The parallel-BCI speller based on the P300 and SSVEP features

This paper developed a parallel-BCI speller system, consisting of four simultaneously presented sub-spellers, where the sub-speller switching and character selection were simultaneously identified through decoding the dominant frequency of steady-state visual-evoked potential (SSVEP) and the time of P300 occurrence in a parallel mode. Five subjects took part in the offline and online experiments. The canonical correlation analysis (CCA) and stepwise linear discriminant analysis (SWLDA) were jointly used to recognize the target character. Online tests showed that the parallel-BCI speller reached the peak information transfer rate (ITR) of 67.4 bit/min with an average of 48.0 bit/min. The results indicate that the proposed parallel-BCI system can be effectively controlled by users with attention shifting fluently among the sub-spellers, and highly improve the BCI spelling performance.

[1]  Brendan Z. Allison,et al.  Improved signal processing approaches in an offline simulation of a hybrid brain–computer interface , 2010, Journal of Neuroscience Methods.

[2]  S. Coyle,et al.  Brain–computer interfaces: a review , 2003 .

[3]  Ying Sun,et al.  Asynchronous P300 BCI: SSVEP-based control state detection , 2010, 2010 18th European Signal Processing Conference.

[4]  A. Cichocki,et al.  Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives , 2010, Progress in Neurobiology.

[5]  Brendan Z. Allison,et al.  The Hybrid BCI , 2010, Frontiers in Neuroscience.

[6]  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.

[7]  Febo Cincotti,et al.  Tools for Brain-Computer Interaction: A General Concept for a Hybrid BCI , 2011, Front. Neuroinform..

[8]  Dong Ming,et al.  A hybrid BCI speller paradigm combining P300 potential and the SSVEP blocking feature , 2013, Journal of neural engineering.

[9]  Fanglin Chen,et al.  A novel hybrid BCI speller based on the incorporation of SSVEP into the P300 paradigm , 2013, Journal of neural engineering.

[10]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.

[11]  G Pfurtscheller,et al.  Toward a hybrid brain–computer interface based on imagined movement and visual attention , 2010, Journal of neural engineering.

[12]  Ivan Volosyak,et al.  SSVEP-based Bremen–BCI interface—boosting information transfer rates , 2011, Journal of neural engineering.

[13]  D. Regan.  Human brain electrophysiology: Evoked potentials and evoked magnetic fields in science and medicine , 1989 .

[14]  Yuanqing Li,et al.  Target Selection With Hybrid Feature for BCI-Based 2-D Cursor Control , 2012, IEEE Transactions on Biomedical Engineering.

[15]  Christa Neuper,et al.  Combined motor imagery and SSVEP based BCI control of a 2 DoF artificial upper limb , 2011, Medical & Biological Engineering & Computing.