Task estimation of upper-limb using EEG and EMG signals

Kazuo Kiguchi, Yoshiaki Hayashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Many kinds of wearable robots have been proposed. In the control of those robots, surface electromyogram (sEMG) signals are widely used in order to estimate a user's motion intention. However, EMG signals that are needed to estimate a user's motion are not always available with all users. On the other hand, in recent years, an electroencephalogram (EEG) signal that is measured through an electrode on the scalp is used to control robots. It is not easy to estimate a user's motion-intention from measured EEG signals in comparison with sEMG signals in which the increase or decrease of the signals relates closely to the motion. In this study, an electric artificial arm for above elbow amputees is controlled based on EMG and EEG signals. An EEG-based control method is proposed to control the forearm and wrist motions of an electric artificial arm in this paper. The target position of the hand is estimated based on the EEG signals, the shoulder and elbow motions. In the proposed method, the target position is selected based on the shoulder and elbow motions, then EEG signals are used to judge whether the selected target position based on the shoulder and elbow motions is correct.

Original languageEnglish
Title of host publicationAIM 2014 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages548-553
Number of pages6
ISBN (Print)9781479957361
DOIs
Publication statusPublished - Jan 1 2014
Event2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2014 - Besancon, France
Duration: Jul 8 2014Jul 11 2014

Publication series

NameIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM

Other

Other2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2014
CountryFrance
CityBesancon
Period7/8/147/11/14

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Computer Science Applications
  • Electrical and Electronic Engineering

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  • Cite this

    Kiguchi, K., & Hayashi, Y. (2014). Task estimation of upper-limb using EEG and EMG signals. In AIM 2014 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics (pp. 548-553). [6878135] (IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AIM.2014.6878135