In this study, a novel regularized common spatial pattern method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery based brain computer interfaces. Common Spatial Patterns (CSP) method is an effective spatial filter for discriminating different motor imagery signals acquired using large number of EEG electrodes. Unfortunately, CSP method is sensitive to nonstationery sources like artefacts and noise, which cause over fitting. In the literature, some regularization methods developed in order to avert over fitting and generate filters that are less sensitive to noise. In this study, we present a method that regularizes CSP filters by taking care of physiological sources of executed motor imagery tasks and spatial relations between electrodes. We compared our method to well known CSP methods on a publicly available EEG dataset by calculating classifying performances and analyzing the effect of regularizing CSP visually. Results show that proposed method gives the best overall performance among six CSP methods.