Decision-making　is　a　complex　cognitive　process　and　plays　an　important　role　in　the　interaction　between　people.　Many　researchers　are　striving　to　predict　the　individual＇s　decision-making　response(ie.,　acceptance　or　rejection)　by　processing　electroencephalogram(EEG)　trial-by-trial.　In　the　study,　we　proposed　a　supervised　learning　approach,　called　regularized　discriminative　spatial　network　pattern(RDSNP),　to　predict　individual　responses　with　a　small　size　of　training　data　set.　It　constructs　discriminative　brain　networks　by　calculating　the　phase　lock　value　of　different　decision-making　responses　with　single-trial　EEG　data.　Then　the　single-trial　spatial　network　topology　was　applied　to　extract　the　RDSNP　features.　Finally,　a　linear　discriminate　analysis(LDA)　classifier　was　built　on　RDSNP　features　and　used　to　predict　individual　decisions　trial-by-trial.　To　verify　the　performance　of　RDSNP,　we　compared　this　approach　with　such　widely　used　baseline　feature　extraction　methods　as　event　related　potentials,　network　properties,　principal　component　analysis　in　EEG　signals　of　16　subjects,　which　was　acquired　during　the　experiments　of　ultimatum　game,　in　terms　of　accuracy　and　F1-score　suggests　that　our　approach　achieve　a　better　performance　on　predicting　single-trial　decisions.　©　2021　IEEE.