Adaptive tracking of EEG oscillations
Adaptive tracking of EEG oscillations
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DOI：
10.1016/j.jneumeth.2009.10.018
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发表时间：
2010-01-30
影响因子：
3
通讯作者：
Vesin, Jean-Marc
中科院分区：
文献类型：
--
作者：
Van Zaen, Jerome;Uldry, Laurent;Vesin, Jean-Marc
神经元振荡是脑电记录的一个重要方面。这些振荡被认为与几种认知机制有关。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地提取频率分量。这个过程并不是一帆风顺的。特别是，由于振荡的时变特性，提取振荡变得困难。此外，当需要相位信息时，提取窄带信号是至关重要的。本文提出了一种利用自适应滤波器来跟踪和提取这些时变振荡的新方法。该方案旨在最大化自适应滤波器输出端的振荡行为。然后，即使在低幅度时间段期间，它也能够跟踪振荡并描述其时间演变。此外，该方法还可以扩展，以便同时跟踪多个振荡和使用多个信号。这两个扩展在脑电数据处理的框架中特别相关，在不同的频段中，振荡同时被激活，并且信号被多个传感器记录。提出的跟踪方案首先用合成信号进行测试，以突出其能力。然后将其应用于视觉形状辨别实验中记录的数据，以评估其在脑电信号处理和检测功能相关变化中的有用性。这种方法是一个有趣的附加处理步骤，用于提供与经典时频分析相比的替代信息，并用于改进交叉频率耦合的检测和分析。(C)2009爱思唯尔B.V.保留所有权利。
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. (C) 2009 Elsevier B.V. All rights reserved.