Brain-machine interfaces for rehabilitation in stroke: A review

Abstract

Despite encouraging results, motor rehabilitation based on BMIs is still in a preliminary stage, and further improvements are required to boost its efficacy. Invasive and hybrid approaches are promising and might set the stage for the next generation of stroke rehabilitation therapies.

Event-related desynchronization during movement attempt and execution in severely paralyzed stroke patients: An artifact removal relevance analysis

“…Brain machine interfaces have demonstrated their efficacy for rehabilitation after paralysis ( Ang et al, 2015 ; Biasiucci et al, 2018 ; Donati et al, 2016 ; Ono et al, 2014 ; Pichiorri et al, 2015 ; Ramos-Murguialday et al, 2013 ; Trincado-Alonso et al, 2018 ). However, this technology is still in a preliminary stage, and there is a great margin for investigation and improvement before it becomes one standard tool in the portfolio of clinical treatments for motor rehabilitation ( Asín Prieto et al, 2014 ; López-Larraz et al, 2018b ). The results presented in this study demonstrate, on the one hand, that rejecting trials with artifacts from the EEG datasets helps to better quantify the brain activity of stroke patients during motor tasks; and on the other hand, that after rejecting the artifacts from the training datasets, the obtained BMI performances are lower.…”

“…Measuring the cortical signatures of movement with EEG has also allowed the development of non-invasive systems that interpret those signals in real-time to create brain-machine interfaces (BMI) with rehabilitative or assistive purpose ( Chaudhary et al, 2016 ; Lebedev and Nicolelis, 2017 ; López-Larraz et al, 2018b ). Different devices, including robotics and prosthetics, have been controlled by patients using brain activity only, to facilitate the movement of their paralyzed arms and legs ( Hortal et al, 2015 ; Ibáñez et al, 2017 ; López-Larraz et al, 2016 ).…”

Non-invasive brain-spine interface: continuous brain control of trans-spinal magnetic stimulation using EEG

“…Non-invasive brain-machine interfaces (BMIs) allow the transmission of volitional cortical commands to control rehabilitative devices (López-Larraz et al, 2018b;Millán et al, 2010;Wolpaw et al, 2002). For instance, there is ample evidence demonstrating contingent EEG control of robotic exoskeletons with patients (Ang et al, 2015;Ramos-Murguialday et al, 2013).…”

“…These neural signatures associated with motor execution or motor attempt can be also detected even in patients with motor deficits (López-Larraz et al, 2018a, which makes them suitable for BSI control. In order to favor Hebbian plasticity and promote functional recovery, a timely linked brain activity encoding motor intention and peripheral afferent neural activation is essential (Kato et al, 2019;López-Larraz et al, 2018b;Mrachacz-Kersting et al, 2016;Nishimura et al, 2013a;Ramos-Murguialday et al, 2013).…”

Design and effectiveness evaluation of mirror myoelectric interfaces: a novel method to restore movement in hemiplegic patients

“…We anticipate that in a longitudinal study with severe patients, the initial performance will be poor and the real-time control unskilled. Alternatively, those patients with poor or no decodable muscle activity could initially train with an EEG-brain-machine-interface 48 – 52 or a hybrid 47 , 53 , 54 until they recovered sufficient EMG activity to benefit from a myoelectric therapy. We foresee that as patients train with this mirror myoelectric interface, the modular organization of the EMG activity will resemble more that of their healthy upper limb.…”