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Found 4 papers, 1 papers with code
Perturbing a Neural Network to Infer Effective Connectivity: Evidence from Synthetic EEG Data
Specifically, we trained neural networks (i. e., CNN, vanilla RNN, GRU, LSTM, and Transformer) to predict future EEG signals according to historical data and perturbed the networks' input to obtain effective connectivity (EC) between the perturbed EEG channel and the rest of the channels.
Mapping effective connectivity by virtually perturbing a surrogate brain
Effective connectivity (EC), indicative of the causal interactions between brain regions, is fundamental to understanding information processing in the brain.
UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detection
UPCL is designed for learning the consistency-related representation with progressive optimized pseudo annotations.
Online Learning Koopman operator for closed-loop electrical neurostimulation in epilepsy
In this work, rooted in optimal control theory, we propose a Koopman-MPC framework for real-time closed-loop electrical neuromodulation in epilepsy, which integrates i) a deep Koopman operator based dynamical model to predict the temporal evolution of epileptic EEG with an approximate finite-dimensional linear dynamics and ii) a model predictive control (MPC) module to design optimal seizure suppression strategies.
