EEG-based evaluation for perception-assist in upper-limb power-assist exoskeletons

Thilina Dulantha Lalitharatne, Kenbu Teramoto, Yoshiaki Hayashi, Kaori Tamura, Kazuo Kiguchi

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

3 Citations (Scopus)

Abstract

In this paper, we report an attempt to utilize Electroencephalography (EEG) signals for judging the correctness of the performed perception-assist in an upper-limb power-assist exoskeleton. Although Electromyography (EMG) signals can be used for judgments, lack of change in EMG signals and the complexity of the upper-limb motions sometimes make it difficult to evaluate the perception-assist using EMG signals. In this study, we investigate whether EEG signals can alone be used instead of EMG signals to judge the correctness of the perception-assist performed by the exoskeleton. Experiments are carried out with three healthy subjects and the results are presented in this paper. Moreover, we show the potentials and advantages of using EEG signals recorded from brain of the users to judge correctness of the perception-assist in upper-limb power-assist exoskeletons.

Original languageEnglish
Title of host publicationWorld Automation Congress Proceedings
PublisherIEEE Computer Society
Pages307-312
Number of pages6
ISBN (Electronic)9781889335490
DOIs
Publication statusPublished - Oct 24 2014
Event2014 World Automation Congress, WAC 2014 - Waikoloa, United States
Duration: Aug 3 2014Aug 7 2014

Publication series

NameWorld Automation Congress Proceedings
ISSN (Print)2154-4824
ISSN (Electronic)2154-4832

Other

Other2014 World Automation Congress, WAC 2014
CountryUnited States
CityWaikoloa
Period8/3/148/7/14

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

Cite this

Lalitharatne, T. D., Teramoto, K., Hayashi, Y., Tamura, K., & Kiguchi, K. (2014). EEG-based evaluation for perception-assist in upper-limb power-assist exoskeletons. In World Automation Congress Proceedings (pp. 307-312). [6935909] (World Automation Congress Proceedings). IEEE Computer Society. https://doi.org/10.1109/WAC.2014.6935909