EEG based controls are extensively used in applications such as autonomous navigation of remote vehicles and wheelchairs, as prosthetic control for limb movements in health care, in robotics and in gaming. The work aimed at implementing and classifying the intended controls for autonomous navigation, by analyzing the recorded EEG signals. Here, eye closures extracted from the EEG signals were pulse coded to generate the control signals for navigation. The EEG data was acquired using wireless Emotive Epoc EEG headset, with 14 electrodes, from ten healthy subjects. Preprocessing techniques were applied to enhance the signal, by removing noise and baseline variations. The features from the blinks considered were height of the ocular pulses and their respective widths, from four channels. K-Nearest Neighbor Classifier and Artificial Neural Network Classifier were applied to classify the number of blinks. The results of the study showed that, for the data set under consideration, ANN Classifier gave 98.58% accuracy and 94% sensitivity, compared to KNN Classifier, which gave 96.06 % accuracy and 87.42% sensitivity, to classify the blinks for the control application.