3 citations · 7 across the 5 of their papers we have counts for
6 papers
Statistical Inference for Modulation Index in Phase-Amplitude Coupling
Marco Antonio Pinto-Orellana, Hernando Ombao, Beth Lopour
Phase-amplitude coupling is a phenomenon observed in several neurological processes, where the phase of one signal modulates the amplitude of another signal with a distinct frequen…
Dyadic aggregated autoregressive (DASAR) model for time-frequency representation of biomedical signals
Marco A. Pinto-Orellana, Habib Sherkat, Peyman Mirtaheri +1
This paper introduces a new time-frequency representation method for biomedical signals: the dyadic aggregated autoregressive (DASAR) model. Signals, such as electroencephalograms…
SCAU: Modeling spectral causality for multivariate time series with applications to electroencephalograms
Marco Antonio Pinto-Orellana, Peyman Mirtaheri, Hugo L. Hammer +1
Electroencephalograms (EEG) are noninvasive measurement signals of electrical neuronal activity in the brain. One of the current major statistical challenges is formally measuring…
Spectral Dependence
Hernando Ombao, Marco Pinto
This paper presents a general framework for modeling dependence in multivariate time series. Its fundamental approach relies on decomposing each signal in a system into various fre…
Smooth Online Parameter Estimation for time varying VAR models with application to rat's LFP data
Anass El Yaagoubi Bourakna, Marco Pinto, Norbert Fortin +1
Multivariate time series data appear often as realizations of non-stationary processes where the covariance matrix or spectral matrix smoothly evolve over time. Most of the current…
A hemodynamic decomposition model for detecting cognitive load using functional near-infrared spectroscopy
Marco A. Pinto-Orellana, Diego C. Nascimento, Peyman Mirtaheri +3
In the current paper, we introduce a parametric data-driven model for functional near-infrared spectroscopy that decomposes a signal into a series of independent, rescaled, time-sh…