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Erschienen in:
Fusing Dimension Reduction and Classification for Mining Interesting Frequent Patterns in Patients Data Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Recognizing Motor Imagery Tasks Using Deep Multi-Layer Perceptrons

verfasst von : Fernando Arce, Erik Zamora, Gerardo Hernández, Javier M. Antelis, Humberto Sossa

Erschienen in: Machine Learning and Data Mining in Pattern Recognition

Verlag: Springer International Publishing

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Abstract

A brain-computer interface provides individuals with a way to control a computer. However, most of these interfaces remain mostly utilized in research laboratories due to the absence of certainty and accuracy in the proposed systems. In this work, we acquired our own dataset from seven able-bodied subjects and used Deep Multi-Layer Perceptrons to classify motor imagery encephalography signals into binary (Rest vs Imagined and Left vs Right) and ternary classes (Rest vs Left vs Right). These Deep Multi-Layer Perceptrons were fed with power spectral features computed with the Welch’s averaged modified periodogram method. The proposed architectures outperformed the accuracy achieved by the state-of-the-art for classifying motor imagery bioelectrical brain signals obtaining 88.03%, 85.92% and 79.82%, respectively, and an enhancement of 11.68% on average over the commonly used Support Vector Machines.

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Metadaten
Titel
Recognizing Motor Imagery Tasks Using Deep Multi-Layer Perceptrons
verfasst von
Fernando Arce
Erik Zamora
Gerardo Hernández
Javier M. Antelis
Humberto Sossa
Copyright-Jahr
2018
Buch
Machine Learning and Data Mining in Pattern Recognition

Print ISBN: 978-3-319-96132-3

Electronic ISBN: 978-3-319-96133-0

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-96133-0_35