Linear causal filtering: definition and theory

Pascual-Marqui, Roberto D.; Rj, Biscay; Bosch-Bayard, Jorge; Achermann, Peter; Faber, Pascal L.; Kinoshita, Toshihiko; Kochi, Kieko

doi:10.1101/2021.05.01.442232

Cited by 3 publications

(4 citation statements)

References 24 publications

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“…The limitations and outlook are: (1) ξ-π can be extended with the brain connectivity, the electrophysiology source imaging [67], and the dynamic spectra decomposition [21]. (2) Note that the main goal of this paper was to propose the nonparametric model ξ-π and demonstrate its superiority in decomposition and fitting.…”

Section: Discussionmentioning

confidence: 99%

$\bm{\xi}$-$\bm{\pi}$: A Nonparametric Model for Neural Power Spectra Decomposition

Hu,

Zhang,

Zhang

et al. 2024

IEEE J. Biomed. Health Inform.

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The power spectra estimated from the brain recordings are the mixed representation of aperiodic transient activity and periodic oscillations, i.e., aperiodic component (AC) and periodic component (PC). Quantitative neurophysiology requires precise decomposition preceding parameterizing each component. However, the shape, statistical distribution, scale, and mixing mechanism of AC and PCs are unclear, challenging the effectiveness of current popular parametric models such as FOOOF, IRASA, BOSC, etc. Here, ξ-π was proposed to decompose the neural spectra by embedding the nonparametric spectra estimation with penalized Whittle likelihood and the shape language modeling into the expectation maximization framework. ξ-π was validated on the synthesized spectra with loss statistics and on the sleep EEG and the large sample iEEG with evaluation metrics and neurophysiological evidence. Compared to FOOOF, both the simulation presenting shape irregularities and the batch simulation with multiple isolated peaks indicated that ξ-π improved the fit of AC and PCs with less loss and higher F1-score in recognizing the centering frequencies and the number of peaks; the sleep EEG revealed that ξ-π produced more distinguishable AC exponents and improved the sleep state classification accuracy; the iEEG showed that ξ-π approached the clinical findings in peak discovery. Overall, ξ-π offered good performance in the spectra decomposition, which allows flexible parameterization using descriptive statistics or kernel functions. ξ-π is a seminal tool for brain signal decoding in fields such as cognitive neuroscience, brain-computer interface, neurofeedback, and brain diseases.

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Section: Discussionmentioning

confidence: 99%

$\bm{\xi}$-$\bm{\pi}$: A Nonparametric Model for Neural Power Spectra Decomposition

Hu,

Zhang,

Zhang

et al. 2024

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

show abstract

“…The limitations and outlook are: (1) ξ-π can be extended with the brain connectivity, the electrophysiology source imaging [67], and the dynamic spectra decomposition [21]. (2) As FOOOF is the most recent, popular, and typical parametric model, it was taken as the main competitor to compare with ξ-π.…”

Section: Discussionmentioning

confidence: 99%

“…The limitations and outlook are: (1) ξ-π can be extended to decomposition with electrophysiology source imaging [50] and to dynamic spectra decomposition by working with the time-varying spectra estimation [11]. (2) As FOOOF is recent, popular, and can be viewed as the most typical parametric model, we consider FOOOF as the main competitor.…”

Section: Discussionmentioning

confidence: 99%

ξ-π: a nonparametric model for neural power spectra decomposition

Hu,

Zhang,

Zhang

et al. 2023

Preprint

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The power spectra estimated from the iEEG, EEG, MEG recordings, and spike trains are the mixed representation of aperiodic transient activity and periodic oscillations, i.e., aperiodic component (AC) and periodic component (PC). Quantitative neurophysiology requires precise decomposition preceding parameterizing each component. However, the shape, statistical distribution, scale, and mixing mechanism of AC and PCs are unclear, challenging the effectiveness of current popular parametric models such as FOOOF, IRASA, BOSC, etc. Here,ξ-πwas proposed to decompose the neural spectra by embedding the nonparametric spectra estimation with penalized Whittle likelihood and the shape language modeling into the expectation maximization framework.ξ-πwas validated on the synthesized spectra with loss statistics and on the real spectra from the sleep EEG and the large sample iEEG with evaluation metrics and neurophysiological evidence. Compared to FOOOF, both the simulation presenting shape irregularities and the batch simulation with multiple isolated peaks indicated thatξ-πimproved the fit of AC and PCs with less loss and higher F1-score in recognizing the centering frequencies and the number of peaks; the sleep EEG revealed thatξ-πproduced more distinguishable AC exponents and improved the sleep state classification accuracy; the iEEG showed thatξ-πapproached the clinical findings in peak discovery. Overall,ξ-πoffered good performance in the spectra decomposition, which allows flexible parameterization using descriptive statistics or kernel functions.ξ-πmay be a promising tool for brain signal decoding in the fields such as cognitive neuroscience, brain-computer interface, neurofeedback, and brain diseases.

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“…A detection of causal interactions in human epilepsy networks requires much more elaborate approaches (see for example Refs. [125,126]). The key point is that we do not aim at detecting a coupling or even estimating a coupling strength.…”

Section: Discussionmentioning

confidence: 99%

Phase irregularity: A conceptually simple and efficient approach to characterize electroencephalographic recordings from epilepsy patients

Espinoso

Andrzejak

2022

Phys. Rev. E

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The severe neurological disorder epilepsy affects almost 1% of the world population. For patients who suffer from pharmacoresistant focal-onset epilepsy, electroencephalographic (EEG) recordings are essential for the localization of the brain area where seizures start. Apart from the visual inspection of the recordings, quantitative EEG signal analysis techniques proved to be useful for this purpose. Among other features, regularity versus irregularity and phase coherence versus phase independence allowed characterizing brain dynamics from the measured EEG signals. Can phase irregularities also characterize brain dynamics? To address this question, we use the univariate coefficient of phase velocity variation, defined as the ratio of phase velocity standard deviation and the mean phase velocity. Beyond that, as a bivariate measure we use the classical mean phase coherence to quantify the degree of phase locking. All phase-based measures are combined with surrogates to test null hypotheses about the dynamics underlying the signals. In the first part of our analysis, we use the Rössler model system to study our approach under controlled conditions. In the second part, we use the Bern-Barcelona EEG database which consists of focal and nonfocal signals extracted from seizure-free recordings. Focal signals are recorded from brain areas where the first seizure EEG signal changes can be detected, and nonfocal signals are recorded from areas that are not involved in the seizure at its onset. Our results show that focal signals have less phase variability and more phase coherence than nonfocal signals. Once combined with surrogates, the mean phase velocity proved to have the highest discriminative power between focal and nonfocal signals. In conclusion, conceptually simple and easy to compute phase-based measures can help to detect features induced by epilepsy from EEG signals. This holds not only for the classical mean phase coherence but even more so for univariate measures of phase irregularity.

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Linear causal filtering: definition and theory

Cited by 3 publications

References 24 publications

$\bm{\xi}$-$\bm{\pi}$: A Nonparametric Model for Neural Power Spectra Decomposition

$\bm{\xi}$-$\bm{\pi}$: A Nonparametric Model for Neural Power Spectra Decomposition

ξ-π: a nonparametric model for neural power spectra decomposition

Phase irregularity: A conceptually simple and efficient approach to characterize electroencephalographic recordings from epilepsy patients

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