Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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EL
| URI | http://purl.tuc.gr/dl/dias/82C1E63D-7A56-42EC-924C-FD744A27BA2F | - |
| Αναγνωριστικό | https://www.worldscientific.com/doi/abs/10.1142/S0129065716500362 | - |
| Αναγνωριστικό | https://doi.org/10.1142/S0129065716500362 | - |
| Γλώσσα | en | - |
| Μέγεθος | 14 pages | en |
| Τίτλος | Classification of EEG single trial microstates using local global graphs and discrete hidden Markov models | en |
| Δημιουργός | Michalopoulos Konstantinos | en |
| Δημιουργός | Μιχαλοπουλος Κωνσταντινος | el |
| Δημιουργός | Zervakis Michail | en |
| Δημιουργός | Ζερβακης Μιχαηλ | el |
| Δημιουργός | Deiber, Marie-Pierre, 1961- | en |
| Δημιουργός | Bourbakis, Nikolaos G | en |
| Εκδότης | World Scientific Publishing | en |
| Περίληψη | We present a novel synergistic methodology for the spatio-temporal analysis of single Electroencephalogram (EEG) trials. This new methodology is based on the novel synergy of Local Global Graph (LG graph) to characterize define the structural features of the EEG topography as a global descriptor for robust comparison of dominant topographies (microstates) and Hidden Markov Models (HMM) to model the topographic sequence in a unique way. In particular, the LG graph descriptor defines similarity and distance measures that can be successfully used for the difficult comparison of the extracted LG graphs in the presence of noise. In addition, hidden states represent periods of stationary distribution of topographies that constitute the equivalent of the microstates in the model. The transitions between the different microstates and the formed syntactic patterns can reveal differences in the processing of the input stimulus between different pathologies. We train the HMM model to learn the transitions between the different microstates and express the syntactic patterns that appear in the single trials in a compact and efficient way. We applied this methodology in single trials consisting of normal subjects and patients with Progressive Mild Cognitive Impairment (PMCI) to discriminate these two groups. The classification results show that this approach is capable to efficiently discriminate between control and Progressive MCI single trials. Results indicate that HMMs provide physiologically meaningful results that can be used in the syntactic analysis of Event Related Potentials. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2018-10-03 | - |
| Ημερομηνία Δημοσίευσης | 2016 | - |
| Θεματική Κατηγορία | Classification | en |
| Θεματική Κατηγορία | EEG | en |
| Θεματική Κατηγορία | Hidden Markov models | en |
| Θεματική Κατηγορία | LG graphs | en |
| Βιβλιογραφική Αναφορά | K. Michalopoulos, M. Zervakis, M.-P. Deiber and N. Bourbakis, "Classification of EEG single trial microstates using local global graphs and discrete hidden Markov models," Int. J. Neural Syst., vol. 26, no. 6, Sept. 2016. doi: 10.1142/S0129065716500362 | en |
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