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Feature Selection in Electroencephalographic Signals Using a Multicriteria Decision Analysis Method

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Innovation and Competitiveness in Industry 4.0 Based on Intelligent Systems

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

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Abstract

In recent years, industry 4.0 has promoted the rise of new technologies and devices that generate and collect data both in industry and in everyday life. As a result, there is a new challenge of creating robust tools that facilitate the analysis of this information. Feature selection allows the efficient extraction of features that describe objects or phenomena, by removing distractors and redundant information. Thus, classification and decision-making models can be created with stable representations. In this chapter, a set of typical testors was extracted from a database with motor imagery EEG signals employing the testor theory, an approach to feature selection. A typical testor is the smallest possible combination of features that allow objects to be differentiated belonging to different classes. The purpose of this chapter is to select the typical testor with the best performance in an ANN classifier employing TOPSIS, a multicriteria decision analysis method.

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Authors and Affiliations

  1. Benemérita Universidad Autónoma de Aguascalientes, Aguascalientes, Mexico

    Alexis Edmundo Gallegos Acosta, María Dolores Torres Soto, Aurora Torres Soto & Eunice Esther Ponce de León Sentí

  2. Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Mexico

    Carlos Alberto Ochoa Ortiz Zezzatti

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  1. Alexis Edmundo Gallegos Acosta
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  2. María Dolores Torres Soto
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  3. Aurora Torres Soto
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  4. Eunice Esther Ponce de León Sentí
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  5. Carlos Alberto Ochoa Ortiz Zezzatti
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Correspondence to Alexis Edmundo Gallegos Acosta .

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  1. Industrial Engineering and Manufacturing, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chihuahua, Mexico

    Luis Carlos Méndez-González

  2. Industrial Engineering and Manufacturing, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chihuahua, Mexico

    Luis Alberto Rodríguez-Picón

  3. Industrial Engineering and Manufacturing, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chihuahua, Mexico

    Iván Juan Carlos Pérez Olguín

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Gallegos Acosta, A.E., Torres Soto, M.D., Torres Soto, A., Ponce de León Sentí, E.E., Ochoa Ortiz Zezzatti, C.A. (2023). Feature Selection in Electroencephalographic Signals Using a Multicriteria Decision Analysis Method. In: Méndez-González, L.C., Rodríguez-Picón, L.A., Pérez Olguín, I.J.C. (eds) Innovation and Competitiveness in Industry 4.0 Based on Intelligent Systems. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-29775-5_14

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  • DOI: https://doi.org/10.1007/978-3-031-29775-5_14

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