Under　certain　task　conditions,　error-related　potential　(ErrP)　will　be　elicited,　meaning　that　the　subject　is　perceiving　an　error,　responding　to　an　external　error,　or　engaging　in　a　cognitive　process　of　reinforcement　learning.　The　detection　of　ErrP　on　a　single　trial　basis　has　been　studied　and　applied　to　improve　all　kinds　of　brain-computer　interfaces　(BCIs).　However,　the　performance　of　this　kind　of　detection　is　not　currently　good　enough.　In　the　paper,　we　proposed　a　novel　method,　called　window-adjusted　common　spatial　pattern　(WACSP),　for　detecting　ErrP　in　P300　BCI.　In　this　method,　the　coefficient　of　determination　was　introduced　to　measure　the　difference　of　Electroencephalogram　(EEG)　signals　on　a　channel　at　a　moment　and　to　guide　the　search　of　time　windows　in　which　EEG　differences　are　significant,　and　common　spatial　pattern　(CSP)　was　further　used　to　capture　the　stable　spatial　patterns　of　EEG　differences　between　correct　and　incorrect　responses　in　each　time　window.　WACSP　and　the　commonly　used　methods　were　tested　on　the　data　sets　that　were　built　using　the　EEG　signals　acquired　during　the　P300　BCI　experiments　with　different　feedback.　The　comparisons　of　accuracy,　area　under　receiver　operating　characteristics　curve　(AUC)　and　F-measure　show　that　WACSP　significantly　outperforms　the　commonly　used　methods.　The　proposed　method　can　improve　ErrP　detection　based　on　a　single　trial.