Cristhian Potes

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Previous studies demonstrated that brain signals encode information about specific features of simple auditory stimuli or of general aspects of natural auditory stimuli. How brain signals represent the time course of specific features in natural auditory stimuli is not well understood. In this study, we show in eight human subjects that signals recorded(More)
Neuroimaging approaches have implicated multiple brain sites in musical perception, including the posterior part of the superior temporal gyrus and adjacent perisylvian areas. However, the detailed spatial and temporal relationship of neural signals that support auditory processing is largely unknown. In this study, we applied a novel inter-subject analysis(More)
Listening to music moves our minds and moods, stirring interest in its neural underpinnings. A multitude of compositional features drives the appeal of natural music. How such original music, where a composer's opus is not manipulated for experimental purposes, engages a listener's brain has not been studied until recently. Here, we report an in-depth(More)
Many real-world datasets suffer from missing or incomplete data. In the healthcare setting, for example, certain patient measurement parameters, such as vitals and/or lab values, may be missing due to insufficient monitoring. When present, however, these features could be highly discriminative in predicting aspects of patient state. Therefore, it is(More)
Planning for epilepsy surgery depends substantially on the localization of brain cortical areas responsible for sensory, motor, or cognitive functions, clinically also known as eloquent cortex. In this paper, we present the novel software package 'cortiQ' that allows clinicians to localize eloquent cortex, thus providing a safe margin for surgical resection(More)
The goal of the 2016 PhysioNet/CinC Challenge is the development of an algorithm to classify normal/abnormal heart sounds. A total of 124 time-frequency features were extracted from the phonocardiogram (PCG) and input to a variant of the AdaBoost classifier. A second classifier using convolutional neural network (CNN) was trained using PCGs cardiac cycles(More)
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