Flicker　is　the　most　widely　used　steady-state　visual　evoked　potential　(SSVEP)　stimulus.　In　addition,　checkerboard　can　induce　steady-state　motion　visual　evoked　potential　(SSMVEP)　in　the　occipital　area.　More　recently,　the　action　video　is　proposed　to　simultaneously　elicit　SSMVEP　and　induce　sensorimotor　area　activations　via　the　mirror　neuron　systems　through　Action　Observation　(AO).　Integration　of　AO　with　brain-computer　interface　(BCI)　is　appealing　for　neural　rehabilitation　applications.　In　order　to　make　such　a　BCI　paradigm　more　feasible　in　neural　rehabilitation,　it　is　essential　to　discriminate　whether　a　user　is　actively　engaged　with　the　BCI,　i.e.　intentional　control　(IC)　state,　or　not　engaged,　i.e.　non-intentional　control　(NC)　state.　In　this　study,　the　EEG　responses　to　these　three　types　of　visual　stimuli　were　compared　for　the　first　time　and　a　convolutional　neural　network　(CNN)　was　proposed　to　discriminate　IC　and　NC　states.　A　visual　gaiting　stimulus　was　designed　to　realize　BCI-based　AO.　The　results　showed　that　the　power　of　alpha　rhythms　from　frontal　area　decreased　more　when　the　participants　engaged　at　the　gaiting　stimuli　than　when　the　participants　engaged　at　the　other　two　types　of　stimuli.　In　addition,　the　correlation　coefficient　between　the　EEG　from　the　occipital　area　and　the　template　signals　increased　when　the　participants　engaged　at　the　stimuli.　The　results　also　clearly　demonstrated　the　proposed　CNN　method　can　discriminate　the　IC　and　NC　states.　In　addition,　the　combination　of　the　attention　feature　from　the　frontal　area　and　the　SSVEP/SSMVEP　feature　from　the　occipital　area　showed　significant　performance　improvement　for　the　gaiting　stimulus,　but　not　for　the　other　two　types　of　stimuli.