Signal Processing Laboratory, Swiss Federal Institute of Technology, Lausanne, Switzerland.
J Neurosci Methods. 2010 Jan 30;186(1):97-106. doi: 10.1016/j.jneumeth.2009.10.018. Epub 2009 Nov 3.
Neuronal oscillations are an important aspect of EEG recordings. These oscillations are supposed to be involved in several cognitive mechanisms. For instance, oscillatory activity is considered a key component for the top-down control of perception. However, measuring this activity and its influence requires precise extraction of frequency components. This processing is not straightforward. Particularly, difficulties with extracting oscillations arise due to their time-varying characteristics. Moreover, when phase information is needed, it is of the utmost importance to extract narrow-band signals. This paper presents a novel method using adaptive filters for tracking and extracting these time-varying oscillations. This scheme is designed to maximize the oscillatory behavior at the output of the adaptive filter. It is then capable of tracking an oscillation and describing its temporal evolution even during low amplitude time segments. Moreover, this method can be extended in order to track several oscillations simultaneously and to use multiple signals. These two extensions are particularly relevant in the framework of EEG data processing, where oscillations are active at the same time in different frequency bands and signals are recorded with multiple sensors. The presented tracking scheme is first tested with synthetic signals in order to highlight its capabilities. Then it is applied to data recorded during a visual shape discrimination experiment for assessing its usefulness during EEG processing and in detecting functionally relevant changes. This method is an interesting additional processing step for providing alternative information compared to classical time-frequency analyses and for improving the detection and analysis of cross-frequency couplings.
神经元振荡是脑电图记录的一个重要方面。这些振荡被认为参与了几种认知机制。例如，振荡活动被认为是知觉自上而下控制的关键组成部分。然而，测量这种活动及其影响需要精确提取频率分量。这种处理并不简单。特别是，由于其时变特性，提取振荡存在困难。此外，当需要相位信息时，提取窄带信号至关重要。本文提出了一种使用自适应滤波器跟踪和提取这些时变振荡的新方法。该方案旨在使自适应滤波器输出端的振荡行为最大化。然后，它能够跟踪振荡并描述其时间演化，即使在低幅度时间段也是如此。此外，该方法可以扩展为同时跟踪多个振荡并使用多个信号。这两个扩展在 EEG 数据处理的框架中特别相关，其中在不同的频带中同时活跃着多个振荡，并且使用多个传感器记录信号。所提出的跟踪方案首先使用合成信号进行测试，以突出其功能。然后将其应用于视觉形状辨别实验中记录的数据，以评估其在 EEG 处理过程中以及在检测功能相关变化时的有用性。与经典的时频分析相比，该方法是提供替代信息的一个有趣的附加处理步骤，可用于改进交叉频耦合的检测和分析。
