Background:　Brain　activities　could　be　measured　by　devices　like　EEG,　MEG,　MRI　etc.　in　terms　of　electric　or　magnetic　signal,　which　could　provide　information　from　three　domains,　i.e.,　time,　frequency　and　space.　Combinatory　analysis　of　these　features　could　definitely　help　to　improve　the　classification　performance　on　brain　activities.　NMF　(nonnegative　matrix　factorization)　has　been　widely　applied　in　pattern　extraction　tasks　(e.g.,　face　recognition,　gene　data　analysis)　which　could　provide　physically　meaningful　explanation　of　the　data.　However,　brain　signals　also　take　negative　values,　so　only　spectral　feature　has　been　employed　in　existing　NMF　studies　for　brain　computer　interface.　In　addition,　sparsity　is　an　intrinsic　characteristic　of　electric　signals.　New　method:　To　incorporate　sparsity　constraint　and　enable　analysis　of　time　domain　feature　using　NMF,　a　new　solution　for　motor　imagery　classification　is　developed,　which　combinatorially　analyzes　the　ERP　(event　related　potential,　time　domain)　and　ERSP　(event　related　spectral　perturbation,　frequency　domain)　features　via　a　modified　mixed　alternating　least　square　based　NMF　method　(MALS-NMF　for　short).　Results:　Extensive　experiments　have　verified　the　effectivity　the　proposed　method.　The　results　also　showed　that　imposing　sparsity　constraint　on　the　coefficient　matrix　in　ERP　factorization　and　basis　matrix　in　ERSP　factorization　could　better　improve　the　algorithm　performance.　Comparison　with　existing　methods:　Comparisons　with　other　eight　representative　methods　have　further　verified　the　superiority　of　the　proposed　method.　Conclusions:　The　MALS-NMF　method　is　an　effective　solution　for　motor　imagery　classification　and　has　shed　some　new　light　into　the　field　of　brain　dynamics　pattern　analysis.　(C)　2015　Elsevier　B.V.　All　rights　reserved.