Multi-subject MEG/EEG source imaging with sparse multi-task regression
Magnetoencephalography and electroencephalography (M/EEG) are non-invasive
modalities that measure the weak electromagnetic fields generated by neural activity …
modalities that measure the weak electromagnetic fields generated by neural activity …
EEGSourceSim: A framework for realistic simulation of EEG scalp data using MRI-based forward models and biologically plausible signals and noise
Background Electroencephalography (EEG) is widely used to investigate human brain
function. Simulation studies are essential for assessing the validity of EEG analysis methods …
function. Simulation studies are essential for assessing the validity of EEG analysis methods …
Fused group lasso: A new EEG classification model with spatial smooth constraint for motor imagery-based brain–computer interface
S Zhang, Z Zhu, B Zhang, B Feng, T Yu… - IEEE Sensors …, 2020 - ieeexplore.ieee.org
The traditional group sparse optimization method can simultaneously achieve the channel
selection and classification for the motor imagery electroencephalogram (EEG) signals, but it …
selection and classification for the motor imagery electroencephalogram (EEG) signals, but it …
Structured sparsity of convolutional neural networks via nonconvex sparse group regularization
Convolutional neural networks (CNN) have been hugely successful recently with superior
accuracy and performance in various imaging applications, such as classification, object …
accuracy and performance in various imaging applications, such as classification, object …
WRA-MTSI: a robust extended source imaging algorithm based on multi-trial EEG
K Liu, Z Wang, Z Yu, B Xiao, H Yu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Objective: Reconstructing brain activities from electroencephalography (EEG) signals is
crucial for studying brain functions and their abnormalities. However, since EEG signals are …
crucial for studying brain functions and their abnormalities. However, since EEG signals are …
[HTML][HTML] Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation
The accurate characterization of cortical functional connectivity from
Magnetoencephalography (MEG) data remains a challenging problem due to the subjective …
Magnetoencephalography (MEG) data remains a challenging problem due to the subjective …
Deep Recurrent Encoder: an end-to-end network to model magnetoencephalography at scale
Understanding how the brain responds to sensory inputs from non-invasive brain recordings
like magnetoencephalography (MEG) can be particularly challenging:(i) the high …
like magnetoencephalography (MEG) can be particularly challenging:(i) the high …
Deep recurrent encoder: A scalable end-to-end network to model brain signals
Understanding how the brain responds to sensory inputs is challenging: brain recordings
are partial, noisy, and high dimensional; they vary across sessions and subjects and they …
are partial, noisy, and high dimensional; they vary across sessions and subjects and they …
Improving source estimation of retinotopic MEG responses by combining data from multiple subjects
Magnetoencephalography (MEG) is a functional brain imaging modality, which measures
the weak magnetic field arising from neuronal activity. The source amplitudes and locations …
the weak magnetic field arising from neuronal activity. The source amplitudes and locations …
Estimating neural activity from visual areas using functionally defined EEG templates
Electroencephalography (EEG) is a common and inexpensive method to record neural
activity in humans. However, it lacks spatial resolution making it difficult to determine which …
activity in humans. However, it lacks spatial resolution making it difficult to determine which …