Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton

Sensor technology plays a fundamental role in neuro-motor rehabilitation by enabling precise movement analysis and control. This study explores the integration of brain–machine interfaces (BMIs) and wearable sensors to enhance motor recovery in individuals with neuro-motor impairments. Specifically,...

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Autores: Polo Hortigüela, Cristina, Ortiz, Mario, Soriano Segura, Paula, Iáñez, Eduardo, Azorín, José M.
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad Miguel Hernández de Elche
Repositorio:REDIUMH. Depósito Digital de la UMH
OAI Identifier:oai:dnet:rediumh_____::0b5245a9ebb92be2e61764f48eb22ea5
Acceso en línea:https://hdl.handle.net/11000/39637
Access Level:acceso abierto
Palabra clave:electroencephalography (EEG)
brain–machine interface (BMI)
time-frequency transforms
motor imagery
low-cost exoskeleton
neurorehabilitation
inertial measurement units (IMUs)
CDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnología
CDU::6 - Ciencias aplicadas::61 - Medicina::612 - Fisiología
CDU::0 - Generalidades.::04 - Ciencia y tecnología de los ordenadores. Informática.
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spelling Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle ExoskeletonPolo Hortigüela, CristinaOrtiz, MarioSoriano Segura, PaulaIáñez, EduardoAzorín, José M.electroencephalography (EEG)brain–machine interface (BMI)time-frequency transformsmotor imagerylow-cost exoskeletonneurorehabilitationinertial measurement units (IMUs)CDU::6 - Ciencias aplicadas::62 - Ingeniería. TecnologíaCDU::6 - Ciencias aplicadas::61 - Medicina::612 - FisiologíaCDU::0 - Generalidades.::04 - Ciencia y tecnología de los ordenadores. Informática.Sensor technology plays a fundamental role in neuro-motor rehabilitation by enabling precise movement analysis and control. This study explores the integration of brain–machine interfaces (BMIs) and wearable sensors to enhance motor recovery in individuals with neuro-motor impairments. Specifically, different time-frequency transforms are evaluated to analyze the correlation between electroencephalographic (EEG) activity and ankle position, measured by using inertial measurement units (IMUs). A low-cost ankle exoskeleton was designed to conduct the experimental trials. Six subjects performed plantar and dorsal flexion movements while the EEG and IMU signals were recorded. The correlation between brain activity and foot kinematics was analyzed using the Short-Time Fourier Transform (STFT), Stockwell (ST), Hilbert–Huang (HHT), and Chirplet (CT) methods. The 8–20 Hz frequency band exhibited the highest correlation values. For motor imagery classification, the STFT achieved the highest accuracy (92.9%) using an EEGNet-based classifier and a state-machine approach. This study presents a dual approach: the analysis of EEG-movement correlation in different cognitive states, and the systematic comparison of four time-frequency transforms for both correlation and classification performance. The results support the potential of combining EEG and IMU data for BMI applications and highlight the importance of cognitive state in motion analysis for accessible neurorehabilitation technologies.MDPIDepartamentos de la UMH::Ingeniería Mecánica y Energía202620262025info:eu-repo/semantics/articleapplication/pdf24application/pdfhttps://hdl.handle.net/11000/39637reponame:REDIUMH. Depósito Digital de la UMHinstname:Universidad Miguel Hernández de ElcheIngléshttps://doi.org/10.3390/s25102987info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:dnet:rediumh_____::0b5245a9ebb92be2e61764f48eb22ea52026-05-27T13:36:21Z
dc.title.none.fl_str_mv Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton
title Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton
spellingShingle Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton
Polo Hortigüela, Cristina
electroencephalography (EEG)
brain–machine interface (BMI)
time-frequency transforms
motor imagery
low-cost exoskeleton
neurorehabilitation
inertial measurement units (IMUs)
CDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnología
CDU::6 - Ciencias aplicadas::61 - Medicina::612 - Fisiología
CDU::0 - Generalidades.::04 - Ciencia y tecnología de los ordenadores. Informática.
title_short Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton
title_full Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton
title_fullStr Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton
title_full_unstemmed Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton
title_sort Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton
dc.creator.none.fl_str_mv Polo Hortigüela, Cristina
Ortiz, Mario
Soriano Segura, Paula
Iáñez, Eduardo
Azorín, José M.
author Polo Hortigüela, Cristina
author_facet Polo Hortigüela, Cristina
Ortiz, Mario
Soriano Segura, Paula
Iáñez, Eduardo
Azorín, José M.
author_role author
author2 Ortiz, Mario
Soriano Segura, Paula
Iáñez, Eduardo
Azorín, José M.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Departamentos de la UMH::Ingeniería Mecánica y Energía
dc.subject.none.fl_str_mv electroencephalography (EEG)
brain–machine interface (BMI)
time-frequency transforms
motor imagery
low-cost exoskeleton
neurorehabilitation
inertial measurement units (IMUs)
CDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnología
CDU::6 - Ciencias aplicadas::61 - Medicina::612 - Fisiología
CDU::0 - Generalidades.::04 - Ciencia y tecnología de los ordenadores. Informática.
topic electroencephalography (EEG)
brain–machine interface (BMI)
time-frequency transforms
motor imagery
low-cost exoskeleton
neurorehabilitation
inertial measurement units (IMUs)
CDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnología
CDU::6 - Ciencias aplicadas::61 - Medicina::612 - Fisiología
CDU::0 - Generalidades.::04 - Ciencia y tecnología de los ordenadores. Informática.
description Sensor technology plays a fundamental role in neuro-motor rehabilitation by enabling precise movement analysis and control. This study explores the integration of brain–machine interfaces (BMIs) and wearable sensors to enhance motor recovery in individuals with neuro-motor impairments. Specifically, different time-frequency transforms are evaluated to analyze the correlation between electroencephalographic (EEG) activity and ankle position, measured by using inertial measurement units (IMUs). A low-cost ankle exoskeleton was designed to conduct the experimental trials. Six subjects performed plantar and dorsal flexion movements while the EEG and IMU signals were recorded. The correlation between brain activity and foot kinematics was analyzed using the Short-Time Fourier Transform (STFT), Stockwell (ST), Hilbert–Huang (HHT), and Chirplet (CT) methods. The 8–20 Hz frequency band exhibited the highest correlation values. For motor imagery classification, the STFT achieved the highest accuracy (92.9%) using an EEGNet-based classifier and a state-machine approach. This study presents a dual approach: the analysis of EEG-movement correlation in different cognitive states, and the systematic comparison of four time-frequency transforms for both correlation and classification performance. The results support the potential of combining EEG and IMU data for BMI applications and highlight the importance of cognitive state in motion analysis for accessible neurorehabilitation technologies.
publishDate 2025
dc.date.none.fl_str_mv 2025
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/11000/39637
url https://hdl.handle.net/11000/39637
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.3390/s25102987
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
24
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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 15,81155

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