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© 2016 IEEE. Pattern Recognition (PR)-based EMG controllers of multi-functional upper-limb prostheses have been recently deployed on commercial state-of-the-art prostheses, offering intuitive control with the ability to control large number of movements with fast reaction time. Current challenges with such PR systems include the lack of training and deployment protocols that can help optimize the system's performance based on amputees' needs. Selecting the best subset of movements that each individual amputee can perform will help to exclude movements that have poor performance so that a subject-specific training can be achieved. In this paper, we propose to select the best set of movements that each amputee can perform as well as identifying the movements for which the PR system would have the worst performance and, therefore, would require further training. Unlike previous studies in this direction, different feature extraction and classification methods were utilized to examine if the choice of features/classifiers could affect the best movements subset selection. We performed our experiments on EMG signals collected from four transradial amputees with an accuracy > 97.5% on average across all subjects for the selection of best subset of movements.

The Cluster-Span Threshold (CST) is a recently introduced unbiased threshold for functional connectivity networks. This binarisation technique offers a natural trade-off of sparsity and density of information by balancing the ratio of closed to open triples in the network topology. Here we present findings comparing it with the Union of Shortest Paths (USP), another recently proposed objective method. We analyse standard network metrics of binarised networks for sensitivity to clinical Alzheimer's disease in the Beta band of Electroencephalogram activity. We find that the CST outperforms the USP, as well as subjective thresholds based on fixing the network density, as a sensitive threshold for distinguishing differences in the functional connectivity between Alzheimer's disease patients and control. This study provides the first evidence of the usefulness of the CST for clinical research purposes.

Sair na rua, se movimentar e olhar: ir e vir de pessoas, de carros, de trabalhadores. Fotografar o movimento, o momento. Captar o instante, luzes e sombras. Objetos e seres em contínuo movimento, perdidos em estruturas e infraestruturas… Seres dirigindo carros, andando de moto, bicicleta e ônibus… Seres levando suas cargas, seus animais. Cidades povoadas de carros, aviões, carroças, caminhantes... todos de um lado para o outro, em movimento frenético.Agora todos eles imobilizados para sempre nestas fotografias em preto e branco feitas no Brasil...um país onde a mobilidade é sempre um grande desafio.

Supplemental Digital Content is available in the text, Structural abnormalities in schizophrenia (SZ) patients have been well documented with structural magnetic resonance imaging (MRI) data using voxel-based morphometry (VBM) and region of interest (ROI) analyses. However, these analyses can only detect group-wise differences and thus, have a poor predictive value for individuals. In the present study, we applied a machine learning method that combined support vector machine (SVM) with recursive feature elimination (RFE) to discriminate SZ patients from normal controls (NCs) using their structural MRI data. We first employed both VBM and ROI analyses to compare gray matter volume (GMV) and white matter volume (WMV) between 41 SZ patients and 42 age- and sex-matched NCs. The method of SVM combined with RFE was used to discriminate SZ patients from NCs using significant between-group differences in both GMV and WMV as input features. We found that SZ patients showed GM and WM abnormalities in several brain structures primarily involved in the emotion, memory, and visual systems. An SVM with a RFE classifier using the significant structural abnormalities identified by the VBM analysis as input features achieved the best performance (an accuracy of 88.4%, a sensitivity of 91.9%, and a specificity of 84.4%) in the discriminative analyses of SZ patients. These results suggested that distinct neuroanatomical profiles associated with SZ patients might provide a potential biomarker for disease diagnosis, and machine-learning methods can reveal neurobiological mechanisms in psychiatric diseases.

Multiscale entropy (MSE) has been a prevalent algorithm to quantify the complexity of biomedical time series. Recent developments in the field have tried to alleviate the problem of undefined MSE values for short signals. Moreover, there has been a recent interest in using other statistical moments than the mean, i.e., variance, in the coarse-graining step of the MSE. Building on these trends, here we introduce the so-called refined composite multiscale fuzzy entropy based on the standard deviation (RCMFEσ) and mean (RCMFEμ) to quantify the dynamical properties of spread and mean, respectively, over multiple time scales. We demonstrate the dependency of the RCMFEσ and RCMFEμ, in comparison with other multiscale approaches, on several straightforward signal processing concepts using a set of synthetic signals. The results evidenced that the RCMFEσ and RCMFEμ values are more stable and reliable than the classical multiscale entropy ones. We also inspect the ability of using the standard deviation as well as the mean in the coarse-graining process using magnetoencephalograms in Alzheimer's disease and publicly available electroencephalograms recorded from focal and non-focal areas in epilepsy. Our results indicated that when the RCMFEμ cannot distinguish different types of dynamics of a particular time series at some scale factors, the RCMFEσ may do so, and vice versa. The results showed that RCMFEσ-based features lead to higher classification accuracies in comparison with the RCMFEμ-based ones. We also made freely available all the Matlab codes used in this study at http://dx.doi.org/10.7488/ds/1477 .

Alzheimer's disease (AD) is one of the fastest growing neurological diseases in the world. We evaluate multivariate multiscale sample entropy (mvMSE) and multivariate multiscale permutation entropy (mvMPE) approaches to distinguish resting-state magnetoencephalogram (MEG) signals of 36 AD patients from those of 26 normal controls. We also discuss about choosing the appropriate embedding dimension value as an effective parameter for mvMPE and MPE for the first time. The results illustrate that both the mvMPE and mvMSE can be useful in the diagnosis of AD, although with different running times and abilities. In addition, our findings show that the MEG complexity analysis performed on deeper time scales by mvMPE and mvMSE may be a useful tool to characterize AD. In most scale factors, the average of the mvMPE and mvMSE values of AD patients are lower than those of controls.

Breast cancer is one of the most common types of cancer in women all over the world. Early diagnosis of this kind of cancer can significantly increase the chances of long-term survival. Since diagnosis of breast cancer is a complex problem, neural network (NN) approaches have been used as a promising solution. Considering the low speed of the back-propagation (BP) algorithm to train a feed-forward NN, we consider a number of improved NN trainings for the Wisconsin breast cancer dataset: BP with momentum, BP with adaptive learning rate, BP with adaptive learning rate and momentum, Polak–Ribikre conjugate gradient algorithm (CGA), Fletcher-Reeves CGA, Powell–Beale CGA, scaled CGA, resilient BP (RBP), one-step secant and quasi-Newton methods. An NN ensemble, which is a learning paradigm to combine a number of NN outputs, is used to improve the accuracy of the classification task. Results demonstrate that NN ensemble-based classification methods have better performance than NN-based algorithms. The highest overall average accuracy is 97.68% obtained by NN ensemble trained by RBP for 50%-50% training-test evaluation method.

La presente tésis se centra en las espumas poliolefínicas (polietileno y polipropileno) con el objetivo fundamental de mejorar sus propiedades físicas. Durante su desarrollo se presentan dos rutas de producción nuevas para la fabricación de espumas estructurales y para la fabricación de espumas no reticuladas de polipropileno con excelentes propiedades mecánicas. Se presta especial atención a las propiedades barreras a gases las cuales se mejoran mediante la incorporación de nanoarcillas. Las propiedades mecánicas también se mejoran con la incorporación de diversos tipos de nanoarcillas y nanotubos de carbono. Como métodos de espumado se emplea disolución de gas en estado sólido, espumado libre, moldeo a compresión mejorado y stages molding. Finalmente se estudia en detalle la interacción de la espumación con la exfoliación de las nanoarcillas mediante difracción por dispersión de energía empleando radiación sincrotrón, Departamento de Física de la Materia Condensada, Cristalografía y Minerealogía

The present study aims to understand the phenomenology of char gasification by monitoring the chemical, structural and textural char characteristics through the gasification reaction. Chars from beech wood were gasified under 20%H 2 O, 20%CO 2 and 20%H 2 O + 20%CO 2 in N 2 at 900° C. The gasification reactions were stopped at 20%, 50% and 70% of char conversion. The char properties were analysed by different analytical techniques such as temperature programmed desorption coupled to mass spectrometry, Raman spectroscopy, Scanning Electron Microscopy, N 2 manometry and X-ray Fluorescence. These analyses provide valuable information on the unfolding of the gasification reactions with H 2 O, CO 2 and their mixtures. In particular, it is noted that H 2 O and CO 2 gasification reactions follow different pathways. Moreover, during mixed atmosphere, despite that the char reactivity can be fairly expressed by summing the two individual reactivities, this apparent additivity appears to be the result of several competitions and synergies between H 2 O and CO 2 reactions.

Permutation entropy (PE) is a well-known and fast method extensively used in many physiological signal processing applications to measure the irregularity of time series. Multiscale PE (MPE) is based on assessing the PE for a number of coarse-grained sequences representing temporal scales. However, the stability of the conventional MPE may be compromised for short time series. Here, we propose an improved MPE (IMPE) to reduce the variability of entropy measures over long temporal scales, leading to more reliable and stable results. We gain insight into the dependency of MPE and IMPE on several straightforward signal processing concepts which appear in biomedical activity via a set of synthetic signals. We also apply these techniques to real biomedical signals via publicly available electroencephalogram (EEG) recordings acquired with eyes open and closed and to ictal and non-ictal intracranial EEGs. We conclude that IMPE improves the reliability of the entropy estimations in comparison with the traditional MPE and that it is a promising technique to characterize physiological changes affecting several temporal scales. We provide the source codes of IMPE and the synthetic data in the public domain.

Multiscale entropy (MSE) is a widely-used tool to analyze biomedical signals. It was proposed to overcome the deficiencies of conventional entropy methods when quantifying the complexity of time series. However, MSE is undefined for very short signals and slow for real-time applications because of the use of sample entropy (SampEn). To overcome these shortcomings, we introduce multiscale dispersion entropy (DisEn - MDE) as a very fast and powerful method to quantify the complexity of signals. MDE is based on our recently developed DisEn, which has a computation cost of O(N), compared with O(N2) for SampEn. We also propose the refined composite MDE (RCMDE) to improve the stability of MDE. We evaluate MDE, RCMDE, and refined composite MSE (RCMSE) on synthetic signals and find that these methods have similar behaviors but the MDE and RCMDE are significantly faster than MSE and RCMSE, respectively. The results also illustrate that RCMDE is more stable than MDE for short and noisy signals, which are common in biomedical applications. To evaluate the proposed methods on real signals, we employ three biomedical datasets, including focal and non-focal electroencephalograms (EEGs), blood pressure recordings in Fantasia database, and resting-state EEGs activity in Alzheimer's disease (AD). The results again demonstrate a similar behavior of RCMSE, MDE and RCMDE, although the RCMDE and MDE are significantly faster and lead to larger differences between physiological conditions known to alter the complexity of the physiological recordings. To sum up, MDE and RCMDE are expected to be useful for the analysis of physiological signals thanks to their ability to distinguish different types of dynamics., Comment: 8 pages, 6 figures

This article presents a new approach for the analysis of biomedical data to support the management and care of patients with Alzheimer's disease (AD). The increase in prevalence of neurodegenerative disorders such as AD has led to a need for objective means to assist clinicians with the analysis and interpretation of complex biomedical data. To this end, we propose a "Bioprofile" analysis to reveal the pattern of disease in the subject's biodata. From the Bioprofile, personal "Bioindices" that indicate how closely a subject's data resemble the pattern of AD can be derived. We used an unsupervised technique (k-means) to cluster variables of the ADNI database so that subjects are divisible into those with the Bioprofile of AD and those without it. Results revealed that there is an "AD pattern" in the biodata of most AD and mild cognitive impairment (MCI) patients and some controls. This pattern agrees with a recent hypothetical model of AD evolution. We also assessed how the Bioindices changed with time and we found that the Bioprofile was associated with the risk of progressing from MCI to AD. Hence, the Bioprofile analysis is a promising methodology that may potentially provide a complementary new way of interpreting biomedical data. Furthermore, the concept of the Bioprofile could be extended to other neurodegenerative diseases.

Magnetoencephalography (MEG) allows the real-time recording of neural activity and oscillatory activity in distributed neural networks. We applied a non-linear complexity analysis to resting-state neural activity as measured using whole-head MEG. Recordings were obtained from 20 unmedicated patients with major depressive disorder and 19 matched healthy controls. Subsequently, after 6 months of pharmacological treatment with the antidepressant mirtazapine 30 mg/day, patients received a second MEG scan. A measure of the complexity of neural signals, the Lempel–Ziv Complexity (LZC), was derived from the MEG time series. We found that depressed patients showed higher pre-treatment complexity values compared with controls, and that complexity values decreased after 6 months of effective pharmacological treatment, although this effect was statistically significant only in younger patients. The main treatment effect was to recover the tendency observed in controls of a positive correlation between age and complexity values. Importantly, the reduction of complexity with treatment correlated with the degree of clinical symptom remission. We suggest that LZC, a formal measure of neural activity complexity, is sensitive to the dynamic physiological changes observed in depression and may potentially offer an objective marker of depression and its remission after treatment.

In the present paper X-ray microtomography technique has been applied to investigate internal, three dimensional structure of clay/polypropylene nanocomposite foams. Several sets of foam samples were prepared using the improved compression moulding technique that allows controlling independently the cells‘ size, cells‘ shape and density of foams. Then the X-ray images were acquired followed by an application of image processing steps and final reconstruction of foams microstructure. Volume distribution of cells has been extracted from three dimensional images and related to processing parameters. Since not only the size but also the shape of cells influences a load bearing capacity of foams, the sphericity parameter has been selected as a relative quantifier of cells shape. The sphericity parameter has been also related to different processing parameters.

Blind source separation (BSS) is widely used to analyse brain recordings like the magnetoencephalogram (MEG). However, few studies have compared different BSS decompositions of real brain data. Those comparisons were usually limited to specific applications. Therefore, we aimed at studying the consistency (i.e., similarity) of the decompositions estimated for real MEGs from 26 subjects using five widely used BSS algorithms (AMUSE, SOBI, JADE, extended-Infomax and FastICA) for five epoch lengths (10s, 20s, 40s, 60s and 90s). A statistical criterion based on Factor Analysis was applied to calculate the number of components into which each epoch would be decomposed. Then, the BSS techniques were applied. The results indicate that the pair of algorithms 'AMUSE-SOBI', followed by 'JADE-FastICA', provided the most similar separations. On the other hand, the most dissimilar outcomes were computed with 'AMUSE-JADE' and 'SOBI-JADE'. The BSS decompositions were more similar for longer epochs. Furthermore, additional analyses of synthetic signals supported the results of the real MEGs. Thus, when selecting BSS algorithms to explore brain signals, the techniques offering the most different decompositions, such as AMUSE and JADE, may be preferred to obtain complementary, or at least different, perspectives of the underlying components. (C) 2010 IPEM. Published by Elsevier Ltd. All rights reserved.

Objective Constant changes in developing children's brains can pose a challenge in EEG dependant technologies. Advancing signal processing methods to identify developmental differences in paediatric populations could help improve function and usability of such technologies. Taking advantage of the multi-dimensional structure of EEG data through tensor analysis may offer a framework for extracting relevant developmental features of paediatric datasets. A proof of concept is demonstrated through identifying latent developmental features in resting-state EEG. Approach Three paediatric datasets ([Formula: see text]) were analyzed using a two-step constrained parallel factor (PARAFAC) tensor decomposition. Subject age was used as a proxy measure of development. Classification used support vector machines (SVM) to test if PARAFAC identified features could predict subject age. The results were cross-validated within each dataset. Classification analysis was complemented by visualization of the high-dimensional feature structures using t-distributed stochastic neighbour embedding (t-SNE) maps. Main results Development-related features were successfully identified for the developmental conditions of each dataset. SVM classification showed the identified features could accurately predict subject at a significant level above chance for both healthy and impaired populations. t-SNE maps revealed suitable tensor factorization was key in extracting the developmental features. Significance The described methods are a promising tool for identifying latent developmental features occurring throughout childhood EEG.

Heat transport equations have been used to solve, by implementing the Finite Element Method (FEM), three different cases representative of the aluminium foams life: the production process (solidification in the molten state), post-production (water quenching heat treatments) and applications (fire barriers).

This work studied whether a blind source separation (BSS) and component selection procedure could increase the differences between Alzheimer's disease (AD) patients and control subjects' spectral and nonlinear features of magnetoencephalogram (MEG) recordings. MEGs were acquired with a 148-channel whole-head magnetometer from 62 subjects (36 AD patients and 26 controls), who were divided randomly into training and test sets. MEGs were decomposed using the algorithm for multiple unknown signals extraction (AMUSE). The extracted AMUSE components were characterised with two spectral median frequency and spectral entropy (SpecEn) - and two non-linear features: Lempel-Ziv complexity (LZC) and sample entropy (SampEn). One-way analysis of variance with age as a covariate was applied to the training set to decide which components had the most significant differences between groups. Then, partial reconstructions of the MEGs were computed with these significant components. In the test set, the accuracy and area under the ROC curve (AUC) associated with each partial reconstruction of the MEGs were compared with the case where no BSS-preprocessing was applied. This preprocessing increased the AUCs between 0.013 and 0.227, while the accuracy for SpecEn, LZC and SampEn rose between 6.4% and 22.6%, improving the separation between AD patients and control subjects. (C) 2009 IPEM. Published by Elsevier Ltd. All rights reserved.

Structural foams are composed of two solid layers enclosing a foamed core. The application of sandwich structural foams has rapidly increased in the last decade. Injection moulding is currently used to produce these foams, being not common to produce conventional foams of similar densities and chemical compositions in a similar process. In this paper an alternative route to produce structural foams has been used. This method allows fabricating conventional foams with the same chemical composition and density than the structural foams, so comparisons between properties of both kinds of materials can be made in a proper way, i.e. avoiding effects of different chemical compositions and/or different densities. The structural and mechanical properties in tension, compression and bending have been characterized both for structural and conventional foams based on a low density polyethylene. The results have showed that the sandwich structure of structural foams improves a 50% the mechanical behaviour in bending, however no improvements in compression or tension have been found.

In this work, the effective diffusion coefficient of the gas contained in closed cell polyethylene foams under static loading is measured. To do this, compressive creep experiments were performed on low density polyethylene foams produced under a gas diffusion process. Density dependence of this coefficient has been analysed as well as the variation of pressure with time inside the cells. Finally, immediately after compressive creep, the recovery behaviour of the foams was also characterised. Different abilities for recovering were observed depending on the density of the foam and the absolute recovery resulted independent of the initial stress applied.

This study was designed to analyze the magnetoencephalogram (MEG) background activity from 20 patients with probable Alzheimer's disease (AD) and 21 control subjects by using two nonlinear methods: sample entropy (SampEn), and Lempel-Ziv complexity (LZC). The former quantifies the signal regularity, and the latter is a complexity measure. The signals were acquired with a 148-channel whole-head magnetometer placed in a magnetically shielded room. Our results show that MEG recordings are less complex and more regular in patients with AD than in control subjects. Significant differences between both groups were found in 16 MEG channels with SampEn and in 134 with LZC (p

The aim of the present study is to show the usefulness of nonlinear methods to analyse the electroencephalogram (EEG) and magnetoencephalogram (MEG) in patients with Alzheimer's disease (AD). The following nonlinear methods have been applied to study the EEG and MEG background activity in AD patients and control subjects: approximate entropy, sample entropy, multiscale entropy, auto-mutual information and Lempel–Ziv complexity. We discuss why these nonlinear methods are appropriate to analyse the EEG and MEG. Furthermore, the performance of all these methods has been compared when applied to the same databases of EEG and MEG recordings. Our results show that EEG and MEG background activities in AD patients are less complex and more regular than in healthy control subjects. In line with previous studies, our work suggests that nonlinear analysis techniques could be useful in AD diagnosis.

We analysed the electroencephalogram (EEG) from Alzheimer's disease (AD) patients with two nonlinear methods: approximate entropy (ApEn) and auto mutual information (AMI). ApEn quantifies regularity in data, while AMI detects linear and nonlinear dependencies in time series. EEGs from 11 AD patients and 11 age-matched controls were analysed. ApEn was significantly lower in AD patients at electrodes O1, O2, P3 and P4 (p

Objectives: In this pilot study, we intended to assess whether a procedure based on blind source separation (BSS) and subsequent partial reconstruction of magnetoencephalogram (MEG) recordings might enhance the differences between MEGs from Alzheimer's disease (AD) patients and elderly control subjects.Materials and methods: We analysed MEG background activity recordings acquired with a 148-channel whole-head magnetometer from 21 AD patients and 21 control subjects. Artefact-free epochs of 20 s were blindly decomposed using the algorithm for multiple unknown signals extraction (AMUSE), which arranges the extracted components by decreasing linear predictability. Thus, the components of diverse epochs and subjects could be easily compared. Every component was characterised with its median frequency and spectral. entropy (denoted by f(median) and SpecEn, respectively). The differences between subject groups in these variables were statistically evaluated to find out which components could improve the subject classification. Then, these significant components were used to partially reconstruct the MEG recordings.Results: The statistical analysis showed that the AMUSE components which provided the largest differences between demented patients and control subjects were ordered together. Considering this analysis, we defined two subsets, denoted by BSS-{15,35} and BSS-{20,30},which included 21 components (15-35) and 11 components (20-30), respectively. We partially reconstructed the MEGs with these subsets. Then, the classification performance was computed with a leave-one-out cross-validation procedure for the case where no BSS was applied and for the partial reconstructions BSS-{15,35} and BSS-{20,30}. The BSS and component selection procedure improved the classification accuracy from 69.05% to 83.33% using f(median) with BSS-{5,35} and from 61.91% to 73.81% using SpecEn with BSS-{20,30}.Conclusion: These preliminary results lead us to think that the proposed procedure based on BSS and selection of significant components may improve the classification of AD patients using straightforward features from MEG recordings. (c) 2008 Elsevier B.V. All rights reserved.

We analyse the electroencephalogram signals in the beta band of working memory representation recorded from young healthy volunteers performing several different Visual Short-Term Memory (VSTM) tasks which have proven useful in the assessment of clinical and preclinical Alzheimer's disease. We compare network analysis using Maximum Spanning Trees (MSTs) with network analysis obtained using 20% and 25% connection thresholds on the VSTM data. MSTs are a promising method of network analysis negating the more classical use of thresholds which are so far chosen arbitrarily. However, we find that the threshold analyses outperforms MSTs for detection of functional network differences. Particularly, MSTs fail to find any significant differences. Further, the thresholds detect significant differences between shape and shape-colour binding tasks when these are tested in the left side of the display screen, but no such differences are detected when these tasks are tested for in the right side of the display screen. This provides evidence that contralateral activity is a significant factor in sensitivity for detection of cognitive task differences., Comment: Proceeds from the IEEE Engineering in Medicine and Biology Conference 2015

About 1.2 million people in Europe have Parkinson's disease (PD). PD patients often suffer from social exclusion and depression due to progressive lack of control over the disease. Eventually, most require constant care, leading to huge socioeconomic burdens. To partially tackle this issue, 7 partners in 7 countries participated in LiveWell (EU-funded project, 2012-14) to develop an innovative Web-based education, training and social community platform targeting PD patients, carers and clinicians. This paper describes LiveWell from conception to completion, presents lessons learnt over the course of the project, and highlights some identified main areas of best practice. The Project Consortium might not have had the chance to implement everything learnt in the current outputs, but these lessons and guidelines can be later used to improve LiveWell, and can also be generalised to benefit similar e-health services. The combination of appropriate online education and social communities could help patients and carers cope positively with PD, promoting social inclusion and better outcomes.

We analyzed the frequency spectrum of magnetoencephalogram (MEG) background activity in 16 bipolar disorder (BD) patients and 24 age-matched healthy control. Median frequency (MF), spectral entropy (SpEn), and relative power in delta (RPδ), theta (RPθ), alpha (RPα), beta (RPβ), and gamma (RPγ) bands were computed for all 148 MEG channels. Significant differences between the two groups were found in the average level of MF, RPδ, and RPθ in the posterior region of the scalp. Moreover, the MF, SpEn, RPδ, and RPβ values of BD patients had a different dependence on age as compared with the results of control subjects, which may suggest that BD affects how the brain activity develops with age. We conclude that the spectral analysis of the background MEG in BD patients may give insights into how this condition affects the brain activity.

Cortical reorganization in multiple sclerosis (MS) is defined as a compensatory mechanism which requires MS patients to overactivate specific brain areas in order to perform the task as controls. To investigate this process with the Paced Auditory Serial Addition Test (PASAT) task, we selected 15 MS patients who performed the PASAT task within-normal limits and 10 healthy controls. Once selected, we used functional magnetic resonance imaging (fMRI) to investigate brain areas involved in PASAT performance in both groups. Results showed that the task activated the left frontal (BA6 and 9) and parietal cortex (BA7 and 40) in both groups, but MS patients showed a stronger activation in the left prefrontal cortex (BA9, 44 and 45) when compared with controls. These results confirmed those obtained post hoc by Audoin et al. [Audoin, B., Ibarrola, D., Ranjeva, J.P., Confort-Gouny, S., Malikova, I., Ali-Cherif, A.M., Pelletier, J., Cozzone, P., et al., 2003. Compensatory cortical activation observed by fMRI during cognitive task at the earliest stage of MS. Hum. Brain Mapp. 20, 51–58], and we interpreted this as showing true cortical reorganization.

The time spans of the so-called ditched enclosures are key to a better understanding of a type of site characterised by complex palimpsests. Several conclusions can be drawn from the 16 radiocarbon dates obtained from Ditch 4 at Marroquíes Bajos. According to Bayesian modelling, the filling process began in 2510-2310 cal BC at 2 σ and ended in 2115-1795 cal BC at 2 σ, making them the most recent ditched enclosure with depositional activity in Iberia. Comparison with the dating from other sections of Ditch 4 challenges the traditional assumption that radiocarbon samples obtained from deposits at the bottom of the ditch provide the best dates for its construction. The process by which different sections of the same Ditch 4 were filled at different times suggests that its general outline is more the result of an aggregation of segments than of a single build-up. La temporalidad de los denominados recintos de fosos es un aspecto clave para la correcta compresión de un tipo de yacimiento caracterizado por auténticos palimpsestos de complicada secuenciación. La serie de 16 dataciones radiocarbónicas obtenidas en el Foso 4 de Marroquíes Bajos ha permitido establecer varias conclusiones. Según la modelización bayesiana el inicio del proceso de colmatación se sitúa entre el 2510-2310 cal BC a 2 σ y su final entre el 2115-1795 cal BC a 2 σ, convirtiéndose en el más reciente de los recintos de fosos peninsulares. La comparación con las dataciones existentes para otros tramos del Foso 4 invalida la pretensión de fechar la construcción de los fosos a partir de las dataciones obtenidas en los depósitos asociados a su base. Las secuencias temporalmente diferenciadas en la colmatación de diferentes tramos de un mismo foso sugiere que su configuración es más el resultado de agregados de segmentos que construcciones unitarias.

Tesis de Licenciatura

International audience; Biomass gasification is a promising way to make renewable energy. It produces a syngas which can be turned into different kinds of energy: CHP (Combined Heat Power), bio-fuel or combined biomethane fuel (or BioSNG: a green Substitute Natural Gas) and heat. Combined Biomethane/heat is environment-friendly (high energetic and chemical yields, local heat valorisation, reasonable biomass supply volume and radius) and is complementary to the other renewable energies [3]. Project "GAYA", started in June 2010, is aiming to develop and prepare industrialization of the combined 2 nd generation biomethane/heat pathway, through a demonstration operation and an R&D pilot plant to be erected in St Fons (France). This demonstrator unit represents a unique experimental tool in Europe in order to allows industrialization of 2G biomethane pathway from 2017 onward. The project objective is to develop an efficient and environmental friendly 2nd generation pathway from biomass to biomethane and injection in natural gas grid. The project has identified optimized configurations of each process blocks. Innovations and improvements will be developed in order to reach the highest energetic efficiency and a better environmental balance in comparison with others 2 nd generations' pathways. 734

BackgroundThe information obtained from signal recorded with extracellular electrodes is essential in many research fields with scientific and clinical applications. These signals are usually considered as a point process and a spike detection method is needed to estimate the time instants of action potentials. In order to do so, several steps are taken but they all depend on the results of the first step, which filters the signals. To alleviate the effect of noise, selecting the filter parameters is very time-consuming. In addition, spike detection algorithms are signal dependent and their performance varies significantly when the data change.New MethodsWe propose two approaches to tackle the two problems above. We employ ensemble empirical mode decomposition (EEMD), which does not require parameter selection, and a novel approach to choose the filter parameters automatically. Then, to boost the efficiency of each of the existing methods, the Hilbert transform is employed as a pre-processing step. To tackle the second problem, two novel approaches, which use the fuzzy and probability theories to combine a number of spike detectors, are employed to achieve higher performance.Results, Comparison with Existing Method(s) and ConclusionsThe simulation results for realistic synthetic and real neuronal data reveal the improvement of the proposed spike detection techniques over state-of-the art approaches. We expect these improve subsequent steps like spike sorting.

The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.

We examine whether modeling of the causal dynamic relationships between frontal and occipital electroencephalogram (EEG) time-series recordings reveal reliable differentiating characteristics of Alzheimer’s patients versus control subjects in a manner that may assist clinical diagnosis of Alzheimer’s disease (AD). The proposed modeling approach utilizes the concept of principal dynamic modes (PDMs) and their associated nonlinear functions (ANF) and hypothesizes that the ANFs of some PDMs for the AD patients will be distinct from their counterparts in control subjects. To this purpose, global PDMs are extracted from 1-min EEG signals of 17 AD patients and 24 control subjects at rest using Volterra models estimated via Laguerre expansions, whereby the O1 or O2 recording is viewed as the input signal and the F3 or F4 recording as the output signal. Subsequent singular value decomposition of the estimated Volterra kernels yields the global PDMs that represent an efficient basis of functions for the representation of the EEG dynamics in all subjects. The respective ANFs are computed for each subject and characterize the specific dynamics of each subject. For comparison, signal features traditionally used in the analysis of EEG signals in AD are computed as benchmark. The results indicate that the ANFs of two specific PDMs, corresponding to the delta–theta and alpha bands, can delineate the two groups well., Alzheimer's disease (AD) is currently the most common neurodegenerative disorder in the western world and in the future its incidence is expected to double every 20 years. Because a definitive diagnosis can be made only by autopsy and AD pathology can start years before the first symptoms, there is a need for objective, non-invasive and affordable means to support clinicians in the detection and monitoring of the disease. We present an approach for assisting the diagnosis of Alzheimer's disease via modeling the causal dynamic relationships between frontal and occipital lectroencephalogram (EEG) time-series recordings. In a controlled clinical trial of 41 subjects, the proposed input-output modeling methodology was able to differentiate consistently between AD patients and controls.

Around the world, there are thousands of people who lost a hand during war or as a consequence of an accident. Artificial hand prosthesis controlled by surface electromyography (EMG) signals offers a promising solution to improve the quality of life of amputees. As part of the process of prosthesis fitting, an occupational therapist will try to train the amputee with the help of a physical prosthesis [1] that is not actually fitted, but only displayed to provide visual feedback, but these are expensive (>£16,000). This training should be performed for long periods of time at the rehabilitation centre prior to the prosthesis fitting. It aims at improving the generation of nerve signals for capture by EMG probes, and at tuning of the EMG pattern recognition algorithm to the actions most suited for each amputee [2]. This part of the rehabilitation process can be made more efficient and more widely available through the use of a low-cost actuated hand with the same degrees of freedom as the prosthetic device to be fitted.

Although there have been many advances in electromyography (EMG) signal processing and pattern recognition (PR) for the control of multi-functional upper-limb prostheses, some the outstanding problems need to be solved before practical PR-based prostheses can be put into service. Some of these are the lack of training and deployment protocols and the provision of the tools required. Therefore, we present a preliminary procedure to personalize the prosthesis deployment. In the first step, we record the demographic information of each individual amputee person and their background. In the second step of the protocol, the EMG signals are acquired. PR algorithms and parameters will be chosen in the 3(rd) step of the protocol. In the 4(th) step, the best number of EMG sensors to achieve the maximal performance with a full set of gestures is identified. The final step involves finding the best set of movements that the amputee person can produce with an accuracy95% as well as identifying the movements with the worst performance, which would require further training. This proposed approach is validated with 2 transradial amputees.

The myoelectric control of prostheses has been an important area of research for the past 40 years. Significant advances have been achieved with Pattern Recognition (PR) systems regarding the number of movements to be classified with high accuracy. However, practical robustness still needs further research. This paper focuses on investigating the effect of the change in force levels by transradial amputee persons on the performance of PR systems. Two below-elbow amputee persons participated in the study. Three levels of forces (low, medium, and high) were recorded for different hand grips with the help of visual feedback from the Electromyography (EMG) signals. Results showed that changing the force level degraded the performance of the myoelectric control system by up to 60% with 12 EMG channels for 4 hand grips and a rest position. We investigated different EMG feature sets in combination with a Linear Discriminant Analysis (LDA) classifier. The performance was slightly better with Time Domain (TD) features compared to Auto Regression (AR) coefficients and Root Mean Square (RMS) features. Finally, the error of the classification was considerably reduced to approximately 17% when the PR system was trained with all force levels.

2nd International Symposium on Innovation and Technology in the Phosphate Industry (SYMPHOS), Agadir, MOROCCO, MAY 06-10, 2013; International audience; The valorization of gypsum (by-product of phosphoric acid production) into useful product seems to be of important interest. In this study, the adsorption of lead (II) and cadmium (II) in an aqueous solution using both commercial gypsum (CG) and industrial gypsum by-product (IG) as adsorbents, has been investigated. The use of highly-purity CG allows the understanding of the adsorption phenomenon. The use of an IG as received from an industrial site allows the evaluation of its reactivity compared to pure CG. The adsorption experiments were carried out at ambient conditions for 24 h and the pH was recorded along the reaction. FTIR analyses were performed in order to examine the modification in the sulfate bound arrangement in the case of use of lead and/or cadmium. The obtained results have shown that the retention capacity of lead (II) and cadmium (II) by IG was similar that those obtained in the case of CG. However, gypsum by-product may be used as an alternative material to remove heavy metals in wastewaters.

International audience; The rheological behavior of water-calcium sulfates and hydroxyapatite-calcium sulfates binary blends was considered in this study. These blends are to be used as permeable reactive barriers for groundwater remediation. The influence of gypsum and plaster on the rheological behavior of Ca-HA(Gel) has been studied. The results show that all blends and formulations exhibit a shear-thinning effect and thixotropic behavior. The viscosity also depends on the level of concentration of the solid and the reactivity of the blends. The hydration of plaster causes cohesion and agglomeration between particles due to electrostatic forces, while the hydration of gypsum leads to dispersion due to repulsive forces between particles. The zeta potential was used in this study to understand the interaction between particles and its effect on the global behavior of the blends. The dissolution in water of sulfate ions containing calcium sulfate leads to a change in the surface charge of the blend. This allowed the dispersion or agglomeration of the mixtures to be predicted.

There is a need for objective tools to help clinicians to diagnose Alzheimer's Disease (AD) early and accurately and to conduct Clinical Trials (CTs) with fewer patients. Magnetic Resonance Imaging (MRI) is a promising AD biomarker but no single MRI feature is optimal for all disease stages. Machine Learning classification can address these challenges. In this study, we have investigated the classification of MRI features from AD, Mild Cognitive Impairment (MCI), and control subjects from ADNI with four techniques. The highest accuracy rates for the classification of controls against ADs and MCIs were 89.2% and 72.7%, respectively. Moreover, we used the classifiers to select AD and MCI subjects who are most likely to decline for inclusion in hypothetical CTs. Using the hippocampal volume as an outcome measure, we found that the required group sizes for the CTs were reduced from 197 to 117 AD patients and from 366 to 215 MCI subjects.

Improvements in existing hydroelectric power plants can increase quantity of production, but very often they are also designed to improve its quality, that is, peak generation capacity and availability of reserve energy, which in turn leads to a reduction of emissions in the producer part. An adequate valuation and economic recognition of the energy quality in remuneration seems essential to encourage these improvements. Frequent size discrimination acts against this recognition, because quality of energy is more often present in conventional-sized installations.