Multiway Array Decomposition Analysis of EEGs in Alzheimer’s Disease

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer’s disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied three state of the art multiway array decomposition (MAD) methods to extract features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE). We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease.

Maximum likelihood Linear Programming Data Fusion for Speaker Recognition

Biometric system performance can be improved by means of data fusion. Several kinds of information can be fused in order to obtain a more accurate classification (identification or verification) of an input sample. In this paper we present a method for computing the weights in a weighted sum fusion for score combinations, by means of a likelihood model. The maximum likelihood estimation is set as a linear programming problem. The scores are derived from a GMM classifier working on a different feature extractor. Our experimental results assesed the robustness of the system in front a changes on time (different sessions) and robustness in front a change of microphone. The improvements obtained were significantly better (error bars of two standard deviations) than a uniform weighted sum or a uniform weighted product or the best single classifier. The proposed method scales computationaly with the number of scores to be fussioned as the simplex method for linear programming.

Application of the Mutual Information Minimization to speaker recognition / identification improvement

In this paper we propose the inversion of nonlinear distortions in order to improve the recognition rates of a speaker recognizer system. We study the effect of saturations on the test signals, trying to take into account real situations where the training material has been recorded in a controlled situation but the testing signals present some mismatch with the input signal level (saturations). The experimental results for speaker recognition shows that a combination of several strategies can improve the recognition rates with saturated test sentences from 80% to 89.39%, while the results with clean speech (without saturation) is 87.76% for one microphone, and for speaker identification can reduce the minimum detection cost function with saturated test sentences from 6.42% to 4.15%, while the results with clean speech (without saturation) is 5.74% for one microphone and 7.02% for the other one.

Fast Approximation of Nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems

This paper proposes a very fast method for blindly approximating a nonlinear mapping which transforms a sum of random variables. The estimation is surprisingly good even when the basic assumption is not satisfied.We use the method for providing a good initialization for inverting post-nonlinear mixtures and Wiener systems. Experiments show that the algorithm speed is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.

Parametric Approach to Blind Deconvolution of Nonlinear Channels

A parametric procedure for the blind inversion of nonlinear channels is proposed, based on a recent method of blind source separation in nonlinear mixtures. Experiments show that the proposed algorithms perform efficiently, even in the presence of hard distortion. The method, based on the minimization of the output mutual information, needs the knowledge of log-derivative of input distribution (the so-called score function). Each algorithm consists of three adaptive blocks: one devoted to adaptive estimation of the score function, and two other blocks estimating the inverses of the linear and nonlinear parts of the channel, (quasi-)optimally adapted using the estimated score functions. This paper is mainly concerned by the nonlinear part, for which we propose two parametric models, the first based on a polynomial model and the second on a neural network, while [14, 15] proposed non-parametric approaches.

Norbert Elias : una proposta d’intellectual portrait vint anys després de la seva mort

L’estiu de 1990 moria a Amsterdam un dels sociòlegs més preeminents del segle XX, l’alemany Norbert Elias. La seva vida i la seva obra, profundament imbricades, són un reflex de la complexitat, de les llums i les ombres del segle passat. Amb aquesta proposta de retrat intel·lectual pretenem per una banda commemorar l’efemèride dels 20 anys de la mort d’un sociòleg poc reconegut i poc emprat. I d’altra banda, oferir una introducció a un personatge i una obra que, per la seva magnitud i originalitat, mereixen estar entre les més rellevants de la sociologia. Com a fil conductor de l’article proposem una combinació de llocs físics i simbòlics que van marcar la seva trajectòria.

Les nou preguntes que Norbert Elias es va formular : debats, interrogants i pistes per a la sociologia del segle XXI

En aquest article dedicat a Norbert Elias en commemoració dels vint anys de la seva mort, proposem revisar, de manera resumida, la seva obra a partir dels interrogants ontològics, epistemològics i metodològics que Elias va encarar al llarg de la seva dilatada vida acadèmica i de recerca. L’article pretén ser, alhora, un punt i a part en el monogràfic, perquè en vol recollir alguns dels interrogants, debats i conclusions, com també tenir un paper introductori per a aquelles persones que, sense gaires coneixements previs sobre l’autor de Breslau, vulguin conèixer alguns dels trets definitoris de la seva trajectòria intel·lectual al llarg del segle XX. I, sobretot, quin llegat, en forma de grans preguntes, deixa per a la sociologia del segle XXI. L’article acaba amb una bibliografia escollida d’obres clau.

A non-linear VAD for noisy environments

This paper deals with non-linear transformations for improving the performance of an entropy-based voice activity detector (VAD). The idea to use a non-linear transformation has already been applied in the field of speech linear prediction, or linear predictive coding (LPC), based on source separation techniques, where a score function is added to classical equations in order to take into account the true distribution of the signal. We explore the possibility of estimating the entropy of frames after calculating its score function, instead of using original frames. We observe that if the signal is clean, the estimated entropy is essentially the same; if the signal is noisy, however, the frames transformed using the score function may give entropy that is different in voiced frames as compared to nonvoiced ones. Experimental evidence is given to show that this fact enables voice activity detection under high noise, where the simple entropy method fails.

Non-Linear and Non-Conventional Speech Processing: Alternative Techniques

This special issue aims to cover some problems related to non-linear and nonconventional speech processing. The origin of this volume is in the ISCA Tutorial and Research Workshop on Non-Linear Speech Processing, NOLISP’09, held at the Universitat de Vic (Catalonia, Spain) on June 25–27, 2009. The series of NOLISP workshops started in 2003 has become a biannual event whose aim is to discuss alternative techniques for speech processing that, in a sense, do not fit into mainstream approaches. A selected choice of papers based on the presentations delivered at NOLISP’09 has given rise to this issue of Cognitive Computation.

Quasi-Nonparametric Blind Inversion of Wiener Systems

An e cient procedure for the blind inversion of a nonlinear Wiener system is proposed. We proved that the problem can be expressed as a problem of blind source separation in nonlinear mixtures, for which a solution has been recently proposed. Based on a quasi-nonparametric relative gradient descent, the proposed algorithm can perform e ciently even in the presence of hard distortions.

La utilidad del Estado de Cambios en el Patrimonio Neto para los empresarios

Las cuentas anuales del actual Plan General de Contabilidad (PGC07) incluyen un nuevo documento contable: el Estado de Cgmbios en el Patrimonio Neto (ECPN) que deben presentar todas las empresas. En este trabajo se detalla la utilidad y razon de ser de este estado contable que pretende reflejar el resultado global, la riqueza y las garantras empresariales que presenta la empresa. Estas.informaciones son consecuencia del desplazamiento del objetivo de los estados financieros hacia la valoracion del patrimonio en detrimento del calculo del resultado.

L'Experiència autobiogràfica, l'art i el compromís ètic de l'escriptor: tres eixos de la narrativa de Maria Àngels Anglada

L’obra de M. Àngels Anglada constitueix un tramat complex de lectures i influències que s’interpreta a través de nivells de significació i d’interessos que s’entrecreuen. Com a pilar que sosté tot aquest corpus hi ha un gran bagatge clàssic i una vasta formació literària que, juntament amb l’experiència vital i el posicionament ètic de l’autora, proporcionen les claus de lectura dels seus llibres. Partint d’aquesta idea, l’article aprofundeix en els tres aspectes que considerem eixos fonamentals de l’obra de l’escriptora: els elements autobiogràfics, l’art i el compromís social.

EEG windowed statistical wavelet scoring for evaluation and discrimination of muscular artifacts

EEG recordings are usually corrupted by spurious extra-cerebral artifacts, which should be rejected or cleaned up by the practitioner. Since manual screening of human EEGs is inherently error prone and might induce experimental bias, automatic artifact detection is an issue of importance. Automatic artifact detection is the best guarantee for objective and clean results. We present a new approach, based on the time–frequency shape of muscular artifacts, to achieve reliable and automatic scoring. The impact of muscular activity on the signal can be evaluated using this methodology by placing emphasis on the analysis of EEG activity. The method is used to discriminate evoked potentials from several types of recorded muscular artifacts—with a sensitivity of 98.8% and a specificity of 92.2%. Automatic cleaning ofEEGdata are then successfully realized using this method, combined with independent component analysis. The outcome of the automatic cleaning is then compared with the Slepian multitaper spectrum based technique introduced by Delorme et al (2007 Neuroimage 34 1443–9).

Increasing stair climbing in a train station: effects of contextual variables and visibility

Accumulation of physical activity during daily living is a current public health target that is influenced by the layout of the built environment. This study reports how the layout of the environment may influence responsiveness to an intervention. Pedestrian choices (n = 41 717) between stairs and the adjacent escalators were monitored for seven weeks in a train station (Birmingham, UK). After a 3.5 week baseline period, a stair riser banner intervention to increase stair climbing was installed on two staircases adjacent to escalators and monitoring continued for a further 3.5 weeks. Logistic regression analyses revealed that the visibility of the intervention, defined as the area of visibility in the horizontal plane opposite to the direction of travel (termed the isovist) had a major effect on success of the intervention. Only the largest isovist produced an increase in stair climbing (isovist=77.6 m2, OR = 1.10, CIs 1.02-1.19; isovist=40.7 m2, OR = 0.98, CIs 0.91-1.06; isovist=53.2 m2, OR = 1.00, CIs 0.95-1.06). Additionally, stair climbing was more common during the morning rush hour (OR = 1.56, CIs 1.80-2.59) and at higher levels of pedestrian traffic volume (OR = 1.92, CIs 1.68-2.21). The layout of the intervention site can influence responsiveness to point-of-choice interventions. Changes to the design of train stations may maximize the choice of the stairs at the expense of the escalator by pedestrians leaving the station.

El proyecto ‘Ecosportech’ como ejemplo de universidad emprendedora. Una experiencia a base del ‘Problem Based Learning’

El objetivo de este artículo es presentar el proyecto EcoSPORTech, cuya finalidad es la creación de una empresa social con jóvenes para la realización de actividades deportivas/ocio en el medio natural, integrando las nuevas tecnologías. Este proyecto supone una colaboración interdisciplinaria dentro de la Universidad de Vic, entre las facultades de Empresa y Comunicación (FEC), la de Ciencias de la Salud y el Bienestar (FCSB) y la de Educación (FE) e integra un equipo de profesionales procedentes de los ámbitos de la empresa, el marketing, el periodismo, el deporte y la terapia ocupacional. Estos profesores formarán al grupo de jóvenes con los que se creará la empresa y dirigirán la misma. Esta empresa (cooperativa) se integra en el vivero de empresas sociales que se está creando en la Universidad de Vic.