Identifying longitudinal trends within EEG experiments

DOI10.1111/biom.12347zbMath1419.62362OpenAlexW1555193064WikidataQ40714588 ScholiaQ40714588MaRDI QIDQ2809538

Shafali Jeste, Donatello Telesca, Damla Şentürk, Catherine Sugar, Kevin McEvoy, Kyle Hasenstab

Publication date: 30 May 2016

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://escholarship.org/uc/item/4j94x6cx

zbMATH Keywords

smoothingsignal-to-noise ratiorepeated measurementsheteroskedasticityevent-related potentials dataweighted linear mixed effects models

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items

A Multi-Dimensional Functional Principal Components Analysis of EEG Data, Bayesian Inference for Stationary Points in Gaussian Process Regression Models for Event-Related Potentials Analysis, A study of longitudinal trends in time-frequency transformations of EEG data during a learning experiment, Inferring Brain Signals Synchronicity From a Sample of EEG Readings

Cites Work

• Nonparametric Mixed Effects Models for Unequally Sampled Noisy Curves
• An Analysis of Paediatric CD4 Counts for Acquired Immune Deficiency Syndrome Using Flexible Random Curves