Control of a 9-DoF Wheelchair-mounted robotic arm system using a P300 Brain Computer Interface: Initial experiments

Palankar, Mayur; Laurentis, Kathryn J. De; Alqasemi, Redwan; Veras, Eduardo; Dubey, Rajiv; Arbel, Yael; Donchin, Emanuel

doi:10.1109/robio.2009.4913028

Cited by 75 publications

(41 citation statements)

References 15 publications

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“…Interface between a robot and EEG signals is called as Brain Computer Interface (BCI) (8) - (10) . A P300 brain computer interface (11), (12) is often used to control a robot.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Forearm Supination/Pronation Motion Based on EEG Signals to Control an Artificial Arm

Kiguchi

Lalitharatne

Hayashi

2013

JAMDSM

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In recent years, many myoelectric arms that are controlled based on electromyogram (EMG) signals of amputee's stump or residual muscles have been proposed. In the cases of above elbow amputees, however, the muscles which generate the forearm, wrist and hand motions do not remain. Therefore, most myoelectric arms for above elbow amputees have less degree of freedom and its dexterity is relatively poor compared with a human upper-limb. To improve the quality of life of above elbow amputees and to increase their mobility in daily life activities, some additional input signals must be prepared. One of the strong candidates of the additional input signals is an electroencephalogram (EEG) signal. An EEG signal is an electric signal that can be measured along a scalp, so that it can be measured even with an above elbow amputee. In this study, an artificial arm for above elbow amputees is controlled based on EMG and EEG signals. In this paper, the EEG-based motion estimation method is proposed to control the forearm supination/pronation motion of the artificial arm. The angle, angular velocity, and angular acceleration of the forearm motion are estimated under several velocities by using EEG signals.

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“…Interface between a robot and EEG signals is called as Brain Computer Interface (BCI) (8) - (10) . A P300 brain computer interface (11), (12) is often used to control a robot.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Forearm Supination/Pronation Motion Based on EEG Signals to Control an Artificial Arm

Kiguchi

Lalitharatne

Hayashi

2013

JAMDSM

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“…This study begins the performance evaluation with a nonadaptive model, which was used to control a robot system [12], [15], [16]. Assessing the classification accuracy rate uses the 10-fold cross-validation [11] method.…”

Section: Nonadaptive Modelmentioning

confidence: 99%

“…It includes N200 potential that is a negative valley appearing at poststimulus 180-325 ms and P300 potential that is a positive peak appearing at poststimulus 250-800 ms [6]. Since the first P300 speller based on the "oddball" paradigm was developed [7], the ERP-based brain-computer interface (BCI) systems have emerged, e.g., spelling sentences [8], controlling electrical applications in a virtual [9] or a lab environment [10], browsing Internet websites [11], controlling a wheelchair [12], [13], a hospital-bed nursing system [14], a robotic arm [15], and a humanoid robot [16]- [19]. Nevertheless, the telepresence control of a humanoid robot [20]- [24] via brain signals to perform complex operational tasks is not only helpful for the disabilities addressed in biomedical engineering but also very important for operators controlling humanoid robots in military, astronautic, and industrial applications.…”

Section: Introductionmentioning

confidence: 99%

An Event-Related Potential-Based Adaptive Model for Telepresence Control of Humanoid Robot Motion in an Environment Cluttered With Obstacles

Niu

et al. 2017

IEEE Trans. Ind. Electron.

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“…Several user interfaces have been implemented on the 9-DoF WMRA system such as the 3D SpaceBall joystick, laptop touch screen, voice recognition, eye gaze tracking [6], and P300 brain-computer interface (BCI) [5]. Although the system has executed several ADL tasks successfully, it is difficult to teleoperate the 9-DoF system with combined mobility and manipulation.…”

Section: Introductionmentioning

confidence: 99%

Visual servoing control of a 9-DoF WMRA to perform ADL tasks

Pence

Farelo

Alqasemi

et al. 2012

2012 IEEE International Conference on Robotics and Automation

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Abstract-The wheelchair-mounted robotic arm (WMRA) is a mobile manipulator that consists of a 7-DoF robotic arm and a 2-DoF power wheelchair platform. Previous works combined mobility and manipulation control using weighted optimization for dual-trajectory tracking [7]. In this work, we present an image-based visual servoing (IBVS) approach with scaleinvariant feature transform (SIFT) using an eye-in-hand monocular camera for combined control of mobility and manipulation for the 9-DoF WMRA system to execute activities of daily living (ADL) autonomously. We also present results of the physical implementation with a simple "Go to and Pick Up" task and the "Go to and Open the Door" task previously published in simulation, using IBVS to aid the task performance.

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Control of a 9-DoF Wheelchair-mounted robotic arm system using a P300 Brain Computer Interface: Initial experiments

Cited by 75 publications

References 15 publications

Estimation of Forearm Supination/Pronation Motion Based on EEG Signals to Control an Artificial Arm

Estimation of Forearm Supination/Pronation Motion Based on EEG Signals to Control an Artificial Arm

An Event-Related Potential-Based Adaptive Model for Telepresence Control of Humanoid Robot Motion in an Environment Cluttered With Obstacles

Visual servoing control of a 9-DoF WMRA to perform ADL tasks

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