Here, we propose a physics-based framework of Kuramoto model to investigate oxytocin effects on the phase dynamic neural coupling in DMN and FPN.
Our framework combines a deep Koopman-operator based model for seizure prediction in an approximated finite dimensional linear dynamics and the model predictive control (MPC) for designing optimal seizure suppression strategies.
Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging.
Linear discriminant analysis (LDA) is a widely used algorithm in machine learning to extract a low-dimensional representation of high-dimensional data, it features to find the orthogonal discriminant projection subspace by using the Fisher discriminant criterion.
Most methods for dimensionality reduction are based on either tensor representation or local geometry learning.
The recorded electroencephalography (EEG) signals are usually contaminated by many artifacts.
The likelihood-based generative models have been reported to be highly robust to the out-of-distribution (OOD) inputs and can be a detector by assuming that the model assigns higher likelihoods to the samples from the in-distribution (ID) dataset than an OOD dataset.
Here, we present EEGdenoiseNet, a benchmark EEG dataset that is suited for training and testing deep learning-based denoising models, as well as for performance comparisons across models.
To address this problem, a reliable uncertainty estimation is considered to be critical for in-depth understanding of OOD inputs.
Our results demonstrate that IRL is an effective tool to model human decision-making behavior, as well as to help interpret the human psychological process in risk decision-making.