Abstract
Steady state visual evoked potentials (SSVEP) have been identified as a highly viable solution for brain computer interface (BCI) systems. The SSVEP is observed in the scalp-based recordings of electroencephalogram (EEG) signals, and is one component buried amongst the normal brain signals and complex noise. By taking advantage of sample diversity, higher order statistics and statistical dependencies associated with the analysis of multiple datasets, independent vector analysis (IVA) can be used to enhance the detection of the SSVEP signal content. In this paper, we present a novel method for detecting SSVEP signals by treating each EEG signal as a stand alone data set. IVA is used to exploit the correlation across the estimated sources, as well as statistical diversity within datasets to enhance SSVEP detection, offering a significant improvement over averaging based methods for the detection of the SSVEP signal.
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