The identification of subject-specific traits extracted from patterns of brain activity still represents an important challenge. The need to detect distinctive brain features, which is relevant for biometric and brain computer interface systems, has been also emphasized in monitoring the effect of clinical treatments and in evaluating the progression of brain disorders. In this study we propose an approach which aims to investigate the existence of a distinctive functional core (sub-network) using an unbiased reconstruction of network topology. Brain signals from a public and freely available EEG dataset were analysed using a phase synchronization based measure, minimum spanning tree and k -core decomposition. The analysis was performed for each classical brain rhythm separately. Highest classification rates from k -core decomposition were obtained in the gamma (EER = 0.130, AUC = 0.943) and high beta (EER = 0.172, AUC = 0.905) frequency bands. These results confirm that EEG analysis may represent an effective tool to identify subject-specific characteristics that may be of great impact for several bioengineering applications. However, despite the widespread use of these techniques, critical aspects should be considered when dealing with results from high-frequency scalp EEG.

Minimum spanning tree and k-core decomposition as measure of subject-specific EEG traits

CROBE, ALESSANDRA;DEMURU, MATTEO;DIDACI, LUCA;MARCIALIS, GIAN LUCA;FRASCHINI, MATTEO
2016

Abstract

The identification of subject-specific traits extracted from patterns of brain activity still represents an important challenge. The need to detect distinctive brain features, which is relevant for biometric and brain computer interface systems, has been also emphasized in monitoring the effect of clinical treatments and in evaluating the progression of brain disorders. In this study we propose an approach which aims to investigate the existence of a distinctive functional core (sub-network) using an unbiased reconstruction of network topology. Brain signals from a public and freely available EEG dataset were analysed using a phase synchronization based measure, minimum spanning tree and k -core decomposition. The analysis was performed for each classical brain rhythm separately. Highest classification rates from k -core decomposition were obtained in the gamma (EER = 0.130, AUC = 0.943) and high beta (EER = 0.172, AUC = 0.905) frequency bands. These results confirm that EEG analysis may represent an effective tool to identify subject-specific characteristics that may be of great impact for several bioengineering applications. However, despite the widespread use of these techniques, critical aspects should be considered when dealing with results from high-frequency scalp EEG.
EEG; Minimum spanning tree; Networks
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/180130
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