Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition
Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition
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DOI：
10.1016/j.jneumeth.2016.03.018
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发表时间：
2016-07-15
影响因子：
3
通讯作者：
Fernandez，E.
中科院分区：
文献类型：
--
作者：
Alegre-Cortes，J.;Soto-Sanchez，C.;Fernandez，E.
背景线性分析为理解神经元群体的行为提供了强有力的工具，但在某些条件下，神经元对真实世界刺激的反应是非线性的，许多神经元成分表现出强非线性行为。尽管如此，时间和频率动态的神经种群的感觉stimulation.New methodIn本文中，我们提出了使用噪声辅助的多变量经验模式分解（NA-MEMD），一个数据驱动的无模板算法，加上希尔伯特变换作为一个合适的工具，用于分析人口振荡动力学在一个多维空间与瞬时频率（IF）resolution.ResultsThe所提出的方法是能够提取振荡信息的神经生理数据的深触须神经和视觉皮层多单元的记录，没有证明使用线性方法与固定的基地，如Fourier analysis.Comparison与现有的methodsTexture歧视分析性能增加时，噪声辅助的多变量经验模式加希尔伯特变换的实施，相比，线性技术。皮层振荡群体活动进行了分析与精确的时间-频率分辨率。同样，NA-MEMD提供了增加皮层振荡人口activity.ConclusionsNoise-Assisted多元经验模式分解加希尔伯特变换的时间-频率分辨率是一种改进的方法来分析神经元群体振荡动力学克服线性和平稳的假设经典的方法。
BackgroundLinear analysis has classically provided powerful tools for understanding the behavior of neural populations, but the neuron responses to real-world stimulation are nonlinear under some conditions, and many neuronal components demonstrate strong nonlinear behavior. In spite of this, temporal and frequency dynamics of neural populations to sensory stimulation have been usually analyzed with linear approaches.New methodIn this paper, we propose the use of Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD), a data-driven template-free algorithm, plus the Hilbert transform as a suitable tool for analyzing population oscillatory dynamics in a multi-dimensional space with instantaneous frequency (IF) resolution.ResultsThe proposed approach was able to extract oscillatory information of neurophysiological data of deep vibrissal nerve and visual cortex multiunit recordings that were not evidenced using linear approaches with fixed bases such as the Fourier analysis.Comparison with existing methodsTexture discrimination analysis performance was increased when Noise-Assisted Multivariate Empirical Mode plus Hilbert transform was implemented, compared to linear techniques. Cortical oscillatory population activity was analyzed with precise time–frequency resolution. Similarly, NA-MEMD provided increased time–frequency resolution of cortical oscillatory population activity.ConclusionsNoise-Assisted Multivariate Empirical Mode Decomposition plus Hilbert transform is an improved method to analyze neuronal population oscillatory dynamics overcoming linear and stationary assumptions of classical methods.