Mental stress is a prevalent issue in the modern society and a prominent contributing factor to various physical and psychological diseases. This paper investigates the feasibility of detecting different stress levels using electroencephalography (EEG), and evaluates the effectiveness of various stress-relief methods. EEG data were collected from 25 subjects while they were at rest and under 3 different levels of stress induced by mental arithmetic tasks. Nine features that correlate with stress from existing literature were extracted. Subsequently, discriminative features were selected by Fisher Ratio and used to train a Linear Discriminant Analysis classifier. Results from 10-fold cross-validation yielded averaged intra-subject classification accuracy of 85.6% for stress versus rest, 7l.2% for two levels of stress and rest, and 58.4% for three levels of stress and rest. The results showed high promise of using EEG to detect level of stress, and the features selected showed that Beta brain waves (13-30HZ) and prefrontal relative Gamma power are most discriminative. Five different stress-relief methods were then evaluated, and the method of hugging a pillow was found to be the most effective measure relatively in decreasing the stress level detected using EEG. These results show promise of future research in real-time stress detection and reduction using EEG for stress management and relief.