A análise visual do eletroencefalograma (EEG) tem se mostrado útil na ajuda diagnóstica da doença de Alzheimer (DA), sendo indicado em alguns protocolos clínicos quando o diagnóstico permanece em aberto após a avaliação inicial. Porém, tal análise está sujeita naturalmente à imprecisão inerente de equipamentos, movimentos do paciente, registros elétricos e variação da interpretação da análise visual do médico. A teoria das Redes Neurais Artificiais (RNA) tem-se mostrado muito apropriado para tratar problemas como predição e reconhecimento de padrões de sinais em outras áreas do conhecimento. Neste trabalho utilizou-se uma nova classe de RNA, a Rede Neural Artificial Paraconsistente (RNAP), caracterizada pela manipulação de informações incertas, inconsistentes e paracompletas, destinada a reconhecer padrões predeterminados de EEG e de avaliar sua aplicabilidade como método auxiliar para o diagnóstico da DA. Trinta e três pacientes com DA provável e trinta e quatro pacientes controles foram submetidos ao registro de exames de EEG durante a vigília em repouso. Considerou-se como padrão normal de um paciente, a atividade de base entre 8,0 Hz e 12,0 Hz (com uma frequência média de 10 Hz), permitindo uma variação de 0.5 Hz. A RNAP foi capaz de reconhecer ondas de diferentes bandas de frequência (teta, delta, alfa e beta) aplicadas ao uso clínico do EEG, levando a uma concordância com o diagnóstico clínico de 82% de sensibilidade e 61% de especificidade. Com estes resultados, acredita-se que a RNAP possa vir a ser uma ferramenta promissora para manipular análise de EEG, tendo em mente as seguintes considerações: o interesse crescente de especialistas em análise visual de EEG e a capacidade da RNAP tratar diretamente dados imprecisos, inconsistentes e paracompletos, fornecendo uma interessante análise quantitativa e qualitativa / The visual analysis of EEG has shown useful in helping the diagnosis of Alzheimer disease (AD) when the diagnosis remains uncertain, being used in some clinical protocols. However, such analysis is subject to the inherent equipment imprecision, patient movement, electrical records, and physician interpretation of the visual analysis variation. The Artificial Neural Network (ANN) could be a helpful tool, appropriate to address problems such as prediction and pattern recognition. In this work, it has use a new class of ANN, the Paraconsistent Artificial Neural Network (PANN), which is capable of handling uncertain, inconsistent, and paracomplet information, for recognizing predetermined patterns of EEG and to assess its value as a possible auxiliary method for AD diagnosis. Thirty three patients with Alzheimer\'s disease and thirty four controls patients of EEG records were obtained during relaxed wakefulness. It was considered as normal patient pattern, the background EEG activity between 8.0 Hz and 12.0 Hz (with an average frequency of 10 Hz), allowing a range of 0.5 Hz. The PANN was able to recognize waves that belonging to their respective bands of clinical use (theta, delta, alpha, and beta), leading to an agreement with the clinical diagnosis at 82% of sensitivity and at 61% of specificity. Supported with these results, the PANN could be a promising tool to manipulate EEG analysis, bearing in mind the following considerations: the growing interest of specialists in EEG analysis visual and the ability of the PANN to deal directly imprecise, inconsistent and paracomplet data, providing an interesting quantitative and qualitative analysis

Le neurofeedback (NF) est une technique consistant à renvoyer à un individu des informations sur son activité cérébrale en temps réel, lui permettant ainsi d'apprendre à mieux en contrôler certains aspects pour la réorganiser de manière durable. Des effets spécifiques sur les fonctions émotionnelles, cognitives ou comportementales du sujet sont supposés accompagner l'entraînement par NF, ce qui fait du NF une technique prometteuse pour la rééducation du cerveau de patients souffrant de troubles neurologiques ou psychiatriques et pour l'optimisation de la performance chez les sujets sains. Le NF a été étudié comme outil de rééducation cérébrale dans un grand nombre de troubles neurologiques et psychiatriques. Pourtant, son déploiement au sein de l'arsenal thérapeutique est restreint par le manque de preuves concluantes sur sa réelle efficacité. Les limitations inhérentes aux modalités de mesures de l'activité cérébrale pourraient être une des raisons à l'origine de cette efficacité débattue. En effet, la plupart des approches de NF reposent sur l'exploitation d'un seul type de modalité, l'EEG et l'IRMf étant les plus répandues. Alors que l'EEG est peu coûteux et bénéficie d'une haute résolution temporelle (milliseconde), sa résolution spatiale (quelques centimètres) est limitée par la conduction volumique de la tête et le nombre d'électrodes employées. De plus, la localisation de sources à partir de l'EEG est imprécise du fait qu'elle constitue un problème inverse mal posé. De manière complémentaire, l'IRMf rend possible l'auto-régulation de régions cérébrales spécifiques avec une haute résolution spatiale (millimètres) mais pâtit d'une faible résolution temporelle (seconde). La combinaison de l'EEG et de l'IRMf s'est révélée fructueuse dans l'étude des fonctions cérébrales chez l'homme, pourtant elle a rarement été exploitée pour des applications de NF. Dans le cadre du NF, elle permet d'évaluer et de valider différents paradigmes de manière transmodale. Mais surtout, elle ouvre un champ de possibilités pour le développement de nouvelles approches de NF qui mélangeraient les deux modalités, soit à l'étape de calibration soit pour produire un signal de NF bimodal. La combinaison de l'EEG et de l'IRMf pose de nombreux défis relatifs à la physiologie, au design expérimental, à la qualité des données, ainsi qu'à leur analyse/intégration et leur interprétation. Ces défis sont d'autant plus grands si l'EEG et l'IRMf sont destinés à être utilisés simultanément pour le calcul du signal de NF, du fait de la contrainte de temps-réel et de la difficulté de définir des tâches expérimentales compatibles avec les natures divergentes de l'EEG et de l'IRMf. La partie théorique de cette thèse vise à identifier les aspects méthodologiques qui diffèrent entre le NF-EEG et le NF-IRMf ainsi qu'à examiner les motivations et les stratégies pour combiner l'EEG et l'IRMf dans le cadre du NF. Parmi ces différentes stratégies de combinaison, nous avons choisi de nous focaliser sur le NF-EEG-IRMf bimodal car il apparaît comme une approche prometteuse et n'a quasiment pas été étudié. La faisabilité de cette approche a récemment été démontrée, faisant ainsi place à un tout nouveau champ d'investigation. Cette thèse vise à répondre aux questions suivantes : quelle est la valeur ajoutée du NF bimodal par rapport au NF unimodal ; existe-t-il des mécanismes spécifiques engagés lorsqu'un individu apprend à contrôler deux signaux de NF ; comment intégrer l'EEG et l'IRMf pour produire un seul feedback ? La partie expérimentale de cette thèse se focalise donc sur le développement et l'évaluation de méthodes de NF-EEG-IRMf. Afin de conduire des expériences de NF bimodal, nous commençons par mettre en place une plateforme EEG-IRMf temps-réel. Ensuite, dans une première étude, nous comparons les effets du NF-EEG-IRMF, du NF-EEG et du NF-IRMf. Enfin, dans une seconde étude nous proposons et évaluons deux types de feedbacks intégrés pour le NF-EEG-IRMf. / NF is the process of feeding back real-time information to an individual about his/her ongoing brain activity, so that he/she can train to self-regulate neural substrates of specific behavioral functions. NF has been extensively studied for brain rehabilitation of patients with psychiatric and neurological disorders. However its effective deployment in the clinical armamentarium is being held back by the lack of evidence about its efficacy. One of the possible reason for the debated efficacy of current approaches could be the inherent limitations of single imaging modalities. Indeed, most NF approaches rely on the use of a single modality, EEG and fMRI being the two most widely used. While EEG is inexpensive and benefits from a high temporal resolution (millisecond), its spatial resolution (centimeters) is limited by volume conduction of the head and the number of electrodes. Also source localization from EEG is inaccurate because of the ill-posed inverse problem. In a complementary way, fMRI gives access to the self-regulation of specific brain regions at high spatial resolution (millimeter) but has low temporal resolution (second). Combined EEG-fMRI has proven much valuable for the study of human brain function, however it has rarely been exploited for NF purpose. In the context of NF, combining EEG and fMRI enables cross-modal paradigm evaluation and validation. But more interestingly it opens up avenues for the development of new NF approaches that would mix both modalities, either at the calibration phase or to provide a bimodal NF signal. Combined EEG-fMRI poses numerous challenges with regard to basic physiology, study design, data quality, analysis/integration and interpretation. These challenges are even greater if EEG and fMRI are both to be used simultaneously for online NF computation, because of the real-time constraint and the difficulty to find a task design compatible with EEG and fMRI' diverging natures. The theoretical part of this PhD dissertation aims at identifying methodological aspects that differ between EEG-NF and fMRI-NF and at examining the motivations and strategies for combining EEG and fMRI for NF purpose. Among these combination strategies, we choose to focus on bimodal EEG-fMRI-NF as it seems to be one of the most promising approach and is mostly unexplored. The feasibility of this approach was recently demonstrated and opened an entire new field of investigation. First and foremost, we would like to address the following questions: what is the added value of bimodal NF over unimodal NF; are there any specific mechanisms involved when learning to control two NF signals simultaneously; how to integrate EEG and fMRI to derive a single feedback ? The experimental part of this PhD dissertation therefore focuses on the development and evaluation of methods for bimodal EEG-fMRI-NF. In order to conduct bimodal NF experiments, we start by building up a real-time EEG-fMRI platform. Then in a first study, we compare for the first time bimodal EEG-fMRI-NF with unimodal EEG-NF and fMRI-NF. Eventually, in a second study, we introduce and evaluate two integrated feedback strategies for EEG-fMRI-NF.

The primary aim of this study was to establish whether redintegrative effects can be revealed under conditions with complex sensory stimulation.  Specifically, would the cortical activity involved in the single-trial, passive encoding of a movie, be reactivated when subsequently exposed to a unisensory component of that movie, e.g. an audio- or visual-only segment?  High-density electrical neuroimaging analysis in the frequency domain was used to assist this aim.  The statistical comparisons revealed a greater number of oscillating neuronal regions across all frequency bands in participants who received audiovisual stimulation prior to unisensory exposure (compared to participants who experienced the same unisensory stimulus without prior audiovisual stimulation).   This difference between groups was significant in the alpha2 (right frontal lobe) and gamma (right frontal, sub-lobar and temporal lobes) frequencies during audio-only stimulation.    This enhanced cortical activity during unisensory stimulation suggests that participants were retrieving associated memory traces from their prior multisensory experience, although specific redintegrative effects could not be confirmed.

Détection précoce de crises<p>d’épilepsie à l’aide d’une<p>modélisation du comportement<p>oscillatoire neuronal / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

The reach of technological innovation continues to grow, changing all industries as it evolves. In healthcare, technology is increasingly playing a role in almost all processes, from patient registration to data monitoring, from lab tests to self-care tools. The increase in the amount and diversity of generated clinical data requires development of new technologies and procedures capable of integrating and analyzing the BIG generated information as well as providing support in their interpretation. To that extent, this dissertation focuses on the analysis and processing of biomedical signals, specifically brain and heart signals, using advanced machine learning techniques. That is, the design and implementation of automatic biomedical signal pre-processing and monitoring algorithms, the design of novel feature extraction methods, and the design of classification techniques for specific decision making processes. In the first part of this dissertation Electroencephalogram (EEG) signals that are recorded in 14 different locations on the scalp are utilized to detect random eye state change in real-time. In summary, cross channel maximum and minimum is used to monitor real-time EEG signals in 14 channels. Upon detection of a possible change, Multivariate Empirical Mode Decomposes the last two seconds of the signal into narrow-band Intrinsic Mode Functions. Common Spatial Pattern is then employed to create discriminating features for classification purpose. Logistic Regression, Artificial Neural Network, and Support Vector Machine classifiers all could detect the eye state change with 83.4% accuracy in less than two seconds. We could increase the detection accuracy to 88.2% by extracting relevant features from Intrinsic Mode Functions and directly feeding it to the classification algorithms. Our approach takes less than 2 seconds to detect an eye state change which provides a significant improvement and promising real-life applications when compared to slow and computationally intensive instance based classification algorithms proposed in literatures. Increasing the training examples could even improve the accuracy of our analytic algorithms. We employ our proposed analytic method in detecting the three different dance moves that honey bees perform to communicate the location of a food source. The results are significantly better than other alternative methods in the literature in terms of both accuracy and run time. The last chapter of the dissertation brings out a collaborative research on Parkinson's disease. As a Parkinson’s Progression Markers Initiative (PPMI) investigator, I had access to the vast database of The Michael J. Fox Foundation for Parkinson's Research. We utilized available data to study the heredity factors leading to Parkinson's disease by using Maximum Likelihood and Bayesian approach. Through sophisticated modeling, we incorporated information from healthy individuals and those diagnosed with Parkinson's disease (PD) to available historical data on their grandparents' family to draw Bayesian estimations for the chances of developing PD in five types of families. That is, families with negative history of PD (type 1) and families with positive history in which estimations provided for the prevalence of developing PD when none of the parents (type 2), one of the parents (type 3 and 4), or both of the parents (type 5) carried the disease. The results in the provided data shows that for the families with negative history of PD the prevalence is estimated to be 20% meaning that a child in this family has 20% chance of developing Parkinson. If there is positive history of PD in the family the chance increases to 33% when none of the parents had PD and to 44% when both of the parents had the disease. The chance of developing PD in a family whose solely mother is diagnosed with the disease is estimated to be 26% in comparison to 31% when only father is diagnosed with Parkinson's.

Abstract There is a lack of reliable methods of evaluation of brain ischemic injury in patients undergoing cardiac surgery. The present study was, therefore, planned to evaluate whether serum S100β protein (I), brain cortical microdialysis (II), intracranial pressure (III) and electroencephalography (EEG) (IV) are predictive of postoperative death and brain ischemic injury in an experimental surviving porcine model of hypothermic circulatory arrest (HCA). One hundred and twenty eight (128) female, juvenile (8 to 10 weeks of age) pigs of native stock, weighing 21.0 to 38.2 kg, underwent cardio-pulmonary bypass prior to, and following, a 75-minute period of HCA at a brain temperature of 18°C. During the operation, hemodynamic, electrocardiograph and temperature monitoring was performed continuously. Furthermore, metabolic parameters were monitored at baseline, end of cooling, at intervals of two, four and eight hours after HCA and before extubation. Electroencephalographic recording was performed in all animals, serum S100β protein measurement in 18 animals, cortical microdialysis in 109 animals, and intracranial pressure monitoring in 58 animals. After the operation, assessment of behavior was made on a daily basis until death or elective sacrifice on the seventh postoperative day. All four studies showed that these parameters were predictive of postoperative outcome. Animals with severe histopathological injury had higher serum S100β protein levels at every time interval after HCA. Analysis of cortical brain microdialysis showed that the lactate/glucose ratio was significantly lower and the brain glucose concentration significantly higher among survivors during the early postoperative hours. Intracranial pressure increased significantly after 75 minutes of HCA, and this was associated with a significantly increased risk of postoperative death and brain infarction. A slower recovery of EEG burst percentage after HCA was significantly associated with the development of severe cerebral cortex, brain stem and cerebellum ischemic injury. In conclusion, serum S100β protein proved to be a reliable marker of brain ischemic injury as assessed on histopathological examination. Cerebral microdialysis is a useful method of cerebral monitoring during experimental HCA. Low brain glucose concentrations and high brain lactate/ glucose ratios after HCA are strong predictors of postoperative death. Increased intracranial pressure severely affected the postoperative outcome and may be a potential target for treatment. EEG burst percentage as a sum effect of anesthetic agent and ischemic brain damage is a useful tool for early prediction of severe brain damage after HCA. Among these monitoring methods, brain cortical microdialysis seems to be the most powerful one in predicting brain injury after experimental hypothermic circulatory arrest.

Le cerveau humain est un réseau «large-échelle» formé de régions corticales distribuées et fonctionnellement interconnectées. Le traitement de l'information par le cerveau est un processus dynamique mettant en jeu une réorganisation rapide des réseaux cérébraux fonctionnels, sur une échelle de temps très courte (inférieure à la seconde). Dans le champ des neurosciences cognitives, deux grandes questions restent ouvertes concernant ces réseaux. D'une part, est-il possible de suivre leur dynamique spatio-temporelle avec une résolution temporelle nettement supérieure à celle de l'IRM fonctionnelle? D'autre part, est-il possible de mettre en évidence des différences significatives dans ces réseaux lorsque le cerveau traite des stimuli (visuels, par exemple) ayant des caractéristiques différentes. Ces deux questions ont guidé les développements méthodologiques élaborés dans cette thèse. En effet, de nouvelles méthodes basées sur l'électroencéphalographie sont proposées. Ces méthodes permettent, d'une part de suivre la reconfiguration dynamique des réseaux cérébraux fonctionnels à une échelle de temps inférieure à la seconde. Elles permettent, d'autre part, de comparer deux réseaux cérébraux activés dans des conditions spécifiques. Nous proposons donc un nouvel algorithme bénéficiant de l'excellente résolution temporelle de l'EEG afin de suivre la reconfiguration rapide des réseaux fonctionnels cérébraux à l'échelle de la milliseconde. L'objectif principal de cet algorithme est de segmenter les réseaux cérébraux en un ensemble d' «états de connectivité fonctionnelle» à l'aide d'une approche de type « clustering ». L'algorithme est basé sur celui des K-means et a été appliqué sur les graphes de connectivité obtenus à partir de l'estimation des valeurs de connectivité fonctionnelle entre les régions d'intérêt considérées. La seconde question abordée dans ce travail relève de la mesure de similarité entre graphes. Ainsi, afin de comparer des réseaux de connectivité fonctionnelle, nous avons développé un algorithme (SimNet) capable de quantifier la similarité entre deux réseaux dont les nœuds sont définis spatialement. Cet algorithme met en correspondance les deux graphes en « déformant » le premier pour le rendre identique au second sur une contrainte de coût minimal associée à la déformation (insertion, suppression, substitution de nœuds et d’arêtes). Il procède selon deux étapes, la première consistant à calculer une distance sur les nœuds et la seconde une distance sur les arrêtes. Cet algorithme fournit un indice de similarité normalisé: 0 pour aucune similarité et 1 pour deux réseaux identiques. Il a été évalué sur des graphes simulés puis comparé à des algorithmes existants. Il montre de meilleures performances pour détecter la variation spatiale entre les graphes. Il a également été appliqué sur des données réelles afin de comparer différents réseaux cérébraux. Les résultats ont montré des performances élevées pour comparer deux réseaux cérébraux réels obtenus à partir l'EEG à haute résolution spatiale, au cours d'une tâche cognitive consistant à nommer des éléments de deux catégories différentes (objets vs animaux). / The human brain is a "large-scale" network consisting of distributed and functionally interconnected regions. The information processing in the brain is a dynamic process that involves a fast reorganization of functional brain networks in a very short time scale (less than one second). In the field of cognitive neuroscience, two big questions remain about these networks. Firstly, is it possible to follow the spatiotemporal dynamics of the brain networks with a temporal resolution significantly higher than the functional MRI? Secondly, is it possible to detect a significant difference between these networks when the brain processes stimuli (visual, for example) with different characteristics? These two questions are the main motivations of this thesis. Indeed, we proposed new methods based on dense electroencephalography. These methods allow: i) to follow the dynamic reconfiguration of brain functional networks at millisecond time scale and ii) to compare two activated brain networks under specific conditions. We propose a new algorithm benefiting from the excellent temporal resolution of EEG to track the fast reconfiguration of the functional brain networks at millisecond time scale. The main objective of this algorithm is to segment the brain networks into a set of "functional connectivity states" using a network-clustering approach. The algorithm is based on K-means and was applied on the connectivity graphs obtained by estimation the functional connectivity values between the considered regions of interest. The second challenge addressed in this work falls within the measure of similarity between graphs. Thus, to compare functional connectivity networks, we developed an algorithm (SimNet) that able to quantify the similarity between two networks whose node coordinates is known. This algorithm maps one graph to the other using different operations (insertion, deletion, substitution of nodes and edges). The algorithm is based on two main parts, the first one is based on calculating the nodes distance and the second one is to calculate the edges distance. This algorithm provides a normalized similarity index: 0 for no similarity and 1 for two identical networks. SimNet was evaluated with simulated graphs and was compared with previously-published graph similarity algorithms. It shows high performance to detect the similarity variation between graphs involving a shifting of the location of nodes. It was also applied on real data to compare different brain networks. Results showed high performance in the comparison of real brain networks obtained from dense EEG during a cognitive task consisting in naming items of two different categories (objects vs. animals).

Cette thèse explore comment l’informatique physiologique peut contribuer aux interactions homme-machine (IHM) et encourager l’apparition de nouveaux canaux de communication parmi le grand public. Nous avons examiné comment des capteurs physiologiques,tels que l’électroencéphalographie (EEG), pourraient être utilisés afin d’estimer l’état mental des utilisateurs et comment ils se positionnent par rapport à d’autres méthodes d’évaluation. Nous avons créé la première interface cerveau-ordinateur capable de discriminer le confort visuel pendant le visionnage d’images stéréoscopiques et nous avons esquissé un système qui peux aider à estimer l’expérience utilisateur dans son ensemble, en mesurant charge mentale, attention et reconnaissance d’erreur. Pour abaisser la barrière entre utilisateurs finaux et capteurs physiologiques, nous avons participé à l’intégration logicielle d’un appareil EEG bon marché et libre, nous avons utilisé des webcams du commerce pour mesurer le rythme cardiaque à distance, nous avons confectionné des wearables dont les utilisateurs peuvent rapidement s’équiper afin qu’électrocardiographie, activité électrodermale et EEG puissent être mesurées lors de manifestations publiques. Nous avons imaginé de nouveaux usages pour nos capteurs, qui augmenteraient la présence sociale. Dans une étude autour de l’interaction humain agent,les participants avaient tendance à préférer les avatars virtuels répliquant leurs propres états internes. Une étude ultérieure s’est concentrée sur l’interaction entre utilisateurs, profitant d’un jeu de plateau pour décrire comment l’examen de la physiologie pourrait changer nos rapports. Des avancées en IHM ont permis d’intégrer de manière transparente du biofeedback au monde physique. Nous avons développé Teegi, une poupée qui permet aux novices d’en découvrir plus sur leur activité cérébrale, par eux-mêmes. Enfin avec Tobe, un toolkit qui comprend plus de capteurs et donne plus de liberté quant à leurs visualisations, nous avons exploré comment un tel proxy décalenos représentations, tant de nous-mêmes que des autres. / This thesis explores how physiological computing can contribute to human-computer interaction (HCI) and foster new communication channels among the general public. We investigated how physiological sensors, such as electroencephalography (EEG), could be employed to assess the mental state of the users and how they relate to other evaluation methods. We created the first brain-computer interface that could sense visual comfort during the viewing of stereoscopic images and shaped a framework that could help to assess the over all user experience by monitoring workload, attention and error recognition.To lower the barrier between end users and physiological sensors,we participated in the software integration of a low-cost and open hardware EEG device; used off-the shelf webcams to measure heart rate remotely, crafted we arables that can quickly equip users so that electrocardiography, electrodermal activity or EEG may be measured during public exhibitions. We envisioned new usages for our sensors, that would increase social presence. In a study about human-agent interaction, participants tended to prefer virtual avatars that were mirroring their own internal state. A follow-up study focused on interactions between users to describe how physiological monitoringcould alter our relationships. Advances in HCI enabled us to seam lesslyintegrate biofeedback to the physical world. We developped Teegi, apuppet that lets novices discover by themselves about their brain activity. Finally, with Tobe, a toolkit that encompasses more sensors and give more freedom about their visualizations, we explored how such proxy shifts our representations, about our selves as well as about the others.

The goal of this thesis is to investigate if it is possible to use measurements of physiological parameters to accelerate learning of target shooting for novice marksmen in Saab’s Ground combat indoor trainer (GC-IDT). This was done through a literature study that identified brain activity, eye movements, heart activity, muscle activity and breathing as related to shooting technique. The sensors types Electroencephalography (EEG), Electroocculography (EOG), Electrocardiogram (ECG), Electromyography (EMG) and impedance pneumography (IP) were found to be suitable for measuring the respective parameters in the GC-IDT. The literature study also showed that previous studies had found differences in the physiological parameters in the seconds leading up to the shot when comparing experts and novices. The studies further showed that it was possible to accelerate learning by giving feedback to the novices about their physiological parameters allowing them to mimic the behavior of the experts. An experiment was performed in the GC-IDT by measuring EOG, ECG, EMG and IP on expert and novice marksmen to investigate if similar results as seen in previous studies were to be found. The experiment showed correlation between eye movements and shooting score, which was in line with what previous studies had shown. The respiration measurement did not show any correlation to the shooting scores in this experiment, it was however possible to see a slight difference between expert and novices. The other measurements did not show any correlation to the shooting score in this experiment. In the future, further experiments needs to be made as not all parameters could be explored in depth in this experiment. Possible improvements to such experiments are i.e. increasing the number of participants and/or the number of shots as well as marking shots automatically in the data and increasing the time between shots.

Human motor control is a complex process involving both central and peripheral components of the nervous system. Type Ia afferent input contributes to both motor unit recruitment and firing frequency, however, whether maximal force production is dependent on this input is unclear. Therefore, chapter 2 examined maximal and explosive force production of the knee extensors following prolonged infrapatellar tendon vibration; designed to attenuate the efficacy of the homonymous Ia afferent-α-motoneuron pathway. Despite a marked decrease in H-reflex amplitude, indicating an attenuated efficacy of the Ia afferent-α-motoneuron pathway, both maximal and explosive force production were unaffected after vibration. This suggested that maximal and explosive isometric quadriceps force production was not dependent upon Ia afferent input to the homonymous motor unit pool. Voluntary movements are linked with various modulations in ongoing neural oscillations within the supraspinal sensorimotor system. Despite considerable interest in the oscillatory responses to movements per se, the influence of the motor parameters that define these movements is poorly understood. Subsequently, chapters 3 and 4 investigated how the motor parameters of voluntary contractions modulated the oscillatory amplitude. Chapter 3 recorded electroencephalography from the leg area of the primary sensorimotor cortex in order to investigate the oscillatory responses to isometric unilateral contractions of the knee-extensors at four torque levels (15, 30, 45 and 60% max.). An increase in movement-related gamma (30-50 Hz) activity was observed with increments in knee-extension torque, whereas oscillatory power within the delta (0.5-3 Hz), theta (3-7 Hz), alpha (7-13 Hz) and beta (13-30 Hz) bands were unaffected. Chapter 4 examined the link between the motor parameters of voluntary contraction and modulations in beta (15-30 Hz) oscillations; specifically, movement-related beta decrease (MRBD) and post-movement beta rebound (PMBR). Magnetoencephalography (MEG) was recorded during isometric ramp and constant-force wrist-flexor contractions at distinct rates of force development (10.4, 28.9 and 86.7% max./s) and force output (5, 15, 35 and 60%max.), respectively. MRBD was unaffected by RFD or force output, whereas systematic modulation of PMBR by both contraction force and RFD was identified for the first time. Specifically, increments in isometric contraction force increased PMBR amplitude, and increments in RFD increased PMBR amplitude but decreased PMBR duration. Physical fatigue arises not only from peripheral processes within the active skeletal muscles but also from supraspinal mechanisms within the brain. However, exactly how cortical activity is modulated during fatigue has received a paucity of attention. Chapter 5 investigated whether oscillatory activity within the primary sensorimotor cortex was modulated when contractions were performed in a state of physical fatigue. MEG was recorded during submaximal isometric contractions of the wrist-flexors performed both before and after a fatiguing series of isometric wrist-flexions or a time matched control intervention. Physical fatigue offset the attenuation in MRBD observed during the control trial, whereas PMBR was increased when submaximal contractions were performed in a fatigued state.