Indirect Control of an Autonomous Wheelchair Using SSVEP BCI
-
Published:2020-08-20
Issue:4
Volume:32
Page:761-767
-
ISSN:1883-8049
-
Container-title:Journal of Robotics and Mechatronics
-
language:en
-
Short-container-title:J. Robot. Mechatron.
Author:
Ng Danny Wee-Kiat, ,Goh Sing Yau
Abstract
Having the capability to control a wheelchair using brain signals would be a major benefit to patients suffering from motor disabling diseases. However, one major challenge such systems are facing is the amount of input needed over time by the patient for control. Such a navigation control system results in a significant mental burden for the patient. The objective of this study is to develop a BCI system that requires a low number of inputs from a subject to operate. We propose an autonomous wheelchair that uses steady-state visual evoked potential based brain computer interfaces to achieve the objective. A dual mode system was implemented in this study to allow the autonomous wheelchair to work in both unknown and known environments. Robot operating system is used as the middleware in this study for the development of the algorithm to operate the wheelchair. The mental task for the subject using this wheelchair is reduced by relegating the responsibility of navigation control from the subject to the navigation software.
Publisher
Fuji Technology Press Ltd.
Subject
Electrical and Electronic Engineering,General Computer Science
Reference26 articles.
1. J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clinical Neurophysiology, Vol.113, No.6, pp. 767-791, 2002. 2. T. Ito, S. Ushii, T. Sameshima, Y. Mitsui, S. Ohgi, and C. Mizuike, “Design of brain-machine interface using near-infrared spectroscopy,” J. Robot. Mechatron., Vol.25, No.6, pp. 1000-1010, 2013. 3. H. Touyama and M. Sakuda, “Online control of a virtual object with collaborative ssvep,” J. Adv. Comput. Intell. Intell. Inform., Vol.21, No.7, pp. 1291-1297, 2017. 4. Y. Yu, Z. Zhou, J. Jiang, E. Yin, K. Liu, J. Wang, Y. Liu, and D. Hu, “Toward a hybrid bci: Self-paced operation of a p300-based speller by merging a motor imagery-based “brain switch” into a p300 spelling approach,” Int. J. of Human-Computer Interaction, Vol.33, No.8, pp. 623-632, 2017. 5. J. R. Wolpaw, R. S. Bedlack, D. J. Reda, R. J. Ringer, P. G. Banks, T. M. Vaughan, S. M. Heckman, L. M. McCane, C. S. Carmack, S. Winden et al., “Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis,” Neurology, Vol.91, No.3, pp. e258-e267, 2018.
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|