Singular spectrum analysis for tracking of P300

In this work, we introduce a complex-valued singular spectrum analysis for the analysis of electroencephalogram (EEG), which typically exhibits noncircular probability distribution. To exploit such prior knowledge, our technique makes use of recent advances in complex-valued statistics to exploit the power difference or the correlation between the data channels, in contrast to current methods which cater only for the restrictive class of circular data. In particular, the principal component analysis-like technique was employed to detect the onset of P300, and tracked this event-related potential. In this way, the classification of EEG can be made possible to differentiate between a healthy subject and a schizophrenic patient. In particular, we illuminate how features such as P3a and P3b can be used to perform such classification.