This　work　is　intended　to　increase　the　classification　accuracy　of　single　EEG　epoch,　reduce　the　number　of　repeated　stimuli,　and　improve　the　information　transfer　rate　(ITR)　of　P300　Speller.　Target　EEG　epochs　and　nontarget　EEG　ones　are　both　mapped　to　a　space　by　Wavelet.　In　this　space,　Fisher　Criterion　is　used　to　measure　the　difference　between　target　and　nontarget　ones.　Only　a　few　Daubechies　wavelet　bases　corresponding　to　big　differences　are　selected　to　construct　a　matrix,　by　which　EEG　epochs　are　transformed　to　feature　vectors.　To　ensure　the　online　experiments,　the　computation　tasks　are　distributed　to　several　computers　that　are　managed　and　integrated　by　Storm　so　that　they　could　be　parallelly　carried　out.　The　proposed　feature　extraction　was　compared　with　the　typical　methods　by　testing　its　performance　of　classifying　single　EEG　epoch　and　detecting　characters.　Our　method　achieved　higher　accuracies　of　classification　and　detection.　The　ITRs　also　reflected　the　superiority　of　our　method.　The　parallel　computing　scheme　of　our　method　was　deployed　on　a　small　scale　Storm　cluster　containing　three　desktop　computers.　The　average　feedback　time　for　one　round　of　EEG　epochs　was　1.57　ms.　The　proposed　method　can　improve　the　performance　of　P300　Speller　BCI.　Its　parallel　computing　scheme　is　able　to　support　fast　feedback　required　by　online　experiments.　The　number　of　repeated　stimuli　can　be　significantly　reduced　by　our　method.　The　parallel　computing　scheme　not　only　supports　our　wavelet　feature　extraction　but　also　provides　a　framework　for　other　algorithms　developed　for　P300　Speller.