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Yi-Ou Li
Researcher at University of Maryland, Baltimore County
Publications - 28
Citations - 2351
Yi-Ou Li is an academic researcher from University of Maryland, Baltimore County. The author has contributed to research in topics: Independent component analysis & Blind signal separation. The author has an hindex of 16, co-authored 28 publications receiving 2160 citations. Previous affiliations of Yi-Ou Li include University of California, San Francisco & University of Baltimore.
Papers
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Journal ArticleDOI
Estimating the number of independent components for functional magnetic resonance imaging data.
TL;DR: This work uses the software package ICASSO to analyze the independent component estimates at different orders and shows that, when ICA is performed at overestimated orders, the stability of the IC estimates decreases and the estimation of task related brain activations show degradation.
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Joint Blind Source Separation by Multiset Canonical Correlation Analysis
TL;DR: A generative model of joint BSS based on the correlation of latent sources within and between datasets using multiset canonical correlation analysis (M-CCA) and its utility in estimating meaningful brain activations from a visuomotor task is proposed.
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Canonical Correlation Analysis for Data Fusion and Group Inferences
TL;DR: Two CCA-based approaches for data fusion and group analysis of biomedical imaging data and their utility on fMRI, sMRI, and EEG data are presented and it is important to note that both approaches provide complementary perspectives, and hence it is beneficial to study the data using different analysis techniques.
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Multi-set canonical correlation analysis for the fusion of concurrent single trial ERP and functional MRI
TL;DR: A data fusion method for simultaneously acquired fMRI and EEG data that combines these steps using a single criterion for finding the cross-modality associations and performing source separation is proposed and the promise of the proposed method is demonstrated in finding covarying trial-to-trial amplitude modulations in an auditory task involving implicit pattern learning.
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Canonical Correlation Analysis for Feature-Based Fusion of Biomedical Imaging Modalities and Its Application to Detection of Associative Networks in Schizophrenia
TL;DR: This work proposes a data fusion scheme at the feature level using canonical correlation analysis (CCA) to determine inter-subject covariations across modalities and shows an interesting joint relationship between fMRI and gray matter with patients with schizophrenia showing more functional activity in motor areas and less activity in temporal areas.