On the use of EEG features towards person identification via neural networks

Person identification based on spectral information extracted from the EEG is addressed in this work- a problem that has not yet been seen in a signal processing framework. Spectral features are extracted non-parametrically from real EEG data recorded from healthy individuals. Neural network classification is applied on these features using a Learning Vector Quantizer in an attempt to experimentally investigate the connection between a person's EEG and genetically- specific information. The proposed method, compared with previously proposed methods, has yielded encouraging correct classification scores in the range of 80 % to 100%, (case-dependent). These results are in agreement with previous research showing evidence that the EEG carries genetic information.