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Classification of Motor Imagery EEG Signals with CSP Filtering Through Neural Networks Models

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Book cover Advances in Soft Computing (MICAI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11288))

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Abstract

The paper reports the development and evaluation of brain signals classifiers. The proposal consisted of three main stages: organization of EEG signals, feature extraction and execution of classification algorithms. The EEG signals used, represent four motor actions: Left Hand, Right Hand, Tongue and Foot movements; in the frame of the Motor Imagery Paradigm. These EEG signals were obtained from a database provided by the Technological University of Graz. From this dataset, only the EEG signals of two healthy subjects were used to carry out the proposed work. The feature extraction stage was carried out by applying an algorithm known as Common Spatial Pattern, in addition to the statistical method called Root Mean Square. The classification algorithms used were: K-Nearest Neighbors, Support Vector Machine, Multilayer Perceptron and Dendrite Morphological Neural Networks. This algorithms was evaluated with two studies. The first one aimed to evaluate the performance in the recognition between two classes of Motor Imagery tasks; Left Hand vs. Right Hand, Left Hand vs. Tongue, Left Hand vs. Foot, Right Hand vs. Tongue, Right Hand vs. Foot and Tongue vs. Foot. The second study aimed to employ the same algorithms in the recognition between four classes of Motor Imagery tasks; Subject 1 - \(93.9\% \pm 3.9\%\) and Subject 2 - \(68.7\% \pm 7\%\).

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Acknowledgements

We would like to express our sincere appreciation to the Instituto Politécnico Nacional and the Secretaria de Investigación y Posgrado for the economic support provided to carry out this research. This project was supported economically by SIP-IPN (numbers 20180730, 20180943 and 20180846) and CONACYT (65 Frontiers of Science).

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Authors and Affiliations

  1. Centro de Investigación en Computación – Instituto Politécnico Nacional, Av. Juan de Dios Bátiz and M. Othón de Mendizabal, 07738, Mexico City, Mexico

    Carlos Daniel Virgilio Gonzalez, Juan Humberto Sossa Azuela, Elsa Rubio Espino & Victor H. Ponce Ponce

  2. Tecnológico de Monterrey, Escuela de Ingeniería y Ciencias, Av. General Ramón Corona 2514, Zapopan, Jalisco, Mexico

    Juan Humberto Sossa Azuela

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  1. Carlos Daniel Virgilio Gonzalez
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  2. Juan Humberto Sossa Azuela
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  3. Elsa Rubio Espino
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  4. Victor H. Ponce Ponce
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Correspondence to Carlos Daniel Virgilio Gonzalez .

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Editors and Affiliations

  1. Instituto Politécnico Nacional, Mexico City, Mexico

    Ildar Batyrshin

  2. Universidad Panamericana, Mexico City, Mexico

    María de Lourdes Martínez-Villaseñor

  3. Faculty of Engineering, Universidad Panamericana, Mexico City, Mexico

    Hiram Eredín Ponce Espinosa

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Virgilio Gonzalez, C.D., Sossa Azuela, J.H., Rubio Espino, E., Ponce Ponce, V.H. (2018). Classification of Motor Imagery EEG Signals with CSP Filtering Through Neural Networks Models. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Soft Computing. MICAI 2018. Lecture Notes in Computer Science(), vol 11288. Springer, Cham. https://doi.org/10.1007/978-3-030-04491-6_10

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  • DOI: https://doi.org/10.1007/978-3-030-04491-6_10

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