Decomposing EEG data into space-time-frequency components using parallel factor analysis and its relation with cerebral blood flow

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4984 LNCS, Issue PART 1, 2008, Pages 802-810

  Miwakeichi, Fumikazu ^a   Valdes Sosa, Pedro A ^c   Aubert Vazquez, Eduardo ^c   Bayard, Jorge Bosch ^c   Watanabe, Jobu ^d   Mizuhara, Hiroaki ^b   Yamaguchi, Yoko ^b  

^a CHIBA UNIVERSITY   (Japan)

^b RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

^c CUBAN NEUROSCIENCE CENTER   (Cuba)

^d WASEDA UNIVERSITY   (Japan)

Author keywords

Concurrent fMRI EEG data; EEG space frequency time decomposition; Nonlinear time series analysis; Parallel factor analysis

Indexed keywords

ARGON; ATOMIC PHYSICS; ATOMIC SPECTROSCOPY; ATOMS; BLOOD; BRAIN; DATA FLOW ANALYSIS; DECOMPOSITION; ELECTROENCEPHALOGRAPHY; HEMODYNAMICS; MAGNETIC RESONANCE IMAGING; REGRESSION ANALYSIS; RESONANCE; SIGNAL THEORY; TIME DOMAIN ANALYSIS;

ATOMIC DECOMPOSITIONS; AUTOREGRESSIVE MODELS; BOLD SIGNALS; BRAIN ELECTRICAL ACTIVITIES; CEREBRAL BLOOD FLOWS; CONCURRENT FMRI/EEG DATA; DECOMPOSING; DYNAMICAL EFFECTS; EEG DATUM; EEG SPACE/FREQUENCY/TIME DECOMPOSITION; FUNCTIONAL MAGNETIC RESONANCE IMAGING (FMRI); MODELING; NONLINEAR TERMS; NONLINEAR TIME SERIES ANALYSIS; PARAFAC; PARALLEL FACTOR ANALYSIS; STANDING PROBLEMS; STATISTICAL SIGNIFICANCES; TEMPORAL SIGNATURES; TIME-FREQUENCY;

TIME SERIES ANALYSIS;

EID: 54249101831     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-69158-7_83     Document Type: Conference Paper

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