A combination strategy based brain–computer interface for two-dimensional movement control

Xia, Bin; Oladazimi, Maysam; Veser, Sandra; Cao, Lei; Li, Jie; Jia, Jie; Xie, Hong; Birbaumer, Niels

doi:10.1088/1741-2560/12/4/046021

Cited by 23 publications

(15 citation statements)

References 30 publications

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“…The concept of machine control via non-invasive EEG has been proposed in previous research, and explored in successful offline analyses or online cases including control of a virtual object111213, real objects such as wheelchair, quadcopter1415, and various other rehabilitation and assistive devices1617. To the best of our knowledge, few research groups have attempted control of a prosthetic or a robotic arm using scalp EEG based BCIs.…”

mentioning

confidence: 99%

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

Meng

Zhang

Bekyo

et al. 2016

Sci Rep

390

292

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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.

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mentioning

confidence: 99%

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

Meng

Zhang

Bekyo

et al. 2016

Sci Rep

390

292

View full text Add to dashboard Cite

show abstract

“…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: 85%

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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“…A brain-computer interface (BCI) provides a method of communication between the human brain and an external device that is based on the neural activity generated by the brain and is independent of normal output pathways such as the peripheral nerves and muscles. 1 Recently, both noninvasive [2][3][4][5][6][7][8][9][10] and invasive [11][12][13][14][15][16] BCI technologies have been proposed for various purposes. The ultimate goal of BCI studies is to enable anthropomorphic movement of wearable robotic devices, such as a prosthesis [17][18][19] or exoskeleton [20][21][22] acting as an assistive device, 4 based on the intended motion of the user in real time for more effective use of these devices in assisting with activities of daily living (ADL or ADLs) or rehabilitation tasks.…”

Section: Introductionmentioning

confidence: 99%

A noninvasive brain–computer interface approach for predicting motion intention of activities of daily living tasks for an upper-limb wearable robot

Bandara

Arata

Kiguchi

2018

International Journal of Advanced Robotic Systems

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Brain-computer interfaces are emerging as an important research area and are intended to create an understanding between a computer and the human brain to ensure that robot-human interactions become more intuitive and userfriendly. However, encoding of brain information to derive the intended motion of the user in real time continues to present a problem with respect to the control of wearable robots with multiple degrees of freedom. In this study, a new approach to control several degrees of freedom in a wearable robot is proposed and its feasibility is studied by estimating the user's motion intention in real time, in terms of the user's intended tasks to perform, by using electroencephalography signals measured from the scalp of the user. A time-delayed feature matrix is introduced to provide inputs to neural network and support vector machine-based classifiers that harvest the dynamic nature of the electroencephalography signals for motion intention prediction. The experimental results indicate the effectiveness of the proposed methodology in the estimation of user motion intention, in terms of intended task to perform.

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A combination strategy based brain–computer interface for two-dimensional movement control

Abstract: The results demonstrated that users were able to effectively control the 2D movement using the proposed strategy. The present system may be used as a tool to interact with the external world.

Cited by 23 publications

References 30 publications

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

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

Motor Imagery Based Continuous Teleoperation Robot Control with Tactile Feedback

A noninvasive brain–computer interface approach for predicting motion intention of activities of daily living tasks for an upper-limb wearable robot

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