Search Results for author:
Found 7 papers, 3 papers with code
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
To achieve this, we propose a new building block for geometric deep learning, which we denote SPD domain-specific momentum batch normalization (SPDDSMBN).
On the interpretation of linear Riemannian tangent space model parameters in M/EEG
Riemannian tangent space methods offer state-of-the-art performance in magnetoencephalography (MEG) and electroencephalography (EEG) based applications such as brain-computer interfaces and biomarker development.
Neural dSCA: demixing multimodal interaction among brain areas during naturalistic experiments
In this paper, we develop a new method for cross-areal interaction analysis that uses the rich task or stimulus parameters to reveal how and what types of information are shared by different neural populations.
Demixed shared component analysis of neural population data from multiple brain areas
This yields interpretable components that express which variables are shared between different brain regions and when this information is shared across time.
Neurons and Cognition
SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling
We present a novel probabilistic framework for a hierarchical extension of independent component analysis (ICA), with a particular motivation in neuroscientific data analysis and modeling.
Bregman divergence as general framework to estimate unnormalized statistical models
We show that the Bregman divergence provides a rich framework to estimate unnormalized statistical models for continuous or discrete random variables, that is, models which do not integrate or sum to one, respectively.
Structural equations and divisive normalization for energy-dependent component analysis
Here, we propose a principled probabilistic model to model the energy- correlations between the latent variables.
