EEG toolbox

Benjamin G Schultz, Emmanuel Biau, Sonja A Kotz
BACKGROUND: Researchers rely on the specified capabilities of their hardware and software even though, in reality, these capabilities are often not achieved. Considering that the number of experiments examining neural oscillations has increased steadily, easy-to-implement tools for testing the capabilities of hardware and software are necessary. NEW METHOD: We present an open-source MATLAB toolbox, the Schultz Cigarette Burn Toolbox (SCiBuT) that allows users to benchmark the capabilities of their visual display devices and align neural and behavioral responses with veridical timing of visual stimuli...
June 27, 2020: Journal of Neuroscience Methods
Krzysztof Rykaczewski, Jan Nikadon, Włodzisław Duch, Tomasz Piotrowski
Brain activity pattern recognition from EEG or MEG signal analysis is one of the most important method in cognitive neuroscience. The SUPFUNSIM library is a new MATLAB toolbox which generates accurate EEG forward model and implements a collection of spatial filters for EEG source reconstruction, including the linearly constrained minimum-variance (LCMV), eigenspace LCMV, nulling (NL), and minimum-variance pseudo-unbiased reduced-rank (MV-PURE) filters in various versions. It also enables source-level directed connectivity analysis using partial directed coherence (PDC) measure...
June 21, 2020: Neuroinformatics
David Meunier, Annalisa Pascarella, Dmitrii Altukhov, Mainak Jas, Etienne Combrisson, Tarek Lajnef, Daphné Bertrand-Dubois, Vanessa Hadid, Golnoush Alamian, Jordan Alves, Fanny Barlaam, Anne-Lise Saive, Arthur Dehgan, Karim Jerbi
Recent years have witnessed a massive push towards reproducible research in neuroscience. Unfortunately, this endeavor is often challenged by the large diversity of tools used, project-specific custom code and the difficulty to track all user-defined parameters. NeuroPycon is an open-source multi-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, EEG, functional and anatomical MRI data, with a focus on connectivity and graph theoretical analyses...
June 6, 2020: NeuroImage
Emine Elif Tülay, Bahar Güntekin, Görsev Yener, Ali Bayram, Canan Başar-Eroğlu, Tamer Demiralp
In recent years, quantitative variables derived from the electroencephalogram (EEG) attract an increasing interest for the evaluation of neurodegenerative diseases, as EEG registers the neuro-electric activity with a high temporal resolution and provides a cost-effective and easily accessible, non-invasive method. Event-related oscillations (EROs) as oscillatory responses in the EEG to specific events further provide the possibility to track the cognitive decline in a task-specific manner. Current study in search for potential ERO biomarkers to distinguish different stages of cognitive decline along the Alzheimer's Disease (AD) continuum re-analyzed a combined set of data collected and analyzed in previous studies by Başar and coworkers...
June 6, 2020: International Journal of Psychophysiology
Yudhajit Das, Hanli Liu, Fenghua Tian, Srinivas Kota, Rong Zhang, Lina F Chalak
Birth asphyxia constitutes a major global public health burden for millions of infants with a critical need for real time physiological biomarkers. This proof of concept study targets the translational rigor of such biomarkers and aims to examine whether the variability in the amplitude-integrated EEG (aEEG) outputs impact the determination of neurovascular coupling (NVC) in newborns with encephalopathy. A convenience sample with neonatal asphyxia were monitored for twenty hours in the first day of life with EEG and near infrared spectroscopy (NIRS)-based cerebral tissue oxygen saturation (SctO2)...
June 8, 2020: Scientific Reports
Matthieu Hein, Jean-Pol Lanquart, Gwénolé Loas, Philippe Hubain, Paul Linkowski
BACKGROUND: Sleep plays an important role in vulnerability to mood disorders. However, despite the existence of sex differences in vulnerability to mood disorders, no study has yet investigated the sex effect on sleep network organization and its potential involvement in vulnerability to mood disorders. The aim of our study was to empirically investigate the sex effect on network organization during REM and slow-wave sleep using the effective connectivity measured by Granger causality...
April 25, 2020: Biology of Sex Differences
Guang Ouyang
BACKGROUND: For various reasons, the brain response activities in electroencephalography (EEG) signals are not perfectly synchronized between trials with respect to event markers-a problem commonly referred to as latency jitter. Experimental technologies have been greatly advanced to reduce technical timing errors and thereby reduce jitter. However, there remain intrinsic sources of jitter that are difficult to remove. The problem becomes more complicated when multiple sub-components possess different degrees and features of jitter...
April 9, 2020: Journal of Neuroscience Methods
Amit Jaiswal, Jukka Nenonen, Matti Stenroos, Alexandre Gramfort, Sarang S Dalal, Britta U Westner, Vladimir Litvak, John C Mosher, Jan-Mathijs Schoffelen, Caroline Witton, Robert Oostenveld, Lauri Parkkonen
Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations...
April 8, 2020: NeuroImage
Michael Wiesing, Gereon R Fink, Ralph Weidner
The increasing interest in Virtual Reality (VR) as a tool for neuroscientific research contrasts with the current lack of established toolboxes and standards. In several recent studies, game engines like Unity or Unreal Engine were used. It remains to be tested whether these software packages provide sufficiently precise and accurate stimulus timing and time measurements that allow inferring ongoing mental and neural processes. We here investigated the precision and accuracy of the timing mechanisms of Unreal Engine 4 and SteamVR in combination with the HTC Vive VR system...
2020: PloS One
Anna Castelnovo, Matteo Zago, Cecilia Casetta, Caroline Zangani, Francesco Donati, Mariapaola Canevini, Brady A Riedner, Giulio Tononi, Fabio Ferrarelli, Simone Sarasso, Armando D'Agostino
Abnormal sleep oscillations have recently been proposed as endophenotypes of schizophrenia. However, optimization of methodological approaches is still necessary to standardize analyses of their microstructural characteristics. Additionally, some relevant features of these oscillations remain unexplored in pathological conditions. Among others, slow wave traveling is a promising proxy for diurnal processes of brain connectivity and excitability. The study of slow oscillations propagation appears particularly relevant when schizophrenia is conceptualized as a dys-connectivity syndrome...
March 24, 2020: Schizophrenia Research
Christoph Zrenner, Dragana Galevska, Jaakko O Nieminen, David Baur, Maria-Ioanna Stefanou, Ulf Ziemann
Instantaneous phase of brain oscillations in electroencephalography (EEG) is a measure of brain state that is relevant to neuronal processing and modulates evoked responses. However, determining phase at the time of a stimulus with standard signal processing methods is not possible due to the stimulus artifact masking the future part of the signal. Here, we quantify the degree to which signal-to-noise ratio and instantaneous amplitude of the signal affect the variance of phase estimation error and the precision with which "ground truth" phase is even defined, using both the variance of equivalent estimators and realistic simulated EEG data with known synthetic phase...
March 17, 2020: NeuroImage
F Briend, E Leroux, C Nathou, N Delcroix, S Dollfus, O Etard
NonInvasive Brain Stimulation (NIBS) is a potential therapeutic tool with growing interest, but neuronavigation-guided software and tools available for the target determination are mostly either expensive or closed proprietary applications. To address these limitations, we propose GeodesicSlicer, a customizable, free, and open-source NIBS therapy research toolkit. GeodesicSlicer is implemented as an extension for the widely used 3D Slicer medical image visualization and analysis application platform. GeodesicSlicer uses cortical stimulation target from either functional or anatomical images to provide functionality specifically designed for NIBS therapy research...
March 3, 2020: Neuroinformatics
Panteleimon Chriskos, Christos A Frantzidis, Polyxeni T Gkivogkli, Emmanouil Papanastasiou, Chrysoula Kourtidou-Papadeli, Panagiotis D Bamidis
In this paper we present the first steps in developing SmartHypnos, an easy to use and user friendly graphical user interface, which aims to provide polysomngographic data visualization and the detection and classification of sleep related events. Currently SmartHypnos supports the visualization of EEG, ECG, EOG and EMG signals, and respiratory signals such as nasal pressure, thermistor, oxygen saturation, thoracic and abdominal belt recordings. All these are incorporated into an interface that provides quick and effortless access to the signals mentioned above...
July 2019: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Benedikt V Ehinger, Olaf Dimigen
Electrophysiological research with event-related brain potentials (ERPs) is increasingly moving from simple, strictly orthogonal stimulation paradigms towards more complex, quasi-experimental designs and naturalistic situations that involve fast, multisensory stimulation and complex motor behavior. As a result, electrophysiological responses from subsequent events often overlap with each other. In addition, the recorded neural activity is typically modulated by numerous covariates, which influence the measured responses in a linear or non-linear fashion...
2019: PeerJ
Gaia Amaranta Taberna, Roberto Guarnieri, Dante Mantini
Recent studies have highlighted the importance of an accurate individual head model for reliably using high-density electroencephalography (hdEEG) as a brain imaging technique. Correct identification of sensor positions is fundamental for accurately estimating neural activity from hdEEG recordings. We previously introduced a method of automated localization and labelling of hdEEG sensors using an infrared colour-enhanced 3D scanner. Here, we describe an extension of this method, the spatial positioning toolbox for head markers using 3D scans (SPOT3D), which integrates a graphical user interface (GUI)...
September 6, 2019: Scientific Reports
Bahar Güntekin, Lütfü Hanoğlu, Tuba Aktürk, Ezgi Fide, Derya Durusu Emek-Savaş, Ece Ruşen, Ebru Yıldırım, Görsev G Yener
Behavioral studies have shown that the recognition of facial expressions may be impaired in patients with Alzheimer's disease (AD). The identification and recognition of a facial expression might be represented by event-related brain oscillations. The present study aims to analyze EEG event-related oscillations and determine the electrophysiological indicators of impaired facial expression recognition in AD patients. EEGs of 30 healthy controls and 30 AD patients were recorded during their perception of three different facial expressions (angry, happy, neutral)...
July 2, 2019: Psychophysiology
Andreas Pedroni, Amirreza Bahreini, Nicolas Langer
Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of artifact contamination and the quality of EEG recordings often substantially differs between subjects. Although there exists a variety of standardized preprocessing methods to clean EEG from artifacts, currently there is no method to objectively quantify the quality of preprocessed EEG...
June 21, 2019: NeuroImage
Aaron R Shifman, John E Lewis
The transmembrane ionic currents that underlie changes in a cell's membrane potential give rise to electric fields in the extracellular space. In the context of brain activity, these electric fields form the basis for extracellularly recorded signals, such as multiunit activity, local field potentials and electroencephalograms. Understanding the underlying neuronal dynamics and localizing current sources using these signals is often challenging, and therefore effective computational modeling approaches are critical...
2019: Frontiers in Neuroinformatics
Razieh Alemi, Alexandre Lehmann
BACKGROUND: Cochlear implant (CI) outcomes can be assessed using objective measures that reflect the integrity of the auditory pathway. One such measure is the middle latency response (MLR), which can provide valuable information for clinicians. PURPOSE: Traditional stimuli for evoking MLRs, that is, clicks or tone bursts, do not stimulate all parts of the cochlea simultaneously, whereas chirp stimuli compensate for the cochlear neural delay and, therefore, produce more synchronous responses from the different neural elements of the cochlea...
April 26, 2019: Journal of the American Academy of Audiology
Yusuke Takeda, Keita Suzuki, Mitsuo Kawato, Okito Yamashita
Variational Bayesian Multimodal EncephaloGraphy (VBMEG) is a MATLAB toolbox that estimates distributed source currents from magnetoencephalography (MEG)/electroencephalography (EEG) data by integrating functional MRI (fMRI) ( VBMEG also estimates whole-brain connectome dynamics using anatomical connectivity derived from a diffusion MRI (dMRI). In this paper, we introduce the VBMEG toolbox and demonstrate its usefulness. By collaborating with VBMEG's tutorial page (
2019: Frontiers in Neuroscience
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