Examinando por Materia "ONDAS"

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  • Artículo de Publicación Periódica
    EEG waveform analysis of P300 ERP with applications to brain computer interfaces
    (2018-11) Ramele, Rodrigo; Villar, Ana Julia; Santos, Juan Miguel
    "The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition."
  • Proyecto final de Grado
    Evaluación del entorno de desarrollo Golang como herramienta para la detección de onda dícrota
    (2019-07-22) Boschetti, Marco; Mogni, Guido Matías; Madorno, Matías
    "El objetivo principal de este trabajo fue probar el leguaje de desarrollo GoLang en una situación concreta, buscando dar una solución a un problema real. GoLang, también conocido como Go, fue desarrollado por Google en 2007 con el objetivo de mejorar la productividad de los desarrolladores, destacándose por su manejo de conexión de redes de información y multi-procesos concurrentes."
  • Artículo de Publicación Periódica
    Histogram of gradient orientations of signal plots applied to P300 detection
    (2019-07) Ramele, Rodrigo; Villar, Ana Julia; Santos, Juan Miguel
    "The analysis of Electroencephalographic (EEG) signals is of ulterior importance to aid in the diagnosis of mental disease and to increase our understanding of the brain. Traditionally, clinical EEG has been analyzed in terms of temporal waveforms, looking at rhythms in spontaneous activity, subjectively identifying troughs and peaks in Event-Related Potentials (ERP), or by studying graphoelements in pathological sleep stages. Additionally, the discipline of Brain Computer Interfaces (BCI) requires new methods to decode patterns from non-invasive EEG signals. This field is developing alternative communication pathways to transmit volitional information from the Central Nervous System. The technology could potentially enhance the quality of life of patients affected by neurodegenerative disorders and other mental illness. This work mimics what electroencephalographers have been doing clinically, visually inspecting, and categorizing phenomena within the EEG by the extraction of features from images of signal plots. These features are constructed based on the calculation of histograms of oriented gradients from pixels around the signal plot. It aims to provide a new objective framework to analyze, characterize and classify EEG signal waveforms. The feasibility of the method is outlined by detecting the P300, an ERP elicited by the oddball paradigm of rare events, and implementing an offline P300-based BCI Speller. The validity of the proposal is shown by offline processing a public dataset of Amyotrophic Lateral Sclerosis (ALS) patients and an own dataset of healthy subjects."
  • Artículo de Publicación Periódica
    Quasi-analytical perturbation analysis of the generalized nonlinear Schrödinger equation
    (2019) Bonetti, Juan I.; Hernández, Santiago M.; Fierens, Pablo Ignacio; Temprana, Eduardo G.; Grosz, Diego
    "The Generalized Nonlinear Schrödinger Equation (GNLSE) finds several applications, especially in describing pulse propagation in nonlinear fiber optics. A well-known and thoroughly studied phenomenon in nonlinear wave propagation is that of modulation instability (MI). MI is approached as a weak perturbation to a pump and the analysis is based on preserving those terms linear on the perturbation and disregarding higher-order terms. In this sense, the linear MI analysis is relevant to the understanding of the onset of many other nonlinear phenomena, but its application is limited to the evolution of the perturbation over short distances. In this work, we propose quasi-analytical approximations to the propagation of a perturbation consisting of additive white noise that go beyond the linear modulation instability analysis. Moreover, we show these approximations to be in excellent agreement with numerical simulations and experimental measurements. "