A coloring and timing brain-computer interface for the nursing bed robot. (October 2021)

• Record Type: Journal Article
• Authors: Li, Yuancheng; Zheng, Tianwei; Wang, Mei; Dong, Lihong; Wang, Pai; Qin, Xuebin
• Is Part Of: Computers & electrical engineering. Volume 95(2021)
• Publication Date: 2021-10
• Subjects: Brain–computer interface -- Electroencephalograph -- Intention recognition -- Nursing bed robot -- Wavelet -- Internet of things; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107415
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The patient with a severely spinal injury needs an intention-controlled nursing bed (NB) robot to help the body actions. To recognize human intentions, the brain–computer interface (BCI) of the steady-state visual evoked potentials (SSVEPs) is usually used. For the BCI of the SSVEPs, the visual fatigue should be alleviated, and the intention recognition accuracy should be improved. Therefor this paper proposed a coloring and timing BCI (CT-BCI) to alleviate the visual fatigue, and find the optimal Wavelet vanishing moment parameter to improve the intention recognition accuracy. In addition, the intention coding and decoding are designed and implemented. Furthermore, the wireless communications are used among the CT-BCI and the robots in the internet of things. Experiments show that the proposed CT-BCI decreases the average evocation time for each intention by 0.8 s, and prolongs the eye non-fatigue duration by 5.45 s, and improves the intention recognition accuracy by 6.7% compared with the traditional BCI. Graphical abstract: Highlights: A coloring and timing brain-computer interface is proposed. Optimal wavelet vanishing moment parameter is found for the brain data processing. Coding and decoding of intentions are designed and implemented, and the system are realized. The nursing bed can be controlled by the EEG and EOG as well as MI.