title: Classification of EEG Single Trial Microstates Using Local Global Graphs and Discrete Hidden Markov Models.
abstract: 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.
authors.affiliation: 1 Center of Assistive Research Technologies, Wright State University, Dayton OH 45435, USA.
authors.forename: Kostas
authors.initials: K
authors.lastname: Michalopoulos
authors.affiliation: 2 School of ECE, Technical University of Crete, Chania, Crete, Greece.
authors.forename: Michalis
authors.initials: M
authors.lastname: Zervakis
authors.affiliation: 3 Faculty of Medicine, INSERM Unit 1039, La Tronche, France.|4 Biomarkers of Vulnerability Unit, Dep. of Psychiatry, University Hospitals, Geneva, Switzerland.
authors.forename: Marie-Pierre
authors.initials: MP
authors.lastname: Deiber
authors.affiliation: 1 Center of Assistive Research Technologies, Wright State University, Dayton OH 45435, USA.
authors.forename: Nikolaos
authors.initials: N
authors.lastname: Bourbakis
mesh_terms: D001921:Brain / Q000503:physiopathology; D060825:Cognitive Dysfunction / Q000175:diagnosis / Q000503:physiopathology; D066264:Datasets as Topic; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008390:Markov Chains; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted
keywords: EEG; Hidden Markov models; LG graphs; classification