Examinando por Autor "Villa L.F."

Mostrando 1 - 7 de 7
  • No hay miniatura disponible
    Ítem
    Cerebral Cortex Atlas of Emotional States Through EEG Processing
    (SPRINGER, 2019-10-14) Gómez A.; Quintero O.L.; Lopez-Celani N.; Villa L.F.; Universidad EAFIT. Escuela de Ciencias; Modelado Matemático
    This paper addresses the cerebral cortex maps construction from EEG signals getting an information simplification method for an emotional state phenomenon description. Bi-dimensional density distribution of main signal features are identified and a comparison to a previous approach is presented. Feature extraction scheme is performed via windowed EEG signals Stationary Wavelet Transform with the Daubechies Family (1–10); nine temporal and spectral descriptors are computed from the decomposed signal. Recursive feature selection method based on training a Random forest classifier using a one-vs-all scheme with the full features space, then a ranking procedure via gini importance, eliminating the bottom features and restarting the entire process over the new subset. Stopping criteria is the maximum accuracy. The main contribution is the analysis of the resulting subset features as a proxy for cerebral cortex maps looking for the cognitive processes understanding from surface signals. Identifying the common location of different emotional states in the central and frontal lobes, allowing to be strong parietal and temporal lobes differentiators for different emotions. © 2020, Springer Nature Switzerland AG.
  • No hay miniatura disponible
    Ítem
    Emotion Recognition from EEG and Facial Expressions: A Multimodal Approach
    (Institute of Electrical and Electronics Engineers Inc., 2018-01-01) Chaparro V.; Gomez A.; Salgado A.; Quintero O.L.; Lopez N.; Villa L.F.; Chaparro V.; Gomez A.; Salgado A.; Quintero O.L.; Lopez N.; Villa L.F.; Universidad EAFIT. Departamento de Ciencias; Modelado Matemático
    The understanding of a psychological phenomena such as emotion is of paramount importance for psychologists, since it allows to recognize a pathology and to prescribe a due treatment for a patient. While approaching this problem, mathematicians and computational science engineers have proposed different unimodal techniques for emotion recognition from voice, electroencephalography, facial expression, and physiological data. It is also well known that identifying emotions is a multimodal process. The main goal in this work is to train a computer to do so. In this paper we will present our first approach to a multimodal emotion recognition via data fusion of Electroencephalography and facial expressions. The selected strategy was a feature-level fusion of both Electroencephalography and facial microexpressions, and the classification schemes used were a neural network model and a random forest classifier. Experimental set up was out with the balanced multimodal database MAHNOB-HCI. Results are promising compared to results from other authors with a 97% of accuracy. The feature-level fusion approach used in this work improves our unimodal techniques up to 12% per emotion. Therefore, we may conclude that our simple but effective approach improves the overall results of accuracy. © 2018 IEEE.
  • No hay miniatura disponible
    Ítem
    Emotional Networked maps from EEG signals
    (Institute of Electrical and Electronics Engineers Inc., 2020-01-01) Gomez A.; Quintero O.L.; Lopez-Celani N.; Villa L.F.; Gomez A.; Quintero O.L.; Lopez-Celani N.; Villa L.F.; Universidad EAFIT. Departamento de Ciencias; Modelado Matemático
    The EEG has showed that contains relevant information about recognition of emotional states. It is important to analyze the EEG signals to understand the emotional states not only from a time series approach but also determining the importance of the generating process of these signals, the location of electrodes and the relationship between the EEG signals. From the EEG signals of each emotional state, a functional connectivity measurement was used to construct adjacency matrices: lagged phase synchronization (LPS), averaging adjacency matrices we built a prototype network for each emotion. Based on these networks, we extracted a set node features seeking to understand their behavior and the relationship between them. We found through the strength and degree, the group of representative electrodes for each emotional state, finding differences from intensity of measurement and the spatial location of these electrodes. In addition, analyzing the cluster coefficient, degree, and strength, we find differences between the networks from the spatial patterns associated with the electrodes with the highest coefficient. This analysis can also gain evidence from the connectivity elements shared between emotional states, allowing to cluster emotions and concluding about the relationship of emotions from EEG perspective. © 2020 IEEE.
  • No hay miniatura disponible
    Ítem
    An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data
    (SPRINGER, 2019-10-01) Ariza-Jiménez L.; Pinel N.; Villa L.F.; Quintero O.L.; Universidad EAFIT. Escuela de Ciencias; Modelado Matemático
    Unsupervised learning methods are commonly used to perform the non-trivial task of uncovering structure in biological data. However, conventional approaches rely on methods that make assumptions about data distribution and reduce the dimensionality of the input data. Here we propose the incorporation of entropy related measures into the process of constructing graph-based representations for biological datasets in order to uncover their inner structure. Experimental results demonstrated the potential of the proposed entropy-based graph data representation to cope with biological applications related to unsupervised learning problems, such as metagenomic binning and neuronal spike sorting, in which it is necessary to organize data into unknown and meaningful groups. © 2020, Springer Nature Switzerland AG.
  • No hay miniatura disponible
    Ítem
    An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data
    (SPRINGER, 2019-10-01) Ariza-Jiménez L.; Pinel N.; Villa L.F.; Quintero O.L.; Universidad EAFIT. Departamento de Ciencias; Biodiversidad, Evolución y Conservación
    Unsupervised learning methods are commonly used to perform the non-trivial task of uncovering structure in biological data. However, conventional approaches rely on methods that make assumptions about data distribution and reduce the dimensionality of the input data. Here we propose the incorporation of entropy related measures into the process of constructing graph-based representations for biological datasets in order to uncover their inner structure. Experimental results demonstrated the potential of the proposed entropy-based graph data representation to cope with biological applications related to unsupervised learning problems, such as metagenomic binning and neuronal spike sorting, in which it is necessary to organize data into unknown and meaningful groups. © 2020, Springer Nature Switzerland AG.
  • No hay miniatura disponible
    Ítem
    An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data
    (SPRINGER, 2019-10-01) Ariza-Jiménez L.; Pinel N.; Villa L.F.; Quintero O.L.; Universidad EAFIT. Departamento de Ciencias; Ciencias Biológicas y Bioprocesos (CIBIOP)
    Unsupervised learning methods are commonly used to perform the non-trivial task of uncovering structure in biological data. However, conventional approaches rely on methods that make assumptions about data distribution and reduce the dimensionality of the input data. Here we propose the incorporation of entropy related measures into the process of constructing graph-based representations for biological datasets in order to uncover their inner structure. Experimental results demonstrated the potential of the proposed entropy-based graph data representation to cope with biological applications related to unsupervised learning problems, such as metagenomic binning and neuronal spike sorting, in which it is necessary to organize data into unknown and meaningful groups. © 2020, Springer Nature Switzerland AG.
  • No hay miniatura disponible
    Ítem
    Memberships Networks for High-Dimensional Fuzzy Clustering Visualization
    (Springer Verlag, 2019-01-01) Ariza-Jiménez L.; Villa L.F.; Quintero O.L.; Universidad EAFIT. Escuela de Ciencias; Modelado Matemático
    Visualizing the cluster structure of high-dimensional data is a non-trivial task that must be able to deal with the large dimensionality of the input data. Unlike hard clustering structures, visualization of fuzzy clusterings is not as straightforward because soft clustering algorithms yield more complex clustering structures. Here is introduced the concept of membership networks, an undirected weighted network constructed based on the fuzzy partition matrix that represents a fuzzy clustering. This simple network-based method allows understanding visually how elements involved in this kind of complex data clustering structures interact with each other, without relying on a visualization of the input data themselves. Experiment results demonstrated the usefulness of the proposed method for the exploration and analysis of clustering structures on the Iris flower data set and two large and unlabeled financial datasets, which describes the financial profile of customers of a local bank. © 2019, Springer Nature Switzerland AG.