Brain Mapping Based Stress Identification Using Portable EEG Based Device

A high amount of stress is experienced by people of all ages nowadays and is affecting their physical and mental health adversely. Traditionally, Perceived Stress Score (PSS-14) questionnaire is used to calculate the stress score, which requires manual intervention of people. In this work, we look into the usage of Electroencephalography (EEG) to help predict the stress level of people without manual intervention and recording by a Brain-Computer Interface(BCI) instrument. EEG can estimate and record the electrical signals in brains utilizing sensors exhibited over the scalp. We propose the design of a brain mapping based stress identification system using a single electrode EEG device. Here we have taken EEG recording of 63 students using a single electrode EEG device NeuroSky Mindwave Mobile. A novel feature combination of bandpower 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. Target class of the training set is assigned using the stress score calculated from the response of the PSS-14 questionnaire. We achieve the highest average classification accuracy of 74.43 % using K-NN algorithm. We observe that there exist a correlation of bandpower ratios of different bands obtained from recorded EEG signals from the frontal portion of the brain with the stress level of subjects.