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Found 6 papers, 0 papers with code
Correntropy-Based Improper Likelihood Model for Robust Electrophysiological Source Imaging
Hence the conventional Gaussian likelihood model is a suboptimal choice for the real-world source imaging task.
Sparse Bayesian Correntropy Learning for Robust Muscle Activity Reconstruction from Noisy Brain Recordings
Sparse Bayesian learning has promoted many effective frameworks for brain activity decoding, especially for the reconstruction of muscle activity.
Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition
DMs can improve the accuracy of neural decoding when used with the nonlinear Grassmann kernel, compared to conventional power features.
Adaptive sparseness for correntropy-based robust regression via automatic relevance determination
In the present study, regarding the maximum correntropy criterion (MCC) based robust regression algorithm, we investigate to integrate the MCC method with the automatic relevance determination (ARD) technique in a Bayesian framework, so that MCC-based robust regression could be implemented with adaptive sparseness.
Discovering the neural correlate informed nosological relation among multiple neuropsychiatric disorders through dual utilisation of diagnostic information
The unravelled nosological relation among diverse types of neuropsychiatric disorders serves as an important precursor in advocating the dimensional approach to psychiatric classification.
Multiple-view clustering for identifying subject clusters and brain sub-networks using functional connectivity matrices without vectorization
However, when one applies an existing multiple-view clustering method to fMRI data, there is a need to simplify the data structure, independently dealing with elements in a FC matrix, i. e., vectorizing a correlation matrix.
