Impact of electrode positions and harmonic frequency components in SSVEP-based BCIs

Steady state visual evoked potential SSVEP based brain computer interfaces BCIs have been recognised as having major potential to be used practically in everyday daily life as they typically outperform more traditional BCIs in terms of detection accuracy and information transfer rates. It is also essential to improve the ease of use such as reducing the number of necessary electrodes without decreasing speed or accuracy. This study investigated the extent to which electrode positions, derivation methods, and harmonics will affect the performance of a SSVEP-based BCI. A pilot study was carried out on a 4-class SSVEP-based BCI with six healthy subjects. Detection accuracy increased from 63.75 ± 5.62% mean ± SD with monopolar derivation to 89.42 ± 5.58% with two-channel bipolar derivation using only harmonic H1. The significantly improved detection accuracy with two electrodes implies that it is possible to achieve high classification accuracy with a very low number of electrodes.

[1]  A. Graser,et al.  Spelling with Steady-State Visual Evoked Potentials , 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.

[2]  A. S. Rodionov,et al.  Comparison of linear, nonlinear and feature selection methods for EEG signal classification , 2004, International Conference on Actual Problems of Electron Devices Engineering, 2004. APEDE 2004..

[3]  Horst Bischof,et al.  Toward Self-Paced Brain–Computer Communication: Navigation Through Virtual Worlds , 2008, IEEE Transactions on Biomedical Engineering.

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

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

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

[7]  P. Poryzała,et al.  SSVEP-Based Brain-Computer Interface: On the Effect of Stimulus Parameters on VEPs Spectral Characteristics , 2012 .

[8]  Mario Sarcinelli-Filho,et al.  Using a SSVEP-BCI to command a robotic wheelchair , 2011, 2011 IEEE International Symposium on Industrial Electronics.

[9]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[10]  J. Masdeu,et al.  Human Cerebral Activation during Steady-State Visual-Evoked Responses , 2003, The Journal of Neuroscience.

[11]  Reinhold Scherer,et al.  Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components , 2005, Journal of neural engineering.

[12]  Gernot R. Müller-Putz,et al.  Comparison of DFT and lock-in amplifier features and search for optimal electrode positions in SSVEP-based BCI , 2008, Journal of Neuroscience Methods.

[13]  G. Pfurtscheller,et al.  An SSVEP BCI to Control a Hand Orthosis for Persons With Tetraplegia , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  Hubert Cecotti,et al.  Effect of the visual signal structure on Steady-State Visual Evoked Potentials detection , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[15]  Ivan Volosyak,et al.  Optimal visual stimuli on LCD screens for SSVEP based brain-computer interfaces , 2009, 2009 4th International IEEE/EMBS Conference on Neural Engineering.

[16]  Bernhard Schölkopf,et al.  Support vector channel selection in BCI , 2004, IEEE Transactions on Biomedical Engineering.

[17]  D. Regan,et al.  An Effect of Stimulus Colour on Average Steady-state Potentials evoked in Man , 1966, Nature.

[18]  A. H Kemp,et al.  Cortical neurophysiology of anticipatory anxiety: an investigation utilizing steady state probe topography (SSPT) , 2003, NeuroImage.

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

[20]  Raveendran Paramesran,et al.  VEP optimal channel selection using genetic algorithm for neural network classification of alcoholics , 2002, IEEE Trans. Neural Networks.

[21]  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.

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

[23]  Song Xing,et al.  Brain controlled robotic exoskeleton for neurorehabilitation , 2013, 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[24]  Ronald M. Aarts,et al.  A Survey of Stimulation Methods Used in SSVEP-Based BCIs , 2010, Comput. Intell. Neurosci..

[25]  Song Xing,et al.  Reading the mind: The potential of electroencephalography in brain computer interfaces , 2012, 2012 19th International Conference on Mechatronics and Machine Vision in Practice (M2VIP).

[26]  Feng Wan,et al.  An online SSVEP-based chatting system , 2011, Proceedings 2011 International Conference on System Science and Engineering.

[27]  S. A. Hillyard,et al.  Sustained division of the attentional spotlight , 2003, Nature.

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

[29]  Ramaswamy Palaniappan,et al.  Analogue mouse pointer control via an online steady state visual evoked potential (SSVEP) brain-computer interface. , 2011, Journal of neural engineering.

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

[31]  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.

[32]  Barbara G. Tabachnick,et al.  Experimental designs using ANOVA , 2006 .

[33]  R. C. Oldfield.  The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[34]  W. Perlstein,et al.  Steady-state visual evoked potentials reveal frontally-mediated working memory activity in humans , 2003, Neuroscience Letters.

[35]  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.

[36]  Cuntai Guan,et al.  Robust EEG channel selection across sessions in brain-computer interface involving stroke patients , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

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

[38]  Chang-Hwan Im,et al.  Development of an SSVEP-based BCI spelling system adopting a QWERTY-style LED keyboard , 2012, Journal of Neuroscience Methods.

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

[40]  G Calhoun,et al.  Brain-computer interfaces based on the steady-state visual-evoked response. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[41]  Bernhard Schölkopf,et al.  Robust EEG Channel Selection across Subjects for Brain-Computer Interfaces , 2005, EURASIP J. Adv. Signal Process..