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Real cepstrums on electroencephalogram biometric identification

In a time when personal data circulates constantly between devices and within the cloud, biometric security systems represents one of the most viable security solutions. A relatively new biometric modality based on the individual's Electroencephalogram (EEG) is starting now to gain popularity among researchers. Its relevance relay mainly on its prospects of high security and robustness against intruders and the proliferation of consumer EEG devices. In this work we propose the use of real cepstrums as descriptors of the subject traits within the EEG. When evaluated with each of the 14 conditions of the 100-subjects BCI2000 database, the proposed approach achieved classification accuracies between 86.88% and 94.91% using only the first 5% of the computed cepstral coefficients (13 coefficients).