Autism Spectrum Disorder (ASD) is a neural development disorder affecting the information processing capability of the brain by altering how nerve cells and their synapses interconnect and organize. Electroencephalograph or EEG signals records the electrical activity of the brain from the scalp which can be utilized to identify and investigate the brain wave pattern which are specific to individuals with ASD. Therefore, the analysis of ASD can be done by scrutinizing the specific bands (Theta, Mu and Beta) of the EEG signal. However, EEG signals are mainly contaminated by Ocular (Eye-blink) and Myogenic artefacts which pose problems in EEG interpretation. In this paper an automated real-time method for detection and removal of Ocular and Myogenic artefacts for multichannel EEG signal is proposed which would enhance the diagnostic accuracy. The proposed methodology has been validated against 20 subjects from Caltech, Physionet, Swartz Center for Computational Neuroscience and the computed average correlation and regression are 0.7574 and 0.6992 respectively.