Design and Implementation of Intelligent Wheelchair System Based on EEG Control

DOI

10.2991/assehr.k.200401.085How to use a DOI?

Keywords

EEG, intelligent wheelchair, Wavenet De-nosing, feature-extraction, multi-class SVM

Abstract

In order to improve the self-care ability of disabled people, this paper designs and implements an intelligent wheelchair system based on EEG control. The human EEG signal collected by the sensor is sent to the core control board through Bluetooth device. And the wavelet packet transform is used for noise reduction and feature extraction. Then the multi-class SVM technology is used to recognize the EEG signal and realize the intelligent control of the wheelchair in various motion states. In the experiment, the average Kappa coefficient reach 0.622, which is better than BP neural network. Moreover, the system has short response time and high recognition rate, which further showing the effectiveness and reliability of the proposed method.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hongsen Zhou
AU  - Guolong Zhang
PY  - 2020
DA  - 2020/04/06
TI  - Design and Implementation of Intelligent Wheelchair System Based on EEG Control
BT  - Proceedings of the International Conference on Education, Economics and Information Management (ICEEIM 2019)
PB  - Atlantis Press
SP  - 413
EP  - 417
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.200401.085
DO  - 10.2991/assehr.k.200401.085
ID  - Zhou2020
ER  -