Bump time-frequency toolbox: a toolbox for time-frequency oscillatory bursts extraction in electrophysiological signals

Background: oscillatory activity, which can be separated in background and oscillatory burst pattern activities, is supposed to be representative of local synchronies of neural assemblies. Oscillatory burst events should consequently play a specific functional role, distinct from background EEG activity – especially for cognitive tasks (e.g. working memory tasks), binding mechanisms and perceptual dynamics (e.g. visual binding), or in clinical contexts (e.g. effects of brain disorders). However extracting oscillatory events in single trials, with a reliable and consistent method, is not a simple task. Results: in this work we propose a user-friendly stand-alone toolbox, which models in a reasonable time a bump time-frequency model from the wavelet representations of a set of signals. The software is provided with a Matlab toolbox which can compute wavelet representations before calling automatically the stand-alone application. Conclusion: The tool is publicly available as a freeware at the address: http:// www.bsp.brain.riken.jp/bumptoolbox/toolbox_home.html

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.

Comunidad literaria y deconstrucción. Entre el fin del relato y la archi-escritura: Blanchot, Kofman, Derrida

El presente ensayo se propone relacionar tres figuras del texto literario después de la deconstrucción de la comunidad: el fin del relato literario en La folie du jour y"L"Idylle" de Maurice Blanchot, la palabra sofocada [La parole soffoquées] de Sarah Kofman y la archi-escritura en el pensamiento de Jacques Derrida. Abordaremos la escritura como un gesto filosófico con la finalidad de pensar la interrelación de lo literario y lo filosófico en estos autores franceses de la segunda mitad del siglo xx.

Comunidad literaria y deconstrucción. Entre el fin del relato y la archi-escritura: Blanchot, Kofman, Derrida

El presente ensayo se propone relacionar tres figuras del texto literario después de la deconstrucción de la comunidad: el fin del relato literario en La folie du jour y"L"Idylle" de Maurice Blanchot, la palabra sofocada [La parole soffoquées] de Sarah Kofman y la archi-escritura en el pensamiento de Jacques Derrida. Abordaremos la escritura como un gesto filosófico con la finalidad de pensar la interrelación de lo literario y lo filosófico en estos autores franceses de la segunda mitad del siglo xx.

Improving the Quality of EEG Data in Patients With Alzheimers Disease Using ICA

Does Independent Component Analysis (ICA) denature EEG signals? We applied ICA to two groups of subjects (mild Alzheimer patients and control subjects). The aim of this study was to examine whether or not the ICA method can reduce both group di®erences and within-subject variability. We found that ICA diminished Leave-One- Out root mean square error (RMSE) of validation (from 0.32 to 0.28), indicative of the reduction of group di®erence. More interestingly, ICA reduced the inter-subject variability within each group (¾ = 2:54 in the ± range before ICA, ¾ = 1:56 after, Bartlett p = 0.046 after Bonfer- roni correction). Additionally, we present a method to limit the impact of human error (' 13:8%, with 75.6% inter-cleaner agreement) during ICA cleaning, and reduce human bias. These ¯ndings suggests the novel usefulness of ICA in clinical EEG in Alzheimer's disease for reduction of subject variability.

De la metodologia a la pràctica : anàlisi de les metodologies d'ensenyament musical i elaboració d'una proposta metodològica per a la pràctica docent en l'àmbit del llenguatge musical a les escoles de música

Aquest treball vol ser una revisió d'algunes de les principals metodologies desenvolupades al llarg del segle XX per l'ensenyament del llenguatge musical. Aquesta revisió serà completada amb una anàlisi comparada de les visions filosòfiques i els objectius didàctics de les diferents propostes, d'on extreure unes conclusions per a una possible nova proposta didàctica. Les metodologies que s'analitzen són les dels següents autors: Émile Jaques-Dalcroze, Carl Orff, Zoltán Kodály, Edgar Willems i Maurice Martenot.