Abstract
As per a report from National Crime Records Bureau (NCRB), in India a total of 10,159 students committed suicide during 2018, which implies that average of 28 students reported suicide every day. This increase in the number that leads to an impact on study and analysis of stress among college students. An experimental research carried out by acquiring EEG data from six students under two different conditions. Before going to write their examination EEG data acquired for 3 minutes with recollection of topics for the examination and after examination 3 minutes of data collected with psychological test carried out for all the eight students using eight channel EEG device. Preprocessing of raw EEG with using band pass filter with corner frequencies of 0.1Hz to 40Hz to eliminate low as well as high frequency signal. EEG features extracted by using discrete wavelet transformation technique with six level of decomposition using db4 as mother wavelet. The main EEG features, such as theta, alpha, and beta relative sub band wave energies, were computed using MATLAB. Four different machine learning algorithms, such as decision trees, support vector machine (SVM), k-Nearest Neighbour (k-NN), and ensemble classifier were used to classify stress or non-stress state. Among four ML classifier algorithm ensemble classifier obtained highest classification accuracy of 87%.