Full Text:   <667>

Summary:  <175>

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


Fei Chao


-   Go to

Article info.
Open peer comments

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


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.

@article{title="Electroencephalogram-based brain-computer interface for the Chinese spelling system:a survey",
author="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",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Electroencephalogram-based brain-computer interface for the Chinese spelling system:a survey
%A Ming-hui Shi
%A Chang-le Zhou
%A Jun Xie
%A Shao-zi Li
%A Qing-yang Hong
%A Min Jiang
%A Fei Chao
%A Wei-feng Ren
%A Xiang-qian Liu
%A Da-jun Zhou
%A Tian-yu Yang
%J Frontiers of Information Technology & Electronic Engineering
%V 19
%N 3
%P 423-436
%@ 2095-9184
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601509

T1 - Electroencephalogram-based brain-computer interface for the Chinese spelling system:a survey
A1 - Ming-hui Shi
A1 - Chang-le Zhou
A1 - Jun Xie
A1 - Shao-zi Li
A1 - Qing-yang Hong
A1 - Min Jiang
A1 - Fei Chao
A1 - Wei-feng Ren
A1 - Xiang-qian Liu
A1 - Da-jun Zhou
A1 - Tian-yu Yang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 3
SP - 423
EP - 436
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601509

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.




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


[1]Akram F, Han HS, Kim TS, 2014. A P300-based brain computer interface system for words typing. Comput Biol Med, 45:118-125.

[2]Allison BZ, Pineda JA, 2003. ERPs evoked by different matrix sizes:implications for a brain computer interface (BCI) system. IEEE Trans Neur Syst Rehabil Eng, 11(2):110-113.

[3]Allison BZ, Dunne S, Leeb R, et al., 2013. Towards Practical Brain-Computer Interfaces:Bridging the Gap from Research to Real-World Applications. Springer, Berlin, Germany.

[4]Amiri S, Fazel-Rezai R, Asadpour V, 2013a. A review of hybrid brain-computer interface systems. Adv Hum-Comput Interact, 2013:187024.

[5]Amiri S, Rabbi A, Azinfar L, et al., 2013b. A review of P300, SSVEP, and hybrid P300/SSVEP brain-computer interface systems. In:Fazel-Rezai R (Ed.), Brain-Computer Interface Systems—Recent Progress and Future Prospects. InTechOpen, London, UK, p.195-213.

[6]Bai LJ, Yu TY, Li YQ, 2015. A brain computer interface-based explorer. J Neurosci Methods, 244:2-7.

[7]Birbaumer N, Ghanayim N, Hinterberger T, et al., 1999. A spelling device for the paralysed. Nature, 398(6725):297-298.

[8]Blankertz B, Dornhege G, Krauledat M, et al., 2006. The Berlin brain-computer interface presents the novel mental typewriter Hex-o-Spell. 3rd Int Brain-Computer Interface Workshop and Training Course, p.108-109.

[9]Blankertz B, Tomioka R, Lemm S, et al., 2008. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process Mag, 25(1):41-56.

[10]Blankertz B, Tangermann M, Vidaurre C, et al., 2010. The Berlin brain-computer interface:non-medical uses of BCI technology. Front Neurosci, 4:198.

[11]Blankertz B, Lemm S, Treder M, et al., 2011. Single-trial analysis and classification of ERP components—a tutorial. Neuroimage, 56(2):814-825.

[12]Brouwer AM, van Erp JB, 2010. A tactile P300 brain-computer interface. Front Neurosci, 4:19.

[13]Brunner P, Joshi S, Briskin S, et al., 2010. Does the ‘P300’ speller depend on eye gaze J Neur Eng, 7(5):056013.

[14]Chen CH, Yang J, Huang YB, et al., 2013. A cursor control based Chinese-English BCI speller. 20th Int Conf on Neural Information Processing, p.403-410.

[15]Colwell KA, Ryan DB, Throckmorton CS, et al., 2014. Channel selection methods for the P300 speller. J Neurosci Methods, 232:6-15.

[16]Coyle S, Ward T, Markham C, 2003. Brain-computer interfaces:a review. Interdiscip Sci Rev, 28(2):112-118.

[17]D’albis T, Blatt R, Tedesco R, et al., 2012. A predictive speller controlled by a brain-computer interface based on motor imagery. ACM Trans Comput-Hum Interact, 19(3):20.

[18]Delorme A, Makeig S, 2004. EEGLAB:an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods, 134(1):9-21.

[19]Farwell LA, Donchin E, 1988. Talking off the top of your head:toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol, 70(6):510-523.

[20]Ford JM, White P, Lim KO, et al., 1994. Schizophrenics have fewer and smaller P300s:a single-trial analysis. Biol Psychiatry, 35(2):96-103.

[21]Halder S, Pinegger A, Käthner I, et al., 2015. Brain-controlled applications using dynamic P300 speller matrices. Artif Intell Med, 63(1):7-17.

[22]Hinterberger T, Schmidt S, Neumann N, et al., 2004. Brain-computer communication and slow cortical potentials. IEEE Trans Biomed Eng, 51(6):1011-1018.

[23]Hoffmann U, Vesin JM, Ebrahimi T, et al., 2008. An efficient P300-based brain-computer interface for disabled subjects. J Neurosci Methods, 167(1):115-125.

[24]Höhne J, Schreuder M, Blankertz B, et al., 2010. Two-dimensional auditory P300 speller with predictive text system. Annual Int Conf of the IEEE Engineering in Medicine and Biology Society, p.4185-4188.

[25]Höhne J, Schreuder M, Blankertz B, et al., 2011. A novel 9-class auditory ERP paradigm driving a predictive text entry system. Front Neurosci, 5:99.

[26]Hong B, Guo F, Liu T, et al., 2009. N200-speller using motion-onset visual response. Clin Neurophysiol, 120(9):1658-1666.

[27]Huang TW, Tai YH, Tian YJ, et al., 2013. The fastest BCI for writing Chinese characters using brain waves. 4th Global Congress on Intelligent Systems, p.346-349.

[28]Jin J, Allison BZ, Brunner C, et al., 2010. P300 Chinese input system based on Bayesian LDA. Biomed Eng/Biomed Tech, 55(1):5-18.

[29]Jin J, Allison BZ, Kaufmann T, et al., 2012a. The changing face of P300 BCIs:a comparison of stimulus changes in a P300 BCI involving faces, emotion, and movement. PLOS ONE, 7(11):e49688.

[30]Jin J, Allison BZ, Wang XY, et al., 2012b. A combined brain-computer interface based on P300 potentials and motion-onset visual evoked potentials. J Neurosci Methods, 205(2):265-276.

[31]Jin J, Allison BZ, Zhang Y, et al., 2014a. An ERP-based BCI using an oddball paradigm with different faces and reduced errors in critical functions. Int J Neur Syst, 24(8):1450027.

[32]Jin J, Daly I, Zhang Y, et al., 2014b. An optimized ERP brain-computer interface based on facial expression changes. J Neur Eng, 11(3):036004.

[33]Jin J, Sellers EW, Zhou SJ, et al., 2015. A P300 brain-computer interface based on a modification of the mismatch negativity paradigm. Int J Neur Syst, 25(3):1550011.

[34]Kaufmann T, Schulz SM, Grünzinger C, et al., 2011. Flashing characters with famous faces improves ERP-based brain-computer interface performance. J Neur Eng, 8(5):056016.

[35]Kaufmann T, Holz EM, Kübler A, 2013. Comparison of tactile, auditory, and visual modality for brain-computer interface use:a case study with a patient in the locked-in state. Front Neurosci, 7:129.

[36]Khan YU, Sepulveda F, 2012. EEG single-trial classification of different motor imagery tasks using measures of dispersion and power in frequency bands. Int J Biomed Eng Technol, 8(4):343-356.

[37]Kindermans PJ, Verstraeten D, Buteneers P, et al., 2011. How do you like your P300 speller:adaptive, accurate and simple 5th Int Conf on Brain-Computer Interface, p.1-4.

[38]Kindermans PJ, Verschore H, Schrauwen B, 2013. A unified probabilistic approach to improve spelling in an event-related potential-based brain-computer interface. IEEE Trans Biomed Eng, 60(10):2696-2705.

[39]Kindermans PJ, Tangermann M, Muller KR, et al., 2014. Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller. J Neur Eng, 11(3):35005.

[40]Li YQ, Guan CT, Li HQ, et al., 2008. A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system. Patt Recogn Lett, 29(9):1285-1294.

[41]Liu Q, Chen K, Ai QS, et al., 2014. Review:recent development of signal processing algorithms for SSVEP-based brain computer interfaces. J Med Biol Eng, 34(4):299-309.

[42]Minett JW, Peng G, Zhou L, et al., 2010. An assistive communication brain-computer interface for Chinese text input. 4th IEEE Int Conf on Bioinformatics and Biomedical Engineering, p.1-4.

[43]Minett JW, Zheng HY, Fong MCM, et al., 2012. A Chinese text input brain-computer interface based on the P300 speller. Int J Hum-Comput Interact, 28(7):472-483.

[44]Mora-Cortes A, Manyakov NV, Chumerin N, et al., 2014. Language model applications to spelling with brain-computer interfaces. Sensors, 14(4):5967-5993.

[45]Nicolas-Alonso LF, Gomez-Gil J, 2012. Brain computer interfaces:a review. Sensors, 12(2):1211-1279.

[46]Nijholt A, 2008. BCI for games:a ‘state of the art’ survey. 7th Int Conf on Entertainment Computing, p.225-228.

[47]Parini S, Maggi L, Turconi AC, et al., 2009. A robust and self-paced BCI system based on a four class SSVEP paradigm:algorithms and protocols for a high-transfer-rate direct brain communication. Comput Intell Neurosci, 2009:864564.

[48]Ramoser H, Muller-Gerking J, Pfurtscheller G, 2000. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng, 8(4):441-446.

[49]Ryan DB, Frye GE, Townsend G, et al., 2010. Predictive spelling with a P300-based brain-computer interface:increasing the rate of communication. Int J Hum-Comput Interact, 27(1):69-84.

[50]Schreuder M, Rost T, Tangermann M, 2011. Listen, you are writing! Speeding up online spelling with a dynamic auditory BCI. Front Neurosci, 5:112.

[51]Schreuder M, Höhne J, Blankertz B, et al., 2013. Optimizing event-related potential based brain-computer interfaces:a systematic evaluation of dynamic stopping methods. J Neur Eng, 10(3):036025.

[52]Sellers EW, Krusienski DJ, McFarland DJ, et al., 2006. A P300 event-related potential brain-computer interface (BCI):the effects of matrix size and inter stimulus interval on performance. Biol Psychol, 73(3):242-252.

[53]Semlitsch HV, Anderer P, Schuster P, et al., 1986. A solution for reliable and valid reduction of ocular artifacts, applied to the P300 ERP. Psychophysiology, 23(6):695-703.

[54]Shende PM, Jabade VS, 2015. Literature review of brain computer interface (BCI) using electroencephalogram signal. Int Conf on Pervasive Computing, p.1-5.

[55]Speier W, Arnold C, Lu J, et al., 2012. Natural language processing with dynamic classification improves P300 speller accuracy and bit rate. J Neur Eng, 9(1):016004.

[56]Sun KT, Huang TW, Chen MC, 2011. Design of Chinese spelling system based on ERP. IEEE 11th Int Conf on Bioinformatics and Bioengineering, p.310-313.

[57]Tangermann M, Schreuder M, Dähne S, et al., 2011. Optimized stimulation events for a visual ERP BCI. Int J Bioelectromagn, 13(3):119-120.

[58]Townsend G, LaPallo BK, Boulay CB, et al., 2010. A novel P300-based brain-computer interface stimulus presentation paradigm:moving beyond rows and columns. Clin Neurophysiol, 121(7):1109-1120.

[59]Verschore H, Kindermans PJ, Verstraeten D, et al., 2012. Dynamic stopping improves the speed and accuracy of a P300 speller. 22nd Int Conf on Artificial Neural Networks and Machine Learning, p.661-668.

[60]Vidal JJ, 1973. Toward direct brain-computer communication. Ann Rev Biophys Bioeng, 2:157-180.

[61]Wu B, Su Y, Zhang JH, et al., 2009. A virtual Chinese keyboard BCI system based on P300 potentials. Acta Electron Sin, 37(8):1733-1738, 1745 (in Chinese).

[62]Xia B, Yang H, Zhang QM, et al., 2012. Control 2-dimensional movement using a three-class motor imagery based brain-computer interface. Annual Int Conf of the IEEE Engineering in Medicine and Biology Society.

[63]Xu MP, Qi HZ, Wan BK, et al., 2013. A hybrid BCI speller paradigm combining P300 potential and the SSVEP blocking feature. J Neur Eng, 10(2):026001.

[64]Xu X, Fang HJ, 2015. A P300-based BCI system for online Chinese input. J Huaqiao Univ (Nat Sci), 36(3):269-274 (in Chinese).

[65]Yin EW, Zhou ZT, Jiang J, et al., 2013. A novel hybrid BCI speller based on the incorporation of SSVEP into the P300 paradigm. J Neur Eng, 10(2):026012.

[66]Yin EW, Zhou ZT, Jiang J, et al., 2014. A speedy hybrid BCI spelling approach combining P300 and SSVEP. IEEE Trans Biomed Eng, 61(2):473-483.

[67]Yin EW, Zhou ZT, Jiang J, et al., 2015. A dynamically optimized SSVEP brain-computer interface (BCI) speller. IEEE Trans Biomed Eng, 62(6):1447-1456.

[68]Zhang JX, Fang Z, Du YC, et al., 2012. Centro-parietal N200:an event-related potential component specific to Chinese visual word recognition. Chin Sci Bull, 57(13):1516-1532.

[69]Zhang Y, Zhao QB, Jin J, et al., 2012. A novel BCI based on ERP components sensitive to configural processing of human faces. J Neur Eng, 9(2):026018.

[70]Zhao G, 2015. The contemporary Chinese dictionary. Int J Lexicography, 28(1):107-123.

[71]Zhao JB, 2012. Steady-State Visual Evoked Potential:the Attentional Mechanism and the Application in Brain Computer Interface. PhD Thesis, Zhejiang University, Hangzhou, China (in Chinese).

[72]Zhu DH, Bieger J, Molina GG, et al., 2010. A survey of stimulation methods used in SSVEP-based BCIs. Comput Intell Neurosci, 2010:702357.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE