Classification of motor imagery EEG signals with CSP filtering through neural networks models

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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferencia

7 Citas (Scopus)

Resumen

© 2018, Springer Nature Switzerland AG. 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% ± 3.9% and Subject 2 - 68.7% ± 7%.
Idioma originalInglés estadounidense
Título de la publicación alojadaClassification of motor imagery EEG signals with CSP filtering through neural networks models
Páginas123-135
Número de páginas109
ISBN (versión digital)9783030044909
DOI
EstadoPublicada - 1 ene 2018
EventoLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duración: 1 ene 2019 → …

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen11288 LNAI
ISSN (versión impresa)0302-9743

Conferencia

ConferenciaLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Período1/01/19 → …

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