A hybrid BMI-based exoskeleton for paresis: EMG control for assisting arm movements

Kawase, Toshihiro; Sakurada, Takeshi; Koike, Yasuharu; Kansaku, Kenji

doi:10.1088/1741-2552/aa525f

Cited by 75 publications

(43 citation statements)

References 278 publications

(554 reference statements)

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“…By combining different biosignal sensors such as EEG, EMG, and eye tracking, researchers have demonstrated promising success controlling prosthetic limbs and even quadrotor flight [42,25,23,32]. This paper focuses on safetycritical tasks where robot operation must be corrected by a human supervisor with low latency.…”

Section: Hybrid Control Methods For Human-robot Interactionmentioning

confidence: 99%

Plug-and-Play Supervisory Control Using Muscle and Brain Signals for Real-Time Gesture and Error Detection

DelPreto

Salazar-Gomez

Gil

et al. 2018

Robotics: Science and Systems XIV

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Abstract-Control of robots in safety-critical tasks and situations where costly errors may occur is paramount for realizing the vision of pervasive human-robot collaborations. For these cases, the ability to use human cognition in the loop can be key for recuperating safe robot operation. This paper combines two streams of human biosignals, electrical muscle and brain activity via EMG and EEG, respectively, to achieve fast and accurate human intervention in a supervisory control task. In particular, this paper presents an end-to-end system for continuous rollingwindow classification of gestures that allows the human to actively correct the robot on demand, discrete classification of Error-Related Potential signals (unconsciously produced by the human supervisor's brain when observing a robot error), and a framework that integrates these two classification streams for fast and effective human intervention. The system also allows "plug-and-play" operation, demonstrating accurate performance even with new users whose biosignals have not been used for training the classifiers. The resulting hybrid control system for safety-critical situations is evaluated with 7 untrained human subjects in a supervisory control scenario where an autonomous robot performs a multi-target selection task.

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Section: Hybrid Control Methods For Human-robot Interactionmentioning

confidence: 99%

Plug-and-Play Supervisory Control Using Muscle and Brain Signals for Real-Time Gesture and Error Detection

DelPreto

Salazar-Gomez

Gil

et al. 2018

Robotics: Science and Systems XIV

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“…Signal processing method 1) Feature extraction: It is known that the ipsilateral enhancement of the lower β rhythm and contra-lateral attenuation of the µ rhythm are normally related to motor execution as well as motor imagery [22] and are represented by neuro-physiological phenomena of event-related synchronization (ERS) and desynchronization (ERD). Therefore the temporal filtering of the EEG signals were done in two passbands of [8][9][10][11][12] Hz (µ band) and [16][17][18][19][20][21][22][23][24] Hz (β band), as they were empirically found to generate relatively stable ERD/ERS patterns [34].…”

Section: E Multimodal Neurofeedback During Mpmentioning

confidence: 99%

“…In this regard the BCI based synchronous cortical and peripheral stimulation have been found to be very effective to enhance corticospinal excitability [8]. It facilitates the patient to directly observe the ongoing cortical activity in terms of contingent visual or orthotic feedback [9], [10]. In a controlled study Naros et al [11] established the effectiveness of contingent neurofeedback.…”

Section: Introductionmentioning

confidence: 99%

Active Physical Practice Followed by Mental Practice Using BCI-Driven Hand Exoskeleton: A Pilot Trial for Clinical Effectiveness and Usability

Chowdhury

Meena

Raza

et al. 2018

IEEE J. Biomed. Health Inform.

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Appropriately combining mental practice (MP) and physical practice (PP) in a poststroke rehabilitation is critical for ensuring a substantially positive rehabilitation outcome. Here, we present a rehabilitation protocol incorporating a separate active PP stage followed by MP stage, using a hand exoskeleton and brain-computer interface (BCI). The PP stage was mediated by a force sensor feedback-based assist-as-needed control strategy, whereas the MP stage provided BCI-based multimodal neurofeedback combining anthropomorphic visual feedback and proprioceptive feedback of the impaired hand extension attempt. A six week long clinical trial was conducted on four hemiparetic stroke patients (screened out of 16) with a left-hand disability. The primary outcome, motor functional recovery, was measured in terms of changes in grip-strength (GS) and action research arm test (ARAT) scores; whereas the secondary outcome, usability of the system was measured in terms of changes in mood, fatigue, and motivation on a visual-analog-scale. A positive rehabilitative outcome was found as the group mean changes from the baseline in the GS and ARAT were +6.38 kg and +5.66 accordingly. The VAS scale measurements also showed betterment in mood ( 1.38), increased motivation (+2.10) and reduced fatigue (0.98) as compared to the baseline. Thus, the proposed neurorehabilitation protocol is found to be promising both in terms of clinical effectiveness and usability.

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“…loses in the achieved indicators to modern rehabilitation methods with biological feedback from the patient, involving the activation of motor or sensory functional zones, including mirror activation processes [Simpson et al, 2019;Cervera et al, 2018;Ekerete et al, 2018;Bernhardt et al, 2017]. For example, excellent results are demonstrated in works related to the use of exoskeletal devices [Frolov et al, 2018;Ren et al, 2017;Kawase et al, 2017]. However, such technologies based on robotic devices are expensive and require highly qualified service.…”

Section: Introductionmentioning

confidence: 99%

Brain-computer interface for post-stroke rehabilitation

Grubov

Badarin

Schukovsky

et al. 2019

CAP

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In the paper we proposed the approach for increasing of quality of neurorehabilitation of post-stroke patients based on wavelet analysis of EEG signals recoded during motor imagery. Also we proposed brain-computer interface based on the method. We determined all necessary procedures required to find motor imagery type (kinesthetic or visual) for each individual patient and described subsequent rehabilitation process. We tested developed brain-computer interface on 20 participants with post-stroke motor impairment. We believe that developed system can be used not only in laboratory experimental conditions, but also in clinical ones.

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A hybrid BMI-based exoskeleton for paresis: EMG control for assisting arm movements

Abstract: A BMI-based exoskeleton for paralyzed arms and hands using real-time control was realized by designing a new method to estimate joint angles based on EMG signals, and these may be useful for practical rehabilitation and the support of daily actions.

Cited by 75 publications

References 278 publications

Plug-and-Play Supervisory Control Using Muscle and Brain Signals for Real-Time Gesture and Error Detection

Plug-and-Play Supervisory Control Using Muscle and Brain Signals for Real-Time Gesture and Error Detection

Active Physical Practice Followed by Mental Practice Using BCI-Driven Hand Exoskeleton: A Pilot Trial for Clinical Effectiveness and Usability

Brain-computer interface for post-stroke rehabilitation

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