T
Tulay Adali
Researcher at University of Maryland, Baltimore County
Publications - 466
Citations - 22805
Tulay Adali 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 64, co-authored 429 publications receiving 20040 citations. Previous affiliations of Tulay Adali include Johns Hopkins University & University of Baltimore.
Papers
More filters
Group ica of functional mri data: separability, stationarity, and inference
TL;DR: This work develops three important areas needed for applying ICA to group data: separability, stationarity, and inference and demonstrates the utility of using such a method for making group inferences on fMRI data using ICA.
Journal ArticleDOI
Complex ICA of Brain Imaging Data [Life Sciences]
TL;DR: This paper discusses the advantages of independent component analysis over traditional model based data analysis techniques, e.g. linear regression and its applications to functional magnetic resonance imaging (fMRI).
Proceedings ArticleDOI
Complex backpropagation neural network using elementary transcendental activation functions
Taehwan Kim,Tulay Adali +1 more
TL;DR: The fully complex NN design is extended to employ other complex activation functions of the hyperbolic, circular, and their inverse function family to restore the nonlinear amplitude and phase distortions of non-constant modulus modulated signals.
Multimodal Data Fusion Using Source Separation: Application to Medical Imaging This paper demonstrates the application of the two models introduced in the previous paper to fusion of medical imaging data from three modalities: functional magnetic resonance imaging (MRI), structural MRI, and electroencephalography data and discusses the tradeoffs in various modeling and parameter choices.
TL;DR: The joint independent component analysis (jICA) as mentioned in this paper can be used to identify a set of components that report on differences between the two groups, jointly, for all the modalities used in the study.
Proceedings ArticleDOI
Fusion of fMRI, sMRI, and EEG data using canonical correlation analysis
TL;DR: This work proposes a new data fusion scheme based on canonical correlation analysis that enables the detection of associations across multiple modalities and applies mCCA to fMRI, sMRI, and EEG data collected from patients diagnosed with schizophrenia and healthy controls.