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EEG recordings are often contaminated by environmental noise and physiological factors commonly known as artifacts, which affect the quality of the signal. This paper presents an automatic method to classify between artifactual and neural components in EEG signals using an Independent Component Analysis (ICA) and a Support Vector Machine. With the resultant model, we obtained a classification accuracy...
This work presents a dimensionality reduction (DR) framework that enables users to perform either the selection or mixture of DR methods by means of an interactive model, here named Geo-Desic approach. Such a model consists of linear combination of kernel-based representations of DR methods, wherein the corresponding coefficients are related to coordinated latitude and longitude inside of the world...
Wiener systems represent a linear time invariant (LTI) system followed by a static nonlinearity. The identification of these systems has been a research problem for a long time as it is not a trivial task. A new methodology for identifying Wiener systems is proposed in this paper. The proposed method is a combination of well known techniques, namely the Best Linear Approximation (BLA) from the system...
Artificial Immune Systems are nature inspired techniques which have been applied with success to several areas. The Clonal Selection Algorithm (CLONALG) is one of the most used immune inspired techniques for optimization. Similarly to other metaheuristics, CLONALG requires a large number of objective function evaluations making it impracticable when the objective function is computationally expensive...
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