Artificial neural networks and common spatial patterns for the recognition of motor information from EEG signals

Carlos Daniel Virgilio Gonzalez, Juan Humberto Sossa Azuela, Javier M. Antelis

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

1 Scopus citations

Abstract

© 2018, Springer Nature Switzerland AG. This paper proposes the use of two models of neural networks (Multi Layer Perceptron and Dendrite Morphological Neural Network) for the recognition of voluntary movements from electroencephalographic (EEG) signals. The proposal consisted of three main stages: organization of EEG signals, feature extraction and execution of classification algorithms. The EEG signals were recorded from eighteen healthy subjects performing self-paced reaching movements. Three classification scenarios were evaluated in each participant: Relax versus Intention, Relax versus Execution and Intention versus Execution. The feature extraction stage was carried out by applying an algorithm known as Common Spatial Pattern, in addition to the statistical methods called Root Mean Square, Variance, Standard Deviation and Mean. The results showed that the models of neural networks provided decoding accuracies above chance level, whereby, it is able to detect a movement prior its execution. On the basis of these results, the neural networks are a powerful promising classification technique that can be used to enhance performance in the recognition of motor tasks for BCI systems based on electroencephalographic signals.
Original languageAmerican English
Title of host publicationArtificial neural networks and common spatial patterns for the recognition of motor information from EEG signals
Pages110-122
Number of pages97
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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