2,252 results match your criteria EEG Artifacts


Abnormal visual sensitivity in eyelid myoclonia with absences: Evidence from electrocortical connectivity and non-linear quantitative analysis of EEG signal.

Seizure 2019 Apr 10;69:118-124. Epub 2019 Apr 10.

Department "G.F. Ingrassia", Section of Neurosciences, University of Catania, Via S. Sofia 78, 95123 Catania, Italy. Electronic address:

Purpose: Eyelid myoclonia with absences (EMA) is an epileptic syndrome characterized by eyelid myoclonia with or without absences, eyes closure-induced EEG paroxysms and photosensitivity. Pathophysiological mechanisms of visual sensitivity in EMA are not-fully understood. The objective of the present study was to analyze the electrophysiological dynamics implicated in the visual sensitivity in patients with EMA. Read More

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http://dx.doi.org/10.1016/j.seizure.2019.04.007DOI Listing

Redaction of false high frequency oscillations due to muscle artifact improves specificity to epileptic tissue.

Clin Neurophysiol 2019 Apr 11;130(6):976-985. Epub 2019 Apr 11.

Department of Neurology, University of Michigan, USA; Department of Biomedical Engineering, Biointerfaces Institute, University of Michigan, USA. Electronic address:

Objective: High Frequency Oscillations (HFOs) are a promising biomarker of epilepsy. HFOs are typically acquired on intracranial electrodes, but contamination from muscle artifacts is still problematic in HFO analysis. This paper evaluates the effect of myogenic artifacts on intracranial HFO detection and how to remove them. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S13882457193012
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http://dx.doi.org/10.1016/j.clinph.2019.03.028DOI Listing
April 2019
1 Read

Left Ventricular Assist Device Artifact in EEG.

Neurodiagn J 2019 Apr 16:1-9. Epub 2019 Apr 16.

b Department of Anesthesiology University of Florida College of Medicine , Gainesville , Florida.

Left ventricular assist devices are increasingly used as therapy for patients with severe congestive heart failure. These patients typically receive care in the intensive care unit when EEG monitoring is necessary. Identification of artifacts created by these devices is important for accurate EEG diagnosis, thus avoiding unnecessary therapies that may result in complications or require intubation of the patient. Read More

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https://www.tandfonline.com/doi/full/10.1080/21646821.2019.1
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http://dx.doi.org/10.1080/21646821.2019.1599649DOI Listing
April 2019
3 Reads

Dataset of 24-subject EEG recordings during viewing of real-world objects and planar images of the same items.

Data Brief 2019 Jun 21;24:103857. Epub 2019 Mar 21.

Department of Psychology, University of Nevada, 1664 N Virginia St, Reno, NV 89557-0296, USA.

Here we present a collection of electroencephalographic (EEG) data recorded from 24 observers (14 females, 10 males, mean age: 25.4) while observing individually-presented stimuli comprised of 96 real-world objects, and 96 images of the same items printed in high-resolution. EEG was recorded from 128 scalp channels. Read More

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http://dx.doi.org/10.1016/j.dib.2019.103857DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6446127PMC

The Statistics of EEG Unipolar References: Derivations and Properties.

Brain Topogr 2019 Apr 10. Epub 2019 Apr 10.

The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China.

In this brief communication, which complements the EEG reference review (Yao et al. in Brain Topogr, 2019), we provide the mathematical derivations that show: (1) any EEG reference admits the general form of a linear transformation of the ideal multichannel EEG potentials with reference to infinity; (2) the average reference (AR), the reference electrode standardization technique (REST), and its regularized version (rREST) are solving the linear inverse problems that can be derived from both the maximum likelihood estimate (MLE) and the Bayesian theory; however, REST is based on more informative prior/constraint of volume conduction than that of AR; (3) we show for the first time that REST is also a unipolar reference (UR), allowing us to define a general family of URs with unified notations; (4) some notable properties of URs are 'no memory', 'rank deficient by 1', and 'orthogonal projector centering'; (5) we also point out here, for the first time, that rREST provides the optimal interpolating function that can be used when the reference channel is missing or the 'bad' channels are rejected. The derivations and properties imply that: (a) any two URs can transform to each other and referencing with URs multiple times will not accumulate artifacts; (b) whatever URs the EEG data was previously transformed with, the minimum norm solution to the reference problem will be REST and AR with and without modeling volume conduction, respectively; (c) the MLE and the Bayesian theory show the theoretical optimality of REST. Read More

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http://dx.doi.org/10.1007/s10548-019-00706-yDOI Listing

An EEG-Based Identity Authentication System with Audiovisual Paradigm in IoT.

Sensors (Basel) 2019 Apr 8;19(7). Epub 2019 Apr 8.

College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

With the continuous increment of security risks and the limitations of traditional modes, it is necessary to design a universal and trustworthy identity authentication system for intelligent Internet of Things (IoT) applications such as an intelligent entrance guard. The characteristics of EEG (electroencephalography) have gained the confidence of researchers due to its uniqueness, stability, and universality. However, the limited usability of the experimental paradigm and the unsatisfactory classification accuracy have so far prevented the identity authentication system based on EEG to become commonplace in IoT scenarios. Read More

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http://dx.doi.org/10.3390/s19071664DOI Listing

Eye movements explain decodability during perception and cued attention in MEG.

Neuroimage 2019 Apr 3;195:444-453. Epub 2019 Apr 3.

Donders Institute, Radboud University, Nijmegen, the Netherlands.

Eye movements are an integral part of human perception, but can induce artifacts in many magneto-encephalography (MEG) and electroencephalography (EEG) studies. For this reason, investigators try to minimize eye movements and remove these artifacts from their data using different techniques. When these artifacts are not purely random, but consistent regarding certain stimuli or conditions, the possibility arises that eye movements are actually inducing effects in the MEG signal. Read More

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http://dx.doi.org/10.1016/j.neuroimage.2019.03.069DOI Listing
April 2019
1 Read

Electroencephalographic Resting-State Functional Connectivity of Benign Epilepsy with Centrotemporal Spikes.

J Clin Neurol 2019 Apr;15(2):211-220

Pediatric Clinical Neuroscience Center, Seoul National University Children's Hospital, Seoul, Korea.

Background And Purpose: We aimed to reveal resting-state functional connectivity characteristics based on the spike-free waking electroencephalogram (EEG) of benign epilepsy with centrotemporal spikes (BECTS) patients, which usually appears normal in routine visual inspection.

Methods: Thirty BECTS patients and 30 disease-free and age- and sex-matched controls were included. Eight-second EEG epochs without artifacts were sampled and then bandpass filtered into the delta, theta, lower alpha, upper alpha, and beta bands to construct the association matrix. Read More

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http://dx.doi.org/10.3988/jcn.2019.15.2.211DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6444134PMC
April 2019
1.807 Impact Factor

Towards Decoding Selective Attention from Single-Trial EEG Data in Cochlear Implant Users.

IEEE Trans Biomed Eng 2019 Mar 26. Epub 2019 Mar 26.

Previous results showed that it is possible to decode an attended speech source from EEG data via the reconstruction of the speech envelope in normal hearing (NH) listeners. However, so far it is unknown how the performance of such a decoder is affected by the decrease in spectral resolution and the electrical artifacts introduced by a cochlear implant (CI) in users of these prostheses. NH-listeners and bilateral CI-users participated in the present study. Read More

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http://dx.doi.org/10.1109/TBME.2019.2907638DOI Listing
March 2019
1 Read

Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures.

Front Hum Neurosci 2019 12;13:76. Epub 2019 Mar 12.

The Neural Engineering Data Consortium, Temple University, Philadelphia, PA, United States.

Brain monitoring combined with automatic analysis of EEGs provides a clinical decision support tool that can reduce time to diagnosis and assist clinicians in real-time monitoring applications (e.g., neurological intensive care units). Read More

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http://dx.doi.org/10.3389/fnhum.2019.00076DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6423064PMC

[A review on methods for offline removing of artifacts in electroencephalography induced by transcranial magnetic stimulation].

Authors:
Shan Yin Yingjie Li

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2019 Feb;36(1):146-150

Institute of Biomedical Engineering, school of Communication and Information Engineering, Shanghai University, Shanghai 200444, P.R.China;Qianweichang College, Shanghai University, Shanghai 200444, P.R.China.

Transcranial magnetic stimulation (TMS) combined with electroencephalography(EEG) has become an important tool in brain research. However, it is difficult to remove the large artifacts in EEG signals caused by the online TMS intervention. In this paper, we summed up various types of artifacts. Read More

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http://dx.doi.org/10.7507/1001-5515.201802011DOI Listing
February 2019

[Study of denoising of simultaneous electroencephalogram-functional magnetic resonance imaging signal based on real-time constrained independent components analysis].

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2019 Feb;36(1):7-15

School of Information Science and Engineering, Changzhou University, Changzhou, Jiangsu 213164, P.R.China;Changzhou Key Laboratory of Biomedical Information Technology, Changzhou, Jiangsu 213164,

Simultaneous recording of electroencephalogram (EEG)-functional magnetic resonance imaging (fMRI) plays an important role in scientific research and clinical field due to its high spatial and temporal resolution. However, the fusion results are seriously influenced by ballistocardiogram (BCG) artifacts under MRI environment. In this paper, we improve the off-line constrained independent components analysis using real-time technique (rt-cICA), which is applied to the simulated and real resting-state EEG data. Read More

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http://dx.doi.org/10.7507/1001-5515.201709066DOI Listing
February 2019

Human electrocortical dynamics while stepping over obstacles.

Sci Rep 2019 Mar 18;9(1):4693. Epub 2019 Mar 18.

J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, USA.

To better understand human brain dynamics during visually guided locomotion, we developed a method of removing motion artifacts from mobile electroencephalography (EEG) and studied human subjects walking and running over obstacles on a treadmill. We constructed a novel dual-layer EEG electrode system to isolate electrocortical signals, and then validated the system using an electrical head phantom and robotic motion platform. We collected data from young healthy subjects walking and running on a treadmill while they encountered unexpected obstacles to step over. Read More

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http://dx.doi.org/10.1038/s41598-019-41131-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6423113PMC

Removing muscle artifacts from EEG data of people with cognitive impairment using high order statistic methods.

Hell J Nucl Med 2019 Jan-Apr;22 Suppl:165-173

School of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.

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 between brain regions and activation areas during cognitive performance. EEG recordings are frequently contaminated by muscle artifacts, which obscure and complicate their interpretation. These muscle artifacts are particularly difficult to be removed from the EEG in order the latter to be used for further clinical evaluation. Read More

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January 2019
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EEG representation using multi-instance framework on the manifold of symmetric positive definite matrices.

J Neural Eng 2019 Mar 7;16(3):036016. Epub 2019 Mar 7.

Computer Engineering and Information Technology Department, Amirkabir University of Technology, Hafez Ave., Tehran, Iran.

Objective: The generalization and robustness of an electroencephalogram (EEG)-based system are crucial requirements in actual practices.

Approach: To reach these goals, we propose a new EEG representation that provides a more realistic view of brain functionality by applying multi-instance (MI) framework to consider the non-stationarity of the EEG signal. In this representation, the non-stationarity of EEG is considered by describing the signal as a bag of relevant and irrelevant concepts. Read More

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http://dx.doi.org/10.1088/1741-2552/ab0dadDOI Listing
March 2019
1 Read

Enhanced setup for wired continuous long-term EEG monitoring in juvenile and adult rats: application for epilepsy and other disorders.

BMC Neurosci 2019 Mar 4;20(1). Epub 2019 Mar 4.

Department of Anatomy, Cell Biology and Physiological Sciences, Faculty of Medicine, American University of Beirut, Diana Tamari Sabbagh (DTS) Building, first floor, 117b, P.O. Box 11-0236, Riad El-Solh, Beirut, 1107 2020, Lebanon.

Background: The electroencephalogram (EEG) is a widely used laboratory technique in rodent models of epilepsy, traumatic brain injury (TBI), and other neurological diseases accompanied by seizures. Obtaining prolonged continuous EEG tracings over weeks to months is essential to adequately answer research questions related to the chronobiology of seizure emergence, and to the effect of potential novel treatment strategies. Current EEG recording methods include wired and the more recent but very costly wireless technologies. Read More

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http://dx.doi.org/10.1186/s12868-019-0490-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6398261PMC
March 2019
3 Reads

Removal of Artifacts from EEG Signals: A Review.

Sensors (Basel) 2019 Feb 26;19(5). Epub 2019 Feb 26.

School of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China.

Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Read More

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http://dx.doi.org/10.3390/s19050987DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427454PMC
February 2019
1 Read

Amplitude-integrated electroencephalography for neonatal seizure detection. An electrophysiological point of view.

Authors:
Sebastián Gacio

Arq Neuropsiquiatr 2019 Feb;77(2):122-130

Hospital de Niños Ricardo Gutiérrez, División de Neurología, Ciudad Autónoma de Buenos Aires, Argentina.

Seizures in the newborn are associated with high morbidity and mortality, making their detection and treatment critical. Seizure activity in neonates is often clinically obscured, such that detection of seizures is particularly challenging. Amplitude-integrated EEG is a technique for simplified EEG monitoring that has found an increasing clinical application in neonatal intensive care. Read More

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http://www.scielo.br/scielo.php?script=sci_arttext&pid=S
Publisher Site
http://dx.doi.org/10.1590/0004-282X20180150DOI Listing
February 2019
5 Reads

Investigating the variability of cardiac pulse artifacts across heartbeats in simultaneous EEG-fMRI recordings: A 7T study.

Neuroimage 2019 May 8;191:21-35. Epub 2019 Feb 8.

Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology, University of Lausanne, Lausanne, Switzerland; Department of Radiology, University of Geneva, Geneva, Switzerland.

Electroencephalography (EEG) recordings performed in magnetic resonance imaging (MRI) scanners are affected by complex artifacts caused by heart function, often termed pulse artifacts (PAs). PAs can strongly compromise EEG data quality, and remain an open problem for EEG-fMRI. This study investigated the properties and mechanisms of PA variability across heartbeats, which has remained largely unaddressed to date, and evaluated its impact on PA correction approaches. Read More

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http://dx.doi.org/10.1016/j.neuroimage.2019.02.021DOI Listing

Vibrotactile piezoelectric stimulation system with precise and versatile timing control for somatosensory research.

J Neurosci Methods 2019 Apr 7;317:29-36. Epub 2019 Feb 7.

Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA.

Background: Tactile stimulations systems are useful for studying the somatosensory system in children because they are innocuous and safe. Stimulators based on piezoelectric actuator are useful, but there is still a need for such systems capable of providing accurate and versatile control of timing and pattern of activation.

New Method: We have implemented a vibrotactile stimulating system useful for behavioral and electroencephalography (EEG) and magnetoencephalography (MEG) research. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S01650270193004
Publisher Site
http://dx.doi.org/10.1016/j.jneumeth.2019.02.002DOI Listing
April 2019
22 Reads

Quality Assessment of Single-Channel EEG for Wearable Devices.

Sensors (Basel) 2019 Jan 31;19(3). Epub 2019 Jan 31.

CNRS UMR-7225, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, 75013 Paris, France.

The recent embedding of electroencephalographic (EEG) electrodes in wearable devices raises the problem of the quality of the data recorded in such uncontrolled environments. These recordings are often obtained with dry single-channel EEG devices, and may be contaminated by many sources of noise which can compromise the detection and characterization of the brain state studied. In this paper, we propose a classification-based approach to effectively quantify artefact contamination in EEG segments, and discriminate muscular artefacts. Read More

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http://dx.doi.org/10.3390/s19030601DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6387437PMC
January 2019
1 Read

An Unsupervised Multichannel Artifact Detection Method for Sleep EEG Based on Riemannian Geometry.

Sensors (Basel) 2019 Jan 31;19(3). Epub 2019 Jan 31.

Czech Technical University in Prague, Jugoslávských partyzánů 1580/3, 160 00 Prague, Czech Republic.

In biomedical signal processing, we often face the problem of artifacts that distort the original signals. This concerns also sleep recordings, such as EEG. Artifacts may severely affect or even make impossible visual inspection, as well as automatic processing. Read More

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http://dx.doi.org/10.3390/s19030602DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6387048PMC
January 2019
2 Reads

EEG Dataset and OpenBMI Toolbox for Three BCI Paradigms: An Investigation into BCI Illiteracy.

Gigascience 2019 Jan 30. Epub 2019 Jan 30.

Department of Brain and Cognitive Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea.

Background: Electroencephalography (EEG)-based brain-computer interface (BCI) systems are mainly divided into three major paradigms: motor-imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP). In this paper, we present a BCI dataset that includes the three major BCI paradigms with a large number of subjects over multiple sessions. In addition, information about the psychological and physiological conditions of BCI users was obtained using a questionnaire, and task-unrelated parameters such as resting state, artifacts, and electromyography of both arms were also recorded. Read More

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http://dx.doi.org/10.1093/gigascience/giz002DOI Listing
January 2019
2 Reads

Quantitative Analyses Help in Choosing Between Simultaneous vs. Separate EEG and fMRI.

Front Neurosci 2018 10;12:1009. Epub 2019 Jan 10.

Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium.

Simultaneous registration of scalp electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is considered an attractive approach for studying brain function non-invasively. It combines the better spatial resolution of fMRI with the better temporal resolution of EEG, but comes at the cost of increased measurement artifact and the accompanying artifact preprocessing. This paper presents a study of the residual signal quality based on temporal signal to noise ratio (TSNR) for fMRI and fast Fourier transform (FFT) for EEG, after optimized conventional signal preprocessing. Read More

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http://dx.doi.org/10.3389/fnins.2018.01009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6335318PMC
January 2019
1 Read

Dual responsive neurostimulation implants for epilepsy.

J Neurosurg 2019 Jan 25:1-7. Epub 2019 Jan 25.

Departments of1Neurosurgery and.

Closed-loop brain-responsive neurostimulation via the RNS System is a treatment option for adults with medically refractory focal epilepsy. Using a novel technique, 2 RNS Systems (2 neurostimulators and 4 leads) were successfully implanted in a single patient with bilateral parietal epileptogenic zones. In patients with multiple epileptogenic zones, this technique allows for additional treatment options. Read More

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http://dx.doi.org/10.3171/2018.8.JNS181362DOI Listing
January 2019
7 Reads

Estimation of auditory steady-state responses based on the averaging of independent EEG epochs.

PLoS One 2019 24;14(1):e0206018. Epub 2019 Jan 24.

Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile.

The amplitude of auditory steady-state responses (ASSRs) generated in the brainstem of rats exponentially decreases over the sequential averaging of EEG epochs. This behavior is partially due to the adaptation of the ASSR induced by the continuous and monotonous stimulation. In this study, we analyzed the potential clinical relevance of the ASSR adaptation. Read More

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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0206018PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6345467PMC
January 2019

The Riemannian Potato Field: A Tool for Online Signal Quality Index of EEG.

IEEE Trans Neural Syst Rehabil Eng 2019 Feb 15;27(2):244-255. Epub 2019 Jan 15.

Electroencephalographic (EEG) recordings are contaminated by instrumental, environmental, and biological artifacts, resulting in low signal-to-noise ratio. Artifact detection is a critical task for real-time applications where the signal is used to give a continuous feedback to the user. In these applications, it is therefore necessary to estimate online a signal quality index (SQI) in order to stop the feedback when the signal quality is unacceptable. Read More

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http://dx.doi.org/10.1109/TNSRE.2019.2893113DOI Listing
February 2019
1 Read

A Study on the Feasibility of the Deep Brain Stimulation (DBS) Electrode Localization Based on Scalp Electric Potential Recordings.

Front Physiol 2018 4;9:1788. Epub 2019 Jan 4.

Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States.

Deep Brain Stimulation (DBS) is an effective therapy for patients disabling motor symptoms from Parkinson's disease, essential tremor, and other motor disorders. Precise, individualized placement of DBS electrodes is a key contributor to clinical outcomes following surgery. Electroencephalography (EEG) is widely used to identify the sources of intracerebral signals from the potential on the scalp. Read More

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http://dx.doi.org/10.3389/fphys.2018.01788DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6328462PMC
January 2019
8 Reads

An efficient EEG based deceit identification test using wavelet packet transform and linear discriminant analysis.

J Neurosci Methods 2019 Feb 17;314:31-40. Epub 2019 Jan 17.

Department of Computer Science and Engineering, National Institute of Technology, Goa, India. Electronic address:

Background: Brain-computer interface (BCI) is a combination of hardware and software that provides a non-muscular channel to send various messages and commands to the outside world and control external devices such as computers. BCI helps severely disabled patients having neuromuscular injuries, locked-in syndrome (LiS) to lead their life as a normal person to the best extent possible. There are various applications of BCI not only in the field of medicine but also in entertainment, lie detection, gaming, etc. Read More

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http://dx.doi.org/10.1016/j.jneumeth.2019.01.007DOI Listing
February 2019
6 Reads

Quantitatively detecting postictal hypoperfusion in patients with focal epilepsy using CT perfusion: Determining cross-modality comparisons and electrode artifacts.

J Neurosci Methods 2019 Feb 15;314:13-20. Epub 2019 Jan 15.

Hotchkiss Brain Institute, University of Calgary, Canada; Seaman Family MR Research Centre, University of Calgary, Canada; Department of Clinical Neurosciences, University of Calgary, Canada; Department of Radiology, University of Calgary, Canada. Electronic address:

Background: We previously showed that CT perfusion (CTP) and arterial spin labelled (ASL) MRI can localize the seizure onset zone in humans via postictal perfusion patterns. As a step towards improving the feasibility/ease of collecting postictal CBF data, we determined whether EEG electrodes need to be removed for CTP data collection and whether a cross-modality comparison between baseline ASL and postictal CTP data is possible.

New Method: Five patients with epilepsy underwent postictal CTP scanning. Read More

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http://dx.doi.org/10.1016/j.jneumeth.2019.01.004DOI Listing
February 2019
1 Read

Reducing power line noise in EEG and MEG data via spectrum interpolation.

Neuroimage 2019 Apr 11;189:763-776. Epub 2019 Jan 11.

Center of Functionally Integrative Neuroscience, Aarhus University, 8000, Aarhus, Denmark; Department of Psychology, University of Konstanz, 78457, Konstanz, Germany; Zukunftskolleg, University of Konstanz, 78457, Konstanz, Germany.

Electroencephalographic (EEG) and magnetoencephalographic (MEG) signals can often be exposed to strong power line interference at 50 or 60 Hz. A widely used method to remove line noise is the notch filter, but it comes with the risk of potentially severe signal distortions. Among other approaches, the Discrete Fourier Transform (DFT) filter and CleanLine have been developed as alternatives, but they may fail to remove power line noise of highly fluctuating amplitude. Read More

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http://dx.doi.org/10.1016/j.neuroimage.2019.01.026DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6456018PMC

Conductive gel bridge sensor for motion tracking in simultaneous EEG-fMRI recordings.

Epilepsy Res 2019 01 17;149:117-122. Epub 2018 Dec 17.

Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.

EEG-fMRI allows the localization of the hemodynamic correlates of neural activity and has been shown to be useful as a diagnostic tool in pre-surgical evaluation of refractory epilepsy. However, EEG recordings may be highly contaminated by artifacts induced by movements inside the magnetic field thus rendering the scan difficult for interpretation. Existing methods for motion correction require additional equipment or hardware modification. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S09201211183027
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http://dx.doi.org/10.1016/j.eplepsyres.2018.12.008DOI Listing
January 2019
3 Reads

Removal of Gross Artifacts of Transcranial Alternating Current Stimulation in Simultaneous EEG Monitoring.

Sensors (Basel) 2019 Jan 7;19(1). Epub 2019 Jan 7.

School of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK.

Transcranial electrical stimulation is a widely used non-invasive brain stimulation approach. To date, EEG has been used to evaluate the effect of transcranial Direct Current Stimulation (tDCS) and transcranial Alternating Current Stimulation (tACS), but most studies have been limited to exploring changes in EEG before and after stimulation due to the presence of stimulation artifacts in the EEG data. This paper presents two different algorithms for removing the gross tACS artifact from simultaneous EEG recordings. Read More

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http://dx.doi.org/10.3390/s19010190DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338981PMC
January 2019

NFBLab-A Versatile Software for Neurofeedback and Brain-Computer Interface Research.

Front Neuroinform 2018 24;12:100. Epub 2018 Dec 24.

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

Neurofeedback (NFB) is a real-time paradigm, where subjects learn to volitionally modulate their own brain activity recorded with electroencephalographic (EEG), magnetoencephalographic (MEG) or other functional brain imaging techniques and presented to them via one of sensory modalities: visual, auditory or tactile. NFB has been proposed as an approach to treat neurological conditions and augment brain functions. Although the early NFB studies date back nearly six decades ago, there is still much debate regarding the efficiency of this approach and the ways it should be implemented. Read More

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http://dx.doi.org/10.3389/fninf.2018.00100DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311652PMC
December 2018

An Approach for Brain-Controlled Prostheses Based on a Facial Expression Paradigm.

Front Neurosci 2018 18;12:943. Epub 2018 Dec 18.

Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, Xi'an, China.

One of the most exciting areas of rehabilitation research is brain-controlled prostheses, which translate electroencephalography (EEG) signals into control commands that operate prostheses. However, the existing brain-control methods have an obstacle between the selection of brain computer interface (BCI) and its performance. In this paper, a novel BCI system based on a facial expression paradigm is proposed to control prostheses that uses the characteristics of theta and alpha rhythms of the prefrontal and motor cortices. Read More

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http://dx.doi.org/10.3389/fnins.2018.00943DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6305548PMC
December 2018
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Electroencephalographic Patterns During Common Nursing Interventions in Neurointensive Care: A Descriptive Pilot Study.

J Neurosci Nurs 2019 Feb;51(1):10-15

Kristin Elf, MD PhD, is Clinical Neurophysiologist and Researcher, Department of Neuroscience, Clinical Neurophysiology, Uppsala University, Uppsala, Sweden. Tommy Carlsson, PhD PGDip RN RM, is Postdoctoral Researcher, Department for Health Promoting Science, Sophiahemmet University, Stockholm; and Intensive Care Nurse and Researcher, Department of Neuroscience, Neurosurgery, Uppsala University; and Midwife and Researcher, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden. Liliana Santeliz Rivas, MSc RN, is Intensive Care Nurse, Department of Neuroscience, Neurosurgery, Uppsala University, Uppsala, Sweden. Emma Widnersson, MSc RN, is Intensive Care Nurse, Department of Neuroscience, Neurosurgery, Uppsala University, Uppsala, Sweden.

Background: Many patients with neurological insults requiring neurointensive care have an increased risk of acute symptomatic seizures. Various nursing interventions performed when caring for these patients may elicit pathological cerebral electrical activity including seizures and stimulus-induced rhythmic, periodic, or ictal discharges (SIRPIDs). The aim was to explore changes in electroencephalogram (EEG) due to neurointensive care nursing interventions. Read More

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http://dx.doi.org/10.1097/JNN.0000000000000411DOI Listing
February 2019
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Assessment of Statistically Significant Command-Following in Pediatric Patients with Disorders of Consciousness, Based on Visual, Auditory and Tactile Event-Related Potentials.

Int J Neural Syst 2019 Apr 29;29(3):1850048. Epub 2018 Oct 29.

* Faculty of Physics, University of Warsaw, ul. Pasteura 5, 02-093 Warsaw, Poland.

Disorders of consciousness (DOC) are among the major challenges of contemporary medicine, mostly due to the high rates of misdiagnoses in clinical assessment, based on behavioral scales. This turns our attention to potentially objective neuroimaging methods. Paradigms based on electroencephalography (EEG) are most suited for bedside applications, but sensitive to artifacts. Read More

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http://dx.doi.org/10.1142/S012906571850048XDOI Listing
April 2019
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Deep Convolutional Neural Networks for feature-less automatic classification of Independent Components in multi-channel electrophysiological brain recordings.

IEEE Trans Biomed Eng 2018 Dec 24. Epub 2018 Dec 24.

Objective: Interpretation of the Electroencephalographic (EEG) and Magnetoencephalographic (MEG) signals requires off-line artifacts removal. Since artifacts share frequencies with brain activity, filtering is insufficient. Blind source separation, mainly through Independent Component Analysis (ICA), is the gold-standard procedure for the identification of artifacts in multi-dimensional recordings. Read More

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http://dx.doi.org/10.1109/TBME.2018.2889512DOI Listing
December 2018
3 Reads

Magnetoencephalographic Source Localization of the Eye Area of the Motor Homunculus.

Can J Neurol Sci 2018 Dec 17:1-7. Epub 2018 Dec 17.

1Division of Neurology,Krembil Brain Institute,University Health Network, Toronto Western Hospital,University of Toronto,Toronto, Ontario,Canada.

A patient with intractable epilepsy, previous right frontal resection, and active vagus nerve stimulation (VNS) developed new onset quasi-continuous twitching around the left eye. Electroencephalography showed no correlate to the orbicularis oculi twitches apart from myographic potentials at the left supraorbital and anterior frontal electrodes. Magnetoencephalography was performed using spatiotemporal signal space separation to suppress magnetic artifacts associated with the VNS apparatus. Read More

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https://www.cambridge.org/core/product/identifier/S031716711
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http://dx.doi.org/10.1017/cjn.2018.373DOI Listing
December 2018
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Methodologic recommendations and possible interpretations of video-EEG recordings in immature rodents used as experimental controls: A TASK1-WG2 report of the ILAE/AES Joint Translational Task Force.

Epilepsia Open 2018 Dec 11;3(4):437-459. Epub 2018 Oct 11.

Laboratory of Developmental Epilepsy Saul R. Korey Department of Neurology Dominick P. Purpura Department of Neuroscience Isabelle Rapin Division of Child Neurology Albert Einstein College of Medicine Einstein/Montefiore Epilepsy Center Montefiore Medical Center Bronx New York U.S.A.

The use of immature rodents to study physiologic aspects of cortical development requires high-quality recordings electroencephalography (EEG) with simultaneous video recording (vEEG) of behavior. Normative developmental vEEG data in control animals are fundamental for the study of abnormal background activity in animal models of seizures or other neurologic disorders. Electrical recordings from immature, freely behaving rodents can be particularly difficult because of the small size of immature rodents, their thin and soft skull, interference with the recording apparatus by the dam, and other technical challenges. Read More

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http://dx.doi.org/10.1002/epi4.12262DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6276777PMC
December 2018
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Diffusion geometry approach to efficiently remove electrical stimulation artifacts in intracranial electroencephalography.

J Neural Eng 2018 Nov 21;16(3):036010. Epub 2018 Nov 21.

Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America. Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.

Objective: Cortical oscillations, electrophysiological activity patterns, associated with cognitive functions and impaired in many psychiatric disorders can be observed in intracranial electroencephalography (iEEG). Direct cortical stimulation (DCS) may directly target these oscillations and may serve as therapeutic approaches to restore functional impairments. However, the presence of electrical stimulation artifacts in neurophysiological data limits the analysis of the effects of stimulation. Read More

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http://dx.doi.org/10.1088/1741-2552/aaf2baDOI Listing
November 2018
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Artifacts in EEG of simultaneous EEG-fMRI: pulse artifact remainders in the gradient artifact template are a source of artifact residuals after average artifact subtraction.

J Neural Eng 2019 Feb 29;16(1):016011. Epub 2018 Oct 29.

Institute of Neural Engineering, Graz University of Technology, Graz, Austria.

Objective: The simultaneous application of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) opens up new ways to investigate the human brain. The EEG recordings of simultaneous EEG-fMRI, however, are overlaid to a great degree by fMRI related artifacts and an artifact reduction is mandatory before any EEG analysis. The most severe artifacts-the gradient artifact and the pulse artifact-are repetitive. Read More

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http://dx.doi.org/10.1088/1741-2552/aaec42DOI Listing
February 2019
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Immediate neurophysiological effects of transcranial electrical stimulation.

Nat Commun 2018 11 30;9(1):5092. Epub 2018 Nov 30.

New York University Neuroscience Institute, 435 East 30th Street, New York, NY, 10016, USA.

Noninvasive brain stimulation techniques are used in experimental and clinical fields for their potential effects on brain network dynamics and behavior. Transcranial electrical stimulation (TES), including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), has gained popularity because of its convenience and potential as a chronic therapy. However, a mechanistic understanding of TES has lagged behind its widespread adoption. Read More

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http://www.nature.com/articles/s41467-018-07233-7
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http://dx.doi.org/10.1038/s41467-018-07233-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6269428PMC
November 2018
18 Reads

Multiway canonical correlation analysis of brain data.

Neuroimage 2019 Feb 27;186:728-740. Epub 2018 Nov 27.

City College New York, USA.

Brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratios due to the presence of multiple competing sources and artifacts. A common remedy is to average responses over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Read More

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http://dx.doi.org/10.1016/j.neuroimage.2018.11.026DOI Listing
February 2019

Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals.

Clin Neurophysiol 2019 Jan 15;130(1):25-37. Epub 2018 Nov 15.

Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada. Electronic address:

Objective: Automatic detection of epileptic seizures based on deep learning methods received much attention last year. However, the potential of deep neural networks in seizure detection has not been fully exploited in terms of the optimal design of the model architecture and the detection power of the time-series brain data. In this work, a deep neural network architecture is introduced to learn the temporal dependencies in Electroencephalogram (EEG) data for robust detection of epileptic seizures. Read More

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http://dx.doi.org/10.1016/j.clinph.2018.10.010DOI Listing
January 2019
14 Reads

Neural tracking of the speech envelope in cochlear implant users.

J Neural Eng 2019 Feb 16;16(1):016003. Epub 2018 Nov 16.

Objective: When listening to speech, the brain tracks the speech envelope. It is possible to reconstruct this envelope from EEG recordings. However, in people who hear using a cochlear implant (CI), the artifacts caused by electrical stimulation of the auditory nerve contaminate the EEG. Read More

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http://dx.doi.org/10.1088/1741-2552/aae6b9DOI Listing
February 2019
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A novel algorithm for removing artifacts from EEG data.

Conf Proc IEEE Eng Med Biol Soc 2018 Jul;2018:6014-6017

In recent years, many studies examined if EEG signals from traumatic brain injury (TBI) patients can be used for new rehabilitation technologies, such as BCI systems. However, extraction of the high-gamma band related to movement remains challenging due to the presence of surface electromyogram (sEMG) caused by unconscious facial and head movement of patients. In this paper, we proposed a modified independent component analysis (ICA) model for EMG artifact removal in the EEG data from TBI patients with a hemicraniectomy. Read More

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https://ieeexplore.ieee.org/document/8513658/
Publisher Site
http://dx.doi.org/10.1109/EMBC.2018.8513658DOI Listing
July 2018
19 Reads

A Blind Source-Based Method for Automated Artifact-Correction in Standard Sleep EEG.

Conf Proc IEEE Eng Med Biol Soc 2018 Jul;2018:6010-6013

Electroencephalogram (EEG) is a common tool in sleep medicine, but it is often compromised by non-neural artifacts. Excluding visually identified artifacts is time-consuming and removes relevant EEG information. Blind source separation (BSS) techniques, on the other hand, are capable of separating "brain" from "artifact source components". Read More

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http://dx.doi.org/10.1109/EMBC.2018.8513619DOI Listing

Semi-simulation Experiments for Quantifying the Performance of SSVEP-based BCI after Reducing Artifacts from Trapezius Muscles.

Conf Proc IEEE Eng Med Biol Soc 2018 Jul;2018:4824-4827

Muscular artifacts often contaminate electroencephalograms (EEGs) and deteriorate the performance of brain-computer interfaces (BCIs). Although many artifact reduction techniques are available, most of the studies have focused on their reduction ability (i.e. Read More

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http://dx.doi.org/10.1109/EMBC.2018.8513180DOI Listing
July 2018
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Automatic Independent Component Scalp Map Analysis of Electroencephalogram During Motor Preparation.

Conf Proc IEEE Eng Med Biol Soc 2018 Jul;2018:4689-4692

This work presents a method for automatic independent component (IC) scalp map analysis of electroencephalogram during motor preparation in visuomotor tasks. The strength of this approach is the analysis of the IC scalp maps based on the apriori given mask. This uses an image processing approach, comparable to visual classification used by experts, to automate the selection of relevant ICs in visuomotor tasks. Read More

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http://dx.doi.org/10.1109/EMBC.2018.8513184DOI Listing
July 2018
1 Read