Dataset Information

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks.

ABSTRACT: Brain-computer interface (BCI) technologies aim to provide a bridge between the human brain and external devices. Prior research using non-invasive BCI to control virtual objects, such as computer cursors and virtual helicopters, and real-world objects, such as wheelchairs and quadcopters, has demonstrated the promise of BCI technologies. However, controlling a robotic arm to complete reach-and-grasp tasks efficiently using non-invasive BCI has yet to be shown. In this study, we found that a group of 13 human subjects could willingly modulate brain activity to control a robotic arm with high accuracy for performing tasks requiring multiple degrees of freedom by combination of two sequential low dimensional controls. Subjects were able to effectively control reaching of the robotic arm through modulation of their brain rhythms within the span of only a few training sessions and maintained the ability to control the robotic arm over multiple months. Our results demonstrate the viability of human operation of prosthetic limbs using non-invasive BCI technology.

SUBMITTER: Meng J

PROVIDER: S-EPMC5155290 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Publications

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks.

Meng Jianjun J Zhang Shuying S Bekyo Angeliki A Olsoe Jaron J Baxter Bryan B He Bin B

Scientific reports 20161214

Brain-computer interface (BCI) technologies aim to provide a bridge between the human brain and external devices. Prior research using non-invasive BCI to control virtual objects, such as computer cursors and virtual helicopters, and real-world objects, such as wheelchairs and quadcopters, has demonstrated the promise of BCI technologies. However, controlling a robotic arm to complete reach-and-grasp tasks efficiently using non-invasive BCI has yet to be shown. In this study, we found that a gro ...[more]

PMID: 27966546

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Project description:BackgroundStroke survivors have long-term upper limb impairment, which impacts the quality of life (QOL) and social reintegration, but there is lack of effective therapeutic strategies and novel technologies. Customized multi-muscle functional electrical stimulation (FES) based on the muscle synergy of healthy adults and robotic-assisted therapy (RAT) have been proved efficacy respectively. Synergy-based FES combined with RAT can be a novel and more effective therapy for upper limb recovery of stroke survivors from the perspective of synergistic enhancement. However, few studies have examined the effectiveness of combined synergy-based FES and RAT, especially for motor control evaluated by reach-to-grasp (RTG) movements. The main objective of the following research protocol is to evaluate the effectiveness and efficacy, as well as adoptability, of FES-RAT and FES or RAT rehabilitation program for upper limb function improvement after stroke.MethodsThis will be an assessor-blinded randomized controlled trial involving a 12-week intervention and a 6-month follow-up. Stratified randomization will be used to equally and randomly assign 162 stroke patients into the FES + conventional rehabilitation program (CRP) group, RAT + CRP group and FES-RAT + CRP group. Interventions will be provided in 5 sessions per week, with a total of 60 sessions. The primary outcome measurements will include the Fugl-Meyer Assessment and Biomechanical Assessment of RTG movements. The secondary outcome measurements will include quality of life and brain neuroplasticity assessments by MRI. Evaluations will be performed at five time points, including at baseline, 6 weeks and 12 weeks from the start of treatment, and 3 months and 6 months following the end of treatment. A two-way analysis of variance with repeated measures will be applied to examine the main effects of the group, the time factor and group-time interaction effects.DiscussionThe results of the study protocol will provide high quality evidence for integrated synergy-based FES and RAT, and synergy-based FES alone and guide the design of more effective treatment methods for stroke rehabilitation.Trial registrationChiCTR2300071588.

Project description:This study investigated how modified control of a virtual hand executing reach-to-grasp affects functional performance and agency (perception of control). The objective of this work was to demonstrate positive relationships between reaching performance and grasping agency and motivate greater consideration of agency in movement rehabilitation. We hypothesized that agency and performance have positive correlation across varying control modes of the virtual hand. In this study, each participant controlled motion of a virtual hand through motion of his or her own hand. Control of the virtual hand was modified according to a specific control mode. Each mode involved the virtual hand moving at a modified speed, having noise, or including a level of automation. These specific modes represent potential control features to adapt for a rehabilitation device such as a prosthetic arm and hand. In this study, significant changes in agency and performance were observed across the control modes. Overall, a significant positive relationship (p < 0.001) was observed between the primary performance metric of reach (tracking a minimum path length trajectory) and an implicit measurement of agency (intentional binding). Intentional binding was assessed through participant perceptions of time-intervals between grasp contact and a sound event. Other notable findings include improved movement efficiency (increased smoothness, reduced acceleration) during expression of higher agency and shift toward greater implicit versus explicit agency with higher control speed. Positively relating performance and agency incentivizes control adaptation of powered movement devices, such as prostheses or exoskeletons, to maximize both user engagement and functional performance. Agency-based approaches may foster user-device integration at a cognitive level and facilitate greater clinical retention of the device. Future work should identify robust and automated methods to adapt device control for increased agency. Objectives include how virtual reality (VR) may identify optimal control of real-world devices and assessing real-time agency from neurophysiological signals.

Dataset Information

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks.

Publications

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks.

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