The N200 speller is a recently developed non-flashing visual brain-computer interface (BCI) paradigm utilizing the overt attention modulation effects on motion-onset visual evoked potentials (mVEP). In this study, a novel algorithm is proposed and applied in an online N200 speller. The proposed algorithm integrates the spatial information of the speller matrix to provide a more precise description of the mVEP response patterns, which is defined as the 'spatial profile'. More importantly, only control state data are used in the algorithm to train a classifier that nonetheless can detect the non-control state effectively. Compared to an algorithm with similar structure but not using the spatial profile information, the proposed algorithm shows significantly higher performance for the recognition of the non-control state while achieving a comparable performance for classifying different control states. Offline and online classification results show that the proposed N200 speller is a promising step toward a practical, online non-flashing BCI system for daily use.