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Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals

This paper presents a methodology to detect the intention to make a reaching movement with the arm in healthy subjects before the movement actually starts. This is done by measuring brain activity through electroencephalographic (EEG) signals that are registered by electrodes placed over the scalp....

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Detalles Bibliográficos
Autores principales: Planelles, Daniel, Hortal, Enrique, Costa, Álvaro, Úbeda, Andrés, Iáñez, Eduardo, Azorín, José M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4239925/
https://www.ncbi.nlm.nih.gov/pubmed/25268915
http://dx.doi.org/10.3390/s141018172
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author Planelles, Daniel
Hortal, Enrique
Costa, Álvaro
Úbeda, Andrés
Iáñez, Eduardo
Azorín, José M.
author_facet Planelles, Daniel
Hortal, Enrique
Costa, Álvaro
Úbeda, Andrés
Iáñez, Eduardo
Azorín, José M.
author_sort Planelles, Daniel
collection PubMed
description This paper presents a methodology to detect the intention to make a reaching movement with the arm in healthy subjects before the movement actually starts. This is done by measuring brain activity through electroencephalographic (EEG) signals that are registered by electrodes placed over the scalp. The preparation and performance of an arm movement generate a phenomenon called event-related desynchronization (ERD) in the mu and beta frequency bands. A novel methodology to characterize this cognitive process based on three sums of power spectral frequencies involved in ERD is presented. The main objective of this paper is to set the benchmark for classifiers and to choose the most convenient. The best results are obtained using an SVM classifier with around 72% accuracy. This classifier will be used in further research to generate the control commands to move a robotic exoskeleton that helps people suffering from motor disabilities to perform the movement. The final aim is that this brain-controlled robotic exoskeleton improves the current rehabilitation processes of disabled people.
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spelling pubmed-42399252014-11-21 Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals Planelles, Daniel Hortal, Enrique Costa, Álvaro Úbeda, Andrés Iáñez, Eduardo Azorín, José M. Sensors (Basel) Article This paper presents a methodology to detect the intention to make a reaching movement with the arm in healthy subjects before the movement actually starts. This is done by measuring brain activity through electroencephalographic (EEG) signals that are registered by electrodes placed over the scalp. The preparation and performance of an arm movement generate a phenomenon called event-related desynchronization (ERD) in the mu and beta frequency bands. A novel methodology to characterize this cognitive process based on three sums of power spectral frequencies involved in ERD is presented. The main objective of this paper is to set the benchmark for classifiers and to choose the most convenient. The best results are obtained using an SVM classifier with around 72% accuracy. This classifier will be used in further research to generate the control commands to move a robotic exoskeleton that helps people suffering from motor disabilities to perform the movement. The final aim is that this brain-controlled robotic exoskeleton improves the current rehabilitation processes of disabled people. MDPI 2014-09-29 /pmc/articles/PMC4239925/ /pubmed/25268915 http://dx.doi.org/10.3390/s141018172 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Planelles, Daniel
Hortal, Enrique
Costa, Álvaro
Úbeda, Andrés
Iáñez, Eduardo
Azorín, José M.
Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals
title Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals
title_full Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals
title_fullStr Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals
title_full_unstemmed Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals
title_short Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals
title_sort evaluating classifiers to detect arm movement intention from eeg signals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4239925/
https://www.ncbi.nlm.nih.gov/pubmed/25268915
http://dx.doi.org/10.3390/s141018172
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