2,809 results match your criteria EEG Artifacts

Development of an Adaptive Artifact Subspace Reconstruction Based on Hebbian/Anti-Hebbian Learning Networks for Enhancing BCI Performance.

IEEE Trans Neural Netw Learn Syst 2022 Jun 17;PP. Epub 2022 Jun 17.

Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. Read More

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Optimising the classification of feature-based attention in frequency-tagged electroencephalography data.

Sci Data 2022 Jun 13;9(1):296. Epub 2022 Jun 13.

The University of Queensland, Queensland Brain Institute, St Lucia, 4072, Australia.

Brain-computer interfaces (BCIs) are a rapidly expanding field of study and require accurate and reliable real-time decoding of patterns of neural activity. These protocols often exploit selective attention, a neural mechanism that prioritises the sensory processing of task-relevant stimulus features (feature-based attention) or task-relevant spatial locations (spatial attention). Within the visual modality, attentional modulation of neural responses to different inputs is well indexed by steady-state visual evoked potentials (SSVEPs). Read More

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Ballistocardiogram suppression in concurrent EEG-MRI by dynamic modeling of heartbeats.

Hum Brain Mapp 2022 Jun 13. Epub 2022 Jun 13.

Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada.

The ballistocardiogram (BCG), the induced electric potentials by the head motion originating from heartbeats, is a prominent source of noise in electroencephalography (EEG) data during magnetic resonance imaging (MRI). Although methods have been proposed to suppress the BCG artifact, more work considering the variability of cardiac cycles and head motion across time and subjects is needed to provide highly robust correction. Here, a method called "dynamic modeling of heartbeats" (DMH) is proposed to reduce BCG artifacts in EEG data recorded inside an MRI system. Read More

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SRI-EEG: State-Based Recurrent Imputation for EEG Artifact Correction.

Front Comput Neurosci 2022 20;16:803384. Epub 2022 May 20.

Department of Computer Science, University of California, Santa Barbara, Santa Barbara, CA, United States.

Electroencephalogram (EEG) signals are often used as an input modality for Brain Computer Interfaces (BCIs). While EEG signals can be beneficial for numerous types of interaction scenarios in the real world, high levels of noise limits their usage to strictly noise-controlled environments such as a research laboratory. Even in a controlled environment, EEG is susceptible to noise, particularly from user motion, making it highly challenging to use EEG, and consequently BCI, as a ubiquitous user interaction modality. Read More

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EEG-Based Alzheimer's Disease Recognition Using Robust-PCA and LSTM Recurrent Neural Network.

Sensors (Basel) 2022 May 12;22(10). Epub 2022 May 12.

Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, I-60131 Ancona, Italy.

The use of electroencephalography (EEG) has recently grown as a means to diagnose neurodegenerative pathologies such as Alzheimer's disease (AD). AD recognition can benefit from machine learning methods that, compared with traditional manual diagnosis methods, have higher reliability and improved recognition accuracy, being able to manage large amounts of data. Nevertheless, machine learning methods may exhibit lower accuracies when faced with incomplete, corrupted, or otherwise missing data, so it is important do develop robust pre-processing techniques do deal with incomplete data. Read More

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Parkinson's Disease Detection from Resting-State EEG Signals Using Common Spatial Pattern, Entropy, and Machine Learning Techniques.

Diagnostics (Basel) 2022 Apr 20;12(5). Epub 2022 Apr 20.

Department of Electrical Engineering, King Saud University, Riyadh 11421, Saudi Arabia.

Parkinson's disease (PD) is a very common brain abnormality that affects people all over the world. Early detection of such abnormality is critical in clinical diagnosis in order to prevent disease progression. Electroencephalography (EEG) is one of the most important PD diagnostic tools since this disease is linked to the brain. Read More

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EEG Spectral Changes Linked to Psychiatric Medications: Computational Pipeline for Data Mining and Analysis.

Stud Health Technol Inform 2022 May;294:957-958

Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany.

The presented computational pipeline is designed to analyze drug-induced changes in EEG data from the Temple University EEG Corpus. The data is cleaned from artifacts, pre-processed, the averaged absolute and relative frequency powers are calculated and compared to a control group. Thus, different research hypotheses can be tested with the intention to reuse accessible data collections. Read More

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ICA With CWT and k-means for Eye-Blink Artifact Removal From Fewer Channel EEG.

IEEE Trans Neural Syst Rehabil Eng 2022 27;30:1361-1373. Epub 2022 May 27.

In recent years, there has been an increase in the usage of consumer based EEG devices with fewer channel configuration. Although independent component analysis has been a popular approach for eye-blink artifact removal from multichannel EEG signals, several studies showed that there is a leak of neural information into the eye-blink artifact associated independent components (ICs). Furthermore, the leak increases as the number of input EEG channels decreases and leads to loss of valuable EEG information. Read More

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Activation-Inhibition dynamics of the oscillatory bursts of the human EEG during resting state. The macroscopic temporal range of few seconds.

Cogn Neurodyn 2022 Jun 7;16(3):591-608. Epub 2021 Nov 7.

Human Psychobiology lab, Experimental Psychology Department, Psychology school, University of Sevilla, c/Camilo José Cela s/n, 41018 Sevilla, Spain.

The ubiquitous brain oscillations occur in bursts of oscillatory activity. The present report tries to define the statistical characteristics of electroencephalographical (EEG) bursts of oscillatory activity during resting state in humans to define (i) the statistical properties of amplitude and duration of oscillatory bursts, (ii) its possible correlation, (iii) its frequency content, and (iv) the presence or not of a fixed threshold to trigger an oscillatory burst. The open eyes EEG recordings of five subjects with no artifacts were selected from a sample of 40 subjects. Read More

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Emotion Recognition Using a Reduced Set of EEG Channels Based on Holographic Feature Maps.

Sensors (Basel) 2022 Apr 23;22(9). Epub 2022 Apr 23.

Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia.

An important function of the construction of the Brain-Computer Interface (BCI) device is the development of a model that is able to recognize emotions from electroencephalogram (EEG) signals. Research in this area is very challenging because the EEG signal is non-stationary, non-linear, and contains a lot of noise due to artifacts caused by muscle activity and poor electrode contact. EEG signals are recorded with non-invasive wearable devices using a large number of electrodes, which increase the dimensionality and, thereby, also the computational complexity of EEG data. Read More

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Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis.

Sensors (Basel) 2022 Apr 21;22(9). Epub 2022 Apr 21.

NSU Genome Research Institute (NGRI), North South University, Dhaka 1229, Bangladesh.

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. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove/reduce motion artifacts from EEG and fNIRS signals using reliable and robust methods. In this regard, this paper proposes two robust methods: (i) Wavelet packet decomposition (WPD) and (ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. Read More

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Segmenting electroencephalography wires reduces radiofrequency shielding artifacts in simultaneous electroencephalography and functional magnetic resonance imaging at 7 T.

Magn Reson Med 2022 May 16. Epub 2022 May 16.

CIBM Center for Biomedical Imaging - Animal Imaging and Technology, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland.

Purpose: Simultaneous scalp electroencephalography and functional magnetic resonance imaging (EEG-fMRI) enable noninvasive assessment of brain function with high spatial and temporal resolution. However, at ultra-high field, the data quality of both modalities is degraded by mutual interactions. Here, we thoroughly investigated the radiofrequency (RF) shielding artifact of a state-of-the-art EEG-fMRI setup, at 7 T, and design a practical solution to limit this issue. Read More

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The Masking Impact of Intra-Artifacts in EEG on Deep Learning-Based Sleep Staging Systems: A Comparative Study.

IEEE Trans Neural Syst Rehabil Eng 2022 2;30:1452-1463. Epub 2022 Jun 2.

Elimination of intra-artifacts in EEG has been overlooked in most of the existing sleep staging systems, especially in deep learning-based approaches. Whether intra-artifacts, originated from the eye movement, chin muscle firing, or heart beating, etc., in EEG signals would lead to a positive or a negative masking effect on deep learning-based sleep staging systems was investigated in this paper. Read More

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VR-enabled portable brain-computer interfaces via wireless soft bioelectronics.

Biosens Bioelectron 2022 Aug 29;210:114333. Epub 2022 Apr 29.

George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA; IEN Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA, 30332, USA; Wallace H. Coulter Department of Biomedical Engineering, Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA; Neural Engineering Center, Institute for Materials, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, 30332, USA. Electronic address:

Noninvasive, wearable brain-computer interfaces (BCI) find limited use due to their obtrusive nature and low information. Currently available portable BCI systems are limited by device rigidity, bulky form factors, and gel-based skin-contact electrodes - and therefore more prone to noise and motion artifacts. Here, we introduce virtual reality (VR)-enabled split-eye asynchronous stimulus (SEAS) allowing a target to present different stimuli to either eye. Read More

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Healthcare Professionals' Electroencephalography Competency: A Disconnect Between Self-assessment and Objective Testing.

J Neurosci Nurs 2022 May 5. Epub 2022 May 5.

Abstract: BACKGROUND: The role of the healthcare professional (HCP) in performing high-quality electroencephalography (EEG) is critical to ensuring accurate results. This study analyzes HCPs' subjectively and objectively assessed EEG competence to provide information on their EEG competence and competence needs for the development of their education and training. METHODS: The study was a descriptive cross-sectional study. Read More

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Decoding neural activity preceding balance loss during standing with a lower-limb exoskeleton using an interpretable deep learning model.

J Neural Eng 2022 05 26;19(3). Epub 2022 May 26.

Noninvasive Brain-Machine Interface System Laboratory, Department of Electrical and Computer Engineering, University of Houston, Houston, Texas, 77204, United States of America.

Falls are a leading cause of death in adults 65 and older. Recent efforts to restore lower-limb function in these populations have seen an increase in the use of wearable robotic systems; however, fall prevention measures in these systems require early detection of balance loss to be effective. Prior studies have investigated whether kinematic variables contain information about an impending fall, but few have examined the potential of using electroencephalography (EEG) as a fall-predicting signal and how the brain responds to avoid a fall. Read More

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Low-quality training data detection method of EEG signals for motor imagery BCI system.

J Neurosci Methods 2022 Jul 26;376:109607. Epub 2022 Apr 26.

Anhui Province Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei 230601, China. Electronic address:

Background: The design and implementation of high-performance motor imagery-based brain computer interface (MI-BCI) requires high-quality training samples. However, fluctuation in subjects' physiological and mental states as well as artifacts can produce the low-quality motor imagery electroencephalogram (EEG) signal, which will damage the performance of MI-BCI system.

New Method: In order to select high-quality MI-EEG training data, this paper proposes a low-quality training data detection method combining independent component analysis (ICA) and weak classifier cluster. Read More

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Tracking of rigid head motion during MRI using an EEG system.

Magn Reson Med 2022 Aug 25;88(2):986-1001. Epub 2022 Apr 25.

Section for Magnetic Resonance, DTU Health Tech, Technical University of Denmark, Kgs. Lyngby, Denmark.

Purpose: To demonstrate a novel method for tracking of head movements during MRI using electroencephalography (EEG) hardware for recording signals induced by native imaging gradients.

Theory And Methods: Gradient switching during simultaneous EEG-fMRI induces distortions in EEG signals, which depend on subject head position and orientation. When EEG electrodes are interconnected with high-impedance carbon wire loops, the induced voltages are linear combinations of the temporal gradient waveform derivatives. Read More

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Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis.

Sensors (Basel) 2022 Apr 15;22(8). Epub 2022 Apr 15.

Biomedical Engineering Graduate Program, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates.

Vigilance level assessment is of prime importance to avoid life-threatening human error. Critical working environments such as air traffic control, driving, or military surveillance require the operator to be alert the whole time. The electroencephalogram (EEG) is a very common modality that can be used in assessing vigilance. Read More

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Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-Heuristically Optimized Non-Local Means Filter.

Sensors (Basel) 2022 Apr 12;22(8). Epub 2022 Apr 12.

Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.

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 paper is to remove muscle artifacts without distorting the information contained in the EEG. A novel multi-stage EEG denoising method is proposed for the first time in which wavelet packet decomposition (WPD) is combined with a modified non-local means (NLM) algorithm. Read More

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Validation of Continuous Monitoring System for Epileptic Users in Outpatient Settings.

Sensors (Basel) 2022 Apr 9;22(8). Epub 2022 Apr 9.

Neuroengineering Biomedical Research Group, Miguel Hernández University of Elche, 03202 Elche, Spain.

Epilepsy is a chronic disease with a significant social impact, given that the patients and their families often live conditioned by the possibility of an epileptic seizure and its possible consequences, such as accidents, injuries, or even sudden unexplained death. In this context, ambulatory monitoring allows the collection of biomedical data about the patients' health, thus gaining more knowledge about the physiological state and daily activities of each patient in a more personalized manner. For this reason, this article proposes a novel monitoring system composed of different sensors capable of synchronously recording electrocardiogram (ECG), photoplethysmogram (PPG), and ear electroencephalogram (EEG) signals and storing them for further processing and analysis in a microSD card. Read More

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Spike-Representation of EEG Signals for Performance Enhancement of Brain-Computer Interfaces.

Front Neurosci 2022 4;16:792318. Epub 2022 Apr 4.

Computational Intelligence and Brain Computer Interface Lab, School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia.

Brain-computer interfaces (BCI) relying on electroencephalography (EEG) based neuroimaging mode has shown prospects for real-world usage due to its portability and optional selectivity of fewer channels for compactness. However, noise and artifacts often limit the capacity of BCI systems especially for event-related potentials such as P300 and error-related negativity (ERN), whose biomarkers are present in short time segments at the time-series level. Contrary to EEG, invasive recording is less prone to noise but requires a tedious surgical procedure. Read More

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Naturalistic viewing conditions can increase task engagement and aesthetic preference but have only minimal impact on EEG quality.

Neuroimage 2022 Aug 17;256:119218. Epub 2022 Apr 17.

Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Frankfurt (Main), Germany. Electronic address:

Free gaze and moving images are typically avoided in EEG experiments due to the expected generation of artifacts and noise. Yet for a growing number of research questions, loosening these rigorous restrictions would be beneficial. Among these is research on visual aesthetic experiences, which often involve open-ended exploration of highly variable stimuli. Read More

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Fast parametric curve matching (FPCM) for automatic spike detection.

J Neural Eng 2022 05 6;19(3). Epub 2022 May 6.

Center for Bioelectric Interfaces, Higher School of Economics, Moscow, Russia.

. Epilepsy is a widely spread neurological disease, whose treatment often requires resection of the pathological cortical tissue. Interictal spike analysis observed in the non-invasively collected EEG or MEG data offers an attractive way to localize epileptogenic cortical structures for surgery planning purposes. Read More

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A Flexible Wearable Electrooculogram System With Motion Artifacts Sensing and Reduction.

IEEE Trans Biomed Circuits Syst 2022 04 19;16(2):324-335. Epub 2022 May 19.

Electrooculogram (EOG) is a well-known physiological metric picked up by placing two or more electrodes around the eyeball. EOG signals are known to be extremely susceptible to motion artifacts. This paper presents a single channel, wireless, wearable flexible EOG monitoring system with motion artifacts sensing and reduction feature. Read More

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Optimized deformable convolution network for detection and mitigation of ocular artifacts from EEG signal.

Multimed Tools Appl 2022 Apr 7:1-39. Epub 2022 Apr 7.

Department of ECE, JNTUK, University College of Engineering, Kakinada, AP India.

Electroencephalogram (EEG) is the key component in the field of analyzing brain activity and behavior. EEG signals are affected by artifacts in the recorded electrical activity; thereby it affects the analysis of EGG. To extract the clean data from EEG signals and to improve the efficiency of detection during encephalogram recordings, a developed model is required. Read More

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Autonomic nerve activity and cardiovascular changes during discrete seizures in rats.

Auton Neurosci 2022 07 16;240:102971. Epub 2022 Mar 16.

Department of Physiology & Pharmacology, State University of New York Downstate Health Sciences University, Brooklyn, NY 11203, United States of America; Department of Neurology, State University of New York Downstate Health Sciences University, Brooklyn, NY 11203, United States of America.

Activity in both divisions of the autonomic nervous system (ANS) can increase during seizures and result in tachy- or bradyarrhythmias. We sought to determine the patterns of ANS activity that led to heart rate (HR) changes and whether the character of ANS and HR changes can impact the seizures themselves. Simultaneous recordings of vagus nerve and cervical sympathetic ganglionic or nerve activity, EEG, ECG, and blood pressure were acquired from 16 urethane-anesthetized rats that received systemic kainic acid to induce seizures. Read More

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Removing artifacts from TMS-evoked EEG: A methods review and a unifying theoretical framework.

J Neurosci Methods 2022 Jul 11;376:109591. Epub 2022 Apr 11.

Department of Neurology & Stroke, and Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland. Electronic address:

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a technique for studying cortical excitability and connectivity in health and disease, allowing basic research and potential clinical applications. A major methodological issue, severely limiting the applicability of TMS-EEG, relates to the contamination of EEG signals by artifacts of biologic or non-biologic origin. To solve this problem, several methods, based on independent component analysis (ICA), principal component analysis (PCA), signal space projection (SSP), and other approaches, have been developed over the last decade. Read More

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LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI.

Sensors (Basel) 2022 Mar 28;22(7). Epub 2022 Mar 28.

Department of Mechatronics Engineering, National University of Sciences and Technology, H-12, Islamabad 44000, Pakistan.

Brain-computer interface (BCI) systems based on functional near-infrared spectroscopy (fNIRS) have been used as a way of facilitating communication between the brain and peripheral devices. The BCI provides an option to improve the walking pattern of people with poor walking dysfunction, by applying a rehabilitation process. A state-of-the-art step-wise BCI system includes data acquisition, pre-processing, channel selection, feature extraction, and classification. Read More

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EEG Essentials.

William O Tatum

Continuum (Minneap Minn) 2022 04;28(2):261-305

Purpose Of Review: EEG is the best study for evaluating the electrophysiologic function of the brain. The relevance of EEG is based on an accurate interpretation of the recording. Understanding the neuroscientific basis for EEG is essential. Read More

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