8 research outputs found
Modeling and behavior of the simulation of electric propagation during deep brain stimulation
Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease. In the literature, there are a wide variety of mathematical and computational models to describe electric propagation during DBS; however unfortunately, there is no clarity about the reasons that justify the use of a specific model. In this work, we present a detailed mathematical formulation of the DBS electric propagation that supports the use of a model based on the Laplace Equation. Moreover, we performed DBS simulations for several geometrical models of the brain in order to determine whether geometry size, shape and ground location influence electric stimulation prediction by using the Finite Element Method (FEM). Theoretical and experimental analysis show, firstly, that under the correct assumptions, the Laplace equation is a suitable alternative to describe the electric propagation, and secondly, that geometrical structure, size and grounding of the head volume affect the magnitude of the electric potential, particularly for monopolar stimulation. Results show that, for monopolar stimulation, basic and more realistic models can differ more than 2900%
Assessment of sub-band division and energy computation techniques as fundamental stages for a neuro-feedback training system
The improvement of skills and cognitive abilities by means of neurofeedback training has been turned into an issue of interest in healthy population. These studies have shown a positive correlation between the neurofeedback training and the improvement of the cognitive skills of the people. Typically, in a neurofeedback system the first stage is the artifact remotion, the next stage is the separation of the EEG signal into frequency sub-bands and the last stage is the characterization of the sub-bands energy. Aiming to obtain the desired feedback, the mentioned stages have to be done as quickly and as accurately as possible. A mistake in these stages can lead to consequences as simple as a fruitless training, altering the desired cognitive improvement. In this paper, different techniques for sub-band separation and characterization are compared, aiming to find the most suitable techniques in order to be applied in a neurofeedback system, the techniques are collated according to the non-stationary behavior of the EEG signal and the stability (variability) of the outputs. Results show that the most stable and stationary combination is that determined by the EEG separation through IFFT and the energy calculation through the Teager-Kaiser, followed by its improved version. As conclusion, the IFFT for EEG sub-band separation, and Teager-Kaiser or its improvement for energy calculation, are recommend for a Neurofeedback system for cognitive improvement in healthy population. © 2016 IEEE
Selección de características orientada a sistemas de reconocimiento de granos maduros de café.
This work presents a comparison among feature selection and feature extraction
techniques in training of recognition systems of ripe (green and red) coffee bean.
The evaluated feature selection techniques use filter and wrapper criteria with
heuristic search algorithms. The cost functions are multivariate analysis of
variance and bayes classifier over gaussian distributions, respectively. We use
principal component analysis as a feature extraction technique. Furthermore, we
carry out a statistical data preprocessing, which is necessary for checking the
assumptions of the employed methods.En este trabajo se presenta una comparación de técnicas para la selección y
extracción de características en el entrenamiento de sistemas de reconocimiento
de frutos de café (verde y maduro). Las técnicas de selección evaluadas usan
criterios filtro y wrapper con algoritmos búsqueda heurística. Las funciones de
costo son análisis multivariado de varianza y clasificador bayesiano sobre
distribuciones gaussianas, respectivamente. Se utiliza el análisis de componentes
principales como técnica de extracción. Adicionalmente se realiza el
preprocesamiento estadístico de los datos, el cual es necesario para dar validez a
las suposiciones de las técnicas
Estimation of the neuromodulation parameters from the planned volume of tissue activated in deep brain stimulation
Deep brain stimulation (DBS) is a therapy with promissory results for the
treatment of movement disorders. It delivers electric stimulation via an electrode to a
specific target brain region. The spatial extent of neural response to this stimulation is
known as volume of tissue activated (VTA). Changes in stimulation parameters that control
VTA, such as amplitude, pulse width and electrode configuration can affect the effectiveness
of the DBS therapy. In this study, we develop a novel methodology for estimating suitable DBS
neuromodulation parameters, from planned VTA, that attempts to maximize the therapeutic
effects, and to minimize the adverse effects of DBS. For estimating the continuous outputs
(amplitude and pulse width), we use multi-output support vector regression, taking the
geometry of the VTA as input space. For estimating the electrode polarity configuration, we
perform several classification problems, also using support vector machines from the same
input space. Our methodology attains promising results for both the regression setting,
and for predicting electrode active contacts and their polarity. Combining biological neural
modeling techniques together with machine learning, we introduce a novel area of research
where parameters of neuromodulation in DBS can be tuned by manually specifying a desired
geometric volume
Image pattern recognition in big data: taxonomy and open challenges: survey
Image pattern recognition in the field of big data has gained increasing importance and attention from researchers and practitioners in many domains of science and technology. This paper focuses on the usage of image pattern recognition for big data applications. In this context, the taxonomy of image pattern recognition and big data is revealed. The applications of image pattern recognition for big data, including multimedia, biometrics, and biology/biomedical, are also highlighted. Moreover, the significance of using pattern-based feature reduction in big data is discussed, and machine-learning techniques in pattern recognition applications are presented. A comparison based on the objectives of the approaches is presented to underline the taxonomy. This paper provides a novel review in exploring image recognition approaches for big data, which can be used in future research