Analyzing Steady State Visual Evoked Potentials Using Blind Source Separation

The present study seeks to investigate the limits of scalp EEG (electroencephalogram) SSVEP (Steady State Visual Evoked Potentials) phenomena (to which maximal and minimal frequencies SSVEP can be recorded at the scalp level). SSVEP are periodic evoked signals buried in the non-stationary waves of EEG recordings. EEG signals are furthermore noisy and contain artifacts which may interfere with brain signals. Our primary objective is therefore to enhance and/or extract SSVEP signals, and to reject the possibility of artifacts. Because EEG is recorded by several electrodes distributed over the scalp, and SSVEP have wide spatial extents, blind source separation can be used to enhance SSVEP responses. We explore the potential of blind source separation for SSVEP extraction and analysis, using the IWASOBI algorithm.

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

[2]  A. Yeredor.  Blind separation of Gaussian sources via second-order statistics with asymptotically optimal weighting , 2000, IEEE Signal Processing Letters.

[3]  Huan-qing Feng,et al.  Real time extraction of Visual Evoked Potentials , 2003, International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.

[4]  J. Victor,et al.  A new statistic for steady-state evoked potentials. , 1991, Electroencephalography and clinical neurophysiology.

[5]  Arie Yeredor,et al.  A fast approximate joint diagonalization algorithm using a criterion with a block diagonal weight matrix , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[6]  A. Cichocki,et al.  EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease , 2005, Clinical Neurophysiology.

[7]  Arjan Hillebrand,et al.  The temporal frequency tuning of human visual cortex investigated using synthetic aperture magnetometry , 2004, NeuroImage.

[8]  M. van de Velde,et al.  Signal validation in electroencephalography research , 2000 .

[9]  A. Cichocki.  Blind Signal Processing Methods for Analyzing Multichannel Brain Signals , 2004 .

[10]  Andrzej Cichocki,et al.  Steady State Visual Evoked Potentials in the Delta Range (0.5-5 Hz) , 2008, ICONIP.

[11]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[12]  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.

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

[14]  Arie Yeredor,et al.  A computationally affordable implementation of an asymptotically optimal BSS algorithm for ar sources , 2006, 2006 14th European Signal Processing Conference.

[15]  Yijun Wang,et al.  Lead selection for SSVEP-based brain-computer interface , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  A. Schlögl,et al.  Artifact Processing in Computerized Analysis of Sleep EEG – A Review , 1999, Neuropsychobiology.

[17]  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.