Comparison of linear spatial filters for identifying oscillatory activity in multichannel data
Comparison of linear spatial filters for identifying oscillatory activity in multichannel data
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DOI：
10.1016/j.jneumeth.2016.12.016
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发表时间：
2017-02-15
影响因子：
3
通讯作者：
Cohen, Michael X.
中科院分区：
文献类型：
--
作者：
Cohen, Michael X.
背景资料：大规模的同步神经活动产生的电场可以通过头部外部的电极测量，体积传导确保神经源可以通过许多电极测量。然而，M/EEG研究中的大多数数据分析都是单变量的，这意味着每个电极都被视为单独的测量。认知电生理学文献中已经引入了几种多元线性空间滤波技术，但这些技术并不常用;跨滤波器的比较将对该领域有益。新方法：本文的目的是评估和比较几种线性空间滤波技术的性能，重点是那些使用广义特征分解来促进降维和信噪比最大化的方法。结果：模拟和经验数据用于评估空间滤波结果的准确性、信噪比和可解释性。当模拟信号很强时，不同的空间滤波器提供收敛的结果。然而，更细微的信号需要仔细选择分析参数才能获得最佳结果。与现有方法的比较：线性空间滤波器是认知电生理学中强大的数据分析工具，应该更经常地应用;另一方面，空间滤波器可能会锁定伪影或产生无法解释的结果。假设驱动的分析，仔细的数据检查和适当的参数选择是必要的，以获得高质量的结果时，使用空间滤波器。(C)2016爱思唯尔B. V.保留所有权利。
Background: Large-scale synchronous neural activity produces electrical fields that can be measured by electrodes outside the head, and volume conduction ensures that neural sources can be measured by many electrodes. However, most data analyses in M/EEG research are univariate, meaning each electrode is considered as a separate measurement. Several multivariate linear spatial filtering techniques have been introduced to the cognitive electrophysiology literature, but these techniques are not commonly used; comparisons across filters would be beneficial to the field.New method: The purpose of this paper is to evaluate and compare the performance of several linear spatial filtering techniques, with a focus on those that use generalized eigendecomposition to facilitate dimensionality reduction and signal-to-noise ratio maximization.Results: Simulated and empirical data were used to assess the accuracy, signal-to-noise ratio, and interpretability of the spatial filter results. When the simulated signal is powerful, different spatial filters provide convergent results. However, more subtle signals require carefully selected analysis parameters to obtain optimal results.Comparison with existing methods: Linear spatial filters can be powerful data analysis tools in cognitive electrophysiology, and should be applied more often; on the other hand, spatial filters can latch onto artifacts or produce uninterpretable results.Conclusions: Hypothesis-driven analyses, careful data inspection, and appropriate parameter selection are necessary to obtain high-quality results when using spatial filters. (C) 2016 Elsevier B.V. All rights reserved.