Gonzalez Andino S L, Grave de Peralta Menendez R, Lantz C M, Blank O, Michel C M, Landis T
Functional Brain Mapping Laboratory, Neurology Department, University Hospital Geneva, Switzerland.
Hum Brain Mapp. 2001 Oct;14(2):81-95. doi: 10.1002/hbm.1043.
Localization of the generators of the scalp measured electrical activity is particularly difficult when a large number of brain regions are simultaneously active. In this study, we describe an approach to automatically isolate scalp potential maps, which are simple enough to expect reasonable results after applying a distributed source localization procedure. The isolation technique is based on the time-frequency decomposition of the scalp-measured data by means of a time-frequency representation. The basic rationale behind the approach is that neural generators synchronize during short time periods over given frequency bands for the codification of information and its transmission. Consequently potential patterns specific for certain time-frequency pairs should be simpler than those appearing at single times but for all frequencies. The method generalizes the FFT approximation to the case of distributed source models with non-stationary time behavior. In summary, the non-stationary distributed source approximation aims to facilitate the localization of distributed source patterns acting at specific time and frequencies for non-stationary data such as epileptic seizures and single trial event related potentials. The merits of this approach are illustrated here in the analysis of synthetic data as well as in the localization of the epileptogenic area at seizure onset in patients. It is shown that time and frequency at seizure onset can be precisely detected in the time-frequency domain and those localization results are stable over seizures. The results suggest that the method could also be applied to localize generators in single trial evoked responses or spontaneous activity.
当大量脑区同时活跃时，头皮测量电活动的发生器定位尤其困难。在本研究中，我们描述了一种自动分离头皮电位图的方法，该方法足够简单，在应用分布式源定位程序后有望得到合理的结果。该分离技术基于通过时频表示对头皮测量数据进行时频分解。该方法背后的基本原理是，神经发生器在给定频段的短时间内同步，用于信息编码及其传输。因此，特定时频对的电位模式应该比那些在单一时刻但所有频率下出现的模式更简单。该方法将快速傅里叶变换（FFT）近似推广到具有非平稳时间行为的分布式源模型的情况。总之，非平稳分布式源近似旨在促进对非平稳数据（如癫痫发作和单次试验事件相关电位）在特定时间和频率下起作用的分布式源模式的定位。本文通过对合成数据的分析以及对患者癫痫发作起始时致痫区的定位来说明该方法的优点。结果表明，在时频域中可以精确检测癫痫发作起始的时间和频率，并且这些定位结果在多次发作中是稳定的。结果表明，该方法也可应用于单次试验诱发反应或自发活动中发生器的定位。
