Sequence-based manipulation of robotic arm control in brain machine interface

Justin Kilmarx, Reza Abiri, Soheil Borhani, Yang Jiang, Xiaopeng Zhao

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

In brain machine interfaces (BMI), the brain activities are recorded by invasive or noninvasive approaches and translated into command signals to control external prosthetic devices such as a computer cursor, a wheelchair, or a robotic arm. Although many studies confirmed the capability of BMI systems in controlling multi degrees-of-freedom (DOF) prosthetic devices using invasive approaches, BMI research using noninvasive paradigms is still in its infancy. In this paper, a new robotic BMI platform has been developed using electroencephalography (EEG) technology to control a 6-DOF robotic arm. EEG signals were collected from the scalp using a wireless headset exploiting a new fast-training paradigm named as “imagined body kinematics”. A regression model was employed to decode the kinematic parameters from the EEG signals. The subjects were instructed to voluntarily control a virtual cursor in multiple trials to hit different pre-programmed targets on a screen in an optimized sequence. The command signals generated from hitting the targets during trials were applied to control sequential movements of the robotic arm in a discrete manner to manipulate an object in a two-dimensional workspace. This approach is derived from a basic shared control strategy where the robotic arm is responsible for carrying out complex maneuvers based on the user’s intention. Our proposed BMI platform yielded a high success rate of 70% in a sequence-based manipulation task after only a short time of training (10 min). The developed platform serves as a proof-of-concept for EEG-based neuro-prosthetic devices.

Original languageEnglish
Pages (from-to)149-160
Number of pages12
JournalInternational Journal of Intelligent Robotics and Applications
Volume2
Issue number2
DOIs
StatePublished - Jun 1 2018

Bibliographical note

Funding Information:
This work was in part supported by a NeuroNET seed grant to XZ; and in part by the NIH under grants NIH P30 AG028383 to the UK Sanders-Brown Center on Aging, NIH AG00986 to YJ, and NIH NCRR UL1TR000117 to the UK Center for Clinical and Translational Science. JK’s work was partially supported through a summer internship from the Office of Undergraduate Research at The University of Tennessee.

Publisher Copyright:
© 2018, Springer Nature Singapore Pte Ltd.

Keywords

  • Brain machine interface
  • EEG
  • Fast-training
  • Manipulation task
  • Robotic arm

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence

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