Related papers: Low-Rank + Sparse Decomposition (LR+SD) for EEG Ar…

Simultaneous EEG-fMRI recording combines high temporal and spatial resolution for tracking neural activity. However, its usefulness is greatly limited by artifacts from magnetic resonance (MR), especially gradient artifacts (GA) and…

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 analysis. Here, we…

EEG signals convey important information about brain activity both in healthy and pathological conditions. However, they are inherently noisy, which poses significant challenges for accurate analysis and interpretation. Traditional EEG…

Objective: The concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received much attention due to its potential for combined high temporal and spatial resolution.…

Electroencephalogram (EEG) artifact detection in real-world settings faces significant challenges such as computational inefficiency in multi-channel methods, poor robustness to simultaneous noise, and trade-offs between accuracy and…

Introduction: Electroencephalogram (EEG) signals have gained significant popularity in various applications due to their rich information content. However, these signals are prone to contamination from various sources of artifacts, notably…

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 algorithms, extensive…

This study introduces a novel framework to apply Artifact Subspace Reconstruction (ASR) algorithm on single-channel Electroencephalogram (EEG) data. ASR, renowned for its automated capability to effectively eliminate various artifacts like…

The recent emergence and success of electroencephalography (EEG) in low-cost portable devices, has opened the door to a new generation of applications processing a small number of EEG channels for health monitoring and brain-computer…

Biological data like electroencephalography (EEG) are typically contaminated by unwanted signals, called artifacts. Therefore, many applications dealing with biological data with low signal-to-noise ratio require robust artifact correction.…

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 objective of this…

Research about brain activities involving spoken word production is considerably underdeveloped because of the undiscovered characteristics of speech artifacts, which contaminate electroencephalogram (EEG) signals and prevent the inspection…

This paper introduces a novel method for effectively removing artifacts from EEG signals by combining the Empirical Mode Decomposition (EMD) method with a machine learning architecture. The proposed method addresses the limitations of…

Spatially-varying intensity noise is a common source of distortion in medical images. Bias field noise is one example of such a distortion that is often present in the magnetic resonance (MR) images or other modalities such as retina…

The low-rank plus sparse (L+S) decomposition model has enabled better reconstruction of dynamic magnetic resonance imaging (dMRI) with separation into background (L) and dynamic (S) component. However, use of low-rank prior alone may not…

Simultaneous EEG-fMRI is a multi-modal neuroimaging technique that provides complementary spatial and temporal resolution for inferring a latent source space of neural activity. In this paper we address this inference problem within the…

Electroencephalography (EEG) has countless applications across many of fields. However, EEG applications are limited by low signal-to-noise ratios. Multiple types of artifacts contribute to the noisiness of EEG, and many techniques have…

For real-world BCI applications, lightweight Electroencephalography (EEG) systems offer the best cost-deployment balance. However, such spatial sparsity of EEG limits spatial fidelity, hurting learning and introducing bias. EEG spatial…

Objective. EEG data collected during fMRI acquisition are contaminated with MRI gradients and ballistocardiogram (BCG) artifacts, in addition to artifacts of physiological origin. There have been several attempts for reducing these…

This research addresses a validated TMS EEG cleaning pipeline and a corresponding benchmark dataset. It evaluates two widely used artifact removal pipelines. A reference dataset of carefully preprocessed EEG signals was established to…