相关论文: Removing motion artifacts from mechanomyographic s…
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…
We present a filtering procedure based on singular value decomposition to remove artifacts arising from sample motion during dynamic full field OCT acquisitions. The presented method succeeded in removing artifacts created by environmental…
A photoplethysmography (PPG) is an uncomplicated and inexpensive optical technique widely used in the healthcare domain to extract valuable health-related information, e.g., heart rate variability, blood pressure, and respiration rate. PPG…
Motion artifacts present in magnetic resonance imaging (MRI) can seriously interfere with clinical diagnosis. Removing motion artifacts is a straightforward solution and has been extensively studied. However, paired data are still heavily…
Photoplethysmography (PPG) is a non-invasive and economical technique to extract vital signs of the human body. Although it has been widely used in consumer and research grade wrist devices to track a user's physiology, the PPG signal is…
Electrocardiogram (ECG) artifact contamination often occurs in surface electromyography (sEMG) applications when the measured muscles are in proximity to the heart. Previous studies have developed and proposed various methods, such as…
Automated quality assessment of structural brain MRI is an important prerequisite for reliable neuroimaging analysis, but yet remains challenging due to motion artifacts and poor generalization across acquisition sites. Existing approaches…
Image corruption by motion artifacts is an ingrained problem in Magnetic Resonance Imaging (MRI). In this work, we propose a neural network-based regularization term to enhance Autofocusing, a classic optimization-based method to remove…
Conventional electromyography (EMG) measures the continuous neural activity during muscle contraction, but lacks explicit quantification of the actual contraction. Mechanomyography (MMG) and accelerometers only measure body surface motion,…
Fetal functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful tool for investigating brain development in utero, holding promise for generating developmental disease biomarkers and supporting prenatal diagnosis. However, to…
Wearable head sensor systems use different kinematic signal processing approaches which limits field-based inter-study comparisons, especially when artefacts are present in the signal. The aim of this study is to assess the frequency…
Electrocardiogram (ECG) signal is an important physiological signal which contains cardiac information and is the basis to diagnosis cardiac related diseases. In this paper, several innovative and efficient methods based on adaptive filter…
Objective: Often, people with Subjective Cognitive Impairment (SCI), Mild Cognitive Impairment (MCI) and dementia are underwent to Electroencephalography (EEG) in order to evaluate through biological indexes the functional connectivity…
Electromyography (EMG) signals are obtained from muscle cell activity. The recording and analysis of EMG signals has several applications. The EMG is of diagnostic importance for treating patients suffering from neurological and…
Electromyography (EMG) data has been extensively adopted as an intuitive interface for instructing human-robot collaboration. A major challenge of the real-time detection of human grasp intent is the identification of dynamic EMG from hand…
Objective- Heart rate monitoring using wrist type Photoplethysmographic (PPG) signals is getting popularity because of construction simplicity and low cost of wearable devices. The task becomes very difficult due to the presence of various…
Imaging neural activity in a behaving animal presents unique challenges in part because motion from an animal's movement creates artifacts in fluorescence intensity time-series that are difficult to distinguish from neural signals of…
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…
The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. This paper proposes two robust methods:…
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 neurological…