A Low-Cost Lower-Limb Brain-Machine Interface Triggered by Pedaling Motor Imagery for Post-Stroke Patients Rehabilitation
| Autor: | Teodiano Bastos-Filho, Jorge Henrique Posses Nascimento, Vivianne Cardoso, Maria Alejandra Romero-Laiseca, Dharmendra Gurve, Sridhar Krishnan, Anselmo Frizera-Neto, Denis Delisle-Rodriguez, Flavia Loterio |
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| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
medicine.medical_treatment Biomedical Engineering 02 engineering and technology Electroencephalography 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation Motor imagery 0202 electrical engineering electronic engineering information engineering Internal Medicine medicine Humans Latency (engineering) Stroke Brain–computer interface Rehabilitation medicine.diagnostic_test business.industry General Neuroscience Stroke Rehabilitation medicine.disease Linear discriminant analysis Lower Extremity Feature (computer vision) Brain-Computer Interfaces 020201 artificial intelligence & image processing business 030217 neurology & neurosurgery |
| Zdroj: | IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. 28(4) |
| ISSN: | 1558-0210 |
| Popis: | A low-cost Brain-Machine Interface (BMI) based on electroencephalography for lower-limb motor recovery of post-stroke patients is proposed here, which provides passive pedaling as feedback, when patients trigger a Mini-Motorized Exercise Bike (MMEB) by executing pedaling motor imagery (MI). This system was validated in an On-line phase by eight healthy subjects and two post-stroke patients, which felt a closed-loop commanding the MMEB due to the fast response of our BMI. It was developed using methods of low-computational cost, such as Riemannian geometry for feature extraction, Pair-Wise Feature Proximity (PWFP) for feature selection, and Linear Discriminant Analysis (LDA) for pedaling imagery recognition. The On-line phase was composed of two sessions, where each participant completed a total of 12 trials per session executing pedaling MI for triggering the MMEB. As a result, the MMEB was successfully triggered by healthy subjects for almost all trials (ACC up to 100%), while the two post-stroke patients, PS1 and PS2, achieved their best performance (ACC of 41.67% and 91.67%, respectively) in Session #2. These patients improved their latency (2.03 ± 0.42 s and 1.99 ± 0.35 s, respectively) when triggering the MMEB, and their performance suggests the hypothesis that our system may be used with chronic stroke patients for lower-limb recovery, providing neural relearning and enhancing neuroplasticity. |
| Databáze: | OpenAIRE |
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