Single trial recognition of slow cortical potentials (SCPs) from full-band EEG (FbEEG) faces different challenges to classical EEG such as noisy, high magnitude (~±100µV) infra slow oscillations (ISO) with f<0.1Hz and high frequency spatial noise from a variety of artifacts. We analyze offline the anticipation related SCPs recorded from 11 subjects over two days in a variation of the Contingent Negative Variation (CNV) paradigm with Go and No-go conditions in an assistive technology framework. The results suggest that widely used spatial filters such as Common Average Referencing (CAR) and Laplacian are sub-optimal for the single trial analysis of SCPs. We show that a spatial smoothing filter (SSF), which in combination with CAR enhances the spatially distributed SCP while attenuating high frequency spatial noise. We report, first, that a narrow band filter in the range [0.1 1]Hz captures anticipation related SCP better and effectively reduces ISOs. Second, the SSF in combination with CAR outperforms CAR-alone and Laplacian spatial filters. Third, we compare linear and quadratic classifiers calculated using optimally filtered Cz electrode potentials and report that the best methods resulted in single trial classification accuracies of 83±4%, where classifiers were trained on day 1 and tested using data from day 2, to ensure generalization capabilities across days (1-7 days).