Ocular　movements　are　inevitable　in　electroencephalograme　(EEG)　collection,　and　the　resulting　Ocular　Artifact　(OA)　becomes　one　of　the　main　interferences　of　EEG　due　to　its　great　amplitude.　Many　methods　have　been　proposed　to　remove　OA　from　EEG　recordings　based　on　Blind　Source　Separation　(BSS)　algorithm.　Often　regression　is　performed　in　time　or　frequency　domain　by　completely　deleting　the　OA　components.　This　can　cause　the　overestimation　of　OA　and　the　information　loss　of　EEG,　because　EEG　and　electrooculogram　(EOG)　mix　or　spread　bidirectionally.　Furthermore,　there　exists　a　variety　of　noises,　except　for　OA,　and　interference　coupling　in　EEG,　this　also　affects　the　OA　removal　performance,　such　as　the　robustness　and　anti-interference　ability.　Here,　we　propose　a　novel　and　generally　applicable　method,　denoted　as　FKD,　for　removing　OA　from　mixed　EEG　signals　with　the　Fast　Kernel　Independent　Component　analysis　(FastKICA)　and　Discrete　Wavelet　Transform　(DWT).　In　two　cases　of　linear　and　nonlinear　mixed　models,　many　experiments　are　conducted　with　Brain　Computer　Interface　(BCI)　data　set.　The　experiment　results　show　that　FKD　has　good　performance　comparing　with　other　BBS-based　OA　removal　methods,　and　it　is　more　acceptable　in　actual　BCI　system.