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    Initial Approach for Construction of a Public Dataset for Emotional Analysis Through Brain-Computer Interfaces and Second Language Platforms

    Autor: 
    Restrepo Rodríguez, Andrés Ovidio
    ;
    Ariza Riaño, Maddyzeth
    ;
    Gaona-García, Paulo Alonso
    ;
    Montenegro Marin, Carlos Enrique
    ;
    González-Crespo, Rubén
    Fecha: 
    2023
    Palabra clave: 
    measurement; brain; annotations; time series analysis; data visualization; machine learning; data collection; Scopus(2)
    Revista / editorial: 
    IEEE Global Engineering Education Conference, EDUCON
    Citación: 
    A. O. R. Rodríguez, M. A. Riaño, P. A. G. Garía, C. E. M. Marín and R. G. Crespo, "Initial Approach for Construction of a Public Dataset for Emotional Analysis Through Brain-Computer Interfaces and Second Language Platforms," 2023 IEEE Global Engineering Education Conference (EDUCON), Kuwait, Kuwait, 2023, pp. 1-7, doi: 10.1109/EDUCON54358.2023.10125173.
    Tipo de Ítem: 
    conferenceObject
    URI: 
    https://reunir.unir.net/handle/123456789/16079
    DOI: 
    https://doi.org/10.1109/EDUCON54358.2023.10125173
    Dirección web: 
    https://ieeexplore.ieee.org/document/10125173
    Resumen:
    In recent years, the deepening of the role of emotions in education has increased, since it has been shown that emotions influence what we learn and retain, in different areas such as learning a second language. Likewise, the analysis of emotional data in learning environments has been implemented through various methods, such as the collection of vital signs, where brain-computer interfaces provide a means to capture emotional metrics from brain activity. Furthermore, public data sets in this domain are scarce. Consequently, this work presents the initial approach to generate an Emotional Dataset, for emotional analysis based on the collection of emotions, through an Emotiv Insight as a Brain Computer Interface. Also, through the Babbel application for learning second languages. As a result of this study, it will be possible to obtain the general and projected structure for the resulting data set, based on the generated approach.
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