Measuring randomness by leave-one-out prediction error. Analysis of EEG after painful stimulation (Articolo in rivista)

Type

• Prodotto della ricerca (Classe)
• Articolo in rivista (Classe)

Label

• Measuring randomness by leave-one-out prediction error. Analysis of EEG after painful stimulation (Articolo in rivista) (literal)

Anno

• 2006-01-01T00:00:00+01:00 (literal)

Alternative label

• N. Ancona, L. Angelini, M. De Tommaso, D. Marinazzo, L. Nitti, M. Pellicoro and S. Stramaglia (2006)
  Measuring randomness by leave-one-out prediction error. Analysis of EEG after painful stimulation
  in Physica. A (Print)
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• N. Ancona, L. Angelini, M. De Tommaso, D. Marinazzo, L. Nitti, M. Pellicoro and S. Stramaglia (literal)

Pagina inizio

• 491 (literal)

Pagina fine

• 498 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 365 (literal)

Rivista

• Physica. A (Rivista)

Note

• ISI Web of Science (WOS) (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• Istituto di Studi sui Sistemi Intelligenti per l'Automazione, C.N.R., Bari, Italy TIRES-Center of Innovative Technologies for Signal Detection and Processing, University of Bari, Italy Dipartimento Interateneo di Fisica, Bari, Italy Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy Dipartimento di Scienze Neurologiche e Psichiatriche, University of Bari, Italy Dipartimento di Biochimica Medica, Biologia Medica e Fisica Medica, University of Bari, Italy (literal)

Titolo

• Measuring randomness by leave-one-out prediction error. Analysis of EEG after painful stimulation (literal)

Abstract

• A parametric approach, to measure randomness in time series, is presented. Time series are modelled by a kernel machine performing regularized least squares and the leave-one-out error is used to quantify unpredictability. Analyzing simulated data sets, we find that structure in data leads to a minimum of the leave-one-out error as the regularizing parameter is varied. We consider electroencephalographic signals from migraineurs and healthy humans, after painful stimulation and use the proposed approach to detect changes of physiological state and to find differences between the response from patients and healthy subjects. As painful stimulus causes organization of the local activity in cortex, EEG series become more predictable after stimulation. This phenomenon is less evident in patients: the inadequate cortical response to pain in migraineurs separates patients from controls with probability close to {2}.005$. (literal)

Prodotto di

• Institute of intelligent systems for automation (ISSIA) (Istituto)
• Sistemi Sensoriali per il Controllo di Qualità e Diagnostica (SP.P03.002.001) (Modulo)

Autore CNR

• NICOLA ANCONA (Unità di personale interno)

Incoming links:

Prodotto

• Institute of intelligent systems for automation (ISSIA) (Istituto)
• Sistemi Sensoriali per il Controllo di Qualità e Diagnostica (SP.P03.002.001) (Modulo)

Autore CNR di

• NICOLA ANCONA (Unità di personale interno)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Physica. A (Rivista)