Independence of Amplitude-Frequency and Phase Calibrations in an SSVEP-Based BCI Using Stepping Delay Flickering Sequences

This study proposes a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) independent of amplitude-frequency and phase calibrations. Six stepping delay flickering sequences (SDFSs) at 32-Hz flickering frequency were used to implement a six-command BCI system. EEG signals recorded from Oz position were first filtered within 29-35 Hz, segmented based on trigger events of SDFSs to obtain SDFS epochs, and then stored separately in epoch registers. An epoch-average process suppressed the inter-SDFS interference. For each detection point, the latest six SDFS epochs in each epoch register were averaged and the normalized power of averaged responses was calculated. The visual target that induced the maximum normalized power was identified as the visual target. Eight subjects were recruited in this study. All subjects were requested to produce the “563241” command sequence four times. The averaged accuracy, command transfer interval, and information transfer rate (mean std.) values for all eight subjects were 97.38 5.97%, 3.56 0.68 s, and 42.46 11.17 bits/min, respectively. The proposed system requires no calibration in either the amplitude-frequency characteristic or the reference phase of SSVEP which may provide an efficient and reliable channel for the neuromuscular disabled to communicate with external environments.

[1]  Vaegan,et al.  Visual evoked potentials standard (2004) , 2004, Documenta Ophthalmologica.

[2]  John J. Foxe,et al.  Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  G. Sperling,et al.  Attentional modulation of SSVEP power depends on the network tagged by the flicker frequency. , 2006, Cerebral cortex.

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

[5]  H. Flor,et al.  The thought translation device (TTD) for completely paralyzed patients. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[6]  T. J. Sullivan,et al.  A user-friendly SSVEP-based brain–computer interface using a time-domain classifier , 2010, Journal of neural engineering.

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

[8]  Shangkai Gao,et al.  An online brain–computer interface using non-flashing visual evoked potentials , 2010, Journal of neural engineering.

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

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

[11]  Cuntai Guan,et al.  An Efficient P300-based Brain-Computer Interface with Minimal Calibration Time , 2009, NIPS 2009.

[12]  Ivan Volosyak,et al.  A novel calibration method for SSVEP based brain-computer interfaces , 2010, 2010 18th European Signal Processing Conference.

[13]  Jonathan R Wolpaw,et al.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[14]  G. Pfurtscheller,et al.  Could the beta rebound in the EEG be suitable to realize a “brain switch”? , 2009, Clinical Neurophysiology.

[15]  Klaus-Robert Müller,et al.  Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach , 2006, NIPS.

[16]  Ivan Volosyak,et al.  An SSVEP-Based Brain–Computer Interface for the Control of Functional Electrical Stimulation , 2010, IEEE Transactions on Biomedical Engineering.

[17]  G Pfurtscheller,et al.  Current trends in Graz Brain-Computer Interface (BCI) research. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[18]  Shangkai Gao,et al.  A practical VEP-based brain-computer interface. , 2006, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[19]  F. Varela,et al.  Measuring phase synchrony in brain signals , 1999, Human brain mapping.

[20]  Giuseppe Andreoni,et al.  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 , 2009, Comput. Intell. Neurosci..

[21]  S. Tobimatsu,et al.  Normal variability of the amplitude and phase of steady-state VEPs. , 1996, Electroencephalography and clinical neurophysiology.

[22]  Gernot R. Müller-Putz,et al.  Control of an Electrical Prosthesis With an SSVEP-Based BCI , 2008, IEEE Transactions on Biomedical Engineering.

[23]  Chia-Wei Sun,et al.  An SSVEP-Actuated Brain Computer Interface Using Phase-Tagged Flickering Sequences: A Cursor System , 2010, Annals of Biomedical Engineering.

[24]  Klaus-Robert Müller,et al.  Towards Zero Training for Brain-Computer Interfacing , 2008, PloS one.

[25]  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.

[26]  Po-Lei Lee,et al.  Frequency recognition in an SSVEP-based brain computer interface using empirical mode decomposition and refined generalized zero-crossing , 2011, Journal of Neuroscience Methods.

[27]  J. Odom VISUAL EVOKED POTENTIALS STANDARD , 2004 .

[28]  Po-Lei Lee,et al.  The Brain Computer Interface Using Flash Visual Evoked Potential and Independent Component Analysis , 2006, Annals of Biomedical Engineering.

[29]  A. Wilkins,et al.  Photic‐ and Pattern‐induced Seizures: A Review for the Epilepsy Foundation of America Working Group , 2005, Epilepsia.

[30]  R. Bergholz,et al.  Fourier transformed steady-state flash evoked potentials for continuous monitoring of visual pathway function , 2008, Documenta Ophthalmologica.

[31]  Xiaorong Gao,et al.  A BCI-based environmental controller for the motion-disabled , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[32]  Andrzej Cichocki,et al.  Fully Online Multicommand Brain-Computer Interface with Visual Neurofeedback Using SSVEP Paradigm , 2007, Comput. Intell. Neurosci..

[33]  Xiaorong Gao,et al.  A brain–computer interface using motion-onset visual evoked potential , 2008, Journal of neural engineering.

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

[35]  G. Schalk,et al.  ECoG factors underlying multimodal control of a brain-computer interface , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[37]  John R. Smith,et al.  Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment , 2005, EURASIP J. Adv. Signal Process..

[38]  G F Harding,et al.  Televised Material and Photosensitive Epilepsy , 1999, Epilepsia.

[39]  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.

[40]  Yu-Te Wu,et al.  Brain computer interface using flash onset and offset visual evoked potentials , 2008, Clinical Neurophysiology.

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

[42]  C. Herrmann Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena , 2001, Experimental Brain Research.

[43]  Po-Lei Lee,et al.  Accounting for Phase Drifts in SSVEP-Based BCIs by Means of Biphasic Stimulation , 2011, IEEE Transactions on Biomedical Engineering.

[44]  G Pfurtscheller,et al.  EEG-based communication: improved accuracy by response verification. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.