要与自发脑电图分析不同的方法。那些方法通常在时域中操作，给统计学家带来了许多具有挑战性的问题。分析自发脑电图的方法通常在频域中根据频谱和相干性进行操作。负责设备的生物医学工程师通常也接受过时间序列分析方面的培训。因此，与统计学家相比，他们在脑电图和诱发电位分析方法的进展方面做出了更多贡献。然而，有了受试者样本以及随后在建模和推断分析中出现的相关问题，统计学方面产生更大影响可能会带来很大益处。本文试图对一个引人入胜的领域进行概述。在此过程中，我们更广泛地探讨了一些具有统计学吸引力的问题。这不可避免地导致与我们自己的工作存在一些重叠。
