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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....
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2014
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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. |
format | Online Article Text |
id | pubmed-4239925 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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