In this article, we develop an analytical approach for estimating brain connectivity networks
that accounts for subject heterogeneity. More specifically, we consider a novel extension of a …

Aims and objectives: Individual alpha frequency (IAF) is a biomarker of neurophysiological
functioning. The IAF-guided repetitive transcranial magnetic stimulation (α-rTMS) is …

Electroencephalography (EEG) studies produce region-referenced functional data in the
form of EEG signals recorded across electrodes on the scalp. The high-dimensional data …

Mixed membership models are an extension of finite mixture models, where each
observation can partially belong to more than one mixture component. A probabilistic …

Bayesian methods provide direct inference in functional data analysis applications without
reliance on bootstrap techniques. A major tool in functional data applications is the …

Mixed membership models are a flexible class of probabilistic data representations used for
unsupervised and semi-supervised learning, allowing each observation to partially belong …

Objective: In this article, we reconstruct the hybrid data with hybrid principal component
analysis (HPCA) as a feature extraction step and model them with functional regression as a …

Mixed membership models, or partial membership models, are a flexible unsupervised
learning method that allows each observation to belong to multiple clusters. In this article …

A common concern in the field of functional data analysis is the challenge of temporal
misalignment, which is typically addressed using curve registration methods. Currently, most …

Autism spectrum disorder (ASD), which is a neuro-developmental disability, impairs social
interaction and communication skills of an individual and can include repetitive behavior …