state in sub frequency bands of electroencephalography (EEG) data obtained from 16 different regions (Fp1, Fp2, F3, F4, F7, F8, C3, C4, P3, P4, T3, T4, T5, T6, O1, O2) of the brain based on international 10- 20 electrode system were calculated and healthy individuals and first-episode schizophrenia patients have been classified by using data mining approaches. Multi-resolution wavelet decomposition (MRWD) approach has been used to acquire sub frequency bands of EEG. Single Spectrum Analysis (SSA) has been also applied to extract singular values of EEG bands so called Delta (0.5-4 Hz), Theta (4-8 Hz), Alpha (8-16 Hz), Beta (1632 Hz), Gamma (32-64 Hz). The most successful data mining approach results in classification based on three emotional states (pleasant, unpleasant and neutral), recording place and EEG sub-bands is Rotation Forest algorithm. The best classification results have seen by using data gathering from P3, P4, O1, O2, T5, T6 electrodes at Gamma and Beta frequency bands.