Papers EEG Denoising
“EEG Denoising” 태그가 달린 논문 11편 · 필터 해제
ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals
Artifact removal in electroencephalography (EEG) is a longstanding challenge that significantly impacts neuroscientific analysis and brain-computer interface (BCI) performance. Tackling this problem demands advanced algo…
Brain Computer InterfaceComponent ClassificationDenoisingEEG+1EEGDiR: Electroencephalogram denoising network for temporal information storage and global modeling through Retentive Network
Electroencephalogram (EEG) signals play a pivotal role in clinical medicine, brain research, and neurological disease studies. However, susceptibility to various physiological and environmental artifacts introduces noise…
DenoisingEEGEEG DenoisingElectroencephalogram (EEG)DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature Reuse
Electroencephalography (EEG) signals are easily corrupted by various artifacts, making artifact removal crucial for improving signal quality in scenarios such as disease diagnosis and brain-computer interface (BCI). In t…
Brain Computer InterfaceDenoisingEEGEEG Denoising+2A multi-artifact EEG denoising by frequency-based deep learning
Electroencephalographic (EEG) signals are fundamental to neuroscience research and clinical applications such as brain-computer interfaces and neurological disorder diagnosis. These signals are typically a combination of…
Deep LearningDenoisingEEGEEG DenoisingAutomatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-heuristically Optimized Non-local Means Filter
Electroencephalogram (EEG) signals may get easily contaminated by muscle artifacts, which may lead to wrong interpretation in the brain--computer interface (BCI) system as well as in various medical diagnoses. The main o…
Brain Computer InterfaceDenoisingEEGEEG Denoising+1Embedding Decomposition for Artifacts Removal in EEG Signals
Electroencephalogram (EEG) recordings are often contaminated with artifacts. Various methods have been developed to eliminate or weaken the influence of artifacts. However, most of them rely on prior experience for analy…
DecoderDenoisingEEGEEG Denoising+1Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN AutoEncoder
This paper presents a fractional one-dimensional convolutional neural network (CNN) autoencoder for denoising the Electroencephalogram (EEG) signals which often get contaminated with noise during the recording process, m…
DenoisingEEGEEG DenoisingElectroencephalogram (EEG)EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising
Deep learning networks are increasingly attracting attention in various fields, including electroencephalography (EEG) signal processing. These models provided comparable performance with that of traditional techniques. …
Deep LearningDenoisingEEGEEG Denoising+1Deep learning denoising for EOG artifacts removal from EEG signals
There are many sources of interference encountered in the electroencephalogram (EEG) recordings, specifically ocular, muscular, and cardiac artifacts. Rejection of EEG artifacts is an essential process in EEG analysis si…
Deep LearningDenoisingEEGEEG Denoising+3Improved robust weighted averaging for event-related potentials in EEG
The aim of this study was to improve the robust weighted averaging based on criterion function minimization and assess its effectiveness for extracting event-related brain potentials (ERP) from electroencephalographic (E…
EEGEEG DenoisingElectroencephalogram (EEG)ERPDenoising Time Series Data Using Asymmetric Generative Adversarial Networks
Denoising data is a preprocessing step for several time series mining algorithms. This step is especially important if the noise in data originates from diverse sources. Consequently, it is commonly used in biomedical ap…
DenoisingEEGEEG DenoisingElectroencephalogram (EEG)+3