Brain-machine interfaces for rehabilitation in stroke: A review

BACKGROUND: Motor paralysis after stroke has devastating consequences for the patients, families and caregivers. Although therapies have improved in the recent years, traditional rehabilitation still fails in patients with severe paralysis. Brain-machine interfaces (BMI) have emerged as a promising tool to guide motor rehabilitation interventions as they can be applied to patients with no residual movement. OBJECTIVE: This paper reviews the efficiency of BMI technologies to facilitate neuroplasticity and motor recovery after stroke. METHODS: We provide an overview of the existing rehabilitation therapies for stroke, the rationale behind the use of BMIs for motor rehabilitation, the current state of the art and the results achieved so far with BMI-based interventions, as well as the future perspectives of neural-machine interfaces. RESULTS: Since the first pilot study by Buch and colleagues in 2008, several controlled clinical studies have been conducted, demonstrating the efficacy of BMIs to facilitate functional recovery in completely paralyzed stroke patients with noninvasive technologies such as the electroencephalogram (EEG). CONCLUSIONS: 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.This study was funded by the Bundesministerium für Bildung und Forschung BMBF MOTORBIC (FKZ13GW0053)andAMORSA(FKZ16SV7754), the Deutsche Forschungsgemeinschaft (DFG), the fortüne-Program of the University of Tübingen (2422-0-0 and 2452-0-0), and the Basque GovernmentScienceProgram(EXOTEK:KK2016/00083). NIL was supported by the Basque Government’s scholarship for predoctoral students

Low Latency Estimation of Motor Intentions to Assist Reaching Movements along Multiple Sessions in Chronic Stroke Patients: A Feasibility Study

A corrigendum on Low Latency Estimation of Motor Intentions to Assist Reaching Movements along Multiple Sessions in Chronic Stroke Patients: A Feasibility Study by Ibáñez, J., Monge-Pereira, E., Molina-Rueda, F., Serrano, J. I., del Castillo, M. D., Cuesta-Gómez, A., et al. (2017). Front. Neurosci. 11:126. doi: 10.3389/fnins.2017.00126. In the recently published article, there were incorrect and missing contents in the Acknowledgments section

A synergy-based hand control is encoded in human motor cortical areas

How the human brain controls hand movements to carry out different tasks is still debated. The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown. Here, we combined kinematic, electromyography, and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects. Hand postural information, encoded through kinematic synergies, were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements, significantly outperforming alternative somatotopic or muscle-based models. Importantly, hand postural synergies were predicted by neural activation patterns within primary motor cortex. These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses

Noninvasive Neuroprosthetic Control of Grasping by Amputees

Smooth coordination and fine temporal control of muscles by the brain allows us to effortlessly pre-shape our hand to grasp different objects. Correlates of motor control for grasping have been found across wide-spread cortical areas, with diverse signal features. These signals have been harnessed by implanting intracortical electrodes and used to control the motion of robotic hands by tetraplegics, using algorithms called brain-machine interfaces (BMIs). Signatures of motor control signal encoding mechanisms of the brain in macro-scale signals such as electroencephalography (EEG) are unknown, and could potentially be used to develop noninvasive brain-machine interfaces. Here we show that a) low frequency (0.1 – 1 Hz) time domain EEG contains information about grasp pre-shaping in able-bodies individuals, and b) This information can be used to control pre-shaping motion of a robotic hand by amputees. In the first study, we recorded simultaneous EEG and hand kinematics as 5 able-bodies individuals grasped various objects. Linear decoders using low delta band EEG amplitudes accurately predicted hand pre-shaping kinematics during grasping. Correlation coefficient between predicted and actual kinematics was r = 0.59 ± 0.04, 0.47 ± 0.06 and 0.32 ± 0.05 for the first 3 synergies. In the second study, two transradial amputees (A1 and A2) controlled a prosthetic hand to grasp two objects using a closed-loop BMI with low delta band EEG. This study was conducted longitudinally in 12 sessions spread over 38 days. A1 achieved a 63% success rate, with 11 sessions significantly above chance. A2 achieved a 32% success rate, with 2 sessions significantly above chance. Previous methods of EEG-based BMIs used frequency domain features, and were thought to have a low signal-to-noise ratio making them unsuitable for control of dexterous tasks like grasping. Our results demonstrate that time-domain EEG contains information about grasp pre-shaping, which can be harnessed for neuroprosthetic control.Electrical and Computer Engineering, Department o

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

Corticomuscular co-activation based hybrid brain-computer interface for motor recovery monitoring

The effect of corticomuscular coactivation based hybrid brain-computer interface (h-BCI) on post-stroke neurorehabilitation has not been explored yet. A major challenge in this area is to find an appropriate corticomuscular feature which can not only drive an h-BCI but also serve as a biomarker for motor recovery monitoring. Our previous study established the feasibility of a new method of measuring corticomuscular co-activation called correlation of band-limited power time-courses (CBPT) of EEG and EMG signals, outperforming the traditional EEG-EMG coherence in terms of accurately controlling a robotic hand exoskeleton device by the stroke patients. In this paper, we have evaluated the neurophysiological significance of CBPT for motor recovery monitoring by conducting a 5-week long longitudinal pilot trial on 4 chronic hemiparetic stroke patients. Results show that the CBPT variations correlated significantly (p-value< 0.05) with the dynamic changes in motor outcome measures during the therapy for all the patients. As the bandpower based biomarkers are popular in literature, a comparison with such biomarkers has also been made to cross-verify whether the changes in CBPT are indeed neurophysiological. Thus the study concludes that CBPT can serve as a biomarker for motor recovery monitoring while serving as a corticomuscular co-activation feature for h-BCI based neurorehabilitation. Despite an observed significant positive change between pre- and post-intervention motor outcomes, the question of the clinical effectiveness of CBPT is subject to further controlled trial on a larger cohort

A synergy-based hand control is encoded in human motor cortical areas

abstract: How the human brain controls hand movements to carry out different tasks is still debated. The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown. Here, we combined kinematic, electromyography, and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects. Hand postural information, encoded through kinematic synergies, were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements, significantly outperforming alternative somatotopic or muscle-based models. Importantly, hand postural synergies were predicted by neural activation patterns within primary motor cortex. These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses