Brain–machine interfaces for controlling lower-limb powered robotic systems

He, Yongtian; Eguren, David; Azorín, José M.; Grossman, Robert G.; Luu, Trieu Phat; Contreras-Vidal, José L.

doi:10.1088/1741-2552/aaa8c0

Cited by 180 publications

(107 citation statements)

References 84 publications

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“…In this category, new emerging devices include EXOATLET [106] (Exoatlet, Moscow, Russia), PhoeniX (SuitX, Berkeley, CA, USA), for which limited information is currently available, and REX (REX Bionics, London, UK). In addition, some of these exoskeletons, such as Lokomat and REX, have been employed to develop brain machine interfaces (BMIs) (for a review, see [107]). Then, albeit still in progress, the development of self-balancing exoskeletons might allow for arm swing, which is an important feature during locomotion (no crutches or other external supports are required).…”

Section: Robotic Neurorehabilitation For the Lower Limbmentioning

confidence: 99%

Perspectives and Challenges in Robotic Neurorehabilitation

et al. 2019

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The development of robotic devices for rehabilitation is a fast-growing field. Nowadays, thanks to novel technologies that have improved robots' capabilities and offered more cost-effective solutions, robotic devices are increasingly being employed during clinical practice, with the goal of boosting patients' recovery. Robotic rehabilitation is also widely used in the context of neurological disorders, where it is often provided in a variety of different fashions, depending on the specific function to be restored. Indeed, the effect of robot-aided neurorehabilitation can be maximized when used in combination with a proper training regimen (based on motor control paradigms) or with non-invasive brain machine interfaces. Therapy-induced changes in neural activity and behavioral performance, which may suggest underlying changes in neural plasticity, can be quantified by multimodal assessments of both sensorimotor performance and brain/muscular activity pre/post or during intervention. Here, we provide an overview of the most common robotic devices for upper and lower limb rehabilitation and we describe the aforementioned neurorehabilitation scenarios. We also review assessment techniques for the evaluation of robotic therapy. Additional exploitation of these research areas will highlight the crucial contribution of rehabilitation robotics for promoting recovery and answering questions about reorganization of brain functions in response to disease.In the last decades, innovative robotic technologies have been developed in order to effectively help clinicians during the neurorehabilitation process. The term "robotic technology" in this application domain refers to any mechatronic device with a certain degree of intelligence that can physically intervene on the behavior of the patient, optimizing and speeding up his/her sensorimotor recovery. The two key capabilities of these robots are: (1) Assessing the human sensorimotor function; and (2) re-training the human brain in order to improve the patient's quality of life. However, most of the studies in this field have been focused more on the development of the devices, whereas less effort was made on maximizing their efficacy for promoting recovery. The main challenge consists of designing effective training modalities, supported by appropriate control strategies. Thus, each robotic device supports a pre-defined training modality depending on the low-level control strategy implemented and also on the residual abilities of each patient. Usually, most of the rehabilitation devices implement a passive training modality (robot-driven, position control strategy), where the robot imposes the trajectories, and an active training modality (patient-driven), where the robot modulates its trajectory in response to the subject's intention to move [7,8]. However, among all the different training modalities, the most relevant is the assistive one. Assistive controllers help participants to move their impaired limbs according to the desired postures during grasping, reaching, or walki...

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Section: Robotic Neurorehabilitation For the Lower Limbmentioning

confidence: 99%

Perspectives and Challenges in Robotic Neurorehabilitation

et al. 2019

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“…The feasibility of inducing neurological recovery in paraplegic patients by long term training with a BCI-based gait protocol was shown in [5]. In addition, BCI-based control of virtual object [6], robotic arm [7][8][9], robotic prosthetic [10,11], wheelchair [12], and various rehabilitation devices [13][14][15][16] were also reported in previous research.…”

Section: Introductionmentioning

confidence: 82%

Motor Imagery Based Continuous Teleoperation Robot Control with Tactile Feedback

Bao-guo

et al. 2020

Electronics

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Brain computer interface (BCI) adopts human brain signals to control external devices directly without using normal neural pathway. Recent study has explored many applications, such as controlling a teleoperation robot by electroencephalography (EEG) signals. However, utilizing the motor imagery EEG-based BCI to perform teleoperation for reach and grasp task still has many difficulties, especially in continuous multidimensional control of robot and tactile feedback. In this research, a motor imagery EEG-based continuous teleoperation robot control system with tactile feedback was proposed. Firstly, mental imagination of different hand movements was translated into continuous command to control the remote robotic arm to reach the hover area of the target through a wireless local area network (LAN). Then, the robotic arm automatically completed the task of grasping the target. Meanwhile, the tactile information of remote robotic gripper was detected and converted to the feedback command. Finally, the vibrotactile stimulus was supplied to users to improve their telepresence. Experimental results demonstrate the feasibility of using the motor imagery EEG acquired by wireless portable equipment to realize the continuous teleoperation robot control system to finish the reach and grasp task. The average two-dimensional continuous control success rates for online Task 1 and Task 2 of the six subjects were 78.0% ± 6.1% and 66.2% ± 6.0%, respectively. Furthermore, compared with the traditional EEG triggered robot control using the predefined trajectory, the continuous fully two-dimensional control can not only improve the teleoperation robot system’s efficiency but also give the subject a more natural control which is critical to human–machine interaction (HMI). In addition, vibrotactile stimulus can improve the operator’s telepresence and task performance.

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“…and, when analysing gait with a more demanding attentional task, other areas (prefrontal, posterior parietal cortex) seem specifically involved, with the occurrence of beta/gamma oscillations. The decoding of this brain activity is a necessary step to build valid brain-computer interfaces (BCIs) able to generate gait artificially [85]. As a perspective, a real-time closed-loop BCI that decodes lower limb joint angles from scalp EEG during treadmill walking in order to control the walking movements of a virtual avatar has already been built [86].…”

Section: Discussionmentioning

confidence: 99%

Cortical Oscillations during Gait: Wouldn’t Walking Be So Automatic?

et al. 2020

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Gait is often considered as an automatic movement but cortical control seems necessary to adapt gait pattern with environmental constraints. In order to study cortical activity during real locomotion, electroencephalography (EEG) appears to be particularly appropriate. It is now possible to record changes in cortical neural synchronization/desynchronization during gait. Studying gait initiation is also of particular interest because it implies motor and cognitive cortical control to adequately perform a step. Time-frequency analysis enables to study induced changes in EEG activity in different frequency bands. Such analysis reflects cortical activity implied in stabilized gait control but also in more challenging tasks (obstacle crossing, changes in speed, dual tasks . . . ). These spectral patterns are directly influenced by the walking context but, when analyzing gait with a more demanding attentional task, cortical areas other than the sensorimotor cortex (prefrontal, posterior parietal cortex, etc.) seem specifically implied. While the muscular activity of legs and cortical activity are coupled, the precise role of the motor cortex to control the level of muscular contraction according to the gait task remains debated. The decoding of this brain activity is a necessary step to build valid brain-computer interfaces able to generate gait artificially.

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Brain–machine interfaces for controlling lower-limb powered robotic systems

Cited by 180 publications

References 84 publications

Perspectives and Challenges in Robotic Neurorehabilitation

Perspectives and Challenges in Robotic Neurorehabilitation

Motor Imagery Based Continuous Teleoperation Robot Control with Tactile Feedback

Cortical Oscillations during Gait: Wouldn’t Walking Be So Automatic?

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