Brain　functional　connectivity　has　been　widely　explored　to　reveal　the　functional　interaction　dynamics　between　the　brain　regions.　However,　conventional　connectivity　measures　rely　on　deterministic　models　demanding　application-specific　empirical　analysis,　while　deep　learning　approaches　focus　on　finding　discriminative　features　for　state　classification,　having　limited　capability　to　capture　the　interpretable　connectivity　characteristics.　To　address　the　challenges,　this　study　proposes　a　self-supervised　triplet　network　with　depth-wise　attention　(TripletNet-DA)　to　generate　the　functional　connectivity:　1)　TripletNet-DA　firstly　utilizes　channel-wise　transformations　for　temporal　data　augmentation,　where　the　correlated　&　uncorrelated　sample　pairs　are　constructed　for　self-supervised　training,　2)　Channel　encoder　is　designed　with　a　convolution　network　to　extract　the　deep　features,　while　similarity　estimator　is　employed　to　generate　the　similarity　pairs　and　the　functional　connectivity　representations,　3)　TripletNet-DA　applies　Triplet　loss　with　anchor-negative　similarity　penalty　for　model　training,　where　the　similarities　of　uncorrelated　sample　pairs　are　minimized　to　enhance　model＇s　learning　capability.　Experimental　results　on　pathological　EEG　datasets　(Autism　Spectrum　Disorder,　Major　Depressive　Disorder)　indicate　that　1)　TripletNet-DA　demonstrates　superiority　in　both　ASD　discrimination　and　MDD　classification　than　the　state-of-the-art　counterparts,　where　the　connectivity　features　in　beta　&　gamma　bands　have　respectively　achieved　the　accuracy　of　97.05%,　98.32%　for　ASD　discrimination,　89.88%,　91.80%　for　MDD　classification　in　the　eyes-closed　condition　and　90.90%,　92.26%　in　the　eyes-open　condition,　2)　TripletNet-DA　enables　to　uncover　significant　differences　of　functional　connectivity　between　ASD　EEG　and　TD　ones,　and　the　prominent　connectivity　links　are　in　accordance　with　the　empirical　findings,　thus　providing　potential　biomarkers　for　clinical　ASD　analysis.