Indoor Simulated Training Environment for Brain-Controlled Wheelchair Based on Steady-State Visual Evoked Potentials

被引:14
作者
Liu, Ming [1 ]
Wang, Kangning [1 ,2 ]
Chen, Xiaogang [1 ]
Zhao, Jing [3 ]
Chen, Yuanyuan [4 ]
Wang, Huiquan [2 ]
Wang, Jinhai [2 ]
Xu, Shengpu [1 ]
机构
[1] Chinese Acad Med Sci & Peking Union Med Coll, Inst Biomed Engn, Tianjin, Peoples R China
[2] Tianjin Polytech Univ, Sch Elect & Informat Engn, Tianjin, Peoples R China
[3] Tianjin Univ Tradit Chinese Med, Inst Tradit Chinese Med, Tianjin, Peoples R China
[4] Tianjin Univ, Sch Microelect, Tianjin, Peoples R China
来源
FRONTIERS IN NEUROROBOTICS | 2020年 / 13卷
基金
中国国家自然科学基金;
关键词
simulated environment; brain-controlled wheelchair; indoor training; steady-state visual evoked potentials; path recommendation; COMPUTER-INTERFACE; NEUROPHYSIOLOGICAL PROTOCOL; VIRTUAL ENVIRONMENTS; ACTUATED WHEELCHAIR; BCI; REHABILITATION; MOTIVATION; DESIGN; STROKE;
D O I
10.3389/fnbot.2019.00101
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Brain-controlled wheelchair (BCW) has the potential to improve the quality of life for people with motor disabilities. A lot of training is necessary for users to learn and improve BCW control ability and the performances of BCW control are crucial for patients in daily use. In consideration of safety and efficiency, an indoor simulated training environment is built up in this paper to improve the performance of BCW control. The indoor simulated environment mainly realizes BCW implementation, simulated training scenario setup, path planning and recommendation, simulated operation, and scoring. And the BCW is based on steady-state visual evoked potentials (SSVEP) and the filter bank canonical correlation analysis (FBCCA) is used to analyze the electroencephalography (EEG). Five tasks include individual accuracy, simple linear path, obstacles avoidance, comprehensive steering scenarios, and evaluation task are designed, 10 healthy subjects were recruited and carried out the 7-days training experiment to assess the performance of the training environment. Scoring and command-consuming are conducted to evaluate the improvement before and after training. The results indicate that the average accuracy is 93.55% and improves from 91.05% in the first stage to 96.05% in the second stage (p = 0.001). Meanwhile, the average score increases from 79.88 in the first session to 96.66 in the last session and tend to be stable (p < 0.001). The average number of commands and collisions to complete the tasks decreases significantly with or without the approximate shortest path (p < 0.001). These results show that the performance of subjects in BCW control achieves improvement and verify the feasibility and effectiveness of the proposed simulated training environment.
引用
收藏
页码:1 / 15
页数:15
相关论文
共 56 条
[41]   Robotic and Virtual Reality BCIs Using Spatial Tactile and Auditory Oddball Paradigms [J].
Rutkowski, Tomasz M. .
FRONTIERS IN NEUROROBOTICS, 2016, 10
[42]  
Satti A., 2011, COMPUTATIONAL INTELL, P1
[43]  
Sitaram R, 2008, IEEE SIGNAL PROC MAG, V25, P95, DOI 10.1109/MSP2007.910456
[44]   Electroencephalogram-based control of an electric wheelchair [J].
Tanaka, K ;
Matsunaga, K ;
Wang, HO .
IEEE TRANSACTIONS ON ROBOTICS, 2005, 21 (04) :762-766
[45]   Towards BCI-actuated smart wheelchair system [J].
Tang, Jingsheng ;
Liu, Yadong ;
Hu, Dewen ;
Zhou, ZongTan .
BIOMEDICAL ENGINEERING ONLINE, 2018, 17
[46]   Augmented feedback presented in a virtual environment accelerates learning of a difficult motor task [J].
Todorov, E ;
Shadmehr, R ;
Bizzi, E .
JOURNAL OF MOTOR BEHAVIOR, 1997, 29 (02) :147-158
[47]   Proposal of a SSVEP-BCI to command a robotic wheelchair [J].
Müller S.M.T. ;
Bastos T.F. ;
Filho M.S. .
Müller, S.M.T. (sandramuller@ceunes.ufes.br), 2013, Springer Science and Business Media, LLC (24) :97-105
[48]   Audio-cued motor imagery-based brain-computer interface: Navigation through virtual and real environments [J].
Velasco-Alvarez, Francisco ;
Ron-Angevin, Ricardo ;
da Silva-Sauer, Leandro ;
Sancha-Ros, Salvador .
NEUROCOMPUTING, 2013, 121 :89-98
[49]   Alpha neurofeedback training improves SSVEP-based BCI performance [J].
Wan, Feng ;
da Cruz, Janir Nuno ;
Nan, Wenya ;
Wong, Chi Man ;
Vai, Mang I. ;
Rosa, Agostinho .
JOURNAL OF NEURAL ENGINEERING, 2016, 13 (03)
[50]   The control of a virtual automatic car based on multiple patterns of motor imagery BCI [J].
Wang, Hongtao ;
Li, Ting ;
Bezerianos, Anastasios ;
Huang, Hui ;
He, Yuebang ;
Chen, Peng .
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2019, 57 (01) :299-309