Sparse group representation model for motor imagery EEG classification

Y Jiao, Y Zhang, X Chen, E Yin, J **… - IEEE journal of …, 2018 - ieeexplore.ieee.org
A potential limitation of a motor imagery (MI) based brain-computer interface (BCI) is that it
usually requires a relatively long time to record sufficient electroencephalogram (EEG) data …

Sparse representation for brain signal processing: a tutorial on methods and applications

Y Li, ZL Yu, N Bi, Y Xu, Z Gu… - IEEE Signal Processing …, 2014 - ieeexplore.ieee.org
In many cases, observed brain signals can be assumed as the linear mixtures of unknown
brain sources/components. It is the task of blind source separation (BSS) to find the sources …

Sparse representation of whole-brain fMRI signals for identification of functional networks

J Lv, X Jiang, X Li, D Zhu, H Chen, T Zhang… - Medical image …, 2015 - Elsevier
There have been several recent studies that used sparse representation for fMRI signal
analysis and activation detection based on the assumption that each voxel's fMRI signal is …

Holistic atlases of functional networks and interactions reveal reciprocal organizational architecture of cortical function

J Lv, X Jiang, X Li, D Zhu, S Zhang… - IEEE Transactions …, 2014 - ieeexplore.ieee.org
For decades, it has been largely unknown to what extent multiple functional networks
spatially overlap/interact with each other and jointly realize the total cortical function. Here …

Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations

S Zhang, X Li, J Lv, X Jiang, L Guo, T Liu - Brain imaging and behavior, 2016 - Springer
A relatively underexplored question in fMRI is whether there are intrinsic differences in terms
of signal composition patterns that can effectively characterize and differentiate task-based …