scholarly journals Automagic: Standardized Preprocessing of Big EEG Data

2018 ◽  
Author(s):  
Andreas Pedroni ◽  
Amirreza Bahreini ◽  
Nicolas Langer
Keyword(s):  
Large Scale ◽  
Correction Method ◽  
Pool Data ◽  
Imaging Data ◽  
Eeg Data ◽  
Aging Populations ◽  
Artifact Rejection ◽  
Eeg Recordings ◽  
Study Sites

AbstractElectroencephalography (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. This makes the commonly accepted procedure of excluding subjects from analyses due to exceeding contamination of artifacts highly subjective. As a consequence, P-hacking is fostered, the replicability of results is decreased, and it is difficult to pool data from different study sites. In addition, in large-scale studies, data are collected over years or even decades, requiring software that controls and manages the preprocessing of ongoing and dynamically growing studies. To address these challenges, we developed Automagic, an open-source MATLAB toolbox that acts as a wrapper to run currently available preprocessing methods and offers objective standardized quality assessment for growing studies. The software is compatible with the Brain Imaging Data Structure (BIDS) standard and hence facilitates data sharing. In the present paper we outline the functionality of Automagic and examine the effect of applying combinations of methods on a sample of resting EEG data. This examination suggests that applying a pipeline of algorithms to detect artifactual channels in combination with Multiple Artifact Rejection Algorithm (MARA), an independent component analysis (ICA)-based artifact correction method, is sufficient to reduce a large extent of artifacts.

scholarly journals Electroencephalogram (EEG) Recording Protocol for Cognitive and Affective Human Neuroscience Research

2019 ◽  
Author(s):  
Jaclyn L. Farrens ◽  
Aaron M. Simmons ◽  
Steven J. Luck ◽  
Emily S. Kappenman
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Neuroscience Research ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Brain Processing ◽  
Resolution Measure ◽  
Electroencephalogram Eeg

Abstract Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

scholarly journals Electroencephalogram (EEG) Recording Protocol for Cognitive and Affective Human Neuroscience Research

2020 ◽  
Author(s):  
Jaclyn L. Farrens ◽  
Aaron M. Simmons ◽  
Steven J. Luck ◽  
Emily S. Kappenman
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Neuroscience Research ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Brain Processing ◽  
Resolution Measure ◽  
Electroencephalogram Eeg

Abstract Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

scholarly journals fMRI Artefact Rejection and Sleep Scoring Toolbox

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Yves Leclercq ◽  
Jessica Schrouff ◽  
Quentin Noirhomme ◽  
Pierre Maquet ◽  
Christophe Phillips
Keyword(s):  
Simultaneous Recording ◽  
Imaging Data ◽  
Functional Magnetic Resonance ◽  
Magnetic Resonance Imaging Data ◽  
Data Format ◽  
Data Manipulation ◽  
Sleep Scoring ◽  
Eeg Data ◽  
Continuous Eeg ◽  
Eeg Recordings

We started writing the “fMRI artefact rejection and sleep scoring toolbox”, or “FAST”, to process our sleep EEG-fMRI data, that is, the simultaneous recording of electroencephalographic and functional magnetic resonance imaging data acquired while a subject is asleep. FAST tackles three crucial issues typical of this kind of data: (1) data manipulation (viewing, comparing, chunking, etc.) of long continuous M/EEG recordings, (2) rejection of the fMRI-induced artefact in the EEG signal, and (3) manual sleep-scoring of the M/EEG recording. Currently, the toolbox can efficiently deal with these issues via a GUI, SPM8 batching system or hand-written script. The tools developed are, of course, also useful for other EEG applications, for example, involving simultaneous EEG-fMRI acquisition, continuous EEG eye-balling, and manipulation. Even though the toolbox was originally devised for EEG data, it will also gracefully handle MEG data without any problem. “FAST” is developed in Matlab as an add-on toolbox for SPM8 and, therefore, internally uses its SPM8-meeg data format. “FAST” is available for free, under the GNU-GPL.

scholarly journals Enabling #EEGManyLabs: Quality in Automatically Preprocessed EEG Data and Psychopathological Associations of Frontal Alpha Asymmetry

2022 ◽  
Author(s):  
Niklas Schürmann
Keyword(s):  
Interrater Reliability ◽  
Large Scale ◽  
Electrode Pair ◽  
Alpha Power ◽  
Alpha Asymmetry ◽  
Eeg Data ◽  
Replication Crisis ◽  
State Of Research ◽  
The Relationship

Neuroscience is facing a replication crisis. Little effort is invested in replication projects and low power in many studies indicates a potentially poor state of research. To assess replicability of EEG research, the #EEGManyLabs project aims to reproduce the most influential original EEG studies. A spin-off to the main project shall investigate the relationship between frontal alpha asymmetries and psychopathological symptoms, the predictive qualities of which have lately been considered controversial. To ensure that preprocessing of EEG data can be conducted automatically (via Automagic), we tested 47 healthy participants in an EEG resting state paradigm and collected psychopathological measures. We analyzed reliability and quality of manual and automated preprocessing and performed multiple regressions to investigate the association of frontal alpha asymmetries and depression, worry, trait anxiety and COVID-19 related worry. We hypothesized comparably good interrater reliability of preprocessing methods and higher data quality in automatically preprocessed data. We expected associations of leftward frontal alpha asymmetries and higher depression and anxiety scores and significant associations of rightward frontal alpha asymmetries and higher worrying and COVID-19- related worrying. Interrater reliability of preprocessing methods was mostly good, automatically preprocessed data achieved higher quality scores than manually preprocessed data. We uncovered an association of relative rightward lateralization of alpha power at one electrode pair and depressive symptoms. No further associations of interest emerged. We conclude that Automagic is an appropriate tool for large-scale preprocessing. Findings regarding associations of frontal alpha asymmetries and psychopathology likely stem from sample limitations and shrinking effect sizes.

scholarly journals TRANSPARENCY AND REPRODUCIBILITY IN THE NEUROIMAGING OF AGING

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S23-S24
Author(s):  
Kendra L Seaman
Keyword(s):  
Large Scale ◽  
Meta Analysis ◽  
Social Science Research ◽  
Science Research ◽  
Data Sets ◽  
Imaging Data ◽  
Large Scale Data ◽  
Neuroimaging Data ◽  
Brain Imaging Data

Abstract In concert with broader efforts to increase the reliability of social science research, there are several efforts to increase transparency and reproducibility in neuroimaging. The large-scale nature of neuroimaging data and constantly evolving analysis tools can make transparency challenging. I will describe emerging tools used to document, organize, and share behavioral and neuroimaging data. These tools include: (1) the preregistration of neuroimaging data sets which increases openness and protects researchers from suspicions of p-hacking, (2) the conversion of neuroimaging data into a standardized format (Brain Imaging Data Structure: BIDS) that enables standardized scripts to process and share neuroimaging data, and (3) the sharing of final neuroimaging results on Neurovault which allows the community to do rapid meta-analysis. Using these tools improves workflows within labs, improves the overall quality of our science and provides a potential model for other disciplines using large-scale data.

scholarly journals Electroencephalogram (EEG) Recording Protocol for Cognitive and Affective Human Neuroscience Research

2021 ◽  
Author(s):  
Jaclyn L. Farrens ◽  
Aaron M. Simmons ◽  
Steven J. Luck ◽  
Emily S. Kappenman
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Neuroscience Research ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Brain Processing ◽  
Resolution Measure ◽  
Electroencephalogram Eeg

Abstract Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

scholarly journals INFRARED THERMAL IMAGING AS AN INNOVATIVE APPROACH FOR EARLY DETECTION INFESTATION OF STORED PRODUCT INSECTS IN CERTAIN STORED GRAINS

2020 ◽  
Vol 52 (4) ◽  
pp. 321-331
Author(s):  
A. I. IBRAHIM ◽  
M. A. YOUSRY ◽  
M. I. SAAD ◽  
M. F. MAHMOUD ◽  
Maysa SAID ◽  
...  
Keyword(s):  
Early Detection ◽  
Thermal Imaging ◽  
Large Scale ◽  
Negative Impact ◽  
Imaging Data ◽  
Infrared Thermal Imaging ◽  
Stored Product Insects ◽  
Stored Grains ◽  
White Bean

Grains of field crops, such as wheat, maize, faba bean and white bean, are considered strategic food for humanity worldwide and Egypt. Unfortunately, percent losses of grains quantity may reach to 15-30%, as a result of stored product insect damage, and the losses increased dramatically in the last years, as an outcome of quickly productions of these pests. Experiments were conducted on infrared thermal imaging that demonstrate early detection of infestation by stored product insects in wheat, maize, broad bean, white bean and bean grains. The imaging is dependent on subtle significant differences in temperature between infested and healthy grains. Because the thermal imaging data are digital, computer programs can be used to analysis differences in temperature and mining figures explained for that. Results revealed that the use of thermal imaging offers an alternative method to detect an insect infestation. Data concluded that thermal imaging has the potential to identify whether the grains of crops that tested are infested or not, but is less effective in identifying which developmental stage is present. Moreover, it could apply this technique easily on a large scale in silos, storage, mills and granaries without negative impact on quality of stored grains.

scholarly journals HAPPILEE: The Harvard Automated Processing Pipeline In Low Electrode Electroencephalography, a standardized software for low density EEG and ERP data

2021 ◽  
Author(s):  
Kelsie Lynn Lopez ◽  
Alexa Danielle Monachino ◽  
Santiago Morales ◽  
Stephanie Leach ◽  
Maureen Bowers ◽  
...  
Keyword(s):  
Event Related Potential ◽  
Low Density ◽  
Density Data ◽  
Channel Detection ◽  
Eeg Data ◽  
Artifact Rejection ◽  
Automated Processing ◽  
Brain Function Measurement ◽  
Eeg Recordings ◽  
Processing Steps

Low-density Electroencephalography (EEG) recordings (e.g. fewer than 32 electrodes) are widely-used in research and clinical practice and enable scalable brain function measurement across a variety of settings and populations. Though a number of automated pipelines have recently been proposed to standardize and optimize EEG preprocessing for high-density systems with state-of-the-art methods, few solutions have emerged that are compatible with low-density systems. However, low-density data often include long recording times and/or large sample sizes that would benefit from similar standardization and automation with contemporary methods. To address this need, we propose the HAPPE In Low Electrode Electroencephalography (HAPPILEE) pipeline as a standardized, automated pipeline optimized for EEG recordings with low density channel layouts of any size. HAPPILEE processes task-free (e.g. resting-state) and task-related EEG, and event-related potential (ERP) data, from raw files through a series of processing steps including filtering, line noise reduction, bad channel detection, artifact rejection from continuous data, segmentation, and bad segment rejection that have all been optimized for low density data. HAPPILEE also includes post-processing reports of data and pipeline quality metrics to facilitate the evaluation and reporting of data quality and processing-related changes to the data in a standardized manner. We describe multiple approaches with both recorded and simulated EEG data to optimize and validate pipeline performance. The HAPPILEE pipeline is freely available as part of HAPPE 2.0 software under the terms of the GNU General Public License at: https://github.com/PINE-Lab/HAPPE.

scholarly journals Kajian Vegetasi Kawasan Pasca Tambang Marmer di Kabupaten Timor Tengah Utara

SAINTEKBU ◽  
2018 ◽  
Vol 10 (2) ◽  
pp. 10-16
Author(s):  
Emilia Juliyanti Bria ◽  
Remigius Binsasi
Keyword(s):  
Environmental Factors ◽  
Large Scale ◽  
Economic Value ◽  
Tectona Grandis ◽  
North Central ◽  
Schleichera Oleosa ◽  
Significant Difference ◽  
Study Sites ◽  
Tectona Grandis L.F

Marble is one of the industrial materials of high economic value and very beneficial to people's lives. Therefore, many explorations are done by mining companies. Exploitation of natural resources on a large scale without regard to the carrying capacity of the environment, can lead to drastic decline in the quality of the ecosystem. This is what happened in the post-mine forest area of ​​Oenbit Village, North Central Timor District. This study aims to identify and calculate the abundance of plants and environmental factors that affect the plants in the post-marble area of ​​Oenbit village. The method used is quadratic / plot method. The results showed that plant species with significant values ​​above 80.00% were Anacardium occidentale L. (125.69%), Tamarindus indica L. (122.17%), Tectona grandis L.f (87.32%), and Schleichera oleosa (Lour .) Oken (82.67%). Abiotic environmental factors measured at the study sites are soil pH, soil moisture, air temperature, air humidity and light intensity. The results of these measurements showed no significant difference.

scholarly journals Electroencephalogram (EEG) Recording Protocol for Cognitive and Affective Human Neuroscience Research

2020 ◽  
Author(s):  
Jaclyn L. Farrens ◽  
Aaron M. Simmons ◽  
Steven J. Luck ◽  
Emily S. Kappenman
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Neuroscience Research ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Brain Processing ◽  
Resolution Measure ◽  
Electroencephalogram Eeg

Abstract Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

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