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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

© 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%.
Original languageAmerican English
Title of host publicationClassification of motor imagery EEG signals with CSP filtering through neural networks models
Pages123-135
Number of pages109
ISBN (Electronic)9783030044909
DOIs
StatePublished - 1 Jan 2018
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2019 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11288 LNAI
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/19 → …

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