2,228 results match your criteria EEG Artifacts


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

Neuroimage 2019 Feb 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
February 2019

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

J Neurosci Methods 2019 Feb 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
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http://dx.doi.org/10.1016/j.jneumeth.2019.02.002DOI Listing
February 2019
4 Reads

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
January 2019

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
1 Read

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

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
2 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

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
1 Read

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
2 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 Jan 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
January 2019

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

Epilepsy Res 2019 Jan 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
1 Read

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

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 2018 Oct 29:1850048. 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
October 2018

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

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
2 Reads

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
1 Read

Diffusion geometry approach to efficiently remove electrical stimulation artifacts in intracranial electroencephalography.

J Neural Eng 2018 Nov 21. Epub 2018 Nov 21.

Department of Mathematics, University of Toronto, Toronto, Ontario, CANADA.

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
1 Read

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

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
11 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
11 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
1 Read

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/
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http://dx.doi.org/10.1109/EMBC.2018.8513658DOI Listing
July 2018
12 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
1 Read

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

Resting-state Gamma-band EEG Abnormalities in Autism.

Conf Proc IEEE Eng Med Biol Soc 2018 Jul;2018:1915-1918

Gamma-band rhythmic abnormalities have been of significant interests in autism spectrum disorders (ASD). Most studies used magnetoencephalography (MEG) due to its advantage in measuring weak gamma signals as compared to electroencephalography (EEG). However, EEG is more accessible, portable, and importantly, more sensitive to cortical sources located at the crowns of gyri, than MEG. Read More

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https://ieeexplore.ieee.org/document/8512718/
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http://dx.doi.org/10.1109/EMBC.2018.8512718DOI Listing
July 2018
10 Reads

Introducing a Combination of ICA-EMD to Suppress Muscle and Ocular Artifacts in EEG Signals.

Conf Proc IEEE Eng Med Biol Soc 2018 Jul;2018:1250-1253

This paper presents a combination of Independent Component Analysis (ICA) with Empirical Mode Decomposition (EMD) to suppress muscle and ocular artifacts in electroencephalographic (EEG) signals: By means of ICA, the EEG signals are decomposed into independent components. To avoid the suppression of artifactual components still containing physiological information, EMD is applied to decompose the components in Intrinsic Mode Functions (IMFs). The IMFs with mainly muscle artifacts are removed, and a new data set of independent components without muscle artifacts is generated. Read More

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http://dx.doi.org/10.1109/EMBC.2018.8512581DOI Listing

Evaluation of Artifact Subspace Reconstruction for Automatic EEG Artifact Removal.

Conf Proc IEEE Eng Med Biol Soc 2018 Jul;2018:1242-1245

One of the greatest challenges that hinder the decoding and application of electroencephalography (EEG) is that EEG recordings almost always contain artifacts - non-brain signals. Among existing automatic artifact-removal methods, artifact subspace reconstruction (ASR) is an online and realtime capable, component-based method that can effectively remove transient or large-amplitude artifacts. However, the effectiveness of ASR and the optimal choice of its parameter have not been evaluated and reported, especially on real EEG data. Read More

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https://ieeexplore.ieee.org/document/8512547/
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http://dx.doi.org/10.1109/EMBC.2018.8512547DOI Listing
July 2018
8 Reads

Online Automatic Artifact Rejection using the Real-time EEG Source-mapping Toolbox (REST).

Conf Proc IEEE Eng Med Biol Soc 2018 07;2018:106-109

Non-brain contributions to electroencephalographic (EEG) signals, often referred to as artifacts, can hamper the analysis of scalp EEG recordings. This is especially true when artifacts have large amplitudes (e.g. Read More

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https://ieeexplore.ieee.org/document/8512191/
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http://dx.doi.org/10.1109/EMBC.2018.8512191DOI Listing
July 2018
10 Reads

Motor Unit-Driven Identification of Pathological Tremor in Electroencephalograms.

Front Neurol 2018 29;9:879. Epub 2018 Oct 29.

Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia.

Traditional studies on the neural mechanisms of tremor use coherence analysis to investigate the relationship between cortical and muscle activity, measured by electroencephalograms (EEG) and electromyograms (EMG). This methodology is limited by the need of relatively long signal recordings, and it is sensitive to EEG artifacts. Here, we analytically derive and experimentally validate a new method for automatic extraction of the tremor-related EEG component in pathological tremor patients that aims to overcome these limitations. Read More

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https://www.frontiersin.org/article/10.3389/fneur.2018.00879
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http://dx.doi.org/10.3389/fneur.2018.00879DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6215829PMC
October 2018
7 Reads

A multi-channel approach for cortical stimulation artefact suppression in depth EEG signals using time-frequency and spatial filtering.

IEEE Trans Biomed Eng 2018 Nov 12. Epub 2018 Nov 12.

Objective: The stereo electroencephalogram (SEEG) recordings are the sate of the art tool used in pre-surgical evaluation of drug-unresponsive epileptic patients. Coupled with SEEG, electrical cortical stimulation (CS) offer a complementary tool to investigate the lesioned/healthy brain regions and to identify the epileptic zones with precision. However, the propagation of this stimulation inside the brain masks the cerebral activity recorded by nearby multi-contact SEEG electrodes. Read More

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http://dx.doi.org/10.1109/TBME.2018.2881051DOI Listing
November 2018

An Automatic Channel Selection Approach for ICA-Based Motor Imagery Brain Computer Interface.

J Med Syst 2018 Nov 6;42(12):253. Epub 2018 Nov 6.

The Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, China.

Independent component analysis (ICA) is a potential spatial filtering method for the implementation of motor imagery brain-computer interface (MIBCI). However, ICA-based MIBCI (ICA-MIBCI) is sensitive to electroencephalogram (EEG) channels and the quality of the training data, which are two crucial factors affecting the stability and classification performance of ICA-MIBCI. To address these problems, this paper is mainly focused on the investigation of EEG channel optimization. Read More

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http://link.springer.com/10.1007/s10916-018-1106-3
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http://dx.doi.org/10.1007/s10916-018-1106-3DOI Listing
November 2018
10 Reads

Studying brain activity in sports performance: Contributions and issues.

Prog Brain Res 2018 27;240:247-267. Epub 2018 Aug 27.

Euromov-University of Montpellier, Montpellier, France.

Understanding the interactions between brain activity and behavior comprehensively in achieving optimal exercise performance in sports is still lacking. The existent research in this area has been limited by the constraints of sports environments and the robustness of the most suitable non-invasive functional neuroimaging methods (electroencephalography, EEG and functional near-infrared spectroscopy, fNIRS) to motion artifacts and noise. However, recent advances in brain mapping technology should improve the capabilities of the future brain imaging devices to assess and monitor the level of adaptive cognitive-motor performance during exercise in sports environments. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S00796123183006
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http://dx.doi.org/10.1016/bs.pbr.2018.07.004DOI Listing
August 2018
14 Reads

A novel approach to emotion recognition using local subset feature selection and modified Dempster-Shafer theory.

Behav Brain Funct 2018 Oct 31;14(1):17. Epub 2018 Oct 31.

Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.

Background: Emotion recognition is an increasingly important field of research in brain computer interactions.

Introduction: With the advance of technology, automatic emotion recognition systems no longer seem far-fetched. Be that as it may, detecting neural correlates of emotion has remained a substantial bottleneck. Read More

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http://dx.doi.org/10.1186/s12993-018-0149-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6208176PMC
October 2018
1 Read

EEG-EOG based Virtual Keyboard: Toward Hybrid Brain Computer Interface.

Neuroinformatics 2018 Oct 27. Epub 2018 Oct 27.

Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.

The past twenty years have ignited a new spark in the research of Electroencephalogram (EEG), which was pursued to develop innovative Brain Computer Interfaces (BCIs) in order to help severely disabled people live a better life with a high degree of independence. Current BCIs are more theoretical than practical and are suffering from numerous challenges. New trends of research propose combining EEG to other simple and efficient bioelectric inputs such as Electro-oculography (EOG) resulting from eye movements, to produce more practical and robust Hybrid Brain Computer Interface systems (hBCI) or Brain/Neuronal Computer Interface (BNCI). Read More

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http://link.springer.com/10.1007/s12021-018-9402-0
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http://dx.doi.org/10.1007/s12021-018-9402-0DOI Listing
October 2018
7 Reads

Reducing the Effect of Spurious Phase Variations in Neural Oscillatory Signals.

Front Comput Neurosci 2018 8;12:82. Epub 2018 Oct 8.

Systems Neuroscience & Neurotechnology Unit, Faculty of Medicine, Saarland University, Homburg, Germany.

The phase-reset model of oscillatory EEG activity has received a lot of attention in the last decades for decoding different cognitive processes. Based on this model, the ERPs are assumed to be generated as a result of phase reorganization in ongoing EEG. Alignment of the phase of neuronal activities can be observed within or between different assemblies of neurons across the brain. Read More

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https://www.frontiersin.org/article/10.3389/fncom.2018.00082
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http://dx.doi.org/10.3389/fncom.2018.00082DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6186847PMC
October 2018
13 Reads

Global Epileptic Seizure Identification With Affinity Propagation Clustering Partition Mutual Information Using Cross-Layer Fully Connected Neural Network.

Front Hum Neurosci 2018 2;12:396. Epub 2018 Oct 2.

Computer School, Wuhan University, Wuhan, China.

A longstanding challenge in epilepsy research and practice is the need to classify synchronization patterns hidden in multivariate electroencephalography (EEG) data that is routinely superimposed with intensive noise. It is essential to select a suitable feature extraction method to achieve high recognition performance. A typical approach is to extract the mutual information (MI) between pairs of channels. Read More

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https://www.frontiersin.org/article/10.3389/fnhum.2018.00396
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http://dx.doi.org/10.3389/fnhum.2018.00396DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6176510PMC
October 2018
3 Reads

Eyes-Closed Increases the Usability of Brain-Computer Interfaces Based on Auditory Event-Related Potentials.

Front Hum Neurosci 2018 28;12:391. Epub 2018 Sep 28.

Brain State Decoding Lab, Department of Computer Science, University of Freiburg, Freiburg, Germany.

Recent research has demonstrated how brain-computer interfaces (BCI) based on auditory stimuli can be used for communication and rehabilitation. In these applications, users are commonly instructed to avoid eye movements while keeping their eyes open. This secondary task can lead to exhaustion and subjects may not succeed in suppressing eye movements. Read More

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http://dx.doi.org/10.3389/fnhum.2018.00391DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6172854PMC
September 2018
1 Read

A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network.

Front Neurosci 2018 28;12:680. Epub 2018 Sep 28.

State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China.

High accuracy decoding of electroencephalogram (EEG) signal is still a major challenge that can hardly be solved in the design of an effective motor imagery-based brain-computer interface (BCI), especially when the signal contains various extreme artifacts and outliers arose from data loss. The conventional process to avoid such cases is to directly reject the entire severely contaminated EEG segments, which leads to a drawback that the BCI has no decoding results during that certain period. In this study, a novel decoding scheme based on the combination of Lomb-Scargle periodogram (LSP) and deep belief network (DBN) was proposed to recognize the incomplete motor imagery EEG. Read More

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http://dx.doi.org/10.3389/fnins.2018.00680DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6172343PMC
September 2018

Inherent physiological artifacts in EEG during tDCS.

Neuroimage 2019 01 12;185:408-424. Epub 2018 Oct 12.

Neural Engineering Laboratory, Department of Biomedical Engineering, The City College of New York of the City University of New York, New York, NY, USA; Department of Psychology, The Graduate Center at City University of New York, New York, NY, USA. Electronic address:

Online imaging and neuromodulation is invalid if stimulation distorts measurements beyond the point of accurate measurement. In theory, combining transcranial Direct Current Stimulation (tDCS) with electroencephalography (EEG) is compelling, as both use non-invasive electrodes and image-guided dose can be informed by the reciprocity principle. To distinguish real changes in EEG from stimulation artifacts, prior studies applied conventional signal processing techniques (e. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S10538119183198
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http://dx.doi.org/10.1016/j.neuroimage.2018.10.025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6289749PMC
January 2019
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A Method for Simultaneous Evaluation of Muscular and Neural Prepulse Inhibition.

Front Neurosci 2018 26;12:654. Epub 2018 Sep 26.

Center for Mathematics, Computation and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil.

Prepulse inhibition (PPI) test has been widely used to evaluate sensorimotor gating. In humans, deficits in this mechanism are measured through the orbicularis muscle response using electromyography (EMG). Although this mechanism can be modulated by several brain structures and is impaired in some pathologies as schizophrenia and bipolar disorder, neural PPI evaluation is rarely performed in humans. Read More

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http://dx.doi.org/10.3389/fnins.2018.00654DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6168667PMC
September 2018

Event-related desynchronization during movement attempt and execution in severely paralyzed stroke patients: An artifact removal relevance analysis.

Neuroimage Clin 2018 4;20:972-986. Epub 2018 Oct 4.

Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany; Neural Engineering Laboratory, Health Department, TECNALIA, San Sebastián, Spain.

The electroencephalogram (EEG) constitutes a relevant tool to study neural dynamics and to develop brain-machine interfaces (BMI) for rehabilitation of patients with paralysis due to stroke. However, the EEG is easily contaminated by artifacts of physiological origin, which can pollute the measured cortical activity and bias the interpretations of such data. This is especially relevant when recording EEG of stroke patients while they try to move their paretic limbs, since they generate more artifacts due to compensatory activity. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S22131582183030
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http://dx.doi.org/10.1016/j.nicl.2018.09.035DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6180341PMC
February 2019
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EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis.

Comput Math Methods Med 2018 4;2018:6534041. Epub 2018 Sep 4.

School of Information Science and Engineering, Lanzhou University, Lanzhou, China.

Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of tradition is subjective, time-consuming, and error-prone due to the large bulk of data which have to be processed. Therefore, automatic sleep staging is essential in order to solve these problems. Read More

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https://www.hindawi.com/journals/cmmm/2018/6534041/
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http://dx.doi.org/10.1155/2018/6534041DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6142786PMC
December 2018
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Influence of imputation strategies on the identification of brain functional connectivity networks.

J Neurosci Methods 2018 Nov 19;309:199-207. Epub 2018 Sep 19.

Department of Child and Adolescent Psychiatry, Psychosomatic Medicine and Psychotherapy, Jena University Hospital, Friedrich Schiller University Jena, Germany.

Whenever neurophysiological data, such as EEG data are recorded, occurring artifacts pose an essential problem. This study addresses this issue by using imputation methods whereby whole data sets of a trial, or distinct electrodes, are not removed from the analysis of the EEG data but are replaced. We present different imputation strategies but use only two which are optimal for this particular study; predictive mean matching and data augmentation. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S01650270183028
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http://dx.doi.org/10.1016/j.jneumeth.2018.09.021DOI Listing
November 2018
10 Reads