Ikerbasque, Basque Foundation for Science, Plaza Euskadi 5, 48009 Bilbao, Spain; Tecnalia Research and Innovation, Neuroengineering Group, Health Unit, Donostia, Spain; Dept. of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain.
MRC Cognition and Brain Sciences Unit, University of Cambridge, UK; BCBL. Basque Center on Cognition, Brain and Language, Donostia-San Sebastián, Spain.
Neuroimage. 2023 Aug 1;276:120178. doi: 10.1016/j.neuroimage.2023.120178. Epub 2023 May 25.
Instantaneous and peak frequency changes in neural oscillations have been linked to many perceptual, motor, and cognitive processes. Yet, the majority of such studies have been performed in sensor space and only occasionally in source space. Furthermore, both terms have been used interchangeably in the literature, although they do not reflect the same aspect of neural oscillations. In this paper, we discuss the relation between instantaneous frequency, peak frequency, and local frequency, the latter also known as spectral centroid. Furthermore, we propose and validate three different methods to extract source signals from multichannel data whose (instantaneous, local, or peak) frequency estimate is maximally correlated to an experimental variable of interest. Results show that the local frequency might be a better estimate of frequency variability than instantaneous frequency under conditions with low signal-to-noise ratio. Additionally, the source separation methods based on local and peak frequency estimates, called LFD and PFD respectively, provide more stable estimates than the decomposition based on instantaneous frequency. In particular, LFD and PFD are able to recover the sources of interest in simulations performed with a realistic head model, providing higher correlations with an experimental variable than multiple linear regression. Finally, we also tested all decomposition methods on real EEG data from a steady-state visual evoked potential paradigm and show that the recovered sources are located in areas similar to those previously reported in other studies, thus providing further validation of the proposed methods.
神经振荡的瞬时和峰值频率变化与许多感知、运动和认知过程有关。然而，大多数此类研究都是在传感器空间中进行的，只有偶尔在源空间中进行。此外，这两个术语在文献中经常互换使用，尽管它们并不反映神经振荡的同一方面。在本文中，我们讨论了瞬时频率、峰值频率和局部频率（也称为频谱质心）之间的关系，然后提出并验证了三种不同的方法，从多通道数据中提取源信号，其（瞬时、局部或峰值）频率估计与感兴趣的实验变量最大相关。结果表明，在信噪比低的情况下，局部频率可能是频率变化的更好估计。此外，基于局部和峰值频率估计的源分离方法，分别称为 LFD 和 PFD，比基于瞬时频率的分解提供更稳定的估计。特别是，LFD 和 PFD 能够在使用逼真头部模型进行的模拟中恢复感兴趣的源，与实验变量的相关性比多元线性回归更高。最后，我们还在稳态视觉诱发电位范式的真实 EEG 数据上测试了所有分解方法，并表明恢复的源位于与其他研究中先前报道的相似的区域，从而进一步验证了所提出的方法。
