Generative adversarial networks in time series: A systematic literature review
Generative adversarial network (GAN) studies have grown exponentially in the past few
years. Their impact has been seen mainly in the computer vision field with realistic image …
years. Their impact has been seen mainly in the computer vision field with realistic image …
Generative adversarial networks in time series: A survey and taxonomy
Generative adversarial networks (GANs) studies have grown exponentially in the past few
years. Their impact has been seen mainly in the computer vision field with realistic image …
years. Their impact has been seen mainly in the computer vision field with realistic image …
Human activity prediction based on forecasted IMU activity signals by sequence-to-sequence deep neural networks
Human Activity Recognition (HAR) has gained significant attention due to its broad range of
applications, such as healthcare, industrial work safety, activity assistance, and driver …
applications, such as healthcare, industrial work safety, activity assistance, and driver …
Exploring convolutional neural network architectures for EEG feature extraction
The main purpose of this paper is to provide information on how to create a convolutional
neural network (CNN) for extracting features from EEG signals. Our task was to understand …
neural network (CNN) for extracting features from EEG signals. Our task was to understand …
[HTML][HTML] Neural networks generative models for time series
Nowadays, time series are a widely-exploited methodology to describe phenomena
belonging to different fields. In fact, electrical consumption can be explained, from a data …
belonging to different fields. In fact, electrical consumption can be explained, from a data …
Time series generation with masked autoencoder
This paper shows that masked autoencoder with extrapolator (ExtraMAE) is a scalable self-
supervised model for time series generation. ExtraMAE randomly masks some patches of …
supervised model for time series generation. ExtraMAE randomly masks some patches of …
Denoising EEG signals for real-world BCI applications using GANs
As a measure of the brain's electrical activity, electroencephalography (EEG) is the primary
signal of interest for brain-computer-interfaces (BCI). BCIs offer a communication pathway …
signal of interest for brain-computer-interfaces (BCI). BCIs offer a communication pathway …
Deep autoencoder for real-time single-channel EEG cleaning and its smartphone implementation using TensorFlow Lite with hardware/software acceleration
Objective: To remove signal contamination in electroencephalogram (EEG) traces coming
from ocular, motion, and muscular artifacts which degrade signal quality. To do this in real …
from ocular, motion, and muscular artifacts which degrade signal quality. To do this in real …
An Approach for EEG Denoising Based on Wasserstein Generative Adversarial Network
Electroencephalogram (EEG) recordings often contain artifacts that would lower signal
quality. Many efforts have been made to eliminate or at least minimize the artifacts, and most …
quality. Many efforts have been made to eliminate or at least minimize the artifacts, and most …
Temporal 2D-Cycle-Generation Framework for Time Series Classification
X Chen, X Jin, H Zhang, J Xiong, Y Deng… - Applied Soft Computing, 2025 - Elsevier
In time series classification tasks, most datasets have small data volumes or are
inconvenient to collect. Therefore, we proposed a data augmentation framework based on …
inconvenient to collect. Therefore, we proposed a data augmentation framework based on …