Parametric spectral analysis of EEG alpha rhythm using a learning vector quantizer network can identify individuals with 72-84% accuracy, supporting the presence of genetically-specific information in EEG.
May inform biometric and genetic EEG research; leaves open prospective validation for reliability and applications.
Person identification based on parametric spectral analysis of the EEG signal is addressed in this work-a problem that has not yet been seen in a signal-processing framework, to the best of our knowledge. AR parameters are estimated from a signal containing only the alpha, rhythm activity of the EEG. These parameters are used as features in the classification step, which employs a learning vector quantizer network. The proposed method was applied on a set of real EEG recordings made on healthy individuals, in an attempt to experimentally investigate the connection between a person's EEG and genetically-specific information. Correct classification scores at the range of 72% to 84% show the potential of our approach for person classification/identification and are in agreement with previous research showing evidence that the EEG carries genetic information.
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Poulos et al. (2003) studied this question.
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