On the Quantification of SSVEP Frequency Responses in Human EEG in Realistic BCI Conditions

This article concerns one of the most important problems of brain-computer interfaces (BCI) based on Steady State Visual Evoked Potentials (SSVEP), that is the selection of the a-priori most suitable frequencies for stimulation. Previous works related to this problem were done either with measuring systems that have little in common with actual BCI systems (e.g., single flashing LED) or were presented on a small number of subjects, or the tested frequency range did not cover a broad spectrum. Their results indicate a strong SSVEP response around 10 Hz, in the range 13–25 Hz, and at high frequencies in the band of 40–60 Hz. In the case of BCI interfaces, stimulation with frequencies from various ranges are used. The frequencies are often adapted for each user separately. The selection of these frequencies, however, was not yet justified in quantitative group-level study with proper statistical account for inter-subject variability. The aim of this study is to determine the SSVEP response curve, that is, the magnitude of the evoked signal as a function of frequency. The SSVEP response was induced in conditions as close as possible to the actual BCI system, using a wide range of frequencies (5–30 Hz, in step of 1 Hz). The data were obtained for 10 subjects. SSVEP curves for individual subjects and the population curve was determined. Statistical analysis were conducted both on the level of individual subjects and for the group. The main result of the study is the identification of the optimal range of frequencies, which is 12–18 Hz, for the registration of SSVEP phenomena. The applied criterion of optimality was: to find the largest contiguous range of frequencies yielding the strong and constant-level SSVEP response.

[1]  D Regan,et al.  A high frequency mechanism which underlies visual evoked potentials. , 1968, Electroencephalography and clinical neurophysiology.

[2]  D. Regan,et al.  Recent advances in electrical recording from the human brain , 1975, Nature.

[3]  Ernst Fernando Lopes Da Silva Niedermeyer,et al.  Electroencephalography, basic principles, clinical applications, and related fields , 1982 .

[4]  Z J Koles,et al.  The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG. , 1991, Electroencephalography and clinical neurophysiology.

[5]  S. Makeig Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. , 1993, Electroencephalography and clinical neurophysiology.

[6]  Z. Koles,et al.  Spatial patterns in the background EEG underlying mental disease in man. , 1994, Electroencephalography and clinical neurophysiology.

[7]  Z J Koles,et al.  Spatio-temporal decomposition of the EEG: a general approach to the isolation and localization of sources. , 1995, Electroencephalography and clinical neurophysiology.

[8]  Y. Benjamini,et al.  THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .

[9]  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.

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

[11]  Michael S. Lazar,et al.  Spatial patterns underlying population differences in the background EEG , 2005, Brain Topography.

[12]  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..

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

[14]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

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

[16]  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.

[17]  Wei Wu,et al.  Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs , 2007, IEEE Transactions on Biomedical Engineering.

[18]  Søren K. Andersen,et al.  Attentional bias of competitive interactions in neuronal networks of early visual processing in the human brain , 2008, NeuroImage.

[19]  I. Kaashoek,et al.  Automatic Determination of the Optimum Stimulation Frequencies in an SSVEP based BCI , 2009 .

[20]  Fernando Lopes da Silva,et al.  Comprar Niedermeyer's Electroencephalography, 6/e (Basic Principles, Clinical Applications, and Related Fields ) | Fernando Lopes Da Silva | 9780781789424 | Lippincott Williams & Wilkins , 2010 .

[21]  Gary Garcia Molina,et al.  Phase detection in a visual-evoked-potential based brain computer interface , 2010, 2010 18th European Signal Processing Conference.

[22]  A. Cichocki,et al.  Optimization of SSVEP brain responses with application to eight-command Brain–Computer Interface , 2010, Neuroscience Letters.

[23]  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.

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

[25]  Andrew P. Bradley,et al.  Effect of competing stimuli on SSVEP-based BCI , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[26]  C Neuper,et al.  A comparison of three brain–computer interfaces based on event-related desynchronization, steady state visual evoked potentials, or a hybrid approach using both signals , 2011, Journal of neural engineering.

[27]  M. A. Lopez-Gordo,et al.  Customized stimulation enhances performance of independent binary SSVEP-BCIs , 2011, Clinical Neurophysiology.

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

[29]  Ross Cunnington,et al.  Stimulus specificity of a steady-state visual-evoked potential-based brain–computer interface , 2012, Journal of neural engineering.

[30]  Piotr J. Durka,et al.  User-centered design of brain-computer interfaces: OpenBCI.pl and BCI Appliance , 2012 .