Background:　Although　the　objective　depression　evaluation　is　a　hot　topic　in　recent　years,　less　is　known　concerning　developing　a　pervasive　and　objective　approach　for　quantitatively　evaluating　depression.　Driven　by　the　Wisdom　as　a　Service　architecture,　a　quantitative　analysis　method　for　rating　depressive　mood　status　based　on　forehead　electroencephalograph　(EEG)　and　an　electronic　diary　log　application　named　quantitative　log　for　mental　state　(Q-Log)　is　proposed.　A　regression　method　based　on　random　forest　algorithm　is　adopted　to　train　the　quantitative　model,　where　independent　variables　are　forehead　EEG　features　and　the　dependent　variables　are　the　first　principal　component　(FPC)　values　of　the　Q-Log.　Results:　The　Leave-One-Participant-Out　Cross-Validation　is　adopted　to　estimate　the　performance　of　the　quantitative　model,　and　the　result　shows　that　the　model　outcomes　have　a　moderate　uphill　relationship　(the　average　coefficient　equals　0.6556　and　the　P　value　less　than　0.01)　with　the　FPC　values　of　the　Q-Log.　Furthermore,　an　exemplary　application　of　knowledge　sharing,　which　is　developed　by　using　ontology　technology　and　Jena　inference　subsystem,　is　given　to　illustrate　the　preliminary　work　for　annotating　data　and　facilitating　clinical　users　to　understand　the　meaning　of　the　quantitative　analysis　results.　Conclusions:　This　method　combining　physiological　sensor　data　with　psychological　self-rating　data　could　provide　new　insights　into　the　pervasive　and　objective　depression　evaluation　processes　in　daily　life.　©　2018,　The　Author(s).