scholarly journals The HAPPE plus Event-Related (HAPPE+ER) Software: A Standardized Processing Pipeline for Event-Related Potential Analyses

2021 ◽  
Author(s):  
Alexa Danielle Monachino ◽  
Kelsie Lynn Lopez ◽  
Lara J. Pierce ◽  
laurel Joy Gabard-durnam
Keyword(s):  
Data Processing ◽  
Event Related Potential ◽  
Subjective Time ◽  
Post Processing ◽  
Channel Detection ◽  
Processing Pipeline ◽  
Data Segmentation ◽  
Eeg Data ◽  
Artifact Rejection ◽  
Automated Processing

Event-Related Potential (ERP) designs are a common method for interrogating neurocognitive function with electroencephalography (EEG). However, the gold standard of preprocessing ERP data is manual-editing, a subjective, time-consuming processes. A number of automated pipelines have recently been created to address the need for standardization, automation, and quantification of EEG data processing; however, few are optimized for ERP analyses (especially in developmental or clinical populations). To fill this need, we propose and validate the HAPPE plus Event-Related (HAPPE+ER) software, a standardized and automated processing pipeline optimized for ERP analyses in EEG data. HAPPE+ER processes event-related potential data from raw files through a series of filtering, line noise reduction, bad channel detection, artifact rejection from continuous data, segmentation, and bad segment rejection methods. HAPPE+ER also includes post-processing reports of both data quality and pipeline quality metrics to facilitate the evaluation and reporting of data processing in a standardized manner. Finally, HAPPE+ER includes a post-processing script to facilitate generating ERP figures and measures for statistical analysis. We describe multiple approaches with both adult and developmental data to optimize and validate pipeline performance. HAPPE+ER software is freely available under the terms of GNU General Public License at https://github.com/PINE-Lab/HAPPE.

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 Eliciting and Recording Event Related Potentials (ERPs) in Behaviourally Unresponsive Populations: A Retrospective Commentary on Critical Factors

2021 ◽  
Vol 11 (7) ◽  
pp. 835
Author(s):  
Alexander Rokos ◽  
Richard Mah ◽  
Rober Boshra ◽  
Amabilis Harrison ◽  
Tsee Leng Choy ◽  
...  
Keyword(s):  
Event Related Potentials ◽  
Stimulus Presentation ◽  
Event Related Potential ◽  
Critical Factors ◽  
Healthy Elderly ◽  
States Of Consciousness ◽  
Long Latency ◽  
Eeg Data ◽  
Related Potentials ◽  
The Relationship

A consistent limitation when designing event-related potential paradigms and interpreting results is a lack of consideration of the multivariate factors that affect their elicitation and detection in behaviorally unresponsive individuals. This paper provides a retrospective commentary on three factors that influence the presence and morphology of long-latency event-related potentials—the P3b and N400. We analyze event-related potentials derived from electroencephalographic (EEG) data collected from small groups of healthy youth and healthy elderly to illustrate the effect of paradigm strength and subject age; we analyze ERPs collected from an individual with severe traumatic brain injury to illustrate the effect of stimulus presentation speed. Based on these critical factors, we support that: (1) the strongest paradigms should be used to elicit event-related potentials in unresponsive populations; (2) interpretation of event-related potential results should account for participant age; and (3) speed of stimulus presentation should be slower in unresponsive individuals. The application of these practices when eliciting and recording event-related potentials in unresponsive individuals will help to minimize result interpretation ambiguity, increase confidence in conclusions, and advance the understanding of the relationship between long-latency event-related potentials and states of consciousness.

scholarly journals From 2D to VR Film: A Research on the Load of Different Cutting Rates Based on EEG Data Processing

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 130
Author(s):  
Feng Tian ◽  
Yan Zhang ◽  
Yingjie Li
Keyword(s):  
Virtual Reality ◽  
Data Processing ◽  
Experimental Research ◽  
Objective Data ◽  
Theta Wave ◽  
Theoretical Support ◽  
Eeg Data ◽  
Cutting Rate ◽  
Alpha Beta ◽  
Viewing Experience

Focusing on virtual reality (VR) and film cutting, this study compared and evaluated the effect of visual mode (2D, VR) and cutting rate (fast, medium, slow) on a load, to make an attempt for VR research to enter the cognitive field. This study uses a 2 × 3 experimental research design. Forty participants were divided into one of two groups randomly and watched films with three cutting rates. The subjective and objective data were collected during the experiment. The objective results confirm that VR films bring more powerful alpha, beta, theta wave activities, and bring a greater load. The subjective results confirm that the fast cutting rate brings a greater load. These results provide a theoretical support for further exploring the evaluation methods and standards of VR films and improving the viewing experience in the future.

scholarly journals Extending theFermi-LAT Data Processing Pipeline to the Grid

2012 ◽  
Vol 396 (3) ◽  
pp. 032121 ◽  
Author(s):  
S Zimmer ◽  
L Arrabito ◽  
T Glanzman ◽  
T Johnson ◽  
C Lavalley ◽  
...  
Keyword(s):  
Data Processing ◽  
Processing Pipeline

Using Fictive Powers as Event Markers in EEG Data Processing

1995 ◽  
pp. 323-328
Author(s):  
Richard X. Tang ◽  
Kouichi Sugita ◽  
Walter J. Freeman ◽  
Sven Jakubith
Keyword(s):  
Data Processing ◽  
Eeg Data

scholarly journals ERP CORE: An Open Resource for Human Event-Related Potential Research

2020 ◽  
Author(s):  
Emily S. Kappenman ◽  
Jaclyn Farrens ◽  
Wendy Zhang ◽  
Andrew X Stewart ◽  
Steven J Luck
Keyword(s):  
Data Processing ◽  
Brain Activity ◽  
A Priori ◽  
Event Related Potentials ◽  
Event Related Potential ◽  
Error Related Negativity ◽  
Wide Range ◽  
Experiment Control ◽  
Related Potentials

Event-related potentials (ERPs) are noninvasive measures of human brain activity that index a range of sensory, cognitive, affective, and motor processes. Despite their broad application across basic and clinical research, there is little standardization of ERP paradigms and analysis protocols across studies. To address this, we created ERP CORE (Compendium of Open Resources and Experiments), a set of optimized paradigms, experiment control scripts, data processing pipelines, and sample data (N = 40 neurotypical young adults) for seven widely used ERP components: N170, mismatch negativity (MMN), N2pc, N400, P3, lateralized readiness potential (LRP), and error-related negativity (ERN). This resource makes it possible for researchers to 1) employ standardized ERP paradigms in their research, 2) apply carefully designed analysis pipelines and use a priori selected parameters for data processing, 3) rigorously assess the quality of their data, and 4) test new analytic techniques with standardized data from a wide range of paradigms.

scholarly journals THE GWAC DATA PROCESSING AND MANAGEMENT SYSTEM

2021 ◽  
Vol 53 ◽  
pp. 174-179
Author(s):  
Y. Xu ◽  
L. P. Xin ◽  
X. H. Han ◽  
H. B. Cai ◽  
L. Huang ◽  
...  
Keyword(s):  
Data Processing ◽  
User Interfaces ◽  
Management System ◽  
Scientific Data ◽  
Data Archiving ◽  
Processing Pipeline ◽  
Advanced Technologies ◽  
Single Frame ◽  
Moonless Night ◽  
Processing Requirement

GWAC will have been built an integrated FOV of 5,000 degree2 and have already built 1,800 square degree2. The limit magnitude of a 10-second exposure image in the moonless night is 16R. In each observation night, GWAC produces about 0.7TB of raw data, and the data processing pipeline generates millions of single frame alerts. We describe the GWAC Data Processing and Management System (GPMS), including hardware architecture, database, detection-filtering-validation of transient candidates, data archiving, and user interfaces for the check of transient and the monitor of the system. GPMS combines general technology and software in astronomy and computer field, and use some advanced technologies such as deep learning. Practical results show that GPMS can fully meet the scientific data processing requirement of GWAC. It can online accomplish the detection, filtering and validation of millions of transient candidates, and feedback the final results to the astronomer in real-time. During the observation from October of 2018 to December of 2019, we have already found 102 transients.

scholarly journals Decreased P2 Waveform Reflects Impaired Brain Executive Function Induced by 12 h of Low Homeostatic Sleep Pressure: Evidence From an Event-Related Potential Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Lingjing Zeng ◽  
Haijing Wu ◽  
Jialu Li ◽  
Haiteng Wang ◽  
Songyue Xie ◽  
...  
Keyword(s):  
Executive Function ◽  
Response Time ◽  
Visual Processing ◽  
Repeated Measures ◽  
Young Male ◽  
Event Related Potential ◽  
Peak Latency ◽  
Peak Amplitude ◽  
Eeg Data ◽  
The Impact

Homeostatic sleep pressure can cause cognitive impairment, in which executive function is the most affected. Previous studies have mainly focused on high homeostatic sleep pressure (long-term sleep deprivation); thus, there is still little related neuro-psycho-physiological evidence based on low homeostatic sleep pressure (12 h of continuous wakefulness) that affects executive function. This study aimed to investigate the impact of lower homeostatic sleep pressure on executive function. Our study included 14 healthy young male participants tested using the Go/NoGo task in normal resting wakefulness (10:00 am) and after low homeostatic sleep pressure (10:00 pm). Behavioral data (response time and accuracy) were collected, and electroencephalogram (EEG) data were recorded simultaneously, using repeated measures analysis of variance for data analysis. Compared with resting wakefulness, the participants’ response time to the Go stimulus was shortened after low homeostatic sleep pressure, and the correct response rate was reduced. Furthermore, the peak amplitude of Go–P2 decreased significantly, and the peak latency did not change significantly. For NoGo stimulation, the peak amplitude of NoGo–P2 decreased significantly (p < 0.05), and the peak latency was significantly extended (p < 0.05). Thus, the P2 wave is likely related to the attention and visual processing and reflects the early judgment of the perceptual process. Therefore, the peak amplitude of Go–P2 and NoGo–P2 decreased, whereas the peak latency of NoGo–P2 increased, indicating that executive function is impaired after low homeostatic sleep pressure. This study has shown that the P2 wave is a sensitive indicator that reflects the effects of low homeostatic sleep pressure on executive function, and that it is also an important window to observe the effect of homeostatic sleep pressure and circadian rhythm on cognitive function.

Technical design: data processing pipeline in eHealth

2022 ◽  
pp. 259-283
Author(s):  
Patrick Schneider ◽  
Fatos Xhafa
Keyword(s):  
Data Processing ◽  
Technical Design ◽  
Design Data ◽  
Processing Pipeline

scholarly journals Comparative analysis of two-group supervised classification algorithms in the study of P300-based brain-computer interface

2019 ◽  
Vol 252 ◽  
pp. 03010
Author(s):  
Małgorzata Plechawska-Wójcik ◽  
Monika Kaczorowska ◽  
Bernadetta Michalik
Keyword(s):  
Comparative Analysis ◽  
Supervised Classification ◽  
Brain Computer Interface ◽  
Event Related Potential ◽  
Computer Interface ◽  
Linear Discriminant ◽  
Process Comparison ◽  
Eeg Data ◽  
Comparative Accuracy ◽  
Supervised Classification Methods

The main goal of the paper is to perform a comparative accuracy analysis of the two-group classification of EEG data collected during the P300-based brain-computer interface tests. The brain-computer interface is a technology that allows establishing communication between a human brain and external devices. BCIs may be applied in medicine to improve the life of disabled people and as well for entertainment. The P300 is an event-related potential (ERP) appearing about 300 ms after the occurrence of the stimulus of visual, auditory or sensory nature. It is based on the phenomenon observed in anticipation for a target event among non-target events. The 21-channel 201 Mitsar amplifier was used during the experiment to store EEG data from seven electrodes placed on the dedicated cap. The study was conducted on a group of five persons using P300 scenario available in OpenVibe software. The experiment was based on three steps the classifier learning process, comparison and averaging of the obtained result and the final test of the classifier. The comparative analysis was performed with the application of two supervised classification methods: Linear Discriminant Analysis (LDA) and Multi-layer Perceptron (MLP). The preliminary data analysis, extraction and feature selection was performed prior to the classification.

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