People who are suffering from a Neuro motor disability are in a condition where they are awake and well aware of their surroundings but are unable to perform any action due to paralysis in the body (with the exception of eye movements and blinking). The people who are suffering from such disorder are not able to speak or not understandable enough, so they could not communicate and interact with other people in the word. To help these people to interact better with their surroundings, a machine-learning based methodology for eye wink detection using signals from brain is proposed in this project. A dataset from 50 people was collected using Neurosky Mindwave mobile which provides us various waveforms of EEG signal like Alpha, Beta, Theta and Gamma signals. A model was trained using these features along with Blink Strength to detect the left and right blinks of a person. At the end, we replaced the mouse click functionality with the left and right eye blinks that will help such people to interact with real world.