EEG model stability and online decoding of attentional demand during gait using gamma band features

Rehabilitation therapies are evolving oriented to improve their performances in terms of functional recovery. To achieve such recovery, the patients’ involvement is an important factor that correlates with the plastic properties of the brain. By evaluating electroencephalographic signals, it is poss...

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Authors: Costa García, Álvaro, Láñez, E., Del Ama, A.J., Gil Agudo, A., Azorín Poveda, José María
Format: article
Publication Date:2019
Country:España
Institution:Universidad Miguel Hernández de Elche
Repository:REDIUMH. Depósito Digital de la UMH
OAI Identifier:oai:dspace.umh.es:11000/6014
Online Access:http://hdl.handle.net/11000/6014
Access Level:Open access
Keyword:Attention level
Gait
EEG
Online
62 - Ingeniería. Tecnología
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oai_identifier_str oai:dspace.umh.es:11000/6014
network_acronym_str ES
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repository_id_str
spelling EEG model stability and online decoding of attentional demand during gait using gamma band featuresCosta García, ÁlvaroLáñez, E.Del Ama, A.J.Gil Agudo, A.Azorín Poveda, José MaríaAttention levelGaitEEGOnline62 - Ingeniería. TecnologíaRehabilitation therapies are evolving oriented to improve their performances in terms of functional recovery. To achieve such recovery, the patients’ involvement is an important factor that correlates with the plastic properties of the brain. By evaluating electroencephalographic signals, it is possible to modify, in real time, the parameters of the rehabilitation according to the patients’ cognitive state. In this paper, an online brain–machine interface to measure the attention level during gait is presented. The system is based on the measurement of selective attention mechanisms manifested as power synchronization and desynchronization in the gamma band. A Linear Discriminant Analysis classifier is used to provide an attention index between 0 and 1 in real time. Robust techniques for artifact rejection and signal standardization are used in order to deal with the problems associated to the measurement of cortical signals during walking. The final interface is validated with 4 incomplete Spinal Cord Injury patients and 4 healthy participants. The system shows an average success rate of 68.1% in the classification of 3 attention levels and a stable behavior of these results during timeThis research has been funded by the Commission of the European Union under the BioMot project – Smart Wearable Robots with Bioinspired Sensory-Motor Skills (Grant Agreement number IFP7-ICT- 2013-10-611695)and by the Spanish Ministry of Science, Innovation and Universities, the Spanish State Agency of Research, and the Commission of the European Union through the European Regional Development Fund. under the Walk project - Controlling lower-limb exoskeletons by means of brain-machine interfaces to assist people with walking disabilities (Grant Agreement number RTI2018-096677-B-I00).Departamentos de la UMH::Ingeniería de Sistemas y Automática2020202020192020info:eu-repo/semantics/articleapplication/pdf37application/pdfhttp://hdl.handle.net/11000/6014reponame:REDIUMH. Depósito Digital de la UMHinstname:Universidad Miguel Hernández de ElcheIngléshttp://dx.doi.org/10.1016/j.neucom.2019.06.021info:eu-repo/semantics/openAccessoai:dspace.umh.es:11000/60142026-05-27T13:36:21Z
dc.title.none.fl_str_mv EEG model stability and online decoding of attentional demand during gait using gamma band features
title EEG model stability and online decoding of attentional demand during gait using gamma band features
spellingShingle EEG model stability and online decoding of attentional demand during gait using gamma band features
Costa García, Álvaro
Attention level
Gait
EEG
Online
62 - Ingeniería. Tecnología
title_short EEG model stability and online decoding of attentional demand during gait using gamma band features
title_full EEG model stability and online decoding of attentional demand during gait using gamma band features
title_fullStr EEG model stability and online decoding of attentional demand during gait using gamma band features
title_full_unstemmed EEG model stability and online decoding of attentional demand during gait using gamma band features
title_sort EEG model stability and online decoding of attentional demand during gait using gamma band features
dc.creator.none.fl_str_mv Costa García, Álvaro
Láñez, E.
Del Ama, A.J.
Gil Agudo, A.
Azorín Poveda, José María
author Costa García, Álvaro
author_facet Costa García, Álvaro
Láñez, E.
Del Ama, A.J.
Gil Agudo, A.
Azorín Poveda, José María
author_role author
author2 Láñez, E.
Del Ama, A.J.
Gil Agudo, A.
Azorín Poveda, José María
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Departamentos de la UMH::Ingeniería de Sistemas y Automática
dc.subject.none.fl_str_mv Attention level
Gait
EEG
Online
62 - Ingeniería. Tecnología
topic Attention level
Gait
EEG
Online
62 - Ingeniería. Tecnología
description Rehabilitation therapies are evolving oriented to improve their performances in terms of functional recovery. To achieve such recovery, the patients’ involvement is an important factor that correlates with the plastic properties of the brain. By evaluating electroencephalographic signals, it is possible to modify, in real time, the parameters of the rehabilitation according to the patients’ cognitive state. In this paper, an online brain–machine interface to measure the attention level during gait is presented. The system is based on the measurement of selective attention mechanisms manifested as power synchronization and desynchronization in the gamma band. A Linear Discriminant Analysis classifier is used to provide an attention index between 0 and 1 in real time. Robust techniques for artifact rejection and signal standardization are used in order to deal with the problems associated to the measurement of cortical signals during walking. The final interface is validated with 4 incomplete Spinal Cord Injury patients and 4 healthy participants. The system shows an average success rate of 68.1% in the classification of 3 attention levels and a stable behavior of these results during time
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/11000/6014
url http://hdl.handle.net/11000/6014
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.1016/j.neucom.2019.06.021
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
37
application/pdf
dc.source.none.fl_str_mv reponame:REDIUMH. Depósito Digital de la UMH
instname:Universidad Miguel Hernández de Elche
instname_str Universidad Miguel Hernández de Elche
reponame_str REDIUMH. Depósito Digital de la UMH
collection REDIUMH. Depósito Digital de la UMH
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301629

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