Task based motion intention prediction with EEG signals

D. S.V. Bandara, Jumpei Arata, Kazuo Kigichi

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

3 Citations (Scopus)

Abstract

EEG signal is one of the biological signals that can be useful to control wearable robotic devices, according to the human motion intention. However, the real-time estimation of the user's motion intention from EEG signals is cumbersome. The user's motion intention might not be estimated when the user does not concentrate on the control of the robot, distracted by other things or disturbed by the outside interferences. In this paper, a neural network based real-time estimation method is proposed to detect human motion intention in terms of intended task, using EEG signals. The inputs of bandpower time series signals let the neural network identify the dynamic nature of the tasks performed. Experimental details, methodology and the prediction results are presented.

Original languageEnglish
Title of host publicationIRIS 2016 - 2016 IEEE 4th International Symposium on Robotics and Intelligent Sensors
Subtitle of host publicationEmpowering Robots with Smart Sensors
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages57-60
Number of pages4
ISBN (Electronic)9781509060849
DOIs
Publication statusPublished - Oct 11 2017
Event4th IEEE International Symposium on Robotics and Intelligent Sensors, IRIS 2016 - Tokyo, Japan
Duration: Dec 17 2016Dec 20 2016

Publication series

NameIRIS 2016 - 2016 IEEE 4th International Symposium on Robotics and Intelligent Sensors: Empowering Robots with Smart Sensors

Other

Other4th IEEE International Symposium on Robotics and Intelligent Sensors, IRIS 2016
CountryJapan
CityTokyo
Period12/17/1612/20/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Optimization
  • Instrumentation
  • Social Sciences (miscellaneous)

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