Abstract
Time-varying coherence is a powerful tool for revealing functional dynamics between different regions in the brain. In this paper, we address ways of estimating evolutionary spectrum and coherence using the general Cohen's class distributions. We show that the intimate connection between the Cohen's class-based spectra and the evolutionary spectra defined on the locally stationary time series can be linked by the kernel functions of the Cohen's class distributions. The time-varying spectra and coherence are further generalized with the Stockwell transform, a multiscale time-frequency representation. The Stockwell measures can be studied in the framework of the Cohen's class distributions with a generalized frequency-dependent kernel function. A magnetoencephalography study using the Stockwell coherence reveals an interesting temporal interaction between contralateral and ipsilateral motor cortices under the multisource interference task.
Highlights
Previous studies in neuroscience have shown that corticocortical interactions play a crucial role in the performance of cognitive tasks
Since the Stockwell time-frequency representation often contains artifacts at the two ends of a time series due to circular Fourier spectrum shifting in the implementation, we examine the significant Stockwell transform (ST)-based time-varying coherence
We investigate the estimation of the timevarying spectrum and the time-varying coherence for the locally stationary time series using the Cohen’s class distributions
Summary
Previous studies in neuroscience have shown that corticocortical interactions play a crucial role in the performance of cognitive tasks. The temporal information, missed by Fourier analysis, needs to be addressed in order to better understand the dynamics of brain functionality This leads to the development of timevarying spectrum. In 1965, Priestley [4] defined the class of locally stationary time series and proposed the theory of evolutionary spectra to study their time-varying characteristics. Following the development of wavelet theory [8] over the last two decades, transforms that provide the multiresolution TFRs have been receiving growing attention in the field of time-frequency analysis This is because the multiscale resolution provided by wavelet transforms offers a more accurate description of the nonstationary characteristics of a signal. We show that the time-varying spectrum defined by the Cohen’s class distributions coincides with the definition of the locally stationary time series. Our findings reveal interesting temporal interaction between contralateral and ipsilateral motor (MIc and MIi) cortices under the multisource interference task (MSIT)
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