EEG　signals　contain　information　of　limb　motion　patterns　that　are　difficult　to　be　extracted　using　conventional　methods.　The　purpose　of　this　study　was　to　determine　the　feasibility　of　novel　machine　learning　algorithms　in　extraction　of　hand　motion　intents　from　EEG.　In　this　paper,　3　hand　motion　patterns　(hand　opening,　closing,　resting)　were　recognized　using　the　filter　bank　common　spatial　pattern　(FBCSP)　method.　The　spatial　filtering　method　and　the　CNN　classifier　were　used　to　decode　and　classify　the　EEG　signals.　15　healthy　subjects＇　EEG　datasets　were　used.　Results　demonstrated　that　the　average　correct　rate　of　motion　pattern　recognition　was　83.61%.　The　performance　of　these　algorithms　was　validated　using　off-line　computer-controlled　robotic　hand　system,　the　average　accuracy　of　a　subject　was　77.32%,　and　the　average　time　of　the　system　recognition　action　was　28.4ms.　This　study　demonstrated　that　hand　motion　pattern　recognition　can　be　extracted　from　EEG　signals　and　can　be　utilized　by　brain　computer　interface　(BCI)　to　trajectory　control　a　robotic　hand　motion.　©　2020　ACM.