Abstract
In brain computer interface (BCI) systems based on event related potentials (ERPs), a windowed electroencephalography (EEG) signal is taken into consideration for the assumed duration of the ERP potential. In BCI applications inter stimuli interval is shorter than the ERP duration. This causes temporal dependencies over observation potentials thus disallows taking the data into consideration independently. However, conventionally the data is assumed to be independent for decreasing complexity. In this paper we propose a graphical model which covers the temporal dependency into consideration by labeling each time sample. We also propose a formulation to exploit the time series structure of the EEG.