Many　studies　developed　the　machine　learning　method　for　discriminating　Major　Depressive　Disorder　(MDD)　and　normal　control　based　on　multi-channel　electroencephalogram　(EEG)　data,　less　concerned　about　using　single　channel　EEG　collected　from　forehead　scalp　to　discriminate　the　MDD.　The　EEG　dataset　is　collected　by　the　Fp1　and　Fp2　electrode　of　a　32-channel　EEG　system.　The　result　demonstrates　that　the　classification　performance　based　on　the　EEG　of　Fp1　location　exceeds　the　performance　based　on　the　EEG　of　Fp2　location,　and　shows　that　single-channel　EEG　analysis　can　provide　discrimination　of　MDD　at　the　level　of　multi-channel　EEG　analysis.　Furthermore,　a　portable　EEG　device　collecting　the　signal　from　Fp1　location　is　used　to　collect　the　second　dataset.　The　Classification　and　Regression　Tree　combining　genetic　algorithm　(GA)　achieves　the　highest　accuracy　of　86.67%　based　on　leave-one-participant-out　cross　validation,　which　shows　that　the　single-channel　EEG-based　machine　learning　method　is　promising　to　support　MDD　prescreening　application.