Commercial BCI Control and Functional Brain Networks in Spinal Cord Injury: A Proof-of-Concept
| Autor: | George Arfaras, Nicolas Foroglou, A. Astaras, Panagiotis D. Bamidis, Ioannis Xygonakis, Konstantinos Polyzoidis, Niki Pandria, Kyriaki Rafailia Kavazidi, Panagiotis Kartsidis, Alkinoos Athanasiou |
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| Rok vydání: | 2017 |
| Předmět: |
030506 rehabilitation
medicine.medical_specialty Rehabilitation medicine.diagnostic_test medicine.medical_treatment Electroencephalography medicine.disease 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation Motor imagery Proof of concept Physical therapy medicine 0305 other medical science Psychology Control (linguistics) Robotic arm Spinal cord injury 030217 neurology & neurosurgery Brain–computer interface |
| Zdroj: | CBMS |
| DOI: | 10.1109/cbms.2017.35 |
| Popis: | Spinal Cord Injury (SCI), along with disability, results in changes of brain organization and structure. While sensorimotor networks of patients and healthy individuals share similar patterns, unique functional interactions have been identified in SCI networks. Brain-Computer Interfaces (BCIs) have emerged as a promising technology for movement restoration and rehabilitation of SCI patients. We describe an experimental methodology to combine high-resolution electroencephalography (EEG) for investigation of functional connectivity following SCI and non-invasive BCI control of robotic arms. Two BCI-naïve female subjects, a SCI patient and a healthy control subject participated in the proof-of-concept implementation. They were instructed to perform motor imagery (MI) while watching multiple movements of either arms or legs during walking, while under 128-channel EEG recording. They were, subsequently, asked to control two robotic arms (Mercury v2.0) using a commercial class EEG-BCI. They both achieved comparable performance levels of robotic control, 52.5% for the SCI patient and 56.9% for the healthy control. We performed a feasibility analysis of functional networks on the EEG-BCI recordings. Visual MI allows training on multiple imagined movements and shows promise in investigating differences in functional cortical networks associated with different motor tasks. This approach could allow the implementation of functional network-based BCIs in the future for complex movement control. |
| Databáze: | OpenAIRE |
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