Brain Music : Sistema generativo para la creación de música simbólica a partir de respuestas neuronales afectivas

gráficas, tablasEsta tesis de maestría presenta una metodología de aprendizaje profundo multimodal innovadora que fusiona un modelo de clasificación de emociones con un generador musical, con el propósito de crear música a partir de señales de electroencefalografía, profundizando así en la interconexión entre emociones y música. Los resultados alcanzan tres objetivos específicos: Primero, ya que el rendimiento de los sistemas interfaz cerebro-computadora varía considerablemente entre diferentes sujetos, se introduce un enfoque basado en la transferencia de conocimiento entre sujetos para mejorar el rendimiento de individuos con dificultades en sistemas de interfaz cerebro-computadora basados en el paradigma de imaginación motora. Este enfoque combina datos de EEG etiquetados con datos estructurados, como cuestionarios psicológicos, mediante un método de "Kernel Matching CKA". Utilizamos una red neuronal profunda (Deep&Wide) para la clasificación de la imaginación motora. Los resultados destacan su potencial para mejorar las habilidades motoras en interfaces cerebro-computadora. Segundo, proponemos una técnica innovadora llamada "Labeled Correlation Alignment"(LCA) para sonificar respuestas neurales a estímulos representados en datos no estructurados, como música afectiva. Esto genera características musicales basadas en la actividad cerebral inducida por las emociones. LCA aborda la variabilidad entre sujetos y dentro de sujetos mediante el análisis de correlación, lo que permite la creación de envolventes acústicos y la distinción entre diferente información sonora. Esto convierte a LCA en una herramienta prometedora para interpretar la actividad neuronal y su reacción a estímulos auditivos. Finalmente, en otro capítulo, desarrollamos una metodología de aprendizaje profundo de extremo a extremo para generar contenido musical MIDI (datos simbólicos) a partir de señales de actividad cerebral inducidas por música con etiquetas afectivas. Esta metodología abarca el preprocesamiento de datos, el entrenamiento de modelos de extracción de características y un proceso de emparejamiento de características mediante Deep Centered Kernel Alignment, lo que permite la generación de música a partir de señales EEG. En conjunto, estos logros representan avances significativos en la comprensión de la relación entre emociones y música, así como en la aplicación de la inteligencia artificial en la generación musical a partir de señales cerebrales. Ofrecen nuevas perspectivas y herramientas para la creación musical y la investigación en neurociencia emocional. Para llevar a cabo nuestros experimentos, utilizamos bases de datos públicas como GigaScience, Affective Music Listening y Deap Dataset (Texto tomado de la fuente)This master’s thesis presents an innovative multimodal deep learning methodology that combines an emotion classification model with a music generator, aimed at creating music from electroencephalography (EEG) signals, thus delving into the interplay between emotions and music. The results achieve three specific objectives: First, since the performance of brain-computer interface systems varies significantly among different subjects, an approach based on knowledge transfer among subjects is introduced to enhance the performance of individuals facing challenges in motor imagery-based brain-computer interface systems. This approach combines labeled EEG data with structured information, such as psychological questionnaires, through a "Kernel Matching CKA"method. We employ a deep neural network (Deep&Wide) for motor imagery classification. The results underscore its potential to enhance motor skills in brain-computer interfaces. Second, we propose an innovative technique called "Labeled Correlation Alignment"(LCA) to sonify neural responses to stimuli represented in unstructured data, such as affective music. This generates musical features based on emotion-induced brain activity. LCA addresses variability among subjects and within subjects through correlation analysis, enabling the creation of acoustic envelopes and the distinction of different sound information. This makes LCA a promising tool for interpreting neural activity and its response to auditory stimuli. Finally, in another chapter, we develop an end-to-end deep learning methodology for generating MIDI music content (symbolic data) from EEG signals induced by affectively labeled music. This methodology encompasses data preprocessing, feature extraction model training, and a feature matching process using Deep Centered Kernel Alignment, enabling music generation from EEG signals. Together, these achievements represent significant advances in understanding the relationship between emotions and music, as well as in the application of artificial intelligence in musical generation from brain signals. They offer new perspectives and tools for musical creation and research in emotional neuroscience. To conduct our experiments, we utilized public databases such as GigaScience, Affective Music Listening and Deap DatasetMaestríaMagíster en Ingeniería - Automatización IndustrialInvestigación en Aprendizaje Profundo y señales BiológicasEléctrica, Electrónica, Automatización Y Telecomunicaciones.Sede Manizale

Naturalistic language comprehension : a fMRI study on semantics in a narrative context

Semantiikka tutkii kieleen sisältyviä merkityksiä, joita tarvitaan kielen ymmärryksessä. Kuinka aivomme käsittelevät semantiikkaa ja kuinka ymmärrämme erityisesti luonnollisessa muodossa olevaa kieltä, on vielä aivotutkijoille epäselvää. Tässä tutkimuksessa kysyttiin, miten laajemmassa kontekstissa, narratiivissa, olevan kielen ymmärrys ja semanttinen prosessointi heijastuu aivojen aktiivisuuteen. Koehenkilöt kuulivat narratiivin toiminnallisen magneettiresonanssikuvantamisen (fMRI) aikana. Narratiivin semanttinen sisältö mallinnettiin laskennallisesti word2vec algoritmin avulla, ja tätä mallia verrattiin veren happitasosta riippuvaiseen (BOLD) aivosignaaliin ridge regression avulla vokseli kerrallaan. Lähestymistavalla saatiin eristettyä yksityiskohtaisempaa tietoa jatkuvan stimuluksen aivodatasta perustuen kielen semanttiseen sisältöön. Subjektien välinen BOLD-signaalin korrelaatio (ISC) itsessään paljasti molempien aivopuoliskojen osallistuvan kielen ymmärrykseen laajasti. Alueellista päällekkäisyyttä löytyi muiden aivoverkostojen kanssa, jotka vastaavat mm. mentalisaatiosta, muistista ja keskittymiskyvystä, mikä viittaa kielen ymmärryksen vaativan myös muiden kognition osien toimintaa. Ridge regression tulokset viittaavat bilateraalisten pikkuaivojen, superiorisen, keskimmäisen sekä mediaalisen etuaivokuoren poimujen, inferiorisen ja mediaalisen parietaalikuoren sekä visuaalikuoren, sekä oikean temporaalikuoren osallistuvan narratiivin semanttiseen prosessointiin aivoissa. Aiempi semantiikan tutkimus on tuottanut samankaltaisia tuloksia, joten word2vec vaikuttaisi tämän tutkimuksen perusteella mallintavan semantiikkaa riittävän hyvin aivotutkimuksen tarpeisiin. Tutkimuksen perusteella molemmat aivopuoliskot osallistuvat kielen laajemman kontekstin käsittelyyn, ja semantiikka nähdään aktivaationa eri puolilla aivokuorta. Nämä aktiivisuudet ovat mahdollisesti riippuvaisia kielen sisällöstä, mutta miten paljon kielen sisältö vaikuttaa eri aivoalueiden osallistumiseen kielen semanttisessa prosessoinnissa, on vielä avoin tutkimuskysymys.Semantics is a study of meaning in language and basis for language comprehension. How these phenomena are processed in the brain is still unclear especially in naturalistic context. In this study, naturalistic language comprehension, and how semantic processing in a narrative context is reflected in brain activity were investigated. Subjects were measured with functional magnetic resonance imaging (fMRI) while listening to a narrative. The semantic content of the narrative was modelled computationally with word2vec and compared to voxel-wise blood-oxygen-level dependent (BOLD) brain signal time courses using ridge regression. This approach provides a novel way to extract more detailed information from the brain data based on semantic content of the stimulus. Inter-subject correlation (ISC) of voxel-wise BOLD signals alone showed both hemispheres taking part in language comprehension. Areas involved in this task overlapped with networks of mentalisation, memory and attention suggesting comprehension requiring other modalities of cognition for its function. Ridge regression suggested cerebellum, superior, middle and medial frontal, inferior and medial parietal and visual cortices bilaterally and temporal cortex on right hemisphere having a role in semantic processing of the narrative. As similar results have been found in previous research on semantics, word2vec appears to model semantics sufficiently and is an applicable tool in brain research. This study suggests contextual language recruiting brain areas in both hemispheres and semantic processing showing as distributed activity on the cortex. This activity is likely dependent on the content of language, but further studies are required to distinguish how strongly brain activity is affected by different semantic contents

Spatio-spectral patterns based on stein kernel for EEG signal classification

El trastorno por déficit de atención con hiperactividad (TDAH) es un trastorno neurológico de inicio en la niñez que puede persistir en la adolescencia y la vida adulta, reduciendo la concentración, la memoria y la productividad. El principal inconveniente de las anomalías de la salud mental de este tipo es la técnica de diagnóstico tradicional, ya que se basa exclusivamente en una descripción sintomatológica sin considerar ningún dato biológico, lo que genera altas tasas de sobrediagnóstico. Para abordar el problema anterior, los investigadores clínicos están intentando extraer biomarcadores de TDAH a partir de señales electroencefalográficas (EEG) registradas. Entre los biomarcadores más comunes se encuentran la relación Theta / Beta y P300, de los cuales estudios recientes han demostrado una falta de importancia en las diferencias entre el TDAH y los sujetos de control. Además, otro gran desafío en el procesamiento del electroencefalograma viene dado por la sensibilidad de las señales, ya que pueden verse fácilmente afectadas por ruidos de fondo, artefactos musculares, movimientos de la cabeza y parpadeos que perjudican enormemente su calidad, lo que limita su introducción en aplicaciones del mundo real. Este trabajo propone una metodología de representación de señales de EEG para identificar discrepancias de respuestas inhibitorias en el sujeto, decodificar la estructura de datos y respaldar el diagnóstico de trastornos mentales. Para esto, primero desarrollamos un enfoque de extracción de características basado en los patrones espaciales comunes (CSP) de las señales de EEG para respaldar el diagnóstico de TDAH como se muestra en el capítulo 3. Luego, desarrollamos una metodología para la representación de señales de EEG que utiliza la similitud entre series de tiempo a través de sus matrices de covarianza en la variedad riemanniana de matrices semidefinitas positivas (PSD), utilizando la divergencia logdet de Jensen Bregman, el kernel de Stein y la alineación de kernel centrada (CKA) como una función de costo para realizar una optimización de filtros espaciales. Finalmente, en el capítulo 5 presentamos una metodología para el apoyo diagnóstico del TDAH. La propuesta implica el uso de los patrones espaciales óptimos desarrollados en el capítulo 4, una descomposición en los ritmos cerebrales y la decodificación discriminativa del capítulo 3. Las características subjetivas resultantes alimentaron un análisis discriminante lineal como herramienta de diagnóstico. La tasa de precisión alcanzada del 93% demuestra que el índice discriminativo basado en los patrones espaciales de stein supera a los biomarcadores convencionales en el diagnóstico de TDAH.Attention-Deficit/Hyperactivity Disorder (ADHD) is a childhood-onset neurological disorder that can persist in adolescence and adult life, reducing concentration, memory, and productivity. The main drawback with mental health abnormalities of this type is the traditional diagnostic technique. Since this is based exclusively on a symptomatological description without considering any biological data, leading to high overdiagnosis rates. To address the above problem, clinical researchers are attempting to extract ADHD biomarkers from recorded electroencephalographic (EEG) signals. Among the most common biomarkers are Theta/Beta Ratio and P300, of which recent studies have shown a lack of significance on the differences between ADHD and control subjects. Besides, another great challenge in EEG processing is given by the sensitivity of the signals, since they can be easily affected by background noise, muscle artifacts, head movements and flickering that greatly impair their quality, which limits its introduction into real world applications. This work proposes an EEG signal representation methodology for identifying subject-wise discrepancies of inhibitory responses, decoding the data structure, and supporting diagnosis of mental disorders. For this, first we develop a feature extraction approach based on the common spatial patterns (CSP) from EEG signals to support the ADHD diagnosis as show in chapter 3. Then, we develop a methodology for the representation of EEG signals that uses the similarity between time series through their covariance matrices in the Riemannian manifold of positive semidefinite matrices (PSD), using the logdet-divergence of Jensen Bregman, the Stein kernel, and Centered Kernel Alignment (CKA) as a cost function to perform a spatial filters optimization. Finally, in chapter 5 we present a methodology for the diagnostic support of ADHD. The proposal involves the use of the optimal spatial patterns developed in chapter 4, a decomposition in brain rhythms, and the discriminative decoding of chapter 3. The resulting subject-wise features fed a linear discriminant analysis as the supported-diagnosis tool. Achieved 93% accuracy rate proves that the discriminative index based on the stein spatial patterns outperforms conventional biomarkers in the ADHD diagnosis.MaestríaMagíster en Ingeniería EléctricaContents 1 List of Symbols and Abbreviations 6 1.1 Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2 Abbrevations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 Introduction 8 2.1 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 State of the art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.4 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4.1 General objective . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4.2 Specific objectives . . . . . . . . . . . . . . . . . . . . . . . 12 3 CSP-based discriminative capacity index from EEG 13 3.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.1 Common Spatial Patterns . . . . . . . . . . . . . . . . . . . . 13 3.1.2 Discriminative decoding of CSP . . . . . . . . . . . . . . . . 14 3.2 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2.1 Synthetic EEG records . . . . . . . . . . . . . . . . . . . . . 15 3.2.2 Real EEG records . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2.3 Proposed scheme for feature extraction . . . . . . . . . . . . 19 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.3.1 Discriminative decoding on simulated data . . . . . . . . . . 19 3.3.2 Feature extraction by discriminative decoding . . . . . . . . . 21 3.3.3 Diagnostic support of ADHD . . . . . . . . . . . . . . . . . 21 4 Multiple Kernel Stein Spatial Patterns 24 4.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.1.1 EEG Decomposition . . . . . . . . . . . . . . . . . . . . . . 24 4.1.2 Time-Series Similarity through the Stein Kernel for PSD Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.1.3 Spatial Filter Optimization Using Centered Kernel Alignment 27 4.1.4 Assembling of Multiple Kernel Representations . . . . . . . . 27 4.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.2.1 Dataset IIa from BCI Competition IV (BCICIV2a) . . . . . . 28 4.2.2 Proposed BCI Methodology . . . . . . . . . . . . . . . . . . 29 4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.3.1 Performance Results . . . . . . . . . . . . . . . . . . . . . . 30 4.3.2 Model Interpretability . . . . . . . . . . . . . . . . . . . . . 33 5 SSP-based discriminative capacity index from EEG supporting ADHD di agnosis 37 5.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 5.1.1 Brain rhythms EEG decomposition . . . . . . . . . . . . . . 38 5.1.2 Stein Spatial Patterns (SSP) . . . . . . . . . . . . . . . . . . 39 5.1.3 Discriminative decoding of SSP . . . . . . . . . . . . . . . . 39 5.1.4 Generative-supervised feature relevance . . . . . . . . . . . . 40 5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 6 Conclusions 45 6.1 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Novas estratégias de pré-processamento, extração de atributos e classificação em sistemas BCI

Orientador: Romis Ribeiro de Faissol AttuxTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: As interfaces cérebro-computador (BCIs) visam controlar um dispositivo externo, utilizando diretamente os sinais cerebrais do usuário. Tais sistemas requerem uma série de etapas para processar e extrair atributos relevantes dos sinais observados para interpretar correta e eficientemente as intenções do usuário. Embora o campo tenha se desenvolvido continuamente e algumas dificuldades tenham sido superadas, ainda é necessário aumentar a capacidade de uso, melhorando sua capacidade de classificação e aumentando a confiabilidade de sua resposta. O objetivo clássico da pesquisa de BCI é apoiar a comunicação e o controle para usuários com comunicação prejudicada devido a doenças ou lesões. Aplicações típicas das BCI são a operação de cursores de interface, programas de escrita de texto ou dispositivos externos, como cadeiras de rodas, robôs e diferentes tipos de próteses. O usuário envia informações moduladas para a BCI, realizando tarefas mentais que produzem padrões cerebrais distintos. A BCI adquire sinais do cérebro do usuário e os traduz em comunicação adequada. Esta tese tem como objetivo desenvolver uma comunicação BCI não invasiva mais rápida e confiável baseada no estudo de diferentes técnicas que atuam nas etapas de processamento do sinal, considerando dois aspectos principais, a abordagem de aprendizado de máquina e a redução da complexidade na tarefa de aprendizado dos padrões mentais pelo usuário. A pesquisa foi focada em dois paradigmas de BCI, Imagética Motora (IM) e o potencial relacionado ao evento P300. Algoritmos de processamento de sinais para a detecção de ambos os padrões cerebrais foram aplicados e avaliados. O aspecto do pré-processamento foi a primeira perspectiva estudada, considerando como destacar a resposta dos fenômenos cerebrais, em relação ao ruído e a outras fontes de informação que talvez distorçam o sinal de EEG; isso em si é um passo que influenciará diretamente a resposta dos seguintes blocos de processamento e classificação. A Análise de Componente Independente (ICA) foi usada em conjunto com métodos de seleção de atributos e diferentes classificadores para separar as fontes originais relacionadas à dessincronização produzida pelo fenômeno de IM; esta foi uma tentativa de criar um tipo de filtro espacial que permitisse o sinal ser pré-processado, reduzindo a influência do ruído. Além disso, os resultados dos valores de classificação foram analisados considerando a comparação com métodos padrão de pré-processamento, como o filtro CAR. Os resultados mostraram que é possível separar os componentes relacionados à atividade motora. A proposta da ICA, em média, foi 4\% mais alta em porcentagem de precisão de classificação do que os resultados obtidos usando o CAR, ou quando nenhum filtro foi usado. O papel dos métodos que estudam a conectividade de diferentes áreas do cérebro foi avaliado como a segunda contribuição deste trabalho; Isso permitiu considerar aspectos que contemplam a complexidade da resposta cerebral de um usuário. A área da BCI precisa de uma interpretação mais profunda do que acontece no nível do cérebro em vários dos fenômenos estudados. A técnica utilizada para construir grafos de conectividade funcional foi a correntropia, esta medida foi utilizada para quantificar a similaridade; uma comparação foi feita usando também, as medidas de correlação de Spearman e Pearson. A conectividade funcional relaciona diferentes áreas do cérebro analisando sua atividade cerebral, de modo que o estudo do grafo foi avaliado utilizando três medidas de centralidade, onde a importância de um nó na rede é medida. Também, dois tipos de classificadores foram testados, comparando os resultados no nível de precisão de classificação. Em conclusão, a correntropia pode trazer mais informações para o estudo da conectividade do que o uso da correlação simples, o que trouxe melhorias nos resultados da classificação, especialmente quando ela foi utilizada com o classificador ELM. Finalmente, esta tese demonstra que os BCIs podem fornecer comunicação efetiva em uma aplicação onde a predição da resposta de classificação foi modelada, o que permitiu a otimização dos parâmetros do processamento de sinal realizado usando o filtro espacial xDAWN e um classificador FLDA para o problema do speller P300, buscando a melhor resposta para cada usuário. O modelo de predição utilizado foi Bayesiano e confirmou os resultados obtidos com a operação on-line do sistema, permitindo otimizar os parâmetros tanto do filtro quanto do classificador. Desta forma, foi visto que usando filtros com poucos canais de entrada, o modelo otimizado deu melhores resultados de acurácia de classificação do que os valores inicialmente obtidos ao treinar o filtro xDAWN para os mesmos casos. Os resultados obtidos mostraram que melhorias nos métodos do transdutor BCI, no pré-processamento, extração de características e classificação constituíram a base para alcançar uma comunicação BCI mais rápida e confiável. O avanço nos resultados da classificação foi obtido em todos os casos, comparado às técnicas que têm sido amplamente utilizadas e já mostraram eficácia para esse tipo de problema. No entanto, ainda há aspectos a considerar da resposta dos sujeitos para tipos específicos de paradigmas, lembrando que sua resposta pode variar ao longo de diferentes dias e as implicações reais disso na definição e no uso de diferentes métodos de processamento de sinalAbstract: Brain-computer interfaces (BCIs) aim to control an external device by directly employing user's brain signals. Such systems require a series of steps to process and extract relevant features from the observed signals to correctly and efficiently interpret the user's intentions. Although the field has been continuously developing and some difficulties have been overcome, it is still necessary to increase usability by enhancing their classification capacity and increasing the reliability of their response. The classical objective of BCI research is to support communication and control for users with impaired communication due to illness or injury. Typical BCI applications are the operation of interface cursors, spelling programs or external devices, such as wheelchairs, robots and different types of prostheses. The user sends modulated information to the BCI by engaging in mental tasks that produce distinct brain patterns. The BCI acquires signals from the user¿s brain and translates them into suitable communication. This thesis aims to develop faster and more reliable non-invasive BCI communication based on the study of different techniques that serve in the signal processing stages, considering two principal aspects, the machine learning approach, and the reduction of the complexity in the task of learning the mental patterns by the user. Research was focused on two BCI paradigms, Motor Imagery (MI) and the P300 event related potential (ERP). Signal processing algorithms for the detection of both brain patterns were applied and evaluated. The aspect of the pre-processing was the first perspective studied to consider how to highlight the response of brain phenomena, in relation to noise and other sources of information that maybe distorting the EEG signal; this in itself is a step that will directly influence the response of the following blocks of processing and classification. The Independent Component Analysis (ICA) was used in conjunction with feature selection methods and different classifiers to separate the original sources that are related to the desynchronization produced by MI phenomenon; an attempt was made to create a type of spatial filter that pre-processed the signal, reducing the influence of the noise. Furthermore, some of the classifications values were analyzed considering comparison when used other standard pre-processing methods, as the CAR filter. The results showed that it is possible to separate the components related to motor activity. The ICA proposal on average were 4\% higher in percent of classification accuracy than those obtained using CAR, or when no filter was used. The role of methods that study the connectivity of different brain areas were evaluated as the second contribution of this work; this allowed to consider aspects that contemplate the complexity of the brain response of a user. The area of BCI needs a deeper interpretation of what happens at the brain level in several of the studied phenomena. The technique used to build functional connectivity graphs was correntropy, this quantity was used to measure similarity, a comparison was made using also, the Spearman and Pearson correlation. Functional connectivity relates different brain areas activity, so the study of the graph was evaluated using three measures of centrality of graph, where the importance of a node in the network is measured. In addition, two types of classifiers were tested, comparing the results at the level of classification precision. In conclusion, the correntropy can bring more information for the study of connectivity than the use of the simple correlation, which brought improvements in the classification results especially when it was used with the ELM classifier. Finally, this thesis demonstrates that BCIs can provide effective communication in an application where the prediction of the classification response was modeled, which allowed the optimization of the parameters of the signal processing performed using the xDAWN spatial filter and a FLDA classifier for the problem of the P300 speller, seeking the best response for each user. The prediction model used was Bayesian and confirmed the results obtained with the on-line operation of the system, thus allowing to optimize the parameters of both the filter and the classifier. In this way it was seen that using filters with few inputs the optimized model gave better results of acuraccy classification than the values initially obtained when the training ofthe xDAWN filter was made for the same cases. The obtained results showed that improvements in the BCI transducer, pre-processing, feature extraction and classification methods constituted the basis to achieve faster and more reliable BCI communication. The advance in the classification results were obtained in all cases, compared to techniques that have been widely used and had already shown effectiveness for this type of problemsDoutoradoEngenharia de ComputaçãoDoutora em Engenharia Elétrica153311/2014-2CNP

Dance training shapes action perception and its neural implementation within the young and older adult brain

How we perceive others in action is shaped by our prior experience. Many factors influence brain responses when observing others in action, including training in a particular physical skill, such as sport or dance, and also general development and aging processes. Here, we investigate how learning a complex motor skill shapes neural and behavioural responses among a dance-naïve sample of 20 young and 19 older adults. Across four days, participants physically rehearsed one set of dance sequences, observed a second set, and a third set remained untrained. Functional MRI was obtained prior to and immediately following training. Participants’ behavioural performance on motor and visual tasks improved across the training period, with younger adults showing steeper performance gains than older adults. At the brain level, both age groups demonstrated decreased sensorimotor cortical engagement after physical training, with younger adults showing more pronounced decreases in inferior parietal activity compared to older adults. Neural decoding results demonstrate that among both age groups, visual and motor regions contain experience-specific representations of new motor learning. By combining behavioural measures of performance with univariate and multivariate measures of brain activity, we can start to build a more complete picture of age-related changes in experience-dependent plasticity

Signal Processing Combined with Machine Learning for Biomedical Applications

The Master’s thesis is comprised of four projects in the realm of machine learning and signal processing. The abstract of the thesis is divided into four parts and presented as follows, Abstract 1: A Kullback-Leibler Divergence-Based Predictor for Inter-Subject Associative BCI. Inherent inter-subject variability in sensorimotor brain dynamics hinders the transferability of brain-computer interface (BCI) model parameters across subjects. An individual training session is essential for effective BCI control to compensate for variability. We report a Kullback-Leibler Divergence (KLD)-based predictor for inter-subject associative BCI. An online dataset comprising left/right hand, both feet, and tongue motor imagery tasks was used to show correlation between the proposed inter-subject predictor and BCI performance. Linear regression between the KLD predictor and BCI performance showed a strong inverse correlation (r = -0.62). The KLD predictor can act as an indicator for generalized inter-subject associative BCI designs. Abstract 2: Multiclass Sensorimotor BCI Based on Simultaneous EEG and fNIRS. Hybrid BCI (hBCI) utilizes multiple data modalities to acquire brain signals during motor execution (ME) tasks. Studies have shown significant enhancements in the classification of binary class ME-hBCIs; however, four-class ME-hBCI classification is yet to be done using multiclass algorithms. We present a quad-class classification of ME-hBCI tasks from simultaneous EEG-fNIRS recordings. Appropriate features were extracted from EEG-fNIRS signals and combined for hybrid features and classified with support vector machine. Results showed a significant increase in hybrid accuracy over single modalities and show hybrid method’s performance enhancement capability. Abstract 3: Deep Learning for Improved Inter-Subject EEG-fNIRS Hybrid BCI Performance. Multimodality based hybrid BCI has become famous for performance improvement; however, the inherent inter-subject and inter-session variation between participants brain dynamics poses obstacles in achieving high performance. This work presents an inter-subject hBCI to classify right/left-hand MI tasks from simultaneous EEG-fNIRS recordings of 29 healthy subjects. State-of-art features were extracted from EEG-fNIRS signals and combined for hybrid features, and finally, classified using deep Long short-term memory classifier. Results showed an increase in the inter-subject performance for the hybrid system while making the system more robust to brain dynamics change and hints to the feasibility of EEG-fNIRS based inter-subject hBCI. Abstract 4: Microwave Based Glucose Concentration Classification by Machine Learning. Non-invasive blood sugar measurement attracts increased attention in recent years, given the increase in diabetes-related complications and inconvenience in the traditional ways using blood. This work utilized machine learning (ML) algorithms to classify glucose concentration (GC) from the measured broadband microwave scattering signals (S11). An N-type microwave adapter pair was utilized to measure the sweeping frequency scattering-parameter (S-parameter) of the glucose solutions with GC varying from 50-10,000 dg/dL. Dielectric parameters were retrieved from the measured wideband complex S-parameters based on the modified Debye dielectric dispersion model. Results indicate that the best algorithm can achieve a perfect classification accuracy and suggests an alternate way to develop a GC detection method using ML algorithms

Investigating the neural correlates of ongoing experience

Spontaneous thoughts are heterogeneous and inherently dynamic. Despite their time-variant properties, studies exploring spontaneous thoughts have identified thematic patterns that exhibit trait-like characteristics and are stable across time. Concurrently, structural and functional neuroimaging studies have shown unique and stable whole-brain network configurations linked to behaviour either through the static and dynamic intrinsic communication and activity of their core regions or through informational exchange with each other. This thesis aimed to explore how these within and between network interactions at different temporal scales might relate to variations in ongoing experience. We utilised different neuroimaging modalities (diffusion weighted and functional magnetic resonance imaging) and applied both static and dynamic analyses techniques. We found evidence of inter-individual variation in all cases associated with different patterns of spontaneous thoughts. Experiment 1 found that variation in white matter architecture projecting to the hippocampus, as well as the stable functional interaction of the hippocampus with the medial prefrontal cortex were linked to the tendency of experiencing thoughts related to the future or the past. Experiment 2 found that static functional connectivity of the precuneus and a lateral fronto-temporal network was related to visual imagery. Furthermore, we found that coupling of a lateral visual network with regions of the brainstem and cerebellum was associated with ruminative thinking, self-consciousness and attentional problems. Importantly, our results highlighted an interaction among these associations, where the brainstem visual network coupling moderated the relationship between parietal-frontal regions and reports of visual imagery. Finally, Experiment 3 used hidden Markov modelling to identify dynamic neural states linked to thoughts related to problem-solving and less intrusive thinking, as well as better physical and mental health. Collectively, these studies highlight the utility of using both static and dynamic measures of neural function to understand patterns of ongoing experience

Non-Verbal Working Memory: A functional near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI) comparison

Ulike hjernenettverk virker å bli aktivert for verbal og ikke-verbal visuell og romlig informasjon i arbeidsminnet. Det finnes et bredt spenn av forskning på det visuell-romlige arbeidsminnet. Likevel, har en tilnærming som benytter seg av objekter med flere integrerte egenskaper og en klar spesifikasjon på den verbale dimensjonen blitt mindre anvendt. Det ikke-verbale arbeidsminnet for visuell-romlig informasjon har blitt mer oversett enn det verbale arbeidsminnet. Derfor søker denne studien å adressere de nevrale nettverkene som utnyttes for ikke-verbal arbeidsminneprestasjon. Hjerneaktivitet ble målt fra totalt 12 deltagere mens de utførte en nylig komponert ikke-verbal arbeidsminneoppgave. Både funksjonell nær-infrarød spektroskopi (fNIRS) og funksjonell magnetisk resonans avbildning (fMRI) ble benyttet for formålet. Resultatene indikerte at det ikke-verbale arbeidsminnet involverer høyre-lateraliserte hjerneaktiveringer, hvor frontoparietale nettverk og visuelle traséer ble benyttet for prestasjon. Disse funnene tilbyr viktig tilførsel til den nevrale nettverksmodellen av det ikke-verbale arbeidsminnet. Dermed virker oppgaven å teste konseptet effektivt. fNIRS og fMRI ble også brukt for å måle resting-state fra de samme deltagerne. Resultatene uttrykket forskjeller mellom de to øktene ved både fMRI og fNIRS. Endringene i konnektivitet kan reflektere effekter av oppgaven på resting-state.Masteroppgave i psykologiMAPSYK360INTL-SVINTL-MEDINTL-PSYKINTL-HFINTL-KMDINTL-JUSINTL-MNMAPS-PSY