Unveiling the Biometric Potential of Finger-Based ECG Signals

Lourenço, André; Silva, Hugo; Fred, Ana

doi:10.1155/2011/720971

Cited by 437 publications

(483 citation statements)

References 9 publications

(17 reference statements)

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“…Typically, the amplitude of this 2d vector is used as the response signal of interest [Oostenveld et al, 2011]. However, using the multivariate MI estimate, we can quantify the modulation of the full 2d signal, as well as breaking down the stimulus effects on amplitude and direction separately (Fig.…”

Section: Resultsmentioning

confidence: 99%

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Ince

Giordano

Kayser

et al. 2016

Human Brain Mapping

249

298

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We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open‐source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541–1573, 2017. © 2016 Wiley Periodicals, Inc.

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Section: Resultsmentioning

confidence: 99%

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Ince

Giordano

Kayser

et al. 2016

Human Brain Mapping

249

298

View full text Add to dashboard Cite

show abstract

“…Prior to sensor‐space analyses, the data were pre‐processed using Matlab and the FieldTrip toolbox (Oostenveld, Fries, Maris, & Schoffelen, 2011; http://www.fieldtriptoolbox.org). Trials containing eye movement or muscle artefacts were rejected after visual inspection.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal dynamics in human visual cortex rapidly encode the emotional content of faces

Dima

Perry

Messaritaki

et al. 2018

Human Brain Mapping

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Recognizing emotion in faces is important in human interaction and survival, yet existing studies do not paint a consistent picture of the neural representation supporting this task. To address this, we collected magnetoencephalography (MEG) data while participants passively viewed happy, angry and neutral faces. Using time‐resolved decoding of sensor‐level data, we show that responses to angry faces can be discriminated from happy and neutral faces as early as 90 ms after stimulus onset and only 10 ms later than faces can be discriminated from scrambled stimuli, even in the absence of differences in evoked responses. Time‐resolved relevance patterns in source space track expression‐related information from the visual cortex (100 ms) to higher‐level temporal and frontal areas (200–500 ms). Together, our results point to a system optimised for rapid processing of emotional faces and preferentially tuned to threat, consistent with the important evolutionary role that such a system must have played in the development of human social interactions.

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“…Greenhouse Geisser correction was applied where the sphericity assumption was violated. EEG data analyses were performed using the FieldTrip MATLAB toolbox developed at the Donders Institute for Brain, Cognition, and Behavior, Nijmegen [Oostenveld, Fries, Maris, & Schoffelen, 2011; http://www.fieldtriptoolbox.org]. …”

Section: Methodsmentioning

confidence: 99%

Implicit learning seems to come naturally for children with autism, but not for children with specific language impairment: Evidence from behavioral and ERP data

et al. 2018

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Autism spectrum disorder (ASD) and specific language impairment (SLI) are two neurodevelopmental disorders characterized by deficits in verbal and nonverbal communication skills. These skills are thought to develop largely through implicit—or automatic—learning mechanisms. The aim of the current paper was to investigate the role of implicit learning abilities in the atypical development of communication skills in ASD and SLI. In the current study, we investigated Response Times (RTs) and Event Related Potentials (ERPs) during implicit learning on a Serial Reaction Time (SRT) task in a group of typically developing (TD) children (n = 17), a group of autistic children (n = 16), and a group of children with SLI (n = 13). Findings suggest that learning in both ASD and SLI are similar to that in TD. However, electrophysiological findings suggest that autistic children seem to rely mainly on more automatic processes (as reflected by an N2b component), whereas the children with SLI seem to rely on more controlled processes (as reflected by a P3 component). The TD children appear to use a combination of both learning mechanisms. These findings suggest that clinical interventions should aim at compensating for an implicit learning deficit in children with SLI, but not in children with ASD. Future research should focus on developmental differences in implicit learning and related neural correlates in TD, ASD, and SLI. Autism Res 2018, 11: 1050–1061. © 2018 The Authors Autism Research published by International Society for Autism Research and Wiley Periodicals, Inc.Lay SummaryAutism and Specific Language Impairment (SLI) are two disorders characterized by problems in social communication and language. Social communication and language are believed to be learned in an automatic way. This is called “implicit learning.” We have found that implicit learning is intact in autism. However, in SLI there seems different brain activity during implicit learning. Maybe children with SLI learn differently, and maybe this different learning makes it more difficult for them to learn language.

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Unveiling the Biometric Potential of Finger-Based ECG Signals

Cited by 437 publications

References 9 publications

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Spatiotemporal dynamics in human visual cortex rapidly encode the emotional content of faces

Implicit learning seems to come naturally for children with autism, but not for children with specific language impairment: Evidence from behavioral and ERP data

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