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EEG-to-EEG: Scalp-to-Intracranial EEG Translation Using a Combination of Variational Autoencoder and Generative Adversarial Networks.
Sensors (Basel)
January 2025
Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.
A generative adversarial network (GAN) makes it possible to map a data sample from one domain to another one. It has extensively been employed in image-to-image and text-to image translation. We propose an EEG-to-EEG translation model to map the scalp-mounted EEG (scEEG) sensor signals to intracranial EEG (iEEG) sensor signals recorded by foramen ovale sensors inserted into the brain.
View Article and Find Full Text PDFSTGLR: A Spacecraft Anomaly Detection Method Based on Spatio-Temporal Graph Learning.
Sensors (Basel)
January 2025
Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, China.
Anomalies frequently occur during the operation of spacecraft in orbit, and studying anomaly detection methods is crucial to ensure the normal operation of spacecraft. Due to the complexity of spacecraft structures, telemetry data possess characteristics such as high dimensionality, complexity, and large scale. Existing methods frequently ignore or fail to explicitly extract the correlation between variables, and due to the lack of prior knowledge, it is difficult to obtain the initial relationship of variables.
View Article and Find Full Text PDFTopology Design of Soft Phononic Crystals for Tunable Band Gaps: A Deep Learning Approach.
Materials (Basel)
January 2025
School of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China.
The phononic crystals composed of soft materials have received extensive attention owing to the extraordinary behavior when undergoing large deformations, making it possible to provide tunable band gaps actively. However, the inverse designs of them mainly rely on the gradient-driven or gradient-free optimization schemes, which require sensitivity analysis or cause time-consuming, lacking intelligence and flexibility. To this end, a deep learning-based framework composed of a conditional variational autoencoder and multilayer perceptron is proposed to discover the mapping relation from the band gaps to the topology layout applied with prestress.
View Article and Find Full Text PDFModelling sensory attenuation as Bayesian causal inference across two datasets.
PLoS One
January 2025
Department of Psychology, Theoretical Cognitive Science Group, Philipps-Universität Marburg, Marburg, Germany.
Introduction: To interact with the environment, it is crucial to distinguish between sensory information that is externally generated and inputs that are self-generated. The sensory consequences of one's own movements tend to induce attenuated behavioral- and neural responses compared to externally generated inputs. We propose a computational model of sensory attenuation (SA) based on Bayesian Causal Inference, where SA occurs when an internal cause for sensory information is inferred.
View Article and Find Full Text PDFIntroducing ActiveInference.jl: A Julia Library for Simulation and Parameter Estimation with Active Inference Models.
Entropy (Basel)
January 2025
Interacting Minds Centre, Aarhus University, 8000 Aarhus, Denmark.
We introduce a new software package for the Julia programming language, the library ActiveInference.jl. To make active inference agents with Partially Observable Markov Decision Process (POMDP) generative models available to the growing research community using Julia, we re-implemented the pymdp library for Python.
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