Literature DB >> 23627660

Design of assistive wheelchair system directly steered by human thoughts.

Junhua Li1, Jianyi Liang, Qibin Zhao, Jie Li, Kan Hong, Liqing Zhang.   

Abstract

Integration of brain-computer interface (BCI) technique and assistive device is one of chief and promising applications of BCI system. With BCI technique, people with disabilities do not have to communicate with external environment through traditional and natural pathways like peripheral nerves and muscles, and could achieve it only by their brain activities. In this paper, we designed an electroencephalogram (EEG)-based wheelchair which can be steered by users' own thoughts without any other involvements. We evaluated the feasibility of BCI-based wheelchair in terms of accuracies and real-world testing. The results demonstrate that our BCI wheelchair is of good performance not only in accuracy, but also in practical running testing in a real environment. This fact implies that people can steer wheelchair only by their thoughts, and may have a potential perspective in daily application for disabled people.

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Mesh:

Year:  2013        PMID: 23627660     DOI: 10.1142/S0129065713500135

Source DB:  PubMed          Journal:  Int J Neural Syst        ISSN: 0129-0657            Impact factor:   5.866


  10 in total

1.  Application of Neuroengineering Based on EEG Features in the Industrial Design of Comfort.

Authors:  Xiaojun Zhou; S Ruhaizin; Wei Zhu; Cheng Shen; Xiaobo He
Journal:  Comput Intell Neurosci       Date:  2022-06-10

Review 2.  Past, Present, and Future of EEG-Based BCI Applications.

Authors:  Kaido Värbu; Naveed Muhammad; Yar Muhammad
Journal:  Sensors (Basel)       Date:  2022-04-26       Impact factor: 3.847

3.  Exploring Combinations of Different Color and Facial Expression Stimuli for Gaze-Independent BCIs.

Authors:  Long Chen; Jing Jin; Ian Daly; Yu Zhang; Xingyu Wang; Andrzej Cichocki
Journal:  Front Comput Neurosci       Date:  2016-01-29       Impact factor: 2.380

4.  Brain-Computer Interface application: auditory serial interface to control a two-class motor-imagery-based wheelchair.

Authors:  Ricardo Ron-Angevin; Francisco Velasco-Álvarez; Álvaro Fernández-Rodríguez; Antonio Díaz-Estrella; María José Blanca-Mena; Francisco Javier Vizcaíno-Martín
Journal:  J Neuroeng Rehabil       Date:  2017-05-30       Impact factor: 4.262

5.  A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network.

Authors:  Yaqi Chu; Xingang Zhao; Yijun Zou; Weiliang Xu; Jianda Han; Yiwen Zhao
Journal:  Front Neurosci       Date:  2018-09-28       Impact factor: 4.677

6.  A Fused Multidimensional EEG Classification Method Based on an Extreme Tree Feature Selection.

Authors:  Ruijing Lin; Chaoyi Dong; Pengfei Ma; Shuang Ma; Xiaoyan Chen; Huanzi Liu
Journal:  Comput Intell Neurosci       Date:  2022-08-08

7.  EEG-Based Person Identification during Escalating Cognitive Load.

Authors:  Ivana Kralikova; Branko Babusiak; Maros Smondrk
Journal:  Sensors (Basel)       Date:  2022-09-21       Impact factor: 3.847

Review 8.  Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces.

Authors:  Keum-Shik Hong; M Jawad Khan; Melissa J Hong
Journal:  Front Hum Neurosci       Date:  2018-06-28       Impact factor: 3.169

9.  Evaluation of Switch and Continuous Navigation Paradigms to Command a Brain-Controlled Wheelchair.

Authors:  Álvaro Fernández-Rodríguez; Francisco Velasco-Álvarez; Manon Bonnet-Save; Ricardo Ron-Angevin
Journal:  Front Neurosci       Date:  2018-06-28       Impact factor: 4.677

10.  Effects of Skin Friction on Tactile P300 Brain-Computer Interface Performance.

Authors:  Ying Mao; Jing Jin; Shurui Li; Yangyang Miao; Andrzej Cichocki
Journal:  Comput Intell Neurosci       Date:  2021-02-09
  10 in total

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