IEEE transactions on bio-medical engineering · 2025
A Gait Imagery-Based Brain-Computer Interface With Visual Feedback for Spinal Cord Injury Rehabilitation on Lokomat.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Blanco-Diaz Cristian Felipe, Serafini Ericka Raiane da Silva, Bastos-Filho Teodiano, Dantas Andre Felipe Oliveira de Azevedo, Santo Caroline Cunha do Espirito, Delisle-Rodriguez Denis
Аннотация
OBJECTIVE: Motor Imagery (MI)-based Brain-Computer Interfaces (BCIs) have been proposed for the rehabilitation of people with disabilities, being a big challenge their successful application to restore motor functions in individuals with Spinal Cord Injury (SCI). This work proposes an Electroencephalography (EEG) gait imagery-based BCI to promote motor recovery on the Lokomat platform, in order to allow a clinical intervention by acting simultaneously on both central and peripheral nervous mechanisms.
METHODS: As a novelty, our BCI system accurately discriminates gait imagery tasks during walking and further provides a multi-channel EEG-based Visual Neurofeedback (VNFB) linked to (8-12 Hz) and (15-20 Hz) rhythms around Cz. VNFB is carried out through a cluster analysis strategy-based Euclidean distance, where the weighted mean MI feature vector is used as a reference to teach individuals with SCI to modulate their cortical rhythms.
RESULTS: The developed BCI reached an average classification accuracy of 74.4%. In addition, feature analysis demonstrated a reduction in cluster variance after several sessions, whereas metrics associated with self-modulation indicated a greater distance between both classes: passive walking with gait MI and passive walking without MI.
CONCLUSION: The results suggest that intervention with a gait MI-based BCI with VNFB may allow the individuals to appropriately modulate their rhythms of interest around Cz.
SIGNIFICANCE: This work contributes to the development of advanced systems for gait rehabilitation by integrating Machine Learning and neurofeedback techniques, to restore lower-limb functions of SCI individuals.
METHODS: As a novelty, our BCI system accurately discriminates gait imagery tasks during walking and further provides a multi-channel EEG-based Visual Neurofeedback (VNFB) linked to (8-12 Hz) and (15-20 Hz) rhythms around Cz. VNFB is carried out through a cluster analysis strategy-based Euclidean distance, where the weighted mean MI feature vector is used as a reference to teach individuals with SCI to modulate their cortical rhythms.
RESULTS: The developed BCI reached an average classification accuracy of 74.4%. In addition, feature analysis demonstrated a reduction in cluster variance after several sessions, whereas metrics associated with self-modulation indicated a greater distance between both classes: passive walking with gait MI and passive walking without MI.
CONCLUSION: The results suggest that intervention with a gait MI-based BCI with VNFB may allow the individuals to appropriately modulate their rhythms of interest around Cz.
SIGNIFICANCE: This work contributes to the development of advanced systems for gait rehabilitation by integrating Machine Learning and neurofeedback techniques, to restore lower-limb functions of SCI individuals.