Kernel-Based Phase Transfer Entropy with Enhanced Feature Relevance Analysis for Brain Computer Interfaces
Iván De La Pava Panche,Andrés Marino Álvarez-Meza,Paula Marcela Herrera Gómez,David Cárdenas-Peña,Jorge Iván Ríos Patiño,Álvaro Orozco-Gutiérrez +5 more
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This work proposes a novel methodology to estimate TE between single pairs of instantaneous phase time series using a kernel-based TE estimator defined in terms of Renyi’s α entropy, which sidesteps the need for probability distribution computation withphase time series obtained by complex filtering the neural signals.Abstract:
Neural oscillations are present in the brain at different spatial and temporal scales, and they are linked to several cognitive functions. Furthermore, the information carried by their phases is fundamental for the coordination of anatomically distributed processing in the brain. The concept of phase transfer entropy refers to an information theory-based measure of directed connectivity among neural oscillations that allows studying such distributed processes. Phase TE is commonly obtained from probability estimations carried out over data from multiple trials, which bars its use as a characterization strategy in brain–computer interfaces. In this work, we propose a novel methodology to estimate TE between single pairs of instantaneous phase time series. Our approach combines a kernel-based TE estimator defined in terms of Renyi’s α entropy, which sidesteps the need for probability distribution computation with phase time series obtained by complex filtering the neural signals. Besides, a kernel-alignment-based relevance analysis is added to highlight relevant features from effective connectivity-based representation supporting further classification stages in EEG-based brain–computer interface systems. Our proposal is tested on simulated coupled data and two publicly available databases containing EEG signals recorded under motor imagery and visual working memory paradigms. Attained results demonstrate how the introduced effective connectivity succeeds in detecting the interactions present in the data for the former, with statistically significant results around the frequencies of interest. It also reflects differences in coupling strength, is robust to realistic noise and signal mixing levels, and captures bidirectional interactions of localized frequency content. Obtained results for the motor imagery and working memory databases show that our approach, combined with the relevance analysis strategy, codes discriminant spatial and frequency-dependent patterns for the different conditions in each experimental paradigm, with classification performances that do well in comparison with those of alternative methods of similar nature.read more
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A novel approach GRNTSTE to reconstruct gene regulatory interactions applied to a case study for rat pineal rhythm gene
TL;DR: Wang et al. as discussed by the authors proposed a new method of constructing gene expression regulatory network, named the gene regulatory network based on time series data and entropy transfer (GRNTSTE), which is based on transfer entropy and large-scale time-series gene expression data to infer the causal regulatory relationship between genes in a data-driven mode.
Journal ArticleDOI
Estimating Directed Phase-Amplitude Interactions from EEG Data through Kernel-Based Phase Transfer Entropy
Iván De La Pava Panche,Viviana Gómez-Orozco,Andrés Marino Álvarez-Meza,David Cárdenas-Peña,Álvaro Orozco-Gutiérrez +4 more
TL;DR: In this article, the problem of phase-amplitude interaction detection is recast as a phase TE estimation problem, under the hypothesis that estimating TE from data of the same nature, i.e., two phase time series, will improve the robustness to the common confounding factors that affect connectivity measures, such as the presence of high noise levels.
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Special Issue “Advances in Neuroimaging Data Processing”
TL;DR: The development of in vivo neuroimaging technology has led to an incredible amount of digital information concerning the brain this article , leading to an increased understanding of the brain and its connections.
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Performance Baseline of Phase Transfer Entropy Methods for Detecting Animal Brain Area Interactions
TL;DR: In this paper , phase transfer entropy (TEθ) was applied to the simulated signals generated by a neural mass model and the actual neural data from ferrets with known interaction properties to investigate the accuracy, stability, and computational complexity of the TEθ methods in identifying the directional coupling.
References
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
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