The　event　camera,　a　bio-inspired　asynchronous　triggered　camera,　offers　promising　prospects　for　fusion　with　frame-based　cameras　owing　to　its　low　latency　and　high　dynamic　range.　However,　calibrating　stereo　vision　systems　that　incorporate　both　event-　and　frame-based　cameras　remains　a　significant　challenge.　In　this　letter,　we　present　EF-Calib,　a　spatiotemporal　calibration　framework　for　event-　and　frame-based　cameras　using　continuous-time　trajectories.　A　novel　calibration　pattern　applicable　to　both　camera　types　and　the　corresponding　event　recognition　algorithm　are　proposed.　Leveraging　the　asynchronous　nature　of　events,　a　derivable　piece-wise　B-spline　to　represent　camera　pose　continuously　is　introduced,　enabling　calibration　for　intrinsic　parameters,　extrinsic　parameters,　and　time　offset,　with　analytical　Jacobians　provided.　Various　experiments　are　carried　out　to　evaluate　the　calibration　performance　of　EF-Calib,　including　calibration　experiments　for　intrinsic　parameters,　extrinsic　parameters,　and　time　offset.　Experimental　results　demonstrate　that　EF-Calib　outperforms　current　methods　by　achieving　the　most　accurate　intrinsic　parameters,　comparable　accuracy　in　extrinsic　parameters　to　frame-based　method,　and　precise　time　offset　estimation.　EF-Calib　provides　a　convenient　and　accurate　toolbox　for　calibrating　the　system　that　fuses　events　and　frames.