Examinando por Autor "Lopez-Celani N."

Mostrando 1 - 2 de 2
  • 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
    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.