Time-delay estimation (TDE) finds application in a variety of problems, be it locating fractures or steering cameras towards the speaker in a multi-participant conference application. Underwater acoustic OFDM source localization is another important application of TDE. Existing underwater acoustic source localization techniques use a microphone array consisting of three or four sensors in order to effectively locate the source. Analog-to-digital (ADC) converters at these sensors call for a non-nominal investment in terms of circuitry and memory. A relatively inexpensive source localization algorithm is needed that works with the output of a single sensor. Since an inexpensive process for estimating the location of the source is desired, the ADC used at the sensor is capable only of a relatively low sampling rate. For a given delay, a low sampling rate leads to sub-sample interval delays, which the desired algorithm must be able to estimate. Prevailing TDE algorithms make some a priori assumptions about the nature of the received signal, such as Gaussianity, wide-sense stationarity, or periodicity. The desired algorithm must not be restrictive in so far as the nature of the transmitted signal is concerned. A time-delay estimation algorithm based on the time-frequency ratio of mixtures (TFRM) method is proposed. The experimental set-up consists of two microphones/sensors placed at some distances from the source. The method accepts as input the received signal which consists of the sum of the signal received at the nearer sensor and the signal received at the farther sensor and noise. The TFRM algorithm works in the time-frequency domain and seeks to perform successive source cancellation in the received burst. The key to performing source cancellation is to estimate the ratio in which the sources combine and this ratio is estimated by means of taking a windowed mean of the ratio of the spectrograms of any two pulses in the received burst. The variance of the mean function helps identify single-source regions and regions in which the sources mix. The performance of the TFRM algorithm is evaluated in the presence of noise and is compared against the Cramer-Rao lower bound. It is found that the variance of the estimates returned by the estimator diverge from the predictions of the Cramer-Rao inequality as the farther sensor is moved farther away. Conversely, the estimator becomes more reliable as the farther sensor is moved closer. The time-delay estimates obtained from the TFRM algorithm are used for source localization. The problem of finding the source reduces to finding the locus of points such that the difference of its distances to the two sensors equals the time delay. By moving the pair of sensors to a different location, or having a second time delay sensor, an exact location for the source can be determined by finding the point of intersection of the two loci. The TFRM method does not rely on a priori information about the signal. It is applicable to OFDM sources as well as sinusoidal and chirp sources. / Master of Science

L’analyse de la stabilité posturale chez l’homme a fait l’objet, ces dernières années, d’un intérêt grandissant au sein de la communauté scientifique. Le système postural permet de maintenir la stabilité du corps humain en posture statique ou dynamique. Cette capacité à maintenir cette stabilité devient critique dans le cas des sujets Parkinsoniens. La maladie de Parkinson a en effet une forte incidence sur la stabilité posturale. Un moyen efficace pour évaluer l’équilibre postural consiste à analyser les déplacements dans le plan horizontal du centre de pression du corps humain en posture orthostatique ; les trajectoires mesurées dans la direction medio-latérale (ML) et la direction Antéro-postérieure (AP) sont appelées signaux stabilométriques. Dans cette thèse, nous visons le développement de méthodes efficaces pour l’analyse de l’équilibre en posture orthostatique sous différentes conditions liées à l’entrée visuelle (yeux ouverts/yeux fermés), la position des pieds (pieds joints/pieds écartés) et en considérant d’autres facteurs comme le genre et l’âge. Dans ce cadre, nous proposons, tout d’abord, une méthode exploitant la variante EEMD (Ensemble Empirical Mode Decomposition) de la décomposition en modes empiriques (EMD) et l’analyse de la diffusion du stabilogramme. Dans le contexte du diagnostic de la maladie de Parkinson, la discrimination entre sujets sains et sujets Parkinsoniens est très importante, de même que l’évaluation du stade de la maladie pour les sujets atteints. Dans ce cadre, deux méthodes sont proposées. La première consiste tout d’abord en une extraction et sélection de caractéristiques temporelles et spectrales, à partir des signaux stabilométriques brutes ou des modes de fonctions intrinsèques dérivés de la décomposition EEMD. Des méthodes standards de type KNN, CART, RF et SVM sont ensuite appliquées pour reconnaitre les sujets Parkinsoniens. La deuxième méthode proposée, est une approche de classification qui repose sur l’emploi de HMMs construits en utilisant les signaux stabilométriques brutes dans les directions ML, AP et ML/AP. Enfin, une dernière méthode est proposée pour la segmentation automatique des signaux stabilométriques sous différentes conditions (entrée visuelle, position des pieds). Pour ce faire, un modèle de régression régi par une chaine de Markov cachée (HMMR) est utilisé pour détecter automatiquement les variations des structures des signaux stabilométriques entre ces conditions. Les résultats obtenus montrent clairement la supériorité des performances des méthodes proposées par rapport aux approches standards, aussi bien, en termes d’analyse de l’équilibre postural que de diagnostic de sujets Parkinsoniens / Recently, human balance control analysis has received an increasing interest from the research community. The human postural system maintains the stability of the body both in the static posture (quiet standing) and during locomotion. This ability to maintain stability becomes hard with aging and Parkinson's disease (PD) subjects. PD has a strong effect on postural stability during quiet standing situations, and during locomotion. One effective way to assess human stability is to analyze the center of pressure (CoP) displacements of the human body during quiet standing. The recorded CoP displacements in quiet standing are called stabilometric signals. This thesis aims to develop efficient approaches to analyze the human postural stability in quiet standing under visual and feet position conditions, as well as under age and gender. This is achieved using Empirical Mode Decomposition (EMD) method and stabilogram-diffusion technique. In the other part, the discrimination between healthy and PD subjects is very important for diagnosing Parkinson's disease, as well as for evaluating the disease level of the patient. In this context, two approaches are proposed; the first approach consists of an EMD-based temporal and spectral feature extraction from the stabilometric signals. The second approach is based on a Hidden Markov Model (HMM) using the raw stabilometric signals. The HMM model is an efficient tool to analyze temporal and sequential data. Another approach is proposed in order to segment the stabilometric signals according to the visual and feet position conditions. This is achieved using a Hidden Markov Model Regression (HMMR)-based approach. This study help clinicians to better understand the motor strategies used by the subjects during quiet standing and may guide the rehabilitation process. The obtained results clearly show high performances of the proposed approaches with respect to other standard approaches in both postural stability analysis and discrimination healthy from PD subjects

Conselho Nacional de Desenvolvimento Científico e Tecnológico / The detection and classification of EMG motor unit action potentials (MUAP) is an important tool in the study of the neuromuscular system, allowing for a number os applications, such as the diagnoses of motor disorders. However, although there are several methods described in the literature to perform such actions, the majority relies on complex algorithms and specific instrumentation. Depending on the system, the computational cost or the detection mechanism, sometimes involving electrode arrays, may limit its use in clinical applications, biofeedback or embedded systems for controlling artificial prostheses. Another important issue is the detection and classification of firing MUAPs in signals with low signal to noise ratio (SNR). A method capable of operating with low SNR is paramount for applications, such as the use of electromyography in human machine interfaces (HMI), where the positioning and fixation of the electrodes may be performed by a non-trained user, and the signal can be contaminated by high levels of electromagnetic interference. As a solution for such problems, two complementary methods were proposed: the first (MD-FC) is based on the use of banks of matched filters for detection and classification MUAPs in EMG signals, whereas the second (MAD-FC) is proposed as an improvement from the first, aiming situations with a high probability of overlapping firing MUAPs. The proposed methods sought to achieve those goals without an excessive increase in computational cost, treating signals with variable noise levels and considering the overlapping of MUAPs. The results showed that the MD-FC system is able to accurately detect 96% of isolated MUAPs in signals with SNR of 10 dB and up to 10 active motor units. However, the performance is reduced in the presence of high levels of overlapping MUAPs, as expected. The second method (MAD-FC) was designed to improve the detection of overlapping MUAPs. The results showed that the MAD-FC is able to detect and classify firing MUAPs in signals with up 10 active motor units and SNR of 20 dB at rates of success higher than 79.80%, on average. When the SNR is decreased to 10dB the rates of success reach at least 75.19%, on average (even in this case with a high percentage of overlapping). In general, the MAD-FC showed rates of success around 20% better than the MD-FC method. Both methods are quite efficient when using computational resources. They were created in order to process EMG windows of 50 milliseconds in less than 5 milliseconds, when using a standard desktop computer. This feature allows their use in applications requiring MUAPs detection and classification in real time. / A detecção e classificação dos potenciais de ação de unidade motora (PAUMs) de sinais EMG é uma ferramenta importante no estudo do sistema neuromuscular. A partir de informações dessa classificação é possível diagnosticar distúrbios motores. Entretanto, apesar de existirem diversas propostas na literatura para executar tais ações, a grande maioria depende de algoritmos complexos e instrumentação específica. Dependendo do sistema, o custo computacional ou o mecanismo de captação envolvendo, matrizes de eletrodos, pode limitar sua utilização em aplicações clínicas, biofeedback ou em sistemas embarcados para controle de próteses. Outra questão importante consiste na detecção e classificação de disparos em sinais com baixa relação sinal ruído (SNR). Um método capaz de operar em sinais com baixa SNR é interessante em aplicações onde não se pode controlar completamente a coleta do sinal. Como exemplo, podemos citar aplicações da eletromiografia em interfaces homem máquina (IHM), onde o posicionamento dos eletrodos pode ser realizado por um usuário com pouco treinamento e o ambiente pode conter alto nível de interferência eletromagnética, diminuindo a SNR do sinal captado. Como solução para tais problemas, foram propostas duas metodologias complementares: a primeira delas (MD-FC) se baseia no uso de bancos de filtros casados para detecção e classificação de PAUMs em sinais EMG, enquanto a segunda (MAD-FC) é uma proposta de aprimoramento da primeira para situações com altas probabilidades de sobreposição de disparos de MUAPs. As metodologias propostas buscaram atingir aqueles objetivos sem um aumento excessivo no custo computacional, tratando sinais com níveis variados de ruídos e considerando a questão de sobreposição de PAUMs, comuns em sinais EMG. Os resultados demonstraram que o sistema MD-FC é capaz de detectar disparos isolados com precisão de 96% em média para relação sinal ruído de 10 dB com até 10 unidades motoras ativas, porém seu é desempenho diminuído na presença de altos níveis de sobreposição de PAUMS. O segundo MAD-FC que foi elaborado de forma a aprimorar a detecção sobre ondas sobrepostas, e é capaz de detectar e classificar os disparos de sinais com até 10 unidades motoras ativas com taxa de classificação correta maior do que 79,80% em média e com SNR de 20 dB. Para sinais com SNR de 10 dB esse valor é de 75,19% em média. Em geral, o método MAD-FC apresentou performance superior ao MD-FC em cerca de 20%. Os dois métodos são bastante eficientes no uso de recursos computacionais. Eles foram criadas de forma a analisar os sinais EMG em janelas de 50 milissegundos em menos de 5 milissegundos a partir de um computador desktop padrão, o que permite sua utilização em sistemas de detecção e classificação de PAUMs em tempo real. / Doutor em Ciências

Inhibitory control processes have intensively been studied in cognitive science for the past decades. Even though the neural dynamics underlying these processes are increasingly better understood, a critical open question is how the representational dynamics of the inhibitory control processes are modulated when engaging in response inhibition in a relatively automatic or a controlled mode. Against the background of an overarching theory of perception-action integration, we combine temporal and spatial EEG signal decomposition methods with multivariate pattern analysis and source localization to obtain fine-grained insights into the neural dynamics of the representational content of response inhibition. For this purpose, we used a sample of N = 40 healthy adult participants. The behavioural data suggest that response inhibition was better in a more controlled than a more automated response execution mode. Regarding neural dynamics, effects of response inhibition modes relied on a concomitant coding of stimulus-related information and rules of how stimulus information is related to the appropriate motor programme. Crucially, these fractions of information, which are encoded at the same time in the neurophysiological signal, are based on two independent spatial neurophysiological activity patterns, also showing differences in the temporal stability of the representational content. Source localizations revealed that the precuneus and inferior parietal cortex regions are more relevant than prefrontal areas for the representation of stimulus–response selection codes. We provide a blueprint how a concatenation of EEG signal analysis methods, capturing distinct aspects of neural dynamics, can be connected to cognitive science theory on the importance of representations in action control.

Digital Particle Image Velocimetry (DPIV) is a planar measurement technique to measure the velocity within a fluid by correlating the motion of flow tracers over a sequence of images recorded with a camera-laser system. Sophisticated digital processing algorithms are required to provide a high enough accuracy for quantitative DPIV results. This study explores the potential of a variety of cross-correlation filters to improve the accuracy and robustness of the DPIV estimation. These techniques incorporate the use of the Phase Transform (PHAT) Generalized Cross Correlation (GCC) filter applied to the image cross-correlation. The use of spatial windowing is subsequently examined and shown to be ideally suited for the use of phase correlation estimators, due to their invariance to the loss of correlation effects. The Robust Phase Correlation (RPC) estimator is introduced, with the coupled use of the phase correlation and spatial windowing. The RPC estimator additionally incorporates the use of a spectral filter designed from an analytical decomposition of the DPIV Signal-to-Noise Ratio (SNR). This estimator is validated in a variety of artificial image simulations, the JPIV standard image project, and experimental images, which indicate reductions in error on the order of 50% when correlating low SNR images. Two variations of the RPC estimator are also introduced, the Gaussian Transformed Phase Correlation (GTPC): designed to optimize the subpixel interpolation, and the Spectral Phase Correlation (SPC): estimates the image shift directly from the phase content of the correlation. While these estimators are designed for DPIV, the methodology described here provides a universal framework for digital signal correlation analysis, which could be extended to a variety of other systems. / Master of Science

Electromyographic (EMG) signal decomposition is the process of resolving an EMG signal into its component motor unit potential trains (MUPTs). The extracted MUPTs can aid in the diagnosis of neuromuscular disorders and the study of the neural control of movement, but only if they are valid trains. Before using decomposition results and the motor unit potential (MUP) shape and motor unit (MU) firing pattern information related to each active MU for either clinical or research purposes the fact that the extracted MUPTs are valid needs to be confirmed. The existing MUPT validation methods are either time consuming or related to operator experience and skill. More importantly, they cannot be executed during automatic decomposition of EMG signals to assist with improving decomposition results. To overcome these issues, in this thesis the possibility of developing automatic MUPT validation algorithms has been explored. Several methods based on a combination of feature extraction techniques, cluster validation methods, supervised classification algorithms, and multiple classifier fusion techniques were developed. The developed methods, in general, use either the MU firing pattern or MUP-shape consistency of a MUPT, or both, to estimate its overall validity. The performance of the developed systems was evaluated using a variety of MUPTs obtained from the decomposition of several simulated and real intramuscular EMG signals. Based on the results achieved, the methods that use only shape or only firing pattern information had higher generalization error than the systems that use both types of information. For the classifiers that use MU firing pattern information of a MUPT to determine its validity, the accuracy for invalid trains decreases as the number of missed-classification errors in trains increases. Likewise, for the methods that use MUP-shape information of a MUPT to determine its validity, the classification accuracy for invalid trains decreases as the within-train similarity of the invalid trains increase. Of the systems that use both shape and firing pattern information, those that separately estimate MU firing pattern validity and MUP-shape validity and then estimate the overall validity of a train by fusing these two indices using trainable fusion methods performed better than the single classifier scheme that estimates MUPT validity using a single classifier, especially for the real data used. Overall, the multi-classifier constructed using trainable logistic regression to aggregate base classifier outputs had the best performance with overall accuracy of 99.4% and 98.8% for simulated and real data, respectively. The possibility of formulating an algorithm for automated editing MUPTs contaminated with a high number of false-classification errors (FCEs) during decomposition was also investigated. Ultimately, a robust method was developed for this purpose. Using a supervised classifier and MU firing pattern information provided by each MUPT, the developed algorithm first determines whether a given train is contaminated by a high number of FCEs and needs to be edited. For contaminated MUPTs, the method uses both MU firing pattern and MUP shape information to detect MUPs that were erroneously assigned to the train. Evaluation based on simulated and real MU firing patterns, shows that contaminated MUPTs could be detected with 84% and 81% accuracy for simulated and real data, respectively. For a given contaminated MUPT, the algorithm on average correctly classified around 92.1% of the MUPs of the MUPT. The effectiveness of using the developed MUPT validation systems and the MUPT editing methods during EMG signal decomposition was investigated by integrating these algorithms into a certainty-based EMG signal decomposition algorithm. Overall, the decomposition accuracy for 32 simulated and 30 real EMG signals was improved by 7.5% (from 86.7% to 94.2%) and 3.4% (from 95.7% to 99.1%), respectively. A significant improvement was also achieved in correctly estimating the number of MUPTs represented in a set of detected MUPs. The simulated and real EMG signals used were comprised of 3–11 and 3–15 MUPTs, respectively.

Electromyographic (EMG) signal decomposition is the process of resolving an EMG signal into its component motor unit potential trains (MUPTs). The extracted MUPTs can aid in the diagnosis of neuromuscular disorders and the study of the neural control of movement, but only if they are valid trains. Before using decomposition results and the motor unit potential (MUP) shape and motor unit (MU) firing pattern information related to each active MU for either clinical or research purposes the fact that the extracted MUPTs are valid needs to be confirmed. The existing MUPT validation methods are either time consuming or related to operator experience and skill. More importantly, they cannot be executed during automatic decomposition of EMG signals to assist with improving decomposition results. To overcome these issues, in this thesis the possibility of developing automatic MUPT validation algorithms has been explored. Several methods based on a combination of feature extraction techniques, cluster validation methods, supervised classification algorithms, and multiple classifier fusion techniques were developed. The developed methods, in general, use either the MU firing pattern or MUP-shape consistency of a MUPT, or both, to estimate its overall validity. The performance of the developed systems was evaluated using a variety of MUPTs obtained from the decomposition of several simulated and real intramuscular EMG signals. Based on the results achieved, the methods that use only shape or only firing pattern information had higher generalization error than the systems that use both types of information. For the classifiers that use MU firing pattern information of a MUPT to determine its validity, the accuracy for invalid trains decreases as the number of missed-classification errors in trains increases. Likewise, for the methods that use MUP-shape information of a MUPT to determine its validity, the classification accuracy for invalid trains decreases as the within-train similarity of the invalid trains increase. Of the systems that use both shape and firing pattern information, those that separately estimate MU firing pattern validity and MUP-shape validity and then estimate the overall validity of a train by fusing these two indices using trainable fusion methods performed better than the single classifier scheme that estimates MUPT validity using a single classifier, especially for the real data used. Overall, the multi-classifier constructed using trainable logistic regression to aggregate base classifier outputs had the best performance with overall accuracy of 99.4% and 98.8% for simulated and real data, respectively. The possibility of formulating an algorithm for automated editing MUPTs contaminated with a high number of false-classification errors (FCEs) during decomposition was also investigated. Ultimately, a robust method was developed for this purpose. Using a supervised classifier and MU firing pattern information provided by each MUPT, the developed algorithm first determines whether a given train is contaminated by a high number of FCEs and needs to be edited. For contaminated MUPTs, the method uses both MU firing pattern and MUP shape information to detect MUPs that were erroneously assigned to the train. Evaluation based on simulated and real MU firing patterns, shows that contaminated MUPTs could be detected with 84% and 81% accuracy for simulated and real data, respectively. For a given contaminated MUPT, the algorithm on average correctly classified around 92.1% of the MUPs of the MUPT. The effectiveness of using the developed MUPT validation systems and the MUPT editing methods during EMG signal decomposition was investigated by integrating these algorithms into a certainty-based EMG signal decomposition algorithm. Overall, the decomposition accuracy for 32 simulated and 30 real EMG signals was improved by 7.5% (from 86.7% to 94.2%) and 3.4% (from 95.7% to 99.1%), respectively. A significant improvement was also achieved in correctly estimating the number of MUPTs represented in a set of detected MUPs. The simulated and real EMG signals used were comprised of 3–11 and 3–15 MUPTs, respectively.

The integration of perception and action has long been studied in psychological science using overarching cognitive frameworks. Despite these being very successful in explaining perception-action integration, little is known about its neurophysiological and especially the functional neuroanatomical foundations. It is unknown whether distinct brain structures are simultaneously involved in the processing of perception-action integration codes and also to what extent demands on perception-action integration modulate activities in these structures. We investigate these questions in an EEG study integrating temporal and ICA-based EEG signal decomposition with source localization. For this purpose, we used data from 32 healthy participants who performed a ‘TEC Go/Nogo’ task. We show that the EEG signal can be decomposed into components carrying different informational aspects or processing codes relevant for perception-action integration. Importantly, these specific codes are processed independently in different brain structures, and their specific roles during the processing of perception-action integration differ. Some regions (i.e., the anterior cingulate and insular cortex) take a ‘default role’ because these are not modulated in their activity by demands or the complexity of event file coding processes. In contrast, regions in the motor cortex, middle frontal, temporal, and superior parietal cortices were not activated by ‘default’ but revealed modulations depending on the complexity of perception-action integration (i.e., whether an event file has to be reconfigured). Perception-action integration thus reflects a multi-region processing of specific fractions of information in the neurophysiological signal. This needs to be taken into account when further developing a cognitive science framework detailing perception-action integration.