Abstract
Stroke is the leading cause of sensorimotor disability worldwide. Recently, neurorehabilitation therapies based on neural interfaces have paved the way toward new effective rehabilitation strategies exploiting neural plasticity mechanisms and have produced promising results even in extremely compromised patients. In this chapter, we review and discuss several aspects of the design and use of neural interfaces coupled with upper limb actuators (robotics and functional electrical stimulation (FES)) for motor rehabilitation after stroke. We first describe the burden of stroke and the limitations of currently used rehabilitation strategies. Secondly, we analyze different neural interfacing methods to reinforce the brain-to-muscle link leveraging previous neuroscientific findings on motor learning and functional neuroplasticity. We review current clinical trials using this technology and analyze its effect on the sensorimotor function of stroke patients, reported as clinical and neurophysiological parameters. Thirdly, we provide several guidelines for the optimal design of these systems to boost motor recovery. We conclude with some recommendations and thoughts for future development of this technology in stroke rehabilitation.
Abbreviations
- ARAT:
-
Action Research Arm Test
- ASH:
-
Ashworth scale
- BCI:
-
Brain–computer interface
- BMI:
-
Brain–machine interface
- CCI:
-
Co-contraction index
- CIMT:
-
Constraint-induced movement therapy
- CMC:
-
Cortico-muscular coherence
- CMI:
-
Cortico-muscular interface
- CNS:
-
Central nervous system
- CSP:
-
Common spatial pattern
- CST:
-
Corticospinal tract
- CVA:
-
Cerebrovascular accident
- DoF:
-
Degree of freedom
- ECoG:
-
Electrocorticography
- EEG:
-
Electroencephalography
- EMG:
-
Electromyography
- ERD:
-
Event-related desynchronization
- ERS:
-
Event-related synchronization
- FES:
-
Functional electrical stimulation
- FMA:
-
Fugl-Meyer assessment
- fMRI:
-
Functional Magnetic Resonance Imaging
- fNIRS:
-
Functional near-infrared spectroscopy
- hBMI:
-
Hybrid brain–machine interface
- LFP:
-
Local field potential
- MAS:
-
Modified Ashworth scale
- MEG:
-
Magnetoencephalography
- M1:
-
Primary motor cortex
- MI:
-
Myoelectric interface
- ML:
-
Machine learning
- MRCP:
-
Movement-related slow cortical potentials
- NI:
-
Neural interface
- NIBS:
-
Noninvasive brain stimulation
- NMES:
-
Neuromuscular Magnetic Electrical Stimulation
- OSF:
-
Optimal spatial filter
- PNS:
-
Peripheral nervous system
- RMS:
-
Root mean square
- SCI:
-
Spinal cord injury
- SEP:
-
Sensory evoked potentials
- SIAS:
-
Stroke Impairment Assessment Set
- sLDA:
-
Sparse linear discriminant analysis
- SMR:
-
Sensorimotor rhythm
- SNR:
-
Signal-to-noise ratio
- STDP:
-
Spike time-dependent plasticity
- SVM:
-
Support vector machine
- tDCS:
-
Transcranial direct current stimulation
- WMFT:
-
Wolf Motor Function Test
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Irastorza-Landa, N. et al. (2022). Central and Peripheral Neural Interfaces for Control of Upper Limb Actuators for Motor Rehabilitation After Stroke: Technical and Clinical Considerations. In: Thakor, N.V. (eds) Handbook of Neuroengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-2848-4_120-1
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