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Found 6 papers, 3 papers with code
EEG to fMRI Synthesis Benefits from Attentional Graphs of Electrode Relationships
In addition, we observe that haemodynamic activity at the scalp, in contrast with sub-cortical regions, is relevant to the learned shared space.
Automatic Generation of Neural Architecture Search Spaces
Here, we propose a framework that encodes the structure of a convolutional neural network, respecting the arithmetical relation of the kernel and stride sizes with the input and output shapes.
Fitting Regularized Population Dynamics with Neural Differential Equations
Neural differential equations (neural DEs) are yet to see success in its application as interpretable autoencoders/descriptors, where they directly model a population of signals with the learned vector field.
EEG to fMRI Synthesis: Is Deep Learning a candidate?
EEG to fMRI synthesis offers a way to enhance and augment brain image data, and guarantee access to more affordable, portable and long-lasting protocols of brain activity monitoring.
fMRI Multiple Missing Values Imputation Regularized by a Recurrent Denoiser
Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging technique with pivotal importance due to its scientific and clinical applications.
On the use of Pairwise Distance Learning for Brain Signal Classification with Limited Observations
The increasing access to brain signal data using electroencephalography creates new opportunities to study electrophysiological brain activity and perform ambulatory diagnoses of neuronal diseases.
