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CLC number: TP391; H125.3

On-line Access: 2018-05-07

Received: 2016-08-28

Revision Accepted: 2016-10-25

Crosschecked: 2018-03-10

Cited: 0

Clicked: 2899

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Fei Chao

http://orcid.org/0000-0002-6928-2638

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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.3 P.423-436

http://doi.org/10.1631/FITEE.1601509


Electroencephalogram-based brain-computer interface for the Chinese spelling system:a survey


Author(s):  Ming-hui Shi, Chang-le Zhou, Jun Xie, Shao-zi Li, Qing-yang Hong, Min Jiang, Fei Chao, Wei-feng Ren, Xiang-qian Liu, Da-jun Zhou, Tian-yu Yang

Affiliation(s):  Fujian Provincial Key Lab of Brain-like Intelligent Systems, Xiamen University, Xiamen 361005, China; more

Corresponding email(s):   fchao@xmu.edu.cn

Key Words:  Brain-computer interface (BCI), Electroencephalography (EEG), Chinese speller, English speller


Ming-hui Shi, Chang-le Zhou, Jun Xie, Shao-zi Li, Qing-yang Hong, Min Jiang, Fei Chao, Wei-feng Ren, Xiang-qian Liu, Da-jun Zhou, Tian-yu Yang. Electroencephalogram-based brain-computer interface for the Chinese spelling system:a survey[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(3): 423-436.

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pages="423-436",
year="2018",
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doi="10.1631/FITEE.1601509"
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A1 - Min Jiang
A1 - Fei Chao
A1 - Wei-feng Ren
A1 - Xiang-qian Liu
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Abstract: 
Electroencephalogram (EEG) based brain-computer interfaces allow users to communicate with the external environment by means of their EEG signals, without relying on the brain’s usual output pathways such as muscles. A popular application for EEGs is the EEG-based speller, which translates EEG signals into intentions to spell particular words, thus benefiting those suffering from severe disabilities, such as amyotrophic lateral sclerosis. Although the EEG-based english speller (EEGES) has been widely studied in recent years, few studies have focused on the EEG-based chinese speller (EEGCS). The EEGCS is more difficult to develop than the EEGES, because the English alphabet contains only 26 letters. By contrast, Chinese contains more than 11 000 logographic characters. The goal of this paper is to survey the literature on EEGCS systems. First, the taxonomy of current EEGCS systems is discussed to get the gist of the paper. Then, a common framework unifying the current EEGCS and EEGES systems is proposed, in which the concept of EEG-based choice acts as a core component. In addition, a variety of current EEGCS systems are investigated and discussed to highlight the advances, current problems, and future directions for EEGCS.

基于脑电图中文拼音输入系统的脑机接口综述

概要:基于脑电图(EEG)的脑机接口能够让用户通过脑电信号与外部环境通信,而不必依赖于肌肉等大脑信号的常规输出路径。基于EEG的拼写器是EEG的重要应用,可将脑电信号转换为拼写特定字符或词的意图,从而帮助重度残疾患者(如肌萎缩侧索硬化症患者,ALS患者)。近年来,基于EEG的英文拼写器(EEGES)已得到较广泛研究,而基于脑电的中文拼写器(EEGCS)则研究甚少。由于英文只包含26个字母,而中文包含11000多个图形字符,因此,EEGCS比EEGES更难开发。本文旨在对EEGCS系统的相关文献进行综述,首先,对当前EEGCS系统进行了系统地分类,为后续讨论奠定基础;然后,提出统一当前EEGCS和EEGES的通用系统架构,其中将基于EEG的选择作为核心部件;最后,对当前各种EEGCS系统进行综述和讨论,指出EEGCS研究的当前进展、存在的问题和未来方向。

关键词:脑机接口(BCI);脑电图(EEG);中文拼写器;英文拼写器

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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