An ICA approach to detect functionally different intra-regional neuronal signals in MEG data

Giulia Barbati, Camillo Porcaro, Filippo Zappasodi, Franca Tecchio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Cerebral processing mainly relies on functional connectivity among involved regions. Neuro-imaging techniques able to assess these links with suitable time resolution are electro- and magneto-encephalography (EEG and MEG), even if it is difficult to localize recorded extra-cranial information, particularly within restricted areas, due to complexity of the 'inverse problem'. By means of Independent Component Analysis (ICA) a procedure 'blind' to position and biophysical properties of the generators, our aim in this work was to identify cerebral functionally different sources in a restricted area. MEG data of 5 subjects were collected performing a relax-movement motor task in 5 different days. ICA reliably extracted neural networks differently modulated during the task in the frequency range of interest. In conclusion, a procedure solely based on statistical properties of the signals, disregarding their spatial positions, was demonstrated able to discriminate functionally different neuronal pools activities in a very restricted cortical area.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science
EditorsJ. Cabestany, A. Prieto, F. Sandoval
Pages1083-1090
Number of pages8
Volume3512
Publication statusPublished - 2005
Event8th International Workshop on Artificial Neural Networks, IWANN 2005: Computational Intelligence and Bioinspired Systems - Vilanova i la Geltru, Spain
Duration: Jun 8 2005Jun 10 2005

Other

Other8th International Workshop on Artificial Neural Networks, IWANN 2005: Computational Intelligence and Bioinspired Systems
CountrySpain
CityVilanova i la Geltru
Period6/8/056/10/05

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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    Barbati, G., Porcaro, C., Zappasodi, F., & Tecchio, F. (2005). An ICA approach to detect functionally different intra-regional neuronal signals in MEG data. In J. Cabestany, A. Prieto, & F. Sandoval (Eds.), Lecture Notes in Computer Science (Vol. 3512, pp. 1083-1090)