Identifying inter-ictal activity in EEG amidst of artefacts is a primary challenge for diagnosis of epilepsy. Artefacts corrupt the EEG, such that identifying inter-ictal activity becomes difficult. There are different artefacts that present the EEG, in which some of the prominent ones are movement artefact, eye movement, electrode and EMG artefact. The evaluation of EEG is a subjective procedure and detecting inter-ictal activity among the artefacts is a challenge. We have designed convolution neural network to identify between different artefacts and inter-ictal activity. For this work, five sets have been considered: the first four sets compare each artefact with inter-ictal activity and set E compares all artefacts with inter-ictal activity. The proposed method has achieved 100% accuracy in identifying electrode and EMG artefacts from each other while for other artefacts the method also performed efficiently.