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首页> 外文期刊>Journal of Neuroscience Methods >Adaptive tracking of EEG oscillations.
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Adaptive tracking of EEG oscillations.

机译：EEG振荡的自适应跟踪。

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摘要

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.
机译：神经元振荡是脑电图记录的重要方面。这些振荡被认为与几种认知机制有关。例如，振荡活动被认为是自顶向下控制感知的关键组成部分。但是，测量此活动及其影响需要精确提取频率分量。该处理不是简单的。特别地，由于其随时间变化的特性，在提取振荡方面出现困难。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的新方法，用于跟踪和提取这些随时间变化的振荡。设计该方案以使自适应滤波器输出处的振荡行为最大化。这样，即使在低振幅时间段内，它也能够跟踪振荡并描述其时间演变。此外，可以扩展该方法，以便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重要，在EEG数据处理的框架中，同时在不同的频带中激活振荡，并使用多个传感器记录信号。首先使用合成信号对提出的跟踪方案进行测试，以突出其功能。然后将其应用于在视觉形状识别实验期间记录的数据，以评估其在EEG处理过程中的有用性并检测功能上相关的变化。与经典的时频分析相比，此方法是一个有趣的附加处理步骤，用于提供替代信息，并用于改善对交叉频率耦合的检测和分析。

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