2,408 results match your criteria EEG Artifacts


Independent Low-Rank Matrix Analysis-Based Automatic Artifact Reduction Technique Applied to Three BCI Paradigms.

Front Hum Neurosci 2020 9;14:173. Epub 2020 Jun 9.

Artificial Intelligence Research Center, Department of Information Technology and Human Factors, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan.

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) can potentially enable people to non-invasively and directly communicate with others using brain activities. Artifacts generated from body activities (e.g. Read More

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http://dx.doi.org/10.3389/fnhum.2020.00173DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7296171PMC

Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals.

Sci Data 2020 Jun 16;7(1):179. Epub 2020 Jun 16.

Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA.

EEG signal processing is a fundamental method for neurophysiology research and clinical neurology practice. Historically the classification of EEG into physiological, pathological, or artifacts has been performed by expert visual review of the recordings. However, the size of EEG data recordings is rapidly increasing with a trend for higher channel counts, greater sampling frequency, and longer recording duration and complete reliance on visual data review is not sustainable. Read More

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http://dx.doi.org/10.1038/s41597-020-0532-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297990PMC

Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling.

Neuroimage 2020 Jun 11;219:117044. Epub 2020 Jun 11.

Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Denmark; Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark. Electronic address:

Transcranial brain stimulation (TBS) has been established as a method for modulating and mapping the function of the human brain, and as a potential treatment tool in several brain disorders. Typically, the stimulation is applied using a one-size-fits-all approach with predetermined locations for the electrodes, in electric stimulation (TES), or the coil, in magnetic stimulation (TMS), which disregards anatomical variability between individuals. However, the induced electric field distribution in the head largely depends on anatomical features implying the need for individually tailored stimulation protocols for focal dosing. Read More

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http://dx.doi.org/10.1016/j.neuroimage.2020.117044DOI Listing

Sleep disruption is not observed with brain-responsive neurostimulation for epilepsy.

Epilepsia Open 2020 Jun 21;5(2):155-165. Epub 2020 Feb 21.

Department of Neurology and Weill Institute for Neurosciences University of California San Francisco San Francisco CA USA.

Objective: Neurostimulation devices that deliver electrical impulses to the nervous system are widely used to treat seizures in patients with medically refractory epilepsy, but the effects of these therapies on sleep are incompletely understood. Vagus nerve stimulation can contribute to obstructive sleep apnea, and thalamic deep brain stimulation can cause sleep disruption. A device for brain-responsive neurostimulation (RNS System, NeuroPace, Inc) is well tolerated in clinical trials, but potential effects on sleep are unknown. Read More

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http://dx.doi.org/10.1002/epi4.12382DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7278540PMC

Corneo-retinal-dipole and eyelid-related eye artifacts can be corrected offline and online in electroencephalographic and magnetoencephalographic signals.

Neuroimage 2020 Jun 1;218:117000. Epub 2020 Jun 1.

Institute of Neural Engineering, Graz University of Technology, Graz, 8010, Styria, Austria. Electronic address:

Eye movements and blinks contaminate electroencephalographic (EEG) and magnetoencephalographic (MEG) activity. As the eye moves, the corneo-retinal dipole (CRD) and eyelid introduce potential/field changes in the M/EEG activity. These eye artifacts can affect a brain-computer interface and thereby impinge on neurofeedback quality. Read More

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http://dx.doi.org/10.1016/j.neuroimage.2020.117000DOI Listing

Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network.

J Neural Eng 2020 Jun 1. Epub 2020 Jun 1.

EPFL Institute of Bioengineering, Lausanne, 1015, SWITZERLAND.

Objective: Mobile Brain/Body Imaging (MoBI) frameworks allowed the research community to find evidence of cortical involvement at walking initiation and during locomotion. However, the decoding of gait patterns from brain signals remains an open challenge. The aim of this work is to propose and validate a deep learning model to decode gait phases from Electroenchephalography (EEG). Read More

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http://dx.doi.org/10.1088/1741-2552/ab9842DOI Listing

A natural evolution optimization based deep learning algorithm for neurological disorder classification.

Biomed Mater Eng 2020 ;31(2):73-94

Center for Artificial Intelligence and Robotics (Cairo), Department of Computer Sciences, Aswan University, Egypt.

Background: A neurological disorder is one of the significant problems of the nervous system that affects the essential functions of the human brain and spinal cord. Monitoring brain activity through electroencephalography (EEG) has become an important tool in the diagnosis of brain disorders. The robust automatic classification of EEG signals is an important step towards detecting a brain disorder in its earlier stages before status deterioration. Read More

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http://dx.doi.org/10.3233/BME-201081DOI Listing
January 2020

Intravenous dexmedetomidine sedation for magnetoencephalography: A retrospective study.

Paediatr Anaesth 2020 May 20. Epub 2020 May 20.

Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, OH, USA.

Background: Magnetoencephalography (MEG) plays a preponderant role in the preoperative assessment of patients with drug-resistant epilepsy (DRE). However, the magnetoencephalography of patients with drug-resistant epilepsy can be difficult without sedation and/or general anesthesia. Our objective is to describe our experience with intravenous dexmedetomidine as sedation for magnetoencephalography and its effect, if any, on the ability to recognize epileptic spikes. Read More

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http://dx.doi.org/10.1111/pan.13925DOI Listing
May 2020
1.742 Impact Factor

EEG Signal Reconstruction Using a Generative Adversarial Network With Wasserstein Distance and Temporal-Spatial-Frequency Loss.

Front Neuroinform 2020 30;14:15. Epub 2020 Apr 30.

School of Informatics, Xiamen University, Xiamen, China.

Applications based on electroencephalography (EEG) signals suffer from the mutual contradiction of high classification performance vs. low cost. The nature of this contradiction makes EEG signal reconstruction with high sampling rates and sensitivity challenging. Read More

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http://dx.doi.org/10.3389/fninf.2020.00015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7204859PMC

Toward the Understanding of Topographical and Spectral Signatures of Infant Movement Artifacts in Naturalistic EEG.

Front Neurosci 2020 28;14:352. Epub 2020 Apr 28.

Department of Psychology, University of Cambridge, Cambridge, United Kingdom.

Electroencephalography (EEG) is perhaps the most widely used brain-imaging technique for pediatric populations. However, EEG signals are prone to distortion by motion. Compared to adults, infants' motion is both more frequent and less stereotypical yet motion effects on the infant EEG signal are largely undocumented. Read More

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http://dx.doi.org/10.3389/fnins.2020.00352DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7199478PMC

How to carry out and interpret EEG recordings in COVID-19 patients in ICU?

Clin Neurophysiol 2020 May 13. Epub 2020 May 13.

Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

There are questions and challenges regarding neurologic complications in COVID-19 patients. EEG is a safe and efficient tool for the evaluation of brain function, even in the context of COVID-19. However, EEG technologists should not be put in danger if obtaining an EEG does not significantly advance diagnosis or change management in the patient. Read More

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http://dx.doi.org/10.1016/j.clinph.2020.05.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217782PMC

Concurrent electrophysiological and hemodynamic measurements of evoked neural oscillations in human visual cortex using sparsely interleaved fast fMRI and EEG.

Neuroimage 2020 Aug 7;217:116910. Epub 2020 May 7.

Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland. Electronic address:

Electroencephalography (EEG) concurrently collected with functional magnetic resonance imaging (fMRI) is heavily distorted by the repetitive gradient coil switching during the fMRI acquisition. The performance of the typical template-based gradient artifact suppression method can be suboptimal because the artifact changes over time. Gradient artifact residuals also impede the subsequent suppression of ballistocardiography artifacts. Read More

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http://dx.doi.org/10.1016/j.neuroimage.2020.116910DOI Listing

High-frequency electrical stimulation of the anterior thalamic nuclei increases vigilance in epilepsy patients during relaxed and drowsy wakefulness.

Epilepsia 2020 Jun 9;61(6):1174-1182. Epub 2020 May 9.

Kork Epilepsy Center, Kehl-Kork, Germany.

Objective: High-frequency deep brain stimulation (DBS) of anterior thalamic nuclei (ANT) reduces the frequency and intensity of focal and focal to bilateral tonic-clonic epileptic seizures. We investigated the impact of high-frequency ANT-DBS on vigilance in epilepsy patients during relaxed and drowsy wakefulness, to better understand the effects and the mechanisms of action of this intervention in humans.

Methods: Four patients with different structural epileptic pathologies were included in this retrospective case-cohort study. Read More

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http://dx.doi.org/10.1111/epi.16514DOI Listing

Eyeblink recognition improves fatigue prediction from single-channel forehead EEG in a realistic sustained attention task.

J Neural Eng 2020 Jun 29;17(3):036015. Epub 2020 Jun 29.

Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan, Republic of China. Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Chiao Tung University, Hsinchu, Taiwan, Republic of China. Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung 80708, Taiwan. Author to whom any correspondence should be addressed.

Objective: A passive brain-computer interface recognizes its operator's cognitive state without an explicitly performed control task. This technique is commonly used in conjunction with consumer-grade EEG devices for detecting the conditions of fatigue, attention, emotional arousal, or motion sickness. While it is easy to mount the sensors in the forehead area, which is not covered with hair, the recorded signals become greatly contaminated with eyeblink and movement artifacts, which makes it a challenge to acquire the data of suitable for analysis quality, particularly in few channel systems where a lack of spatial information limits the applicability of sophisticated signal cleaning algorithms. Read More

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http://dx.doi.org/10.1088/1741-2552/ab909fDOI Listing

EEG Signal Analysis for Diagnosing Neurological Disorders Using Discrete Wavelet Transform and Intelligent Techniques.

Sensors (Basel) 2020 Apr 28;20(9). Epub 2020 Apr 28.

Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 800-11421, Saudi Arabia.

Analysis of electroencephalogram (EEG) signals is essential because it is an efficient method to diagnose neurological brain disorders. In this work, a single system is developed to diagnose one or two neurological diseases at the same time (two-class mode and three-class mode). For this purpose, different EEG feature-extraction and classification techniques are investigated to aid in the accurate diagnosis of neurological brain disorders: epilepsy and autism spectrum disorder (ASD). Read More

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http://dx.doi.org/10.3390/s20092505DOI Listing

Applying stochastic spike train theory for high-accuracy human MEG/EEG.

J Neurosci Methods 2020 Jul 25;340:108743. Epub 2020 Apr 25.

Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and Royal Academy of Music, Aarhus/Aalborg, Nørrebrogade 44, 8000 Aarhus C, Denmark; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, 00100 Helsinki, Finland; Department of Education, Psychology, Communication, University of Bari Aldo Moro, Italy.

Background: The accuracy of electroencephalography (EEG) and magnetoencephalography (MEG) in measuring neural evoked responses (ERs) is challenged by overlapping neural sources. This lack of accuracy is a severe limitation to the application of ERs to clinical diagnostics.

New Method: We here introduce a theory of stochastic neuronal spike timing probability densities for describing the large-scale spiking activity in neural assemblies, and a spike density component analysis (SCA) method for isolating specific neural sources. Read More

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http://dx.doi.org/10.1016/j.jneumeth.2020.108743DOI Listing

Development of a transcranial direct current stimulation device based on current limiter for simultaneous measurement of electroencephalography: A feasibility study.

Technol Health Care 2020 ;28(S1):123-130

Department of Electronics and Information Engineering, Korea University, Sejong, Korea.

Background: Electroencephalography (EEG) measured during transcranial direct current stimulation (tDCS) can help understand the accurate impact of tDCS on the brain, but this has been hindered due to significant inflow of tDCS-induced electrical artifacts.

Objective: In this study, we introduce a novel tDCS device developed based on current limiter, which can prevent the generation of significant electrical artifacts.

Methods: To verify the feasibility of our developed tDCS device, we performed simultaneous measurement of EEG during tDCS application with five different current intensities (0, 500, 1,000, 1,500, and 2,000 μA). Read More

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http://dx.doi.org/10.3233/THC-209013DOI Listing
January 2020

The Maryland analysis of developmental EEG (MADE) pipeline.

Psychophysiology 2020 Jun 15;57(6):e13580. Epub 2020 Apr 15.

Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA.

Compared to adult EEG, EEG signals recorded from pediatric populations have shorter recording periods and contain more artifact contamination. Therefore, pediatric EEG data necessitate specific preprocessing approaches in order to remove environmental noise and physiological artifacts without losing large amounts of data. However, there is presently a scarcity of standard automated preprocessing pipelines suitable for pediatric EEG. Read More

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http://dx.doi.org/10.1111/psyp.13580DOI Listing

Cingulate gyrus epilepsy: semiology, invasive EEG, and surgical approaches.

Neurosurg Focus 2020 04;48(4):E8

1School of Medicine and.

Objective: The semiology of cingulate gyrus epilepsy is varied and may involve the paracentral area, the adjacent limbic system, and/or the orbitofrontal gyrus. Invasive electroencephalography (iEEG) recording is usually required for patients with deeply located epileptogenic foci. This paper reports on the authors' experiences in the diagnosis and surgical treatment of patients with focal epilepsy originating in the cingulate gyrus. Read More

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http://dx.doi.org/10.3171/2020.1.FOCUS19914DOI Listing

How Sensitive Are EEG Results to Preprocessing Methods: A Benchmarking Study.

IEEE Trans Neural Syst Rehabil Eng 2020 May 26;28(5):1081-1090. Epub 2020 Mar 26.

Although several guidelines for best practices in EEG preprocessing have been released, even studies that strictly adhere to those guidelines contain considerable variation in the ways that the recommended methods are applied. An open question for researchers is how sensitive the results of EEG analyses are to variations in preprocessing methods and parameters. To address this issue, we analyze the effect of preprocessing methods on downstream EEG analysis using several simple signal and event-related measures. Read More

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http://dx.doi.org/10.1109/TNSRE.2020.2980223DOI Listing

Improving EEG Muscle Artifact Removal With an EMG Array.

IEEE Trans Instrum Meas 2020 Mar 1;69(3):815-824. Epub 2019 May 1.

Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile.

Removal of artifacts induced by muscle activity is crucial for analysis of the electroencephalogram (EEG), and continues to be a challenge in experiments where the subject may speak, change facial expressions, or move. Ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) has been proven to be an efficient method for denoising of EEG contaminated with muscle artifacts. EEMD-CCA, likewise the majority of algorithms, does not incorporate any statistical information of the artifact, namely, electromyogram (EMG) recorded over the muscles actively contaminating the EEG. Read More

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http://dx.doi.org/10.1109/tim.2019.2906967DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7088455PMC
March 2020
1.790 Impact Factor

Effect of interictal epileptiform discharges on EEG-based functional connectivity networks.

Clin Neurophysiol 2020 May 4;131(5):1087-1098. Epub 2020 Mar 4.

Department of Biomedical Engineering, University of California, Irvine, CA, USA. Electronic address:

Objective: Functional connectivity networks (FCNs) based on interictal electroencephalography (EEG) can identify pathological brain networks associated with epilepsy. FCNs are altered by interictal epileptiform discharges (IEDs), but it is unknown whether this is due to the morphology of the IED or the underlying pathological activity. Therefore, we characterized the impact of IEDs on the FCN through simulations and EEG analysis. Read More

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http://dx.doi.org/10.1016/j.clinph.2020.02.014DOI Listing

Adjusting ADJUST: Optimizing the ADJUST algorithm for pediatric data using geodesic nets.

Psychophysiology 2020 Mar 17:e13566. Epub 2020 Mar 17.

Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA.

A major challenge for electroencephalograph (EEG) studies on pediatric populations is that large amounts of data are lost due to artifacts (e.g., movement and blinks). Read More

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http://dx.doi.org/10.1111/psyp.13566DOI Listing

Statistical Analysis to Find out the Optimal Locations for Non Invasive Brain Stimulation.

J Med Syst 2020 Mar 12;44(4):85. Epub 2020 Mar 12.

Biomedical Systems Laboratory, Multimedia, Analytics, Networks and Systems Group, School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Mandi, India.

Non-invasive brain electrical stimulation (NIBES) techniques are progressively used for modulation of neuronal membrane potentials, which alters cortical excitability. The neuronal activity depends on position of channel locations for electrodes and the amount and direction of injected weak current through the target neurons area. In the present paper hybrid near infrared spectroscopy and electroencephalogram (NIRS-EEG) open access dataset for brain computer interface (BCI) has been used to find the best locations for NIBES. Read More

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http://dx.doi.org/10.1007/s10916-020-1535-7DOI Listing

Electroencephalography.

Handb Clin Neurol 2020 ;168:249-262

Institute for Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Graz, Austria. Electronic address:

The electroencephalogram (EEG) was invented almost 100 years ago and is still a method of choice for many research questions, even applications-from functional brain imaging in neuroscientific investigations during movement to real-time applications like brain-computer interfacing. This chapter gives some background information on the establishment and properties of the EEG. This chapter starts with a closer look at the sources of EEG at a micro or neuronal level, followed by recording techniques, types of electrodes, and common EEG artifacts. Read More

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http://dx.doi.org/10.1016/B978-0-444-63934-9.00018-4DOI Listing
January 2020

Brain-computer interfaces: Definitions and principles.

Handb Clin Neurol 2020 ;168:15-23

Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.

Throughout life, the central nervous system (CNS) interacts with the world and with the body by activating muscles and excreting hormones. In contrast, brain-computer interfaces (BCIs) quantify CNS activity and translate it into new artificial outputs that replace, restore, enhance, supplement, or improve the natural CNS outputs. BCIs thereby modify the interactions between the CNS and the environment. Read More

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http://dx.doi.org/10.1016/B978-0-444-63934-9.00002-0DOI Listing
January 2020

Detection of Postictal Generalized Electroencephalogram Suppression: Random Forest Approach.

JMIR Med Inform 2020 Feb 14;8(2):e17061. Epub 2020 Feb 14.

School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States.

Background: Sudden unexpected death in epilepsy (SUDEP) is second only to stroke in neurological events resulting in years of potential life lost. Postictal generalized electroencephalogram (EEG) suppression (PGES) is a period of suppressed brain activity often occurring after generalized tonic-clonic seizure, a most significant risk factor for SUDEP. Therefore, PGES has been considered as a potential biomarker for SUDEP risk. Read More

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http://dx.doi.org/10.2196/17061DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055778PMC
February 2020

A hybrid method for artifact removal of visual evoked EEG.

J Neurosci Methods 2020 Apr 19;336:108638. Epub 2020 Feb 19.

Department of Electrical Engineering, National Institute of Technology, Calicut 673601, Kerala, India. Electronic address:

Background: The visual evoked Electroencephalogram (EEG) signals are useful indicators to explore the hidden neural circuitry in human brain. But these signals are highly contaminated with a plethora of artifacts arising from power interference, eye, muscle and cardiac movements. Since the interference components include neural activity also, the existing techniques result in the distortion of the underlying cerebral signals. Read More

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http://dx.doi.org/10.1016/j.jneumeth.2020.108638DOI Listing

Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes.

Sensors (Basel) 2020 Feb 2;20(3). Epub 2020 Feb 2.

VSB-Technical University Ostrava, FEECS, Department of Cybernetics and Biomedical Engineering, Ostrava-Poruba 708 00, Czech Republic.

This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky-Golay filter. Read More

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http://dx.doi.org/10.3390/s20030807DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038754PMC
February 2020

Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis.

J Healthc Eng 2019 30;2019:4159676. Epub 2019 Dec 30.

Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230026, China.

Electroencephalography (EEG) signals collected from human scalps are often polluted by diverse artifacts, for instance electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) artifacts. Muscle artifacts are particularly difficult to eliminate among all kinds of artifacts due to their complexity. At present, several researchers have proved the superiority of combining single-channel decomposition algorithms with blind source separation (BSS) to make multichannel EEG recordings free from EMG contamination. Read More

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http://dx.doi.org/10.1155/2019/4159676DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6955116PMC

Mixed-Norm Based Broad Learning System for EEG Classification.

Conf Proc IEEE Eng Med Biol Soc 2019 07;2019:7092-7095

How to design a powerful classifier with strong generalization capability is still an active topic in the brain computer interface (BCI) researches. In this paper, we propose a new classifier, which has the same structure of the recently proposed broad learning system (BLS), but the l norm based optimization model in BLS is replaced by a mixed-nrom based one. To optimize the proposed model efficiently, the augmented Lagrange multiplier (ALM) method is utilized. Read More

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http://dx.doi.org/10.1109/EMBC.2019.8856666DOI Listing

HEAR to remove pops and drifts: the high-variance electrode artifact removal (HEAR) algorithm.

Conf Proc IEEE Eng Med Biol Soc 2019 07;2019:5150-5155

A high fraction of artifact-free signals is highly desirable in functional neuroimaging and brain-computer interfacing (BCI). We present the high-variance electrode artifact removal (HEAR) algorithm to remove transient electrode pop and drift (PD) artifacts from electroencephalographic (EEG) signals. Transient PD artifacts reflect impedance variations at the electrode scalp interface that are caused by ion concentration changes. Read More

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http://dx.doi.org/10.1109/EMBC.2019.8857742DOI Listing

Cross-Frequency Coupling Features of Postictal Generalized EEG Suppression State.

Conf Proc IEEE Eng Med Biol Soc 2019 07;2019:5137-5140

In patients with epilepsy, convulsive seizures are often followed by a postictal generalized EEG suppression (PGES) state characterized by reduced background activity. Recent studies found a correlation between seizure termination state and PGES duration, and suggested that PGES is the result of the cessation of neuronal activity. To test that assertion, we investigated ten seizure records obtained from intracranial EEG (iEEG) from six patients, four of which had Engel Class 1 surgical outcome. Read More

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http://dx.doi.org/10.1109/EMBC.2019.8856405DOI Listing

EEG Movement Artifact Suppression in Interactive Virtual Reality.

Conf Proc IEEE Eng Med Biol Soc 2019 07;2019:4576-4579

The integration of electroencephalogram (EEG) sensors into virtual reality (VR) headsets can provide the capability of tracking the user's cognitive state and eventually be used to increase the sense of immersion. Recent developments in wireless, room-scale VR tracking allow users to move freely in the physical and virtual spaces. Such motion can create significant movement artifacts in EEG sensors mounted to the VR headset. Read More

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http://dx.doi.org/10.1109/EMBC.2019.8856961DOI Listing

Subspace techniques for task-independent EEG person identification.

Conf Proc IEEE Eng Med Biol Soc 2019 07;2019:4545-4548

There has been a growing interest in studying electroencephalography signals (EEG) as a possible biometric. The brain signals captured by EEG are rich and carry information related to the individual, tasks being performed, mental state, and other channel/measurement noise due to session variability and artifacts. To effectively extract person-specific signatures present in EEG, it is necessary to define a subspace that enhances the biometric information and suppresses other nuisance factors. Read More

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http://dx.doi.org/10.1109/EMBC.2019.8857426DOI Listing

Time Warping Solutions for Classifying Artifacts in EEG.

Conf Proc IEEE Eng Med Biol Soc 2019 07;2019:4537-4540

The most common brain-computer interface (BCI) devices use electroencephalography (EEG). EEG signals are noisy owing to the presence of many artifacts, namely head movement, and facial movements like eye blinks or jaw movements. Removal of these artifacts from EEG signals is essential for the success of any downstream BCI application. Read More

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http://dx.doi.org/10.1109/EMBC.2019.8856669DOI Listing

Mining EEG scalp maps of independent components related to HCT tasks.

Conf Proc IEEE Eng Med Biol Soc 2019 07;2019:3888-3891

This work presents an unsupervised mining strategy, applied to an independent component analysis (ICA) of segments of data collected while participants are answering to the items of the Halstead Category Test (HCT). This new methodology was developed to achieve signal components at trial level and therefore to study signal dynamics which are not available within participants' ensemble average signals. The study will be focused on the signal component that can be elicited by the binary visual feedback which is part of the HCT protocol. Read More

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http://dx.doi.org/10.1109/EMBC.2019.8857600DOI Listing

Design Considerations for Artefact-Free Optoelectronic Systems.

Conf Proc IEEE Eng Med Biol Soc 2019 07;2019:3742-3745

This paper proposes design considerations that need to be followed in order to eliminate potential sources of artefact that could distort a recorded neural signal. The artefact that appears in a recorded signal has a combination of potential sources each of which contributes towards its formation. As such, these sources of artefact have been addressed in three main categories: a) electronics artefact, b) encapsulation artefact and c) interface artefact. Read More

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http://dx.doi.org/10.1109/EMBC.2019.8856525DOI Listing

Semi-supervised Seizure Prediction with Generative Adversarial Networks.

Conf Proc IEEE Eng Med Biol Soc 2019 07;2019:2369-2372

Many outstanding studies have reported promising results in seizure prediction that is considered one of the most challenging predictive data analysis. This is mainly because electroencephalogram (EEG) bio-signal intensity is very small, in μV range, and there are significant sensing difficulties given physiological and non-physiological artifacts. In this article, we propose an approach that can make use of not only labeled EEG signals but also the unlabeled ones which are more accessible. Read More

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http://dx.doi.org/10.1109/EMBC.2019.8857755DOI Listing

Reconstructing Cortical Intrinsic Connectivity Networks Using a Regression Method Combining EEG Data from Sensor and Source Levels

Authors:
Guofa Shou Lei Ding

Conf Proc IEEE Eng Med Biol Soc 2019 07;2019:1698-1701

Intrinsic connectivity networks (ICNs) have been widely studied using functional magnetic resonance imaging (fMRI) data and electrophysiological data (e.g., electroencephalography (EEG) or magnetoencephalography (MEG)). Read More

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http://dx.doi.org/10.1109/EMBC.2019.8857687DOI Listing

A morphological way to remove baseline and spike separation in EEG.

Conf Proc IEEE Eng Med Biol Soc 2019 07;2019:668-671

In this work, we have considered the problem of inconsistent sequence order and varying quality of decompositions by independent component analysis (ICA) and presented an alternate way to separate source signals from the electroencephalogram (EEG) using morphological component analysis (MCA) method based on explicit dictionary of independent redundant bases. Using correlation, qualitatively we have compared ICA with MCA's capability to segregate baseline and spike in different EEG data. Read More

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http://dx.doi.org/10.1109/EMBC.2019.8856828DOI Listing

Towards a Unified Framework for De-noising Neural Signals.

Conf Proc IEEE Eng Med Biol Soc 2019 07;2019:620-623

Neural signals provide key information for decision-making processes in multiple disciplines including medicine, engineering, and neuroscience. The correct interpretation of these signals, however, requires substantial processing, especially when the signals exhibit low Signal to Noise Ratio (SNR). Electroencephalographic (EEG) signals are considered among this group and require effective handling of multiple types of artifactual components. Read More

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http://dx.doi.org/10.1109/EMBC.2019.8856876DOI Listing

Wearable, Fiber-less, Multi-Channel System for Continuous Wave Functional Near Infrared Spectroscopy Based on Silicon Photomultipliers Detectors and Lock-In Amplification.

Conf Proc IEEE Eng Med Biol Soc 2019 07;2019:60-66

Development and in-vivo validation of a Continuous Wave (CW) functional Near Infrared Spectroscopy (fNIRS) system is presented. The system is wearable, fiber-less, multi-channel (16×16, 256 channels) and expandable and it relies on silicon photomultipliers (SiPMs) for light detection. SiPMs are inexpensive, low voltage and resilient semiconductor light detectors, whose performances are analogous to photomultiplier tubes (PMTs). Read More

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http://dx.doi.org/10.1109/EMBC.2019.8857206DOI Listing

Identifying site- and stimulation-specific TMS-evoked EEG potentials using a quantitative cosine similarity metric.

PLoS One 2020 13;15(1):e0216185. Epub 2020 Jan 13.

Behavioral Neurology Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States of America.

The ability to interpret transcranial magnetic stimulation (TMS)-evoked electroencephalography (EEG) potentials (TEPs) is limited by artifacts, such as auditory evoked responses produced by discharge of the TMS coil. TEPs generated from direct cortical stimulation should vary in their topographical activity pattern according to stimulation site and differ from responses to sham stimulation. Responses that do not show these effects are likely to be artifactual. Read More

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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0216185PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6957143PMC

Music Improvisation Is Characterized by Increase EEG Spectral Power in Prefrontal and Perceptual Motor Cortical Sources and Can be Reliably Classified From Non-improvisatory Performance.

Front Hum Neurosci 2019 10;13:435. Epub 2019 Dec 10.

Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Osaka University, Osaka, Japan.

This study expores neural activity underlying creative processes through the investigation of music improvisation. Fourteen guitar players with a high level of improvisation skill participated in this experiment. The experimental task involved playing 32-s alternating blocks of improvisation and scales on guitar. Read More

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http://dx.doi.org/10.3389/fnhum.2019.00435DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6915035PMC
December 2019

Multi-channel EEG epileptic spike detection by a new method of tensor decomposition.

J Neural Eng 2020 01 6;17(1):016023. Epub 2020 Jan 6.

Advanced Institute of Engineering and Technology (AVITECH), VNU University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam.

Objective: Epilepsy is one of the most common brain disorders. For epilepsy diagnosis or treatment, the neurologist needs to observe epileptic spikes from electroencephalography (EEG) data. Since multi-channel EEG records can be naturally represented by multi-way tensors, it is of interest to see whether tensor decomposition is able to analyze EEG epileptic spikes. Read More

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http://dx.doi.org/10.1088/1741-2552/ab5247DOI Listing
January 2020

Comparison of artifacts between paste and collodion method of electrode application in pediatric EEG.

Clin Neurophysiol Pract 2020 30;5:12-15. Epub 2019 Nov 30.

Department of Pediatrics, Cohen Children's Medical Center, NY, USA.

Objectives: Children pose challenges to obtain quality EEG data due to excessive artifact. Collodion is used in EEG electrodes due to its water resistance and strong adhesive qualities. This study was done to evaluate differences in artifacts between the collodion and paste method. Read More

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http://dx.doi.org/10.1016/j.cnp.2019.11.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6931097PMC
November 2019

Optimization of Real-Time EEG Artifact Removal and Emotion Estimation for Human-Robot Interaction Applications.

Front Comput Neurosci 2019 26;13:80. Epub 2019 Nov 26.

CIBER-BBN, Madrid, Spain.

Affective human-robot interaction requires lightweight software and cheap wearable devices that could further this field. However, the estimation of emotions in real-time poses a problem that has not yet been optimized. An optimization is proposed for the emotion estimation methodology including artifact removal, feature extraction, feature smoothing, and brain pattern classification. Read More

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http://dx.doi.org/10.3389/fncom.2019.00080DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6889828PMC
November 2019

Improved EOG Artifact Removal Using Wavelet Enhanced Independent Component Analysis.

Brain Sci 2019 Dec 4;9(12). Epub 2019 Dec 4.

Department of Electrical Engineering and Information Systems, Faculty of Information Technology, University of Pannonia, Egyetem u.10, 8200 Veszprém, Hungary.

Electroencephalography (EEG) signals are frequently contaminated with unwanted electrooculographic (EOG) artifacts. Blinks and eye movements generate large amplitude peaks that corrupt EEG measurements. Independent component analysis (ICA) has been used extensively in manual and automatic methods to remove artifacts. Read More

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http://dx.doi.org/10.3390/brainsci9120355DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6956025PMC
December 2019