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EEG Signal Analysis Using PCA and Logistic Regression

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XXVI Brazilian Congress on Biomedical Engineering

Part of the book series: IFMBE Proceedings ((IFMBE,volume 70/2))

Abstract

The analysis of brain signals, known as EEG, is a field of great interest, especially in rehabilitation medicine. For this area, the development of Brain Computer Interface (BCI) systems is of great value since it offers a new channel of communication between the individual and external systems through the analysis of brain signals. This work aims to analyze brain signals used in Brain Computer Interface systems using one of the most widespread techniques for such, Principal Component Analysis (PCA). An online database (Graz data set B) containing EEG signals of motor imagery activity of left and right hand was used. These signals were analyzed and their characteristics were extracted using PCA. To identify them according to the motor imagery performed, these characteristics were classified using a classification algorithm, Logistic Regression.

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Authors and Affiliations

  1. Electrical Engineering Department, Federal University of Sergipe, Marechal Rondon, Jardim Rosa Elze, São Cristóvão, SE, 49100-000, Brazil

    Celine F. C. Soeiro

Authors
  1. Celine F. C. Soeiro
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Correspondence to Celine F. C. Soeiro .

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Editors and Affiliations

  1. INMETRO-Instituto Nacional de Metrologia, Qualidade e Tecnologia, Duque de Caxias, Rio de Janeiro, Brazil

    Rodrigo Costa-Felix

  2. Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    João Carlos Machado

  3. INMETRO-Instituto Nacional de Metrologia, Qualidade e Tecnologia, Duque de Caxias, Rio de Janeiro, Brazil

    André Victor Alvarenga

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Soeiro, C.F.C. (2019). EEG Signal Analysis Using PCA and Logistic Regression. In: Costa-Felix, R., Machado, J., Alvarenga, A. (eds) XXVI Brazilian Congress on Biomedical Engineering. IFMBE Proceedings, vol 70/2. Springer, Singapore. https://doi.org/10.1007/978-981-13-2517-5_27

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  • DOI: https://doi.org/10.1007/978-981-13-2517-5_27

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2516-8

  • Online ISBN: 978-981-13-2517-5

  • eBook Packages: EngineeringEngineering (R0)

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