In　order　to　recognize　the　major　depressive　mood　status　of　inpatients　and　achieve　its　daily　change　information,　a　POMS-BCN　scale　was　used　to　rate　the　mood　status.　Meanwhile,　a　personalized　quantified　model　based　on　portable　EEG　was　built,　which　aimed　at　objectively　assessing　the　major　depressive　mood　status　for　each　patient.　6　inpatients　were　recruited　to　join　the　experiment.　The　Principal　Component　Analysis　method　is　used　to　extract　first　principal　component　curve　from　the　POMS-BCN　data.　The　feature　extraction　method　is　used　to　extract　linear　and　nonlinear　features　from　portable　EEG　data.　The　regression　analysis　based　on　Random　Forest　is　adopted　to　build　the　personalized　quantified　model.　The　principal　component　analysis　result　shows　that　the　first　principal　component　curve　is　able　to　recognize　the　major　emotional　factor　and　depict　its　daily　change　information.　Additionally,　the　expected　quantitative　value　outputted　from　the　personalized　quantified　model　is　highly　correlated　(the　absolute　value　of　correlation　coefficient　0.7,　P-value　0.05)　with　the　actual　first　principal　component　data,　which　implies　that　the　personalized　quantified　model　can　give　an　accurate　objective　assessment　for　the　major　depressive　mood　status.　©　2017,　Springer　International　Publishing　AG.