Multipurpose Methodology For The Design Of Human-Machine Interfaces Using Electroencephalographic Signals
In this project, we propose to develop an alternative methodology that allows for a better characterization and classification of EEG signals for application purposes in BCI systems. To do so, we firslty perfom a comparative study of the current state-of-the-art characterization methods. Secondly, we develop a computational model for data representation and classification in order to determine a system reaching a good trade-off between efficiency and computational cost. Thirdly, we implement a prototype BCI test system along with its corresponding protocol of use. A a higher level, this project is presented as an applied research and corresponds to the initial stage to consolidate a research program in Neuroscience in order to perform semantic interpretation of brain waves for various purposes, which in turn will be part of the Colombian-Ecuadorian Neuro-center (Still an ongoing big project).
This project is aimed at developing a methodology for the visualization of multidimensional datathrough an approach based on the use of methods of reduction of dimension and models of human-computer interaction. In particular, the study is oriented to explore the interactive combinationof dimensionality reduction (DR) methods in order to expand the embedded spaces that can beobtained from a database of input, with the aim of giving the user the possibility to choose therepresentation of the data that better meet their needs. During the development of this researchproject, several interaction methods are proposed and two forms of combination of RD methodswere taken into account, the first one combining the resulting embedded spaces and the second onethrough the use of kernel approaches. Finally, based on the results obtained, the methodology isapplied to the creation of a data visualization tool, which incorporates: i) interaction models, ii)a mixture of RD methods, iii) traditional visualization techniques (scatter diagrams and parallelcoordinate diagram). Additionally, with the purpose of generating a dynamic interaction (changesin real time), the algorithm of locally linear sub-matrices is implemented to carry out the dimensionreduction process at lower computational cost. It is important to highlight that the whole tool isdeveloped under scalability and modularity criteria, so that future works (improvements) can beeasily incorporated into it.
This project is intended to build a structured database of respiratory-disease diagnosed patients via spirometry data. Also, as another main goal thereof, a comparative study on pattern recognition techniques applied on spirometry records will be performed, which is aimed at finding those techniques achieving a good compromise between effectiveness, computational cost, and interpretability of the physiological concept.
The aim of this research is to propose a modular design methodology for planning and assembly of industrial installations and production systems by using computer simulation as a decision making tool. Particularly, methodology is to be tested over a special coffee production plant, located in Nariño-Colombia. Design and simulation results validate the suitability of the proposed methodology.
This work project is about the design of a low-cost biofeedback devices. Device designing involves mainly two building blocks: The first one is concerned on the signal acquisition and analog filtering. Meanwhile, the second one is the estimation of physiological variable indicators and visual feedback implemented in a circuit platform. The goal is reaching great usability and portability while involving low design costs.
The objective of this emergent research area is to link the field of dimensionality reduction (DR) with that of information visualization (IV), in order to harness the special properties of the latter within DR frameworks. In particular, the properties of controllability and interactivity are of interest, which should make the DR outcomes significantly more understandable and tractable for the (no-necessarily-expert) user. These two properties allow the user to have freedom to select the best way for representing data.
This project aims to to design a methodology of visual analysis of information in Big Data using principles of visualization and interactivity in conjunction with techniques of dimensionality reduction. The premise of this research is the possibility of performing the interaction with the user to select and combine methods of reduction of dimension. Then, this project involves not only the design of an interaction model and interface, but also the formulation of a generalized method of dimensionility reduction that allows for an intuitive selection and combination of methods. A transversal aspect of all the stages of the design of the visual analysis methodology is the computational cost that hinders the goal of the methodology being really interactive, that is, in real time. That said, in this project, it is mandatory to carry out all the designs and test implementations in low-computational-cost environments.