title: Cross-evidence for hypnotic susceptibility through nonlinear measures on EEGs of non-hypnotized subjects.
abstract: Assessment of hypnotic susceptibility is usually obtained through the application of psychological instruments. A satisfying classification obtained through quantitative measures is still missing, although it would be very useful for both diagnostic and clinical purposes. Aiming at investigating the relationship between the cortical brain activity and the hypnotic susceptibility level, we propose the combined use of two methodologies - Recurrence Quantification Analysis and Detrended Fluctuation Analysis - both inherited from nonlinear dynamics. Indicators obtained through the application of these techniques to EEG signals of individuals in their ordinary state of consciousness allowed us to obtain a clear discrimination between subjects with high and low susceptibility to hypnosis. Finally a neural network approach was used to perform classification analysis.
authors.affiliation: 1] Department of Information Engineering and Mathematics, University of Siena, Italy [2] Complex Systems Community, University of Siena, Italy.
authors.forename: Riccardo
authors.initials: R
authors.lastname: Chiarucci
authors.affiliation: 1] Department of Information Engineering and Mathematics, University of Siena, Italy [2] Complex Systems Community, University of Siena, Italy.
authors.forename: Dario
authors.initials: D
authors.lastname: Madeo
authors.affiliation: 1] Department of Information Engineering and Mathematics, University of Siena, Italy [2] Complex Systems Community, University of Siena, Italy.
authors.forename: Maria I
authors.initials: MI
authors.lastname: Loffredo
authors.affiliation: Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Italy.
authors.forename: Eleonora
authors.initials: E
authors.lastname: Castellani
authors.affiliation: Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Italy.
authors.forename: Enrica L
authors.initials: EL
authors.lastname: Santarcangelo
authors.affiliation: 1] Department of Information Engineering and Mathematics, University of Siena, Italy [2] Complex Systems Community, University of Siena, Italy.
authors.forename: Chiara
authors.initials: C
authors.lastname: Mocenni
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D003243:Consciousness / Q000502:physiology*; D056805:Consciousness Monitors; D004569:Electroencephalography / Q000379:methods*; D019317:Evidence-Based Medicine; D006801:Humans; D006990:Hypnosis / Q000379:methods*; D008297:Male; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult