Decision-making　is　a　very　important　cognitive　process　in　our　daily　life.　There　has　been　increasing　interest　in　the　discriminability　of　single-trial　electroencephalogram　(EEG)　during　decision-making.　In　this　study,　we　designed　a　machine　learning　based　framework　to　explore　the　discriminability　of　single-trial　EEG　corresponding　to　different　decisions.　For　each　subject,　the　framework　split　the　decision-making　trials　into　two　parts,　trained　a　feature　model　and　a　classifier　on　the　first　part,　and　evaluated　the　discriminability　on　the　second　part　using　the　feature　model　and　classifier.　A　proposed　algorithm　and　five　existing　algorithms　were　applied　to　fulfill　the　feature　models,　and　the　algorithm　Linear　Discriminative　Analysis　(LDA)　was　used　to　implement　the　classifiers.　We　recruited　21　subjects　to　participate　in　Chicken　Game　(CG)　experiments.　The　results　show　that　there　exists　the　discriminability　of　single-trial　EEG　between　the　cooperation　and　aggression　decisions　during　the　CG　experiments,　with　the　classification　accuray　of　75%　(+/-　6%),　and　the　discriminability　is　mainly　from　the　EEG　information　below　40　Hz.　The　further　analysis　indicates　that　the　contributions　of　different　brain　regions　to　the　discriminability　are　consistent　with　the　existing　knowledge　on　the　cognitive　mechanism　of　decision-making,　confirming　the　reliability　of　the　conclusions.　This　study　exhibits　that　it　is　feasible　to　apply　machine　learning　methods　to　EEG　analysis　of　decision-making　cognitive　process.