On the use of EEG features towards person identification via neural networks (original) (raw)

2001, Medical Informatics and The Internet in Medicine

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%, (casedependent). These results are in agreement with previous research showing evidence that the EEG carries genetic information. 2 2 addressed to informaticists and computer scientists, aiming to investigate new approaches in the field of EEG analysis. However, it is our belief that results of this study would be of interest to clinicians as well. Potential applications of the proposed person identification method are, for example, information encoding and decoding or access to secure information. EEG recording is non-invasive and medically safe; it therefore constitutes a viable and, under certain conditions, attractive alternative to currently existing forms of person identification based on fingerprints, blood test or retinal scanning. It should be noted, however, that the proposed method is merely indicative rather than deterministic in its classification results, in comparison to DNA identification, e.g., because the biochemical bases of the EEG phenomena are, as yet, completely unknown. Thus, it is not legitimate to claim that EEG identification is equivalent to DNA identification.