title: Single trial decoding of belief decision making from EEG and fMRI data using independent components features.
abstract: The complex task of assessing the veracity of a statement is thought to activate uniquely distributed brain regions based on whether a subject believes or disbelieves a given assertion. In the current work, we present parallel machine learning methods for predicting a subject's decision response to a given propositional statement based on independent component (IC) features derived from EEG and fMRI data. Our results demonstrate that IC features outperformed features derived from event related spectral perturbations derived from any single spectral band, yet were similar to accuracy across all spectral bands combined. We compared our diagnostic IC spatial maps with our conventional general linear model (GLM) results, and found that informative ICs had significant spatial overlap with our GLM results, yet also revealed unique regions like amygdala that were not statistically significant in GLM analyses. Overall, these results suggest that ICs may yield a parsimonious feature set that can be used along with a decision tree structure for interpretation of features used in classifying complex cognitive processes such as belief and disbelief across both fMRI and EEG neuroimaging modalities.
authors.affiliation: LINT Laboratory, University of California, Los Angeles Los Angeles, CA, USA.
authors.forename: Pamela K
authors.initials: PK
authors.lastname: Douglas
authors.forename: Edward
authors.initials: E
authors.lastname: Lau
authors.forename: Ariana
authors.initials: A
authors.lastname: Anderson
authors.forename: Austin
authors.initials: A
authors.lastname: Head
authors.forename: Wesley
authors.initials: W
authors.lastname: Kerr
authors.forename: Margalit
authors.initials: M
authors.lastname: Wollner
authors.forename: Daniel
authors.initials: D
authors.lastname: Moyer
authors.forename: Wei
authors.initials: W
authors.lastname: Li
authors.forename: Mike
authors.initials: M
authors.lastname: Durnhofer
authors.forename: Jennifer
authors.initials: J
authors.lastname: Bramen
authors.forename: Mark S
authors.initials: MS
authors.lastname: Cohen
keywords: EEG; ICA; decision making; decision tree; decoding; fMRI; interpretation; machine learning
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