This　research　is　aimed　to　analysis　the　resting　EEG　features　in　depression　and　the　application　in　clinic.　Sixteen　patients　with　depression　and　sixteen　healthy　controls　were　involved　in　this　study.　Both　features　from　the　alpha　and　beta　frequency　bands　were　selected　to　analysis　in　this　study.　First　the　features　sensitivity　to　the　group-difference　and　the　correlation　to　the　clinical　HAMD　scale　score　were　analyzed,　and　then　the　classification　method　was　used　to　further　test　the　role　of　the　resting　EEG　features　in　depression.　The　results　showed　that　the　difference　between　depression　and　healthy　controls　in　the　absolute　power　of　beta　band　in　the　left　prefrontal　lobe　was　significant.　And　the　alpha　left-right　asymmetry　in　the　prefrontal　cortex　had　a　correlation　with　HAMD　scale　score.　In　addition,　the　classification　based　on　the　features　showed　that　there　was　a　relative　higher　accuracy　rate　to　identify　the　depressions　than　to　identify　the　healthy　controls.　Specifically,　the　classification　based　on　alpha　asymmetry　was　higher　than　that　based　on　beta　asymmetry,　and　the　absolute　power　in　beta　band　was　higher　than　that　in　alpha　band.　Alpha　asymmetry　is　a　traditional　sensitive　resting　EEG　features　for　depression,　this　study　provide　new　evidence　to　support　the　view.　The　findings　here　further　suggest　that　absolute　power　in　beta　band　would　be　important　biomarker　in　depression.