Background　and　Objective:　Strands　of　evidence　have　supported　existence　of　negative　attentional　bias　in　patients　with　depression.　This　study　aimed　to　assess　the　behavioral　and　electrophysiological　signatures　of　attentional　bias　in　major　depressive　disorder　(MDD)　and　explore　whether　ERP　components　contain　valuable　information　for　discriminating　between　MDD　patients　and　healthy　controls　(HCs).　Methods:　Electroencephalography　data　were　collected　from　17　patients　with　MDD　and　17　HCs　in　a　dot-probe　task,　with　emotional-neutral　pairs　as　experimental　materials.　Fourteen　features　related　to　ERP　waveform　shape　were　generated.　Then,　Correlated　Feature　Selection　(CFS),　ReliefF　and　GainRatio　(GR)　were　applied　for　feature　selection.　For　discriminating　between　MDDs　and　HCs,　k-nearest　neighbor　(KNN),　C4.5,　Sequential　Minimal　Optimization　(SMO)　and　Logistic　Regression　(LR)　were　used.　Results:　Behaviorally,　MDD　patients　showed　significantly　shorter　reaction　time　(RT)　to　valid　than　invalid　sad　trials,　with　significantly　higher　bias　score　for　sad-neutral　pairs.　Analysis　of　split-half　reliability　in　RT　indices　indicated　a　strong　reliability　in　RT,　while　coefficients　of　RT　bias　scores　neared　zero.　These　behavioral　effects　were　supported　by　ERP　results.　MDD　patients　had　higher　P300　amplitude　with　the　probe　replacing　a　sad　face　than　a　neutral　face,　indicating　difficult　attention　disengagement　from　negative　emotional　faces.　Meanwhile,　data　mining　analysis　based　on　ERP　components　suggested　that　CFS　was　the　best　feature　selection　algorithm.　Especially　for　the　P300　induced　by　valid　sad　trials,　the　classification　accuracy　of　CFS　combination　with　any　classifier　was　above　85%,　and　the　KNN　(k　=　3)　classifier　achieved　the　highest　accuracy　(94%).　Conclusions:　MDD　patients　show　difficulty　in　attention　disengagement　from　negative　stimuli,　reflected　by　P300.　The　CFS　over　other　methods　leads　to　a　good　overall　performance　in　most　cases,　especially　when　KNN　classifier　is　used　for　P300　component　classification,　illustrating　that　ERP　component　may　be　applied　as　a　tool　for　auxiliary　diagnosis　of　depression.　(C)　2018　Elsevier　B.V.　All　rights　reserved.