Decomposition of event-related brain potentials into multiple functional components using wavelet transform
Decomposition of event-related brain potentials into multiple functional components using wavelet transform
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DOI：
10.1177/155005940103200307
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发表时间：
2001-07-01
期刊：
影响因子：
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通讯作者：
Ademoglu, A
中科院分区：
文献类型：
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作者：
Demiralp, T;Ademoglu, A
事件相关脑电位(Event Related Brain Potential，ERP)波形由多个在时间、频率和地形空间上扩展的成分组成，因此，对涉及信号的时间、频率和空间特征的数据进行有效的处理，有助于了解大脑的功能、解剖结构和神经生理机制之间的可能联系。小波变换(WT)是提取不同时间点、不同频点的事件相关电位成分的有力信号处理工具。本文介绍了小波变换在ERP处理中的技术解释及其四种不同的应用。前两个应用的目的是根据地形图记录中的增量、θ和阿尔法频带中的某些小波系数来识别和定位功能古怪的ERP成分。第三个应用程序执行类似的描述，涉及三个刺激范例。第四个应用是单次扫描ERP处理，以在单次试验中检测P300。最后一个案例是将小波变换与源定位技术相结合，对ERP组件识别进行了扩展。其目的是定位三维大脑结构中的时频分量，而不是头皮表面。小波变换的时频分析有助于分离和描述ERP产生过程中的顺序和/或重叠的功能过程，并为研究这些认知过程和在实验过程中跟踪其动态提供了可能性。
Event related brain potential (ERP) waveforms consist of several components extending in time, frequency and topographical space, Therefore, an efficient processing of data which involves the time, frequency and space features of the signal, may facilitate understanding the plausible connections among the functions, the anatomical structures and neurophysiological mechanisms of the brain. Wavelet transform (WT) is a powerful signal processing tool for extracting the ERP components occurring at different time and frequency spots. A technical explanation of WT in ERP processing and its four distinct applications are presented here. The first two applications aim to identify and localize the functional oddball ERP components in terms of certain wavelet coefficients in delta, theta and alpha bands in a topographical recording. The third application performs a similar characterization that involves a three stimulus paradigm. The fourth application is a single sweep ERP processing to detect the P300 in single trials. The last case is an extension of ERP component identification by combining the WT with a source localization technique. The aim is to localize the time-frequency components in three dimensional brain structure instead of the scalp surface. The time-frequency analysis using WT helps isolate and describe sequential and/or overlapping functional processes during ERP generation, and provides a possibility for studying these cognitive processes and following their dynamics in single trials during an experimental session.