Biometrics such as fingerprints, retinal or iris scanning and face recognition are actively used for identifications. Cognitive biometrics using brain signals have become interesting identification tools because the brain is the most complex biological structure known and its wave signals are very difficult to mimic or steal. In this dissertation, EEG signals are used to identify a person as different persons have different EEG patterns. EEG signals can be measured from different locations. However, many signals can degrade recognition speed and accuracy. A practical technique combining independent component analysis (ICA) for signal cleaning and a supervised neural network for person identification is proposed. From 16 different EEG signal locations, four truly relevant locations of 1,000 data points (F₄, C₄, P₄, O₂), 1,500 data points (F₈, F₃, C₃, P₄), and 3,000 data points (Fp₁, F₄, P₄, O₂) by SOBIRO algorithm were selected. This selection was used to identify a group of 20 persons with high accuracy and can separate the persons who are not in the group. The significant location for identification is position P₄ which is the parietal lobe of the brain.