A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients

Including supplementary information from the brain or other body parts in the control of brain-machine interfaces (BMIs) has been recently proposed and investigated. Such enriched interfaces are referred to as hybrid BMIs (hBMIs) and have been proven to be more robust and accurate than regular BMIs...

Full description

Saved in:
Bibliographic Details
Main Authors: Sarasola-Sanz, Andrea, Irastorza-Landa, Nerea, Lopez-Larraz, Eduardo, Bibian, Carlos, Helmhold, Florian, Broetz, Doris, Birbaumer, Niels, Ramos-Murguialday, Ander
Format: Conference Proceeding
Language:English
Subjects:
Adult
Brain-Computer Interfaces
Calibration
Decoding
Electroencephalography
Electroencephalography - instrumentation
Electroencephalography - methods
Electromyography
Electromyography - instrumentation
Electromyography - methods
Exoskeletons
Humans
Male
Middle Aged
Muscles
Real-time systems
Reproducibility of Results
Signal Processing, Computer-Assisted
Stroke Rehabilitation - instrumentation
Stroke Rehabilitation - methods
Citations: Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Including supplementary information from the brain or other body parts in the control of brain-machine interfaces (BMIs) has been recently proposed and investigated. Such enriched interfaces are referred to as hybrid BMIs (hBMIs) and have been proven to be more robust and accurate than regular BMIs for assistive and rehabilitative applications. Electromyographic (EMG) activity is one of the most widely utilized biosignals in hBMIs, as it provides a quite direct measurement of the motion intention of the user. Whereas most of the existing non-invasive EEG-EMG-hBMIs have only been subjected to offline testings or are limited to one degree of freedom (DoF), we present an EEG-EMG-hBMI that allows the simultaneous control of 7-DoFs of the upper limb with a robotic exoskeleton. Moreover, it establishes a biologically-inspired hierarchical control flow, requiring the active participation of central and peripheral structures of the nervous system. Contingent visual and proprioceptive feedback about the user's EEG and EMG activity is provided in the form of velocity modulation during functional task training. We believe that training with this closed-loop system may facilitate functional neuroplastic processes and eventually elicit a joint brain and muscle motor rehabilitation. Its usability is validated during a real-time operation session in a healthy participant and a chronic stroke patient, showing encouraging results for its application to a clinical rehabilitation scenario.
ISSN:1945-7901
DOI:10.1109/ICORR.2017.8009362