It has been long known that neurons are electrically excitable cells that process and transmit information via electrical and chemical signalling. Electroencephalography (EEG) is the estimation and recording of these electrical signals utilizing sensors exhibited over the scalp. A Brain-Computer Interface(BCI) is an immediate correspondence pathway between the outside gadget and the human cerebrum. The .field of BCI is a primary purpose for utilizing electroencephalography innovation. In the past, the standard focus has been about making applications in a therapeutic setting, helping paralysed or weakened patients to coordinate with the external world mapping brain signals to human intellect and sensory-motor actions. With the innovative work, BCI progress is at no time later on compelled to simply patients or for treatment; there is a move of focus towards general well-being of people. Stress identification using EEG signals is one of the critical areas of research in this direction. A high amount of stress is experienced by people of all ages nowadays and is affecting their physical and mental health adversely. The purpose of this research work is to design and build a brain mapping based stress identification system using single electrode EEG device and use cognitive questionnaires along with machine learning techniques to predict stress using EEG device NeuroSky Mindwave Mobile. A novel feature combination of band power ratios of alpha, beta, delta and theta bands are extracted and fed to the classifiers. We evaluate the performance of Support Vector Machine and K-Nearest Neighbour machine learning algorithms with around 75% classification accuracy.