Wavelet-based method for coherence analysis with suppression of low frequency envelope modulation in non-stationary signals

Suwansawang, Sopapun; Halliday, David M.

doi:10.1109/ieecon48109.2020.229461

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…Another common use of the WT is for filtering or denoising purposes. The above is crucial in physiological signal processing since the noise, or specific frequency bands, can alter the sampled signals, and their analysis [51]. For instance, the study of Wiklendt et al [44] presented a technique that allows for the retention of frequencies that encode the periodicity of spike trains in EMG signals while filtering out frequencies that contribute to the morphologies of action potentials by employing the CWT.…”

Section: Applications Of the Generalized Morse Wavelets A Medical And...mentioning

confidence: 99%

Applications of the Generalized Morse Wavelets: A Review

et al. 2023

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The study of signals, processes, and systems has motivated the development of different representations that can be used to analyze and understand them. Classical ways of studying the behavior of signals are the time domain and frequency domain representations. For the analysis of non-stationary signals, time-frequency representations have become an essential tool to understand how the frequency content of signals changes with time. A common time-frequency technique employed in the literature is the wavelet transform. Nevertheless, selecting an adequate mother wavelet to perform the wavelet transform has become challenging due to the diverse available wavelet families. This paper reviews the applications and uses of a particular class of wavelet basis known as the Generalized Morse Wavelets. This class of wavelet family provides a systematic framework to choose and generate a wavelet for general-purpose use. This study reviews the application of Generalized Morse Wavelets in biomedical engineering, dynamical systems analysis, electrical engineering, geophysics, and communication systems. Moreover, the parameters of the Generalized Morse Wavelets used in each study are presented. The results of this study reveal that Generalized Morse Wavelets have proven helpful in studying signals, systems, and processes in areas ranging from biomedical engineering to geophysics. Nonetheless, the parameters of the Generalized Morse Wavelets are yet to be chosen through a rigorous methodology and argumentation. Therefore, there is an opportunity to generate methods for selecting the parameters of the Generalized Morse Wavelets based on the characteristics of the signals, systems, or processes under research.INDEX TERMS Generalized Morse wavelets, mother wavelet selection, continuous wavelet transform, applications, time-frequency analysis.

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Section: Applications Of the Generalized Morse Wavelets A Medical And...mentioning

confidence: 99%

Applications of the Generalized Morse Wavelets: A Review

et al. 2023

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“…Despite the potential importance of CFC in understanding neural circuits, a number of issues related to the statistical assessment of PAC using existing estimators have been reported [29][30][31][32][33][34][35][36][37][38][39][40]. These issues have led to a flurry of activity described in [33,36,[41][42][43][44][45][46][47][48][49][50][51][52]. In [41] a computationally efficient method of assessing PAC is introduced along with theoretical confidence bounds.…”

Section: Introductionmentioning

confidence: 99%

“…In [47], contributions of other, biasing, frequency contributions are assessed in PAC by generalizing the modulation index first introduced in [54]. PAC is addressed in the transient setting in [48][49][50]55]. In [55] dynamic PAC is assessed using a temporally-local measure of mutual information.…”

Section: Introductionmentioning

confidence: 99%

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Multitaper estimates of phase-amplitude coupling

Lepage¹,

Fleming²,

Witcher³

et al. 2021

J. Neural Eng.

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Phase-amplitude coupling (PAC) is the association of the amplitude of a high-frequency oscillation with the phase of a low-frequency oscillation. In neuroscience, this relationship provides a mechanism by which neural activity might be coordinated between distant regions. The dangers and pitfalls of assessing PAC with commonly used statistical measures have been well-documented. The limitations of these measures include: (1) response to non-oscillatory, high-frequency, broad-band activity, (2) response to high-frequency components of the low-frequency oscillation, (3) adhoc selection of analysis frequency-intervals, and (4) reliance upon data shuffling to assess statistical significance. Objective. To address issues (1)-( 4) by introducing a nonparametric multitaper estimator of PAC. Approach. In this work, a multitaper PAC estimator is proposed that addresses these issues. Specifically, issue (1) is addressed by replacing the analytic signal envelope estimator computed using the Hilbert transform with a multitaper estimator that down-weights non-sinusoidal activity using a classical, multitaper super-resolution technique. Issue (2) is addressed by replacing coherence between the low-frequency and high-frequency components in a standard PAC estimator with multitaper partial coherence, while issue (3) is addressed with a physical argument regarding meaningful neural oscillation. Finally, asymptotic statistical assessment of the multitaper estimator is introduced to address issue (4). Main results. Multitaper estimates of PAC are introduced. Their efficacy is demonstrated in simulation and on human intracranial recordings obtained from epileptic patients. Significance. This work facilitates a more informative statistical assessment of PAC, a phenomena exhibited by many neural systems, and provides a basis upon which further nonparametric multitaper-related methods can be developed.

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Multitaper Estimates of Phase-Amplitude Coupling

Lepage

Fleming

Witcher

et al. 2021

Preprint

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Phase-amplitude coupling (PAC) is the association of the amplitude of a high-frequency oscillation with the phase of a low-frequency oscillation. In neuroscience, this relationship provides a mechanism by which neural activity might be coordinated between distant regions. The dangers and pitfalls of assessing phase-amplitude coupling with existing statistical measures have been well-documented. The limitations of these measures include: (i) response to non-oscillatory, high-frequency, broad-band activity, (ii) response to high-frequency components of the low-frequency oscillation, (iii) adhoc selection of analysis frequency-intervals, and (iv) reliance upon data shuffling to assess statistical significance. In this work, a multitaper phase-amplitude coupling estimator is proposed that addresses issues (i)-(iv) above. Specifically, issue (i) is addressed by replacing the analytic signal envelope estimator computed using the Hilbert transform with a multitaper estimator that down-weights non-sinusoidal activity using a classical, multitaper super-resolution technique. Issue (ii) is addressed by replacing coherence between the low-frequency and high-frequency components in a standard PAC estimator with multitaper partial coherence, while issue (iii) is addressed with a physical argument regarding meaningful neural oscillation. Finally, asymptotic statistical assessment of the multitaper estimator is introduced to address issue (iv).

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Wavelet-based method for coherence analysis with suppression of low frequency envelope modulation in non-stationary signals

Cited by 3 publications

References 13 publications

Applications of the Generalized Morse Wavelets: A Review

Applications of the Generalized Morse Wavelets: A Review

Multitaper estimates of phase-amplitude coupling

Multitaper Estimates of Phase-Amplitude Coupling

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