Literature DB >> 22692936

A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair.

Jinyi Long1, Yuanqing Li, Hongtao Wang, Tianyou Yu, Jiahui Pan, Feng Li.   

Abstract

Brain-computer interfaces (BCIs) are used to translate brain activity signals into control signals for external devices. Currently, it is difficult for BCI systems to provide the multiple independent control signals necessary for the multi-degree continuous control of a wheelchair. In this paper, we address this challenge by introducing a hybrid BCI that uses the motor imagery-based mu rhythm and the P300 potential to control a brain-actuated simulated or real wheelchair. The objective of the hybrid BCI is to provide a greater number of commands with increased accuracy to the BCI user. Our paradigm allows the user to control the direction (left or right turn) of the simulated or real wheelchair using left- or right-hand imagery. Furthermore, a hybrid manner can be used to control speed. To decelerate, the user imagines foot movement while ignoring the flashing buttons on the graphical user interface (GUI). If the user wishes to accelerate, then he/she pays attention to a specific flashing button without performing any motor imagery. Two experiments were conducted to assess the BCI control; both a simulated wheelchair in a virtual environment and a real wheelchair were tested. Subjects steered both the simulated and real wheelchairs effectively by controlling the direction and speed with our hybrid BCI system. Data analysis validated the use of our hybrid BCI system to control the direction and speed of a wheelchair.

Mesh:

Year:  2012        PMID: 22692936     DOI: 10.1109/TNSRE.2012.2197221

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  30 in total

1.  An asynchronous wheelchair control by hybrid EEG-EOG brain-computer interface.

Authors:  Hongtao Wang; Yuanqing Li; Jinyi Long; Tianyou Yu; Zhenghui Gu
Journal:  Cogn Neurodyn       Date:  2014-05-24       Impact factor: 5.082

2.  Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose.

Authors:  Saugat Bhattacharyya; Amit Konar; D N Tibarewala
Journal:  Med Biol Eng Comput       Date:  2014-09-30       Impact factor: 2.602

3.  Novel hybrid brain-computer interface system based on motor imagery and P300.

Authors:  Cili Zuo; Jing Jin; Erwei Yin; Rami Saab; Yangyang Miao; Xingyu Wang; Dewen Hu; Andrzej Cichocki
Journal:  Cogn Neurodyn       Date:  2019-10-21       Impact factor: 5.082

4.  Exploring Cognitive Flexibility With a Noninvasive BCI Using Simultaneous Steady-State Visual Evoked Potentials and Sensorimotor Rhythms.

Authors:  Bradley J Edelman; Jianjun Meng; Nicholas Gulachek; Christopher C Cline; Bin He
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2018-05       Impact factor: 3.802

Review 5.  Review of brain encoding and decoding mechanisms for EEG-based brain-computer interface.

Authors:  Lichao Xu; Minpeng Xu; Tzyy-Ping Jung; Dong Ming
Journal:  Cogn Neurodyn       Date:  2021-04-10       Impact factor: 3.473

Review 6.  Bacomics: a comprehensive cross area originating in the studies of various brain-apparatus conversations.

Authors:  Dezhong Yao; Yangsong Zhang; Tiejun Liu; Peng Xu; Diankun Gong; Jing Lu; Yang Xia; Cheng Luo; Daqing Guo; Li Dong; Yongxiu Lai; Ke Chen; Jianfu Li
Journal:  Cogn Neurodyn       Date:  2020-03-17       Impact factor: 3.473

7.  A low-cost EEG system-based hybrid brain-computer interface for humanoid robot navigation and recognition.

Authors:  Bongjae Choi; Sungho Jo
Journal:  PLoS One       Date:  2013-09-04       Impact factor: 3.240

Review 8.  Closed-loop brain-machine-body interfaces for noninvasive rehabilitation of movement disorders.

Authors:  Frédéric D Broccard; Tim Mullen; Yu Mike Chi; David Peterson; John R Iversen; Mike Arnold; Kenneth Kreutz-Delgado; Tzyy-Ping Jung; Scott Makeig; Howard Poizner; Terrence Sejnowski; Gert Cauwenberghs
Journal:  Ann Biomed Eng       Date:  2014-05-15       Impact factor: 3.934

Review 9.  Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review.

Authors:  Daniela Camargo-Vargas; Mauro Callejas-Cuervo; Stefano Mazzoleni
Journal:  Sensors (Basel)       Date:  2021-06-24       Impact factor: 3.576

10.  A P300-based brain-computer interface with stimuli on moving objects: four-session single-trial and triple-trial tests with a game-like task design.

Authors:  Ilya P Ganin; Sergei L Shishkin; Alexander Y Kaplan
Journal:  PLoS One       Date:  2013-10-31       Impact factor: 3.240

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