A High-Speed Brain Speller using steady-State Visual evoked potentials

Implementing a complex spelling program using a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) remains a challenge due to difficulties in stimulus presentation and target identification. This study aims to explore the feasibility of mixed frequency and phase coding in building a high-speed SSVEP speller with a computer monitor. A frequency and phase approximation approach was developed to eliminate the limitation of the number of targets caused by the monitor refresh rate, resulting in a speller comprising 32 flickers specified by eight frequencies (8-15 Hz with a 1 Hz interval) and four phases (0°, 90°, 180°, and 270°). A multi-channel approach incorporating Canonical Correlation Analysis (CCA) and SSVEP training data was proposed for target identification. In a simulated online experiment, at a spelling rate of 40 characters per minute, the system obtained an averaged information transfer rate (ITR) of 166.91 bits/min across 13 subjects with a maximum individual ITR of 192.26 bits/min, the highest ITR ever reported in electroencephalogram (EEG)-based BCIs. The results of this study demonstrate great potential of a high-speed SSVEP-based BCI in real-life applications.

[1]  Yan Wang,et al.  Visual stimulus design for high-rate SSVEP BCI , 2010 .

[2]  Ian N. Hsiao,et al.  Functional Neuromuscular Stimulation of the Respiratory Muscles for Patients With Spinal Cord Injury , 2008, Proceedings of the IEEE.

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

[4]  Xiaorong Gao,et al.  Enhancing the classification accuracy of steady-state visual evoked potential-based brain–computer interfaces using phase constrained canonical correlation analysis , 2011, Journal of neural engineering.

[5]  Tzyy-Ping Jung,et al.  An approximation approach for rendering visual flickers in SSVEP-based BCI using monitor refresh rate , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[6]  Wei Wu,et al.  Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs , 2006, IEEE Transactions on Biomedical Engineering.

[7]  Tzyy-Ping Jung,et al.  14.4: Polychromatic High‐Frequency Steady‐State Visual Evoked Potentials for Brain‐Display Interaction , 2013 .

[8]  Yijun Wang,et al.  Brain-Computer Interfaces Based on Visual Evoked Potentials , 2008, IEEE Engineering in Medicine and Biology Magazine.

[9]  Erich E. Sutter,et al.  The brain response interface: communication through visually-induced electrical brain responses , 1992 .

[10]  Yu-Te Wang,et al.  Generating Visual Flickers for Eliciting Robust Steady-State Visual Evoked Potentials at Flexible Frequencies Using Monitor Refresh Rate , 2014, PloS one.

[11]  J. Wolpaw,et al.  Brain–computer interfaces in neurological rehabilitation , 2008, The Lancet Neurology.

[12]  S. Andersen,et al.  Behavioral performance follows the time course of neural facilitation and suppression during cued shifts of feature-selective attention , 2010, Proceedings of the National Academy of Sciences.

[13]  Gernot R. Müller-Putz,et al.  A Single-Switch BCI Based on Passive and imagined movements: toward Restoring Communication in Minimally Conscious patients , 2013, Int. J. Neural Syst..

[14]  Tzyy-Ping Jung,et al.  Noninvasive Neural Prostheses Using Mobile and Wireless EEG , 2008, Proceedings of the IEEE.

[15]  Yijun Wang,et al.  A high-speed BCI based on code modulation VEP , 2011, Journal of neural engineering.

[16]  Donatella Spinelli,et al.  Electrophysiological evidence for an early attentional mechanism in visual processing in humans , 1999, Vision Research.

[17]  Yijun Wang,et al.  VEP-based brain-computer interfaces: time, frequency, and code modulations [Research Frontier] , 2009, IEEE Computational Intelligence Magazine.

[18]  J. Wolpaw,et al.  Towards an independent brain–computer interface using steady state visual evoked potentials , 2008, Clinical Neurophysiology.

[19]  Toshihisa Tanaka,et al.  SSVEP-Based Brain–Computer Interfaces Using FSK-Modulated Visual Stimuli , 2013, IEEE Transactions on Biomedical Engineering.

[20]  A. McGeorge,et al.  Visual Evoked Potentials in Man: New Developments , 1978 .

[21]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces: past, present and future , 2006, Trends in Neurosciences.

[22]  Gerwin Schalk,et al.  Rapid Communication with a “P300” Matrix Speller Using Electrocorticographic Signals (ECoG) , 2010, Front. Neurosci..

[23]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[24]  Makeba Parramore Wilbourn,et al.  Attentional dynamics of infant visual foraging , 2012, Proceedings of the National Academy of Sciences.

[25]  Tzyy-Ping Jung,et al.  A Cell-Phone Based Brain-Computer Interface for Communication in Daily Life , 2010, AICI.

[26]  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.

[27]  Pedro J. García-Laencina,et al.  Efficient Automatic Selection and Combination of EEG Features in Least Squares Classifiers for Motor Imagery Brain-Computer Interfaces , 2013, Int. J. Neural Syst..

[28]  Xiaorong Gao,et al.  Design and implementation of a brain-computer interface with high transfer rates , 2002, IEEE Transactions on Biomedical Engineering.

[29]  D. Regan.  Some characteristics of average steady-state and transient responses evoked by modulated light. , 1966, Electroencephalography and clinical neurophysiology.

[30]  Po-Lei Lee,et al.  An SSVEP-Based BCI Using High Duty-Cycle Visual Flicker , 2011, IEEE Transactions on Biomedical Engineering.

[31]  H. Adeli,et al.  Brain-computer interface technologies: from signal to action , 2013, Reviews in the neurosciences.

[32]  Xiaorong Gao,et al.  A high-ITR SSVEP-based BCI speller , 2014 .

[33]  Wei Yen Hsu,et al.  Application of Competitive Hopfield Neural Network to Brain-Computer Interface Systems , 2012, Int. J. Neural Syst..

[34]  J. Kelsey,et al.  Visual Evoked Potentials in Man: New Developments , 1978 .

[35]  Xingyu Wang,et al.  Multiway Canonical Correlation Analysis for Frequency Components Recognition in SSVEP-Based BCIs , 2011, ICONIP.

[36]  Peng Yuan,et al.  A study of the existing problems of estimating the information transfer rate in online brain–computer interfaces , 2013, Journal of neural engineering.

[37]  Xingyu Wang,et al.  Frequency Recognition in SSVEP-Based BCI using Multiset Canonical Correlation Analysis , 2013, Int. J. Neural Syst..

[38]  Simon P. Kelly,et al.  Visual spatial attention control in an independent brain-computer interface , 2005, IEEE Transactions on Biomedical Engineering.

[39]  Xiaorong Gao,et al.  An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method , 2009, Journal of neural engineering.

[40]  Brendan Z. Allison,et al.  P300 brain computer interface: current challenges and emerging trends , 2012, Front. Neuroeng..

[41]  Marc M. Van Hulle,et al.  Multichannel Decoding for Phase-Coded SSVEP Brain-Computer Interface , 2012, Int. J. Neural Syst..

[42]  S. Hillyard,et al.  Selective attention to stimulus location modulates the steady-state visual evoked potential. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[43]  Scott E. Kerick,et al.  Brain–Computer Interface Technologies in the Coming Decades , 2012, Proceedings of the IEEE.

[44]  Rajesh P. N. Rao,et al.  Towards adaptive classification for BCI , 2006, Journal of neural engineering.

[45]  Bo Hong,et al.  A practical VEP-based brain-computer interface , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[46]  Jie Li,et al.  Design of assistive Wheelchair System directly Steered by Human Thoughts , 2013, Int. J. Neural Syst..

[47]  Tianrui Li.  Fuzziness in systems modelling , 2013, Int. J. Gen. Syst..

[48]  Xingyu Wang,et al.  Optimized stimulus presentation patterns for an event-related potential EEG-based brain–computer interface , 2011, Medical & Biological Engineering & Computing.

[49]  Xiaorong Gao,et al.  Frequency and Phase Mixed Coding in SSVEP-Based Brain--Computer Interface , 2011, IEEE Transactions on Biomedical Engineering.

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

[51]  Arne Robben,et al.  Sampled sinusoidal stimulation profile and multichannel fuzzy logic classification for monitor-based phase-coded SSVEP brain–computer interfacing , 2013, Journal of neural engineering.

[52]  Tzyy-Ping Jung,et al.  A Collaborative Brain-Computer Interface for Improving Human Performance , 2011, PloS one.

[53]  T. Jung,et al.  Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[54]  Gao Xiaorong,et al.  Brain-computer interface based on the high-frequency steady-state visual evoked potential , 2005, Proceedings. 2005 First International Conference on Neural Interface and Control, 2005..

[55]  Francisco J. Pelayo,et al.  An auditory Brain-Computer Interface with Accuracy Prediction , 2012, Int. J. Neural Syst..

[56]  Wolfgang Rosenstiel,et al.  Online Adaptation of a c-VEP Brain-Computer Interface(BCI) Based on Error-Related Potentials and Unsupervised Learning , 2012, PloS one.

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

[58]  A. Engel,et al.  An independent brain–computer interface using covert non-spatial visual selective attention , 2010, Journal of neural engineering.