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In this paper, we propose a new feature extraction algorithm for brain-computer interfaces (BCIs). This algorithm is based on inverse models and uses the novel concept of fuzzy region of interest (ROI). It can automatically identify the relevant ROIs and their reactive frequency bands. The activity in these ROIs can be used as features for any classifier. A first evaluation of the algorithm, using a support vector machine (SVM) as classifier, is reported on data set IV from BCI competition 2003. Results are promising as we reached an accuracy on the test set ranging from 85 % to 86 % whereas the winner of the competition on this data set reached 84%.