Measuring instantaneous frequency of local field potential oscillations using the Kalman smoother.

Measuring instantaneous frequency of local field potential oscillations using the Kalman smoother.
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测量使用卡尔曼更光滑的局部场电位振荡的瞬时频率。

DOI:
10.1016/j.jneumeth.2009.08.012
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发表时间:
2009-11-15
影响因子:
3
通讯作者:
Barbieri R
Barbieri R
中科院分区:
医学4区
文献类型:
--
作者:
Nguyen DP;Wilson MA;Brown EN;Barbieri R

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节律性局部场电位(LFP)来自协调的神经活动。基于脑节律特性的神经功能推断仍然是一个具有挑战性的数据分析问题。以高时间和频谱分辨率表征非平稳节律的算法可能有助于在它们生成的时间尺度上解释LFP活动。我们提出了一种基于卡尔曼平滑器的动态自回归模型,用于跟踪噪声和非平稳正弦波(如LFP数据中发现的)的瞬时频率(iFreq)和调频(FM)。我们验证了我们的算法的性能,使用模拟数据与广泛的光谱内容,并证明其应用程序使用行为学习实验记录的真实的数据。在分析从啮齿动物海马记录的涟漪振荡(100-250 Hz)时,我们的算法识别出了新的重复的短时间尺度频率动态。我们的研究结果表明,iFreq和FM可能是有用的措施,小时间尺度LFP动态的量化。
Rhythmic local field potentials (LFP) arise from coordinated neural activity. Inference of neural function based on the properties of brain rhythms remains a challenging data analysis problem. Algorithms that characterize non-stationary rhythms with high temporal and spectral resolution may be useful for interpreting LFP activity on the timescales in which they are generated. We propose a Kalman smoother based dynamic autoregressive model for tracking the instantaneous frequency (iFreq) and frequency modulation (FM) of noisy and non-stationary sinusoids such as those found in LFP data. We verify the performance of our algorithm using simulated data with broad spectral content, and demonstrate its application using real data recorded from behavioral learning experiments. In analyses of ripple oscillations (100-250 Hz) recorded from the rodent hippocampus, our algorithm identified novel repetitive, short timescale frequency dynamics. Our results suggest that iFreq and FM may be useful measures for the quantification of small timescale LFP dynamics.
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