Mesh of SSVEP-Based BCI and Eye-Tracker for Use of Higher Frequency Stimuli and Lower Number of EEG Channels

Steady-State Visually Evoked Potential (SSVEP) is widely used in Brain-Computer Interface (BCI) systems. However, the use of flickering stimuli at low frequencies causes visual fatigue for users. The visual fatigue increases when multiple stimuli are used, flickering at different frequencies. To overcome this problem, this paper present a solution by using single high frequency (30 Hz) stimulus interface with 30 targets. In the proposed system, the initial recognition of the target was achieved through the eye gaze position using an eye-tracker, and the selection/classification of command was provided by EEG. As only a single stimulating frequency was used (i.e. 30 Hz), thus, only two EEG electrodes (at positions Pz and Oz) were used along with g.USBamp amplifier. This reduced the setup-time for the preparation of the users. A new calibration technique for the eye tracker was designed and developed, which resulted in better eye gaze tracking. The results showed that higher classification accuracy can be achieved by using the mesh of SSVEP-based BCI system and eye-tracker as compared to the SSVEP-based BCI system.

[1]  K. Müller,et al.  Effect of higher frequency on the classification of steady-state visual evoked potentials , 2016, Journal of neural engineering.

[2]  Chang-Hwan Im,et al.  evelopment of a hybrid mental spelling system combining SVEP-based brain – computer interface and webcam-based eye racking , 2015 .

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

[4]  Brendan Z. Allison,et al.  How Many People Could Use an SSVEP BCI? , 2012, Front. Neurosci..

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

[6]  Piotr Stawicki,et al.  Comparison of Speed, accuracy, and User Friendliness between SSVEP-based BCI and Eyetracker , 2017, GBCIC.

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

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

[9]  Chris P. Brennan,et al.  An SSVEP and Eye Tracking Hybrid BNCI: Potential Beyond Communication and Control , 2016, HCI.

[10]  Ivan Volosyak,et al.  Impact of Frequency Selection on LCD Screens for SSVEP Based Brain-Computer Interfaces , 2009, IWANN.

[11]  Ivan Volosyak,et al.  Evaluation of an SSVEP based Brain-Computer Interface on the command and application levels , 2009, 2009 4th International IEEE/EMBS Conference on Neural Engineering.

[12]  Toshihisa Tanaka,et al.  A comparison study of visually stimulated brain-computer and eye-tracking interfaces. , 2017, Journal of neural engineering.

[13]  Piotr Stawicki,et al.  A Novel Hybrid Mental Spelling Application Based on Eye Tracking and SSVEP-Based BCI , 2017, Brain sciences.

[14]  J. Wolpaw,et al.  Brain-Computer Interfaces: Principles and Practice , 2012 .

[15]  Piotr Stawicki,et al.  Age-related differences in SSVEP-based BCI performance , 2017, Neurocomputing.

[16]  A Graser,et al.  BCI Demographics II: How Many (and What Kinds of) People Can Use a High-Frequency SSVEP BCI? , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[17]  Koji Tsuru,et al.  A New Stimulation for Steady-State Visually Evoked Potentials Based Brain-Computer Interface Using Semi-transmissive Patterns with Smartglasses , 2015, 2015 International Conference on Cyberworlds (CW).