@misc{10481/94820,
year = {2024},
url = {https://hdl.handle.net/10481/94820},
abstract = {This dissertation addresses the analysis of data emerging in the field of music neuroscience,
specifically data collected from neurophysiological monitoring techniques that
can be modeled as random objects in spaces of smooth functions. Spaces equipped with
a Hilbert structure offer a versatile and elegant framework for the generalization of various
statistical techniques, ensuring adaptability and robustness in analyzing complex data
structures. Within the context of functional data analysis, these spaces serve as essential
tools for understanding and interpreting dynamic data trends over continuous domains.
Given the relevance of independent component analysis (ICA) in neuroscience research,
our investigation is directed towards its functional counterpart, a technique whose potential
still remains relatively overlooked. Functional ICA can be considered a refinement of
functional principal component analysis, aimed at identifying low-dimensional structures
"as independent as possible" by exploiting the underlying topological features of the data.
We provide a comprehensive account of the theoretical foundations of functional ICA
and extend the method to Sobolev spaces of smoother functions. Some theoretical properties
regarding functional data classification are also presented. Additionally, we develop
a repertoire of related functional data techniques tailored for pre-processing and analyzing
data in the emerging field of embodied music neuroscience, which investigates the
neurological basis of how the body influences musical experience. Two methods based
on nonlinear wavelet and polynomial approximations are developed for pre-processing
artifactual activity in EEG signals and pupillometry. These methods yield excellent outcomes
for neuromotor research, particularly considering the suboptimal condition of the
recorded data due to locomotor activity. We further introduce a set of neural descriptors
derived from data collected through the aforementioned non-invasive methods, aiming
to uncover brain behavior during embodied musical interactions. More specifically, we
focus on methodologies for modeling neurotransmitter activity, a critical aspect shown
to be essential in shaping motor functionality and other proprioceptive sensations. Our
experimental research is portrayed by the concept of emotion transferred into a neurological
domain, providing a unique framework to define and capture the neural essence
of embodiment in music.},
abstract = {This dissertation addresses the analysis of data emerging in the field of music neuroscience,
specifically data collected from neurophysiological monitoring techniques that
can be modeled as random objects in spaces of smooth functions. Spaces equipped with
a Hilbert structure offer a versatile and elegant framework for the generalization of various
statistical techniques, ensuring adaptability and robustness in analyzing complex data
structures. Within the context of functional data analysis, these spaces serve as essential
tools for understanding and interpreting dynamic data trends over continuous domains.
Given the relevance of independent component analysis (ICA) in neuroscience research,
our investigation is directed towards its functional counterpart, a technique whose potential
still remains relatively overlooked. Functional ICA can be considered a refinement of
functional principal component analysis, aimed at identifying low-dimensional structures
"as independent as possible" by exploiting the underlying topological features of the data.
We provide a comprehensive account of the theoretical foundations of functional ICA
and extend the method to Sobolev spaces of smoother functions. Some theoretical properties
regarding functional data classification are also presented. Additionally, we develop
a repertoire of related functional data techniques tailored for pre-processing and analyzing
data in the emerging field of embodied music neuroscience, which investigates the
neurological basis of how the body influences musical experience. Two methods based
on nonlinear wavelet and polynomial approximations are developed for pre-processing
artifactual activity in EEG signals and pupillometry. These methods yield excellent outcomes
for neuromotor research, particularly considering the suboptimal condition of the
recorded data due to locomotor activity. We further introduce a set of neural descriptors
derived from data collected through the aforementioned non-invasive methods, aiming
to uncover brain behavior during embodied musical interactions. More specifically, we
focus on methodologies for modeling neurotransmitter activity, a critical aspect shown
to be essential in shaping motor functionality and other proprioceptive sensations. Our
experimental research is portrayed by the concept of emotion transferred into a neurological
domain, providing a unique framework to define and capture the neural essence
of embodiment in music.},
abstract = {En esta tesis se aborda el análisis de datos emergentes en el campo de la neurociencia de
la música, más concretamente de datos grabados mediante técnicas de monitoreo neurofisiológico
que pueden ser modelados como objetos aleatorios en espacios de funciones
suaves. Los espacios equipados con estructura de Hilbert ofrecen un marco versátil y elegante
para la generalización de un ámplio abanico de técnicas estadísticas, asegurando
adaptabilidad y robustez en el análisis de estructuras de datos complejas. En el contexto
del análisis de datos funcionales, estos espacios sirven como herramientas esenciales para
comprender e interpretar tendencias dinámicas de datos sobre dominios continuos. Dada
la relevancia del análisis en componentes independientes (ICA) para el análisis de datos
neurocientíficos, nuestra investigación se dirige hacia su versión funcional, una técnica
cuyo potencial aún permanece relativamente poco explorado. El ICA funcional puede
considerarse una extensión del análisis en componentes principales funcional, orientado
a identificar componentes "lo más independientes posible" mediante la explotación de las
características topológicas subyacentes de los datos. Se proporciona un análisis exhaustivo
de los fundamentos teóricos del ICA funcional y se extiende el método a espacios
de Sobolev de funciones más suaves. También se presentan algunas propiedades teóricas
sobre la clasificación de datos funcionales en relación al ICA functional. Asimismo, desarrollamos
un repertorio de técnicas relacionadas de datos funcionales diseñadas para el
preprocesamiento y análisis de datos en el campo emergente de la neurociencia musical
encarnada, cuyo objetivo es investigar la base neurológica de cómo el cuerpo influye en
las experiencias musicales. En particular, se desarrollan dos métodos basados en aproximaciones
no lineales de wavelets y polinomios para el preprocesamiento de actividad artefactual
en señales EEG y pupilometría. Estos métodos producen resultados excelentes para la
investigación neuromotora, a pesar de la condición subómptima de los datos registrados
durante la actividad locomotora. Además, presentamos un conjunto de descriptores neurales
derivados de datos recopilados a través de los mencionados métodos no invasivos, con
el objetivo de desvelar el comportamiento cerebral durante interacciones musicales encarnadas.
Más específicamente, nos centramos en metodologías para modelar la actividad
neurotransmisora, un aspecto crítico demostrado como esencial en la funcionalidad motora
y otras sensaciones propioceptivas. Nuestra investigación experimental se presenta
mediante el concepto de emoción transferido al dominio neurológico, proporcionando
un marco único para definir y capturar la esencia neural de la encarnación en la música.},
organization = {Tesis Univ. Granada.},
organization = {Methusalem Funding from the Flemish Government},
organization = {Project FQM-307 of the Government of Andalusia (Spain)},
organization = {Project PID2020-113961GB-100 of the Spanish Ministry of Science and Innovation (also supported by the FEDER programme)},
organization = {Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía (Spain) and the FEDER programme for the project A-FQM-66-UGR20 and the IMAG-María de Maeztu grant CEX2020-001105-M/AEI/10.13039/501100011033},
publisher = {Universidad de Granada},
title = {Hilbertian statistical models in music neuroscience},
author = {Vidal Badía, Marc},
}