GREC CLÀSSIC : curs d'autoaprenentatge introductori

GREC CLÀSSIC. Curs d’autoaprenentatge introductori. Dos anys. El curs consta de tretze lliçons i d’una gramàtica estructurada en quatre blocs: 1. Alfabet i diacrítics, fenòmens fonètics. 2. Morfologia nominal. 3. Morfologia verbal. 4. Infinitius i participis. Sintaxi de les oracions. L’estructura de les lliçons, excepte la primera que inclou l’alfabet, combina qüestions de morfologia nominal i verbal o de morfologia i sintaxi, tal com acostumen a fer els mètodes d’aprenentatge de les llengües modernes. Cada lliçó formula els seus objectius específics, desenvolupa la seva part de continguts i conclou amb uns exercicis pràctics d’autocorrecció. La Gramàtica, per la seva banda, està organitzada com un manual elemental de llengua grega on la persona que segueixi el curs pot ampliar la seva formació i consultar els dubtes. Parts complementàries: presentació on es formulen els objectius, la metodologia i les instruccions concretes per a seguir el curs; terminologia on s’ordenen alfabèticament els conceptes gramaticals emprats en el curs; avaluació final per tal que, més enllà dels exercicis de cada lliçó, hom pugui comprovar si ha assolit els coneixements previstos o si, en cas de no arribar-hi, ha de reforçar algunes lliçons o parts de les mateixes abans de tornar a fer l’avaluació; lèxic, ordenat alfabèticament per tal que hom pugui conèixer el significat dels mots emprats en el curs sense necessitat de consultar un diccionari. A la part d’avaluació hi ha també una enquesta per tal que les persones que segueixin el curs en facin una valoració i ens permetin corregir els seus dèficits o mancances. El projecte 2007MQD00178 ha continuat ampliant els dossiers electrònics, els exercicis autoavaluatius del seu web www.ub.edu/filologiagrega/electra i ha dedicat una part important de la seva tasca a elaborar els continguts i els programes de les assignatures dels dos primers cursos del nou grau de Filologia Clàssica.

Correlation postprocessing-based method for the detection of de focused images

We present a method to detect patterns in defocused scenes by means of a joint transform correlator. We describe analytically the correlation plane, and we also introduce an original procedure to recognize the target by postprocessing the correlation plane. The performance of the methodology when the defocused images are corrupted by additive noise is also considered.

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).

EEG Windowed statitical wavelet deviation for estimation of muscular artifacts

Electroencephalographic (EEG) recordings are, most of the times, corrupted by spurious artifacts, which should be rejected or cleaned by the practitioner. As human scalp EEG screening is error-prone, automatic artifact detection is an issue of capital importance, to ensure objective and reliable results. In this paper we propose a new approach for discrimination of muscular activity in the human scalp quantitative EEG (QEEG), based on the time-frequency shape analysis. The impact of the muscular activity on the EEG can be evaluated from this methodology. We present an application of this scoring as a preprocessing step for EEG signal analysis, in order to evaluate the amount of muscular activity for two set of EEG recordings for dementia patients with early stage of Alzheimer’s disease and control age-matched subjects.

ICA Cleaning procedure for EEG signals analysis: application to Alzheimer's disease detection

To develop systems in order to detect Alzheimer’s disease we want to use EEG signals. Available database is raw, so the first step must be to clean signals properly. We propose a new way of ICA cleaning on a database recorded from patients with Alzheimer's disease (mildAD, early stage). Two researchers visually inspected all the signals (EEG channels), and each recording's least corrupted (artefact-clean) continuous 20 sec interval were chosen for the analysis. Each trial was then decomposed using ICA. Sources were ordered using a kurtosis measure, and the researchers cleared up to seven sources per trial corresponding to artefacts (eye movements, EMG corruption, EKG, etc), using three criteria: (i) Isolated source on the scalp (only a few electrodes contribute to the source), (ii) Abnormal wave shape (drifts, eye blinks, sharp waves, etc.), (iii) Source of abnormally high amplitude ( �100 �V). We then evaluated the outcome of this cleaning by means of the classification of patients using multilayer perceptron neural networks. Results are very satisfactory and performance is increased from 50.9% to 73.1% correctly classified data using ICA cleaning procedure.