A Gait Imagery-Based Brain-Computer Interface with Visual feedback for Spinal Cord Injury Rehabilitation on Lokomat

• Published in: IEEE transactions on biomedical engineering 2024-08, Vol.PP, p.1-10
• Main Authors: 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
• Format: Article
• Language: English
• Subjects: Brain-Computer Interface; Electroencephalography; Feature extraction; Legged locomotion; Lokomat; Lower-limb rehabilitation; Motor imagery; Motors; Neurofeedback; Spinal Cord Injury; Vectors; Visualization; Walking
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2024.3440036

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 \mu (8-12 Hz) and \beta (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.