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,...
| Autores: | ; ; ; ; |
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| Tipo de documento: | Artigo |
| Data de publicação: | 2025 |
| País: | España |
| Recursos: | Universidad Miguel Hernández de Elche |
| Repositório: | REDIUMH. Depósito Digital de la UMH |
| OAI Identifier: | oai:dnet:rediumh_____::0b5245a9ebb92be2e61764f48eb22ea5 |
| Acesso em linha: | https://hdl.handle.net/11000/39637 |
| Nível de acesso: | Acesso aberto |
| Palavra-chave: | 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. |
| Resumo: | 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. |
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