SSVEP-BCI implementation for 37–40 Hz frequency range

This work presents a Brain-Computer Interface (BCI) based on Steady State Visual Evoked Potentials (SSVEP), using higher stimulus frequencies (>30 Hz). Using a statistical test and a decision tree, the real-time EEG registers of six volunteers are analyzed, with the classification result updated each second. The BCI developed does not need any kind of settings or adjustments, which makes it more general. Offline results are presented, which corresponds to a correct classification rate of up to 99% and a Information Transfer Rate (ITR) of up to 114.2 bits/min.

[1]  Mario Sarcinelli-Filho,et al.  Spectral Techniques for Incremental SSVEP Analysis Applied to a BCI Implementation , 2013 .

[2]  Pablo F. Diez,et al.  A comparison of monopolar and bipolar EEG recordings for SSVEP detection , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[3]  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..

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

[5]  Ivan Volosyak,et al.  Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces , 2007, IEEE Transactions on Biomedical Engineering.

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

[7]  Mario Sarcinelli-Filho,et al.  Incremental SSVEP analysis for BCI implementation , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[8]  G. Garcia Molina,et al.  Detection of high frequency steady state visual evoked potentials for Brain-computer interfaces , 2009, 2009 17th European Signal Processing Conference.

[9]  Antonio Mauricio Ferreira Leite Miranda de Sá,et al.  Assessing Time- and Phase-Locked Changes in the EEG during Sensory Stimulation by Means of Spectral Techniques , 2009 .

[10]  Xiaorong Gao,et al.  Frequency Selection for SSVEP-based Binocular Rivalry , 2005, Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005..

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

[12]  Wanderley Cardoso Celeste,et al.  Brain-computer Interface Based on Visual Evoked Potentials to Command Autonomous Robotic Wheelchair , 2010 .

[13]  J R Wolpaw,et al.  Spatial filter selection for EEG-based communication. , 1997, Electroencephalography and clinical neurophysiology.