Brain　activation　detection　is　an　important　problem　in　fMRI　data　analysis.　In　this　paper,　we　propose　a　data-driven　activation　detection　method　called　neighborhood　one-class　SVM　(NOC-SVM).　Based　on　the　probability　distribution　assumption　of　the　one-class　SVM　algorithm　and　the　neighborhood　consistency　hypothesis,　NOC-SVM　identifies　a　voxel　as　either　an　activated　or　non-activated　voxel　by　a　weighted　distance　between　its　near　neighbors　and　a　hyperplane　in　a　high-dimensional　kernel　space.　The　proposed　NOC-SVM　are　evaluated　by　using　both　synthetic　and　real　datasets.　On　two　synthetic　datasets　with　different　SNRs,　NOC-SVM　performs　better　than　K-means　and　fuzzy　K-means　clustering　and　is　comparable　to　POM.　On　a　real　fMRI　dataset,　NOC-SVM　can　discover　activated　regions　similar　to　K-means　and　fuzzy　K-means.　These　results　show　that　the　proposed　algorithm　is　an　effective　activation　detection　method　for　fMRI　data　analysis.　Furthermore,　it　is　stabler　than　K-means　and　fuzzy　K-means　clustering.　(C)　2008　Elsevier　B.V.　All　rights　reserved.