Stress has become a significant mental health problem of the 21st century. The number of people suffering from stress is increasing rapidly. Thus, easy-to-use, inexpensive, and accurate biomarkers are needed to detect stress during its inception. Early detection of stress-related diseases allows people to access healthcare services. This thesis focuses on the development of stress stimuli and the detection of stress induced by these stimuli. Identifying brain regions affected while exposing the subject to these stressful stimuli has also been done. Three different stimuli, viz. videos, gamified application, and a game, are investigated to study their effect as stress induction stimuli. To this end, in this thesis, a system is proposed to classify participants into stressed and non-stressed categories using machine learning, deep learning, and statistical techniques. The statistical significance between stressed and non-stressed was found using Higuchi Fractal Dimensions (HFD) feature extracted from EEG. This feature also helped identify the brain’s most affected region due to stress. Another outcome of this thesis is the extra annotation of the ground truth which further helps to validate the participant’s experience under the influence of stressful stimuli. This annotation was performed by evaluating participant performance under time pressure. In addition, a technique based on in-game analytics is presented to complement the betterment of self-reported data. Further, another dimension utilizing signatures from WiFi Media Access Control (MAC) layer traffic is presented to detect stress indicators in a device-agnostic way.