EEG-Based Person Identification Using Rhythmic Brain Activity During Sleep
| Autor: | George K. Kostopoulos, Athanasios Koutras |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
medicine.diagnostic_test
Computer science Brain activity and meditation business.industry Feature extraction Feature selection Pattern recognition Sleep spindle 02 engineering and technology Electroencephalography Parameter identification problem 03 medical and health sciences 0302 clinical medicine Wavelet Discriminative model 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | Artificial Neural Networks and Machine Learning – ICANN 2018 ISBN: 9783030014230 ICANN (3) |
| Popis: | In this paper we present a novel approach to the person identification problem using rhythmic brain activity of spindles from whole night EEG recordings. The proposed system consists of a feature extraction module and a K-NN based classifier. Different types of features from time, frequency and wavelet domain are used to highlight the topographic, temporal, morphological, spectral and statistical discriminative information of sleep spindles. The feature set’s efficacy is exhaustively tested in order to find the most significant descriptors that maximize intra-subject separability. Extensive experiments resulted in the optimal number of sensors and features that must be used to form the subject-specific unique descriptors. The proposed system showed significant identification accuracy of 99% ~ 90% for 2–20 subjects, and not lower than 86% when identifying 28 persons, indicating that this new type of modality should be further investigated to be used in EEG based identification applications. |
| Databáze: | OpenAIRE |
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