A 9-DoF Wheelchair-Mounted Robotic Arm System: Design, Control, Brain-Computer Interfacing, and Testing

Alqasemi, Redwan; Dubey, Rajiv

doi:10.5772/9678

Cited by 12 publications

(10 citation statements)

References 15 publications

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“…[AD10] and references therein) (Figs. This 9-DOF chain is an example of a redundant biomechanical chain, which has more DOF than its end-effector (e.g., a head, hand, or foot) that represents a single SE(3)-group with a total of 6 DOF.…”

Section: A More Realistic Hamiltonian Chainmentioning

confidence: 99%

Enhancing Performance and Reducing Stress in Sports: Technological Advances

Ivancevic¹,

Greenberg²,

Greenberg³

2015

Cognitive Systems Monographs

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Section: A More Realistic Hamiltonian Chainmentioning

confidence: 99%

Enhancing Performance and Reducing Stress in Sports: Technological Advances

Ivancevic¹,

Greenberg²,

Greenberg³

2015

Cognitive Systems Monographs

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“…The EEG signals caused by visual/light stimulations are mainly belonged to P300 and/or SSVEP (Steady-State Visual Evoked Potential). The changes of these EEG signals are used for robot arm control [1]. Because the reactions of EEGs to the stimulation are relatively easy to be detected, the construction of such BMI system is comparatively easy too.…”

Section: Introductionmentioning

confidence: 99%

Motion classifier generation using EEG for robot control

Yoshioka

Zhu

Yoshikawa

et al. 2013

2013 6th International IEEE/EMBS Conference on Neural Engineering (NER)

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In the past decade, a lot of invasive BMIs (BrainMachine Interfaces) directly using nerve action potentials within brain are reported to control external devices such as prostheses and robots. Meanwhile, non-invasive BMIs are developed to provide a communication tools to the external world mostly for the disabled. In our study, we try to establish a BMI technology not only to be used for the disabled but also for the healthy person to support his/her movement. For this purpose, in this paper, we develop an approach to discriminate the motion intention using alpha and beta rhythms of SMR (sensorimotor rhythms) which are easy to be regulated by means of motor imagery or motion. We design four tasks (eye closing, eye opening, pre-motion, and motion) to measure their corresponding EEGs of four different subjects. Further, we design a relaxation/motion classifier to discriminate whether the subject has the motion or the motion intention by a generalized Mahalanobis distance, in which, the Mahalanobis distance is determined by the distribution of two-dimensional differences between the power spectra of alpha and beta rhythms at two measurement points. Finally, we verify and evaluate the designed discrimination classifier, and the results show the effectiveness of our proposed approach.

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“…In addition, even though the end users of this application are people with severe motor disabilities, most studies only tested their systems with healthy participants. Only three studies had disabled participants -two with P300 signal (32,47) and one with SSVEP signal (50) -which are studies based on exogenous signals that do not require training. The use of a large number of participants with previous experience in BCI systems, and subjects that satisfied certain criteria before proceeding to handle the BCW, should be noted.…”

Section: Participantsmentioning

confidence: 99%

“…Only one P300 BCW used a lowlevel navigation system (33), while three of them used high-level (31,32,59) and two used shared control (29,47). On the one hand, a highlevel system allows the selection of the destination to which the BCW will go autonomously, so the P300 is a great candidate to serve as a communication signal, due to its high success rate, and the possibility of offering many destinations in a GUI simultaneously.…”

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confidence: 99%

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Review of real brain-controlled wheelchairs

2016

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Abstract. This paper presents a review of the state of the art regarding wheelchairs driven by a brain-computer interface (BCI). Using a brain-controlled wheelchair (BCW), disabled users could handle a wheelchair through their brain activity, granting autonomy to move through an experimental environment. A classification is established, based on the characteristics of the BCW, such as the type of electroencephalographic (EEG) signal used, the navigation system employed by the wheelchair, the task for the participants, or the metrics used to evaluate the performance. Furthermore, these factors are compared according to the type of signal used, in order to clarify the differences among them. Finally, the trend of current research in this field is discussed, as well as the challenges that should be solved in the future.

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A 9-DoF Wheelchair-Mounted Robotic Arm System: Design, Control, Brain-Computer Interfacing, and Testing

Cited by 12 publications

References 15 publications

Enhancing Performance and Reducing Stress in Sports: Technological Advances

Enhancing Performance and Reducing Stress in Sports: Technological Advances

Motion classifier generation using EEG for robot control

Review of real brain-controlled wheelchairs

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