fMRI Signal
Functional magnetic resonance imaging (fMRI) signals reflect brain activity, and current research focuses on decoding these signals to understand cognitive processes and potentially build brain-computer interfaces. This involves developing advanced machine learning models, including large language models (LLMs), transformers, and generative models like diffusion models, to reconstruct visual stimuli, spoken text, and even physiological signals from fMRI data. These efforts are improving our understanding of brain function across the lifespan and different cognitive tasks, with implications for diagnosing neurological disorders and enhancing brain-computer interfaces.
Papers
March 26, 2023
March 9, 2023
January 20, 2023
December 13, 2022
July 4, 2022
June 13, 2022
June 7, 2022
May 20, 2022
DELMAR: Deep Linear Matrix Approximately Reconstruction to Extract Hierarchical Functional Connectivity in the Human Brain
Wei Zhang, Yu Bao
DEMAND: Deep Matrix Approximately Nonlinear Decomposition to Identify Meta, Canonical, and Sub-Spatial Pattern of functional Magnetic Resonance Imaging in the Human Brain
Wei Zhang, Yu Bao
February 11, 2022
December 22, 2021
December 11, 2021