A Motor Imagery Based Brain-Computer Interface Speller

Speller is an important application in brain-computer interface researching. In this study, we developed a novel motor imagery based braincomputer interface speller which integrates a 2-D cursor control strategy into a hex-o-spell paradigm to spell a character in two-step. The experimental results (five subjects participated) showed that the average spelling speed is 14.64 characters per minute and that its average information transfer rate is 73.96 bits per minute.

[1]  Christa Neuper,et al.  Graz Brain-Computer Interface (BCI) II , 1994, ICCHP.

[2]  J. Wolpaw,et al.  Brain-computer communication: unlocking the locked in. , 2001, Psychological bulletin.

[3]  Peter Desain,et al.  Introducing the tactile speller: an ERP-based brain–computer interface for communication , 2012, Journal of neural engineering.

[4]  G. Pfurtscheller,et al.  Self-Paced Operation of an SSVEP-Based Orthosis With and Without an Imagery-Based “Brain Switch:” A Feasibility Study Towards a Hybrid BCI , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  Justin Werfel,et al.  BCI competition 2003-data set Ia: combining gamma-band power with slow cortical potentials to improve single-trial classification of electroencephalographic signals , 2004, IEEE Transactions on Biomedical Engineering.

[6]  Xingyu Wang,et al.  Targeting an efficient target-to-target interval for P300 speller brain–computer interfaces , 2012, Medical & Biological Engineering & Computing.

[7]  G. Pfurtscheller,et al.  Critical Decision-Speed and Information Transfer in the “Graz Brain–Computer Interface” , 2003, Applied psychophysiology and biofeedback.

[8]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[9]  Jie Li,et al.  Control 2-dimensional movement using a three-class motor imagery based Brain-Computer Interface , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Chang-Hwan Im,et al.  Development of an SSVEP-based BCI spelling system adopting a QWERTY-style LED keyboard , 2012, Journal of Neuroscience Methods.

[11]  A. Kübler,et al.  A Brain–Computer Interface Controlled Auditory Event‐Related Potential (P300) Spelling System for Locked‐In Patients , 2009, Annals of the New York Academy of Sciences.

[12]  Hubert Cecotti,et al.  A Self-Paced and Calibration-Less SSVEP-Based Brain–Computer Interface Speller , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[13]  Benjamin Blankertz,et al.  THE BERLIN BRAIN-COMPUTER INTERFACE PRESENTS THE NOVEL MENTAL TYPEWRITER HEX-O-SPELL , 2006 .

[14]  Ivan Volosyak,et al.  Evaluation of the Bremen SSVEP based BCI in real world conditions , 2009, 2009 IEEE International Conference on Rehabilitation Robotics.

[15]  Roberto Tedesco,et al.  A PREDICTIVE SPELLER FOR A BRAIN-COMPUTER INTERFACE BASED ON MOTOR IMAGERY , 2008 .

[16]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[17]  E Donchin,et al.  The mental prosthesis: assessing the speed of a P300-based brain-computer interface. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[18]  Yuanqing Li,et al.  An EEG-Based BCI System for 2-D Cursor Control by Combining Mu/Beta Rhythm and P300 Potential , 2010, IEEE Transactions on Biomedical Engineering.