Literature DB >> 24694722

Control of a 2 DoF robot using a brain-machine interface.

Enrique Hortal1, Andrés Ubeda2, Eduardo Iáñez3, José M Azorín4.   

Abstract

In this paper, a non-invasive spontaneous Brain-Machine Interface (BMI) is used to control the movement of a planar robot. To that end, two mental tasks are used to manage the visual interface that controls the robot. The robot used is a PupArm, a force-controlled planar robot designed by the nBio research group at the Miguel Hernández University of Elche (Spain). Two control strategies are compared: hierarchical and directional control. The experimental test (performed by four users) consists of reaching four targets. The errors and time used during the performance of the tests are compared in both control strategies (hierarchical and directional control). The advantages and disadvantages of each method are shown after the analysis of the results. The hierarchical control allows an accurate approaching to the goals but it is slower than using the directional control which, on the contrary, is less precise. The results show both strategies are useful to control this planar robot. In the future, by adding an extra device like a gripper, this BMI could be used in assistive applications such as grasping daily objects in a realistic environment. In order to compare the behavior of the system taking into account the opinion of the users, a NASA Tasks Load Index (TLX) questionnaire is filled out after two sessions are completed.
Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Keywords:  Brain–Machine Interface; Directional control; Hierarchical control; Planar robot; Support Vector Machine

Mesh:

Year:  2014        PMID: 24694722     DOI: 10.1016/j.cmpb.2014.02.018

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  4 in total

1.  A supplementary system for a brain-machine interface based on jaw artifacts for the bidimensional control of a robotic arm.

Authors:  Álvaro Costa; Enrique Hortal; Eduardo Iáñez; José M Azorín
Journal:  PLoS One       Date:  2014-11-12       Impact factor: 3.240

2.  Evaluating classifiers to detect arm movement intention from EEG signals.

Authors:  Daniel Planelles; Enrique Hortal; Alvaro Costa; Andrés Ubeda; Eduardo Iáez; José M Azorín
Journal:  Sensors (Basel)       Date:  2014-09-29       Impact factor: 3.576

3.  Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions.

Authors:  Enrique Hortal; Daniel Planelles; Francisco Resquin; José M Climent; José M Azorín; José L Pons
Journal:  J Neuroeng Rehabil       Date:  2015-10-17       Impact factor: 4.262

4.  Using brain-computer interfaces: a scoping review of studies employing social research methods.

Authors:  Johannes Kögel; Jennifer R Schmid; Ralf J Jox; Orsolya Friedrich
Journal:  BMC Med Ethics       Date:  2019-03-07       Impact factor: 2.652

  4 in total

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