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In ERP P300 recording ( Audio stimulus), the P3 wave shows up as double hump, where to pick Peak for Amplitude and latency?
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Good Evening Blaine. I do have a incertitude about splitting the P300 wave into P3a and P3b component for such waves. Do you have any input on this. Please share. Thankyou – 📷Ranjit Savita Gaur Kumar
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I have a cluster and I am running statistics on component ERPs. I have realized that 2 out of 10 times when I am calculating the statistics I got one extra area of significance. Is that normal? is it because of random permutation? or I shouldn't get that?
Thank you in advance.
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Thank you for your answer Otto! Yes I though that could be due to the random permutations. So in EEGLAB I have the option to set the randomization number to a) a number . b) auto (which I guess it finds the optimal) and c) all (which is a high number of permutations) but I still get the same result.
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I want to compute the average and then difference of the ERP components using the pop_comperp() function in EEGLAB but I realized that I have some issues with the signs. The ERP data for some components are flipped and therefore the average is not correct. Below you can see my output plot.
Do you know how can I solve this problem?
Kind Regards,
Evangelia
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Hello and thanks for your answer!
Yes, I finally did it by just multiplying the inverse weights with (-1) it was easier than I thought :)
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I am working on EEG self-detection of Parkinson's disease and I would need to apply my methods on data from people with Parkinson's disease subjected to ERPs or ssEVPs with a control group of healthy people subjected to the same stimulation under the same conditions.
I have been searching for a year without being able to find a database. So I am looking for a direct link to a laboratory or researcher who can provide me with this data.
Thank you.
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Hi,
See this site, it may help you:
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Hi, I wonder what type of EEG cap do you use/recommend for ERP research, in order to obtain better impedance, but still to be able to get an acceptable time btw cap installing and signal recording? Thank you!
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If you are planning to record from a low-density EEG cap (e.g., 32 electrodes max), I would strongly recommend active (pre-amplified) wet electrodes for recording relatively noise-free signals even in face of rather high impedance (up to 50 kOhms, ). For this sort of settings, I would suggest BrainProducts actiCap. In my experience, the set up time was ~20-30 min for having impedance < 5 kOhms at each electrode.
Best,
Thomas
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Hello everyone,
As many of you are aware, Industry 4.0 and smart manufacturing is widely gaining traction among many manufacturing practitioners.
One of the aims of smart manufacturing is to bring in more real-time intelligence to shop-floor and manufacturing planners, by employing different shop-floor data sources (For eg, smart products, advanced equipment, and machine sensors, collated through the Industrial Internet of things, RFID gateways, etc). The data collected from these shop-floor data sources can be processed by means of Artificial intelligence-based algorithms ( machine learning, evolutionary algorithms, etc) to produce optimized production schedules, process plans, service schedules, and maintenance plans. The application of such techniques has been researched by several researchers and numerous publications are already available.
However, at the same time, central to the functioning of any manufacturing industry are Enterprise resources planning packages, encompassing all the functions of a manufacturing business, ranging from procurement to production planning and control to service management and even auxiliary support functions such as accounting and packages.
As a result, there are two different software systems that can benefit manufacturing firms:
1. AI-based smart manufacturing tools which seem promising in improving production efficiency: Such packages are more manufacturing operation centric
2. Tried and tested ERP work packages, developed by numerous software firms: Such packages can focus on both the business planning and manufacturing operations management.
The questions that I often wonder about in this regard are :
  • Have AI- algorithm-based smart manufacturing tools been integrated into existing ERP software?
  • What is the level of maturity of ERP work packages with respect to AI-based intelligence algorithms ( particularly with respect to integration and hosting of AI-based work packages, API access to databases, software architecture)?
Many ERP work packages support multiple scheduling rules for a host of production scenarios such as flow manufacturing, make to stock, make to order, job shop- etc.
  • But do they actually employ AI Solvers, such as genetic algorithms, search-based algorithms, Neural network models, reinforcement learning-based algorithms?
From my initial assessment of the market, I understand that we are staring at a situation where the development in these two work packages progressing is in two nonintersecting planes, with two separate software packages being the only way out- one for AI-enabled manufacturing execution and the other for ERP systems?
In that case, I'd be keen to know the market feedback-
  • Are industrial engineers of today willing to accept the need for two separate software packages for manufacturing scheduling and control, considering that liaising with software systems is not their core job ?
I've found a few articles on Intelligent ERP systems, but they are pretty generic, and mainly focus on the need for ERP systems to integrate cloud and mobile-based support and automated inspections, but do not discuss much on AI solvers.
A search on google scholar does not reveal much either, with many articles proposing architecture for ERP of the future, integration with smart agents, etc, without discussing a lot on existing maturity levels and capabilities.
Looking forward to your valuable answers!
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ERP systems, e.g. SAP, Sage, Oracle, IFS, and Dynamics, are well developed and adopted by enterprise companies. I trust Big data analytics and related technologies have been utilised for advanced planning. However, the cost and time for ERP implementation and integration is still a severe challenge to many companies. So, Google scholar may not show ERP capabilities and innovations that exist!
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Why is SAP popular in erp systems above all does the use of it really gives a business competitive advantage?
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Resistance in various forms and the degree of Customization are definitely important factors to consider.
There are a growing number of published studies about ERP systems in small and medium enterprises (SMEs). This may be the most enlightening one so far:
Ondrej Zach , Bjørn Erik Munkvold & Dag Håkon Olsen (2014) ERP system implementation in SMEs: exploring the influences of the SME context, Enterprise Information Systems, 8:2, 309-335.
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If any one can help me to Prepare " User requirement Specification " For Production planing and scheduling in ERP system
Thanks
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The same-different task requires to subjects to indicate whether a pair of stimuli seen or heard are the same (say AA or BB) or different (say AB or BA). Researchers often collect offline measures (e.g. response accuracy and latency) in the task.
Is there a way that I can collect online measures using eye-tracking, ERP or some other experimental techniques in psychology? In other words, instead of people reporting whether the pair of stimuli are different, I hope to infer their knowledge based on their fixations and brain potentials. Please recommend papers that I can read (if any). Thank you!
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تصميم استبيان
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One main reason for failure of most ERP implementations is the misfit between business requirements and ERP system business process be reduced. How can the misfit between business requirents and ERP system business process be reduced?
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One main reason for failure of most ERP implementations is the misfit between business requirements and ERP system business process be reduced. How can the misfit between business requirents and ERP system business process be reduced?
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Unless a current-state business process brings a strategic competitive advantage to the company (beyond "we have always done it this way), modify the system to fit, else implement the business process defined in the new ERP. Most ERP systems are purchased with an eye on updated processes and features, yet they are oft implemented to mimic current-state processes designed around an ill-fitting system.
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Hi everybody,
I need to perform reliability analysis on my ERP data. Specifically, I would like to estimate internal consistency reliability through Spearman-Brown corrected split-half reliability. Could anybody help me with this? Do I need to use all the trials for each participant?
I'm not sure how to start the analysis, using trials or averages.....
I hope to get some answer here.
Thanks in advance.
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Not sure, but this paper could help:
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Dear all,
I recently tried to optimize my scripts to preprocess EEG data using EEGLAB2019. I found that including EOG channels in ICA significantly improved the subsequent ERP results, i.e., the classic LPP appeared. However, if I included EOG channels in ICA, the subsequent average re-reference had to also include them. If I performed average re-reference on only brain channels, the eeg_checkset would report an error ( ICA Index exceeds matrix dimensions). If I excluded EOG channels in ICA, average re-reference on only brain channels worked well, which, however, generated a bad ERP wave.
The figure attached is the ERP waves based on four-subject preprocessed data. The only difference between them is whether the EOG channels were included in ICA. ICALabel was used to automatically remove the EOG components.
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its generally not ok to include EOG EMG channels in the rereference, otherwise it would generate huge artifacts in your EEG. On the other hand its beneficial to include when you run the ICA since the the high amplitude of eye-artefacts can help get rid of the artefact-related components in the EEG
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I'm conducting a market audit regarding ERP solutions. I'm looking for quality research work related to PESTEL analysis. I'm glad if you can recommend me some high-quality research work. Thank you in advance.
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Hello,
We have a setup of iMotion and Emotiv EPOC that a previous faculty was using for attentional and emotion recognition purposes. As I understand iMotion gets the Emotiv data and timestamps it, however I want to conduct some research about ERP and BCI (For e.g. P300) where syncrhonisation is critical, it is possible to accomplish this using iMotions and Emotiv EPOC+? I would greatly appreciate your help.
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Hello Ibrahim,
We do not do Emotiv + ERPs of any sort. You would need a higher temporal resolution to track something like a P300. For the temporal resolution to be adequate to execute an ERP experiment in iMotions you would need to use an ABM + Stim tracker and some custom consultation with our experts here at iMotions. Your methods and hardware choice may not need this depending on your research question. You are welcomed to contact us for further information https://imotions.com/contact-us/
Perhaps this paper would be of use. There is a paper which shows that ERPs can be extracted using the Emotiv EPOC+: https://sccn.ucsd.edu/pipermail/eeglablist/2012/005883.html
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I am writing a project proposal for my PhD. The main theme is around Event Related Potentials (ERPs) in Depressed patients. I need expert help regarding this.
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Umema Zafar , If you can be a little more specific, I'll be happy to help!
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Do you know papers that discuss this subject?
We are looking for objective methods to decide about inclusion/exclusion of data sets.
We are doing a study that looks at the detection rate of an effect (N400) on a single subject basis.
We have some very noisy data sets and are wondering where to draw the line for inclusion.
What criteria/methods do you suggest?
- Sufficient number of trials left per condition after preprocessing
- Sufficient SNR
- ...?
We are looking forward to your replies!
Thank you.
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Dear Anna
It actually strongly depends on what you want to do with your data.
If you just doing a simple amplitude/latency assessment you don't need that high signal quality.
If you want to perform source analysis/time-frequency you need much cleaner data (high SNR). Note that the SNR is defined as a baseline to signal of interest ratio (not the overall quality)
Finally in the case of single single-trial analysis you definitely need clean data
On the other hand, what problem do you have with your data?
Is it high noise? - maybe it can be filtered out
is it an artifact (EKG, blinks)? - that can be easily reduced
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I recorded tibial nerve simulations with EEG on two subjects. One subject shows reasonable ERPs as described by literature with a positive peak at ~39ms, the other subject shows the ERPs, but they are inverted ( same processing steps were taken for both datasets). Does this make sense physiologically? 
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I totallly agree, technical issues must be ruled out like active electrode and reference changing. This means active is put on the reference site and reference is put on active site. Nevertheless there are programs that allows to change the polarity of the recorded signal, so maybe you can still use that information. But we are open mindd so, rule out and see.
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Hi all,
I'm writing a protocol (using the PRISMA checklist) for a systematic review on evoked responses (ERPs and ERFs) in typically developing children (aged 0-17).
Can anyone recommend an appropriate risk of bias assessment to use?
The ones I've come across (e.g., Newcastle-Ottowa, Cochrane) don't seem appropriate for the study designs I'm interested in (single group, NOT intervention etc.).
Many thanks,
Hannah
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We had a similar challenge lately on a meta-analysis of behavioral data in kids. It seems that some meta-analyses come up with their own criteria that are pretty random.
I wonder if one way would be to assess the degree to which each paper followed the SPR/Keil et al. guidelines for reporting? Reporting of data/findings may not fully reflect the quality of studies, but it is an objective set of criteria that you could code.
More generally, the CASP checklist for cohort studies may fit better than some others: https://casp-uk.net/casp-tools-checklists/
Please report back if you find a good method!
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Technology is played important role in the disruptive era, how to use it to support accounting process such as ERP or inventory counting. Moreover, if it could support, h ow to measure it.
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The technological development that the world is witnessing has an impact on the accounting and auditing profession, and one of the most important systems is the ERP system for what it possesses as a model for planning, control, and optimal and comprehensive use of human, technical and material resources and harnessing the latest technologies and information systems to accomplish business and services. In addition to the planning advantages achieved by allowing the facility to reduce costs, increase production, avoid inventory shortages, improve delivery performance, and increase flexibility in preparing what the customer needs
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Hi,
i have a question about my master thesis. I am doing a thesis about success and risk factors in offshore software development within ERP Projects. Different countries have had numerous successful ERP implementations but my company does not have success. i am having interviews with IT experts in different countries that have been collaborating with the offshore software development centers.
Is it logic that I am doing a case study about the offshore software center and not about different ERP Projects, because i am figuring out different experiences, success and risk factors about the overall experience with the offshore centers and not just within 1 project.
thanks alot
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Good Answer Anthony Sackey
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Hi,
I am writing my MS Thesis on the ERP system with Blockchain. I need any Idea to present a novelty approach in ERP + Blockchain.
Replies awaiting.
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Integrating ERP and Blockchain is a highly promising topic. Not surprisingly, the big players in the field are working on it. If your thesis has a technical focus, you could discuss the technical problems that arise from this integration. If your focus in more on managerial issues, you can discuss changes in processes or organizational structures.
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Hello, I am looking for a small ERP for an accounting class, specifically one that has sample data if possible. Any suggestions?
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Hi
Please check the following work. I believe it will help you.
Regards
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I am looking for a ERP platform (with case study/tutorial materials and data sets) that could replace SAP ERP GBI software, to help teach my students the practical elements of Enterprise Systems. Does anyone have any suggestions?
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Thanks for the update, I will let you know if I come across anything else that would be fit for teaching.
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Hi, has anyone good experience with dry EEG electrodes, e.g. from ANT, for EVP/EEG recordings?
I have read that impedance is much higher compared to wet electrodes.
Thanks - Johannes
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scientists need to improve SNR with dry electrodes. In my opinion, these are not good at the current development level.
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Hello everyone,
I have a query about ERPs and neural oscillations. Is there an interaction between ERPs and neural oscillations? I am researching motor cognition, and I know the mu rhythm involves motor cognition. I am wondering whether the mu rhythm influences the motor ERPs. Thanks in advance.
Chen
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There are intresting links between oscillating activity and ERP, i suggest you read this interesting paper from Zrenner et al. were the authors investigate the effects of locking alpha oscillation on TMS.
Brain oscillation-synchronized stimulation of the left dorsolateral prefrontal cortex in depression using real-time EEG-triggered TMS.
The phase state in which the brain oscillation is influences ERP, of course to apreciate this effect one must lock the ERP to the oscillation phase with a close loop.
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What areas of the brain can be measured using EEG? What functions are appropriate and not appropriate to study with this method? Can the EEG record accurately activity in deeper brain structures and cortical areas along the midline (MPFC, Precuneus, ACC, subcortical structures)? What are the main advantages and disadvantages of this method?
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What we know by now, it is only appropriate to use EEG for cortex in connection to, not for deeper structures. But there are some newer methods of use of EEG which can give you some answers or at least an idea about the connectivity, functional and other questions, like source reconstruction from EEG (like sLORETA, for example), the graph theory applications, and also Transfer entropy analysis, Granger causality etc. For those analyses you also need to use high-density EEG, meaning more than 64 electrode positions, the more the better.
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I'm new to the field of EEG/ERP studies
For statistical analysis across different components, e.g. N100, I computed average for all the bins (exp conditions) for every individual using EEGLAB.
Then grand averaging was performed for all the ERP sets.
But I'm getting different values after grand averaging and computing mean across ERP sets while plotting ERP waveforms. For N100 the average mean values are different in waveform plot.
Please help.
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The discrepancy you observed is a common occurrence and reflects the non-linear nature of peak-based measurements - the average of individual subjects' data may not match the value in the grand-average waveform. If you use mean amplitude measurement for each subject (i.e., across a series of time points within the interval of interest) instead of the max value, that average across subjects will match the value in the grand-average waveform.
See Reason #5 in the blog by S. Luck:
His book "The Introduction to the Event-Related Potential Technique" is also a great resource and includes a discussion of the pros and cons for using peak vs. mean amplitude/latency measures.
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I want to compare ERP data from a single group of participants between 2 measurement times. I also want to compare the data from this group of participants with a group of control participants. I have used the jackknifing method to get cleaner averages. Sample size is 9 in both groups. I know that usually, t-tests are performed on ERP data even when the sample size is small. But I have been told by some people evaluating my last seminar that it was absolutely not appropriate for me to share (in my thesis) the results of parametric tests considering my sample sizes. I don't necessarily agree with them but I don't have much choice. So my questions are :
1) is it possible to perform non-parametric statistical analysis on jackknifed data? These analysis would mainly be non-parametric equivalents to t-tests (Wilcoxon and Mann-Whitney).
2) if it's possible, given that t values have to be adjusted before looking for the p values, how can I adjust the values I obtain in the non-parametric tests ?
3) if it's not possible, can you detail the reasons why it should not be done?
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I thank you for your answers. They are useful to me, although my questions refer to the suitability of non-parametric tests following the use of the jackknifing procedure. Also, when I talk about an adjustement, it has to do specifically with the jackknifing procedure. For instance, following the jackknifing procedure, when doing a t-test, the t value has to be devided by (n-1) before looking for the corresponding p value. When doing an ANOVA, the F value has to be devided by (n-1)^2. This is necessary because the jackknifing procedure artificially reduces the error variance in the data, which then leads to artificially inflated t values. I wonder if the same can be done with Z values. I was not able to find any reference discussing this issue.
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Hello!
I'm using az 4620 as my PR.
Usually, it is cured at 110 degree celsius under heating for 80 seconds.
I know that, but I have to heat it at 300 degree celsius for 25 hours.
I can't erase PR with acetone and EPR.
I couldn't be more grateful if you let me know what to do to erase my over-cured PR.
Plz... I'm dying to do this experiment...
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If you want to completely remove the PR use piranha cleaning or O2 plasma.
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Carter J. Funkhouser(2019) report that RewP is also referred to as the feedback negativity (FN) or feedback-related negativity (FRN).However, Michael P. Berry(2019) report that the RewP and FN are ERP components elicited by feed-back indicating rewards versus nonrewards, respectively. I wonder if FN and RewP are the same ERP component or two different ERP components ?
Berry, M. P., Tanovic, E., Joormann, J., & Sanislow, C. A. (2019). Relation of depression symptoms to sustained reward and loss sensitivity. Psychophysiology, 56(7). https://doi.org/10.1111/psyp.13364
Funkhouser, C. J., Auerbach, R. P., Kujawa, A., Morelli, S. A., Phan, K. L., & Shankman, S. A. (2019). Social Feedback Valence Differentially Modulates the Reward Positivity, P300, and Late Positive Potential. Journal of Psychophysiology. https://doi.org/10.1027/0269-8803/a000253
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They are terms used "interchangeably". FRN was identified with respect to instances where an individual receives loss-related feedback/non-reward; however, not long later an argument was made that the component was not a negativity but rather a positivity related to experiencing reward/gain-related feedback.
James Glazer has a really great 2018 paper titled "Beyond the FRN: Broadening the time-course of EEG and ERP components implicated in reward processing" that covers almost everything related to EEG/ERP and Reward, and Section 3.1 provides a nice overview of the relationship between FRN and RewP.
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We are a master's students in International Management at Ca' Foscari University of Venice. We are working on a project about ERP in the fashion industry and we will be glad if you can contribute to our research project. Thank you from Allegra, Carlotta, Giorgia, Ilaria, Luca and Stefano.
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I found something in RG:
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I have a hard time finding any literature on the Pc (positive correct) ERP, which is sometimes seen for correct response-locked trials. Much more is known about Pe (positive error, or error positivity) for incorrect responses. Any suggestions are welcome!
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Thank you Brittany for your suggestion. I looked up some of their references after mentioning Pc, more specifically Van der Borght et al., 2016, however this paper only explores Pc/Pe differences. So, Pc only figures as a contrast for Pe, like in the rest of the literature. What I would like to know about is a more specific evaluation of what processes contribute to Pc itself. I will have a closer look at other references and will keep you updated. Thanks again!
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I have collected 2 channels of EEG data with subjects looking at some visual stimulus. I have looked into a bunch of websites regarding EEG processing steps once we acquire the signal. But I have not found any detailed explanation. I want to know these things:-
1. What kind of output file do we obtain after collecting EEG data ? Is it just the value of signal voltages? Does the output depend on the type of devices being used ? For example in my experiment, I have only the voltages value of the 2 channels as the output.
2. I am using ERPLAB and trying to analyse the signals as per the tutorial. There is some information regarding event codes. In my experiment, the visual stimulus is simply an object moving from left to right on the screen. How do I add event codes for this ?
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You may want also use some millisecond accurate triggering ...https://www.braintrends.it/devices.html
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I am trying to estimate measurement time-window(TW) for the spatio-temporal ERP. Briefly, I have two series of the time windows for 20 subjects (i.e. first series are is estimated TWs and second series is actual TWs ). I want to know whether these two series of TWs are similar or not? (I am looking for the not significant difference which is my target)
Currently, the estimated series of TWs are close to the actual TWs (i.e. for example in start point (time sample for the start) the standard deviation error, SD= 2ms, max pair difference =4ms min pair difference=0ms). However, trying repeated ANOVA, T-test and also nonparametric tests such as; U-test methods indicates a significant difference between the two series. Does anyone know other tests to show those two series of results are similar or not?
Sampling rate=428Hz, epoche (-100 to 600ms)
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I am searching for a toolbox that can analyze changes in different physiological states using simultaneously recorded signals with trigger markers including EEG, Heart rate variations, galvanic skin response, and respiration rate.
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The FieldTrip toolbox has functions for heart rate and respiration, but I'm not sure about the others. I would look into it.
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General question, but which metrics or methods would be more of your interest for ERP analysis?
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Being interested in ERPs data, we may want to do Spatial analysis (topographical analysis), Spatio-temporal, Source localization and several other important properties for analysis.
Spatio-temporal ERP analysis is most popular in measuring peak or latency of amplitude for detecting difference within and between subjects statistical power analysis factors.
For EEG, usually spatio-temporal and spectrum power analysis on measurement frequency bins can be critical.
You may decide it based on your research question and hypothesis.
Cheers,
Reza
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Dear all,
my question is about the polarity of an ERP comportment:
Should the peak of an ERP have a positive amplitude and trough a negative value? let say, would it be acceptable to have N100 amplitude equal to 5 micro-volt and P200 equal to -2 micro-volt
For the ERP data that i am currently analyzing, i detect clear peak and through in waveform. But, about half of subjects show negative amplitude at peak or positive amplitude value at trough.
Your opinion is highly appreciated.
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The short answer is: peaks of positive components do not have to have positive values, and of negative components - negative. You're dealing with continuous signals, so earlier components "drive" the later ones in their amplitudes. For example, if your N2 is particularly large, it may cause the following positivity not to reach positive values, even though the values will indeed increase. Early components (N1, P1) often show the respective intuitive negative / positive values, because they usually directly follow baseline correction, but later ones depend a lot on what happens before them. What you care for is the relative change.
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Can somebody teach how to read peaks in ERP in order to observe peak amplitude and latency measurments in ERPLAB.
I am unable to understand why in this figure No positive peak(P100) was found in the latency range of [75 150ms]?
I will be thankful to you if you can help me in learning or reading/identifying peaks in the prescribed range.
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Dear Ty Lees , Mikkel C Vinding , Alan J Lincoln thank you so much for sparing your important time and answering this question. What i have found that by default the ERPLAB takes measurement at 50% of the peak onset and due to which the peak could not be identified. So i changed the setting from 50% to 100% and now the peak could be clearly found and latency value can be obtained. Doing so i have noticed, now it identifies the peak clearly which was earlier not identified. kindly correct me if have done it wrong. Thank you so much .
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I am using a 24-channel system with 19 active electrodes to record the brain activity during flanker task. I run ICA to remove ocular artifacts. Although ICA can identify 1 or 2 eye-blink components, time course and ERP image show there still task-related brain signal in these eye-blink components. My question, is there any tips that can help to keep the real brain signal?
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I agree with Franklin Lue. ICA "takes the signal apart" and identifies components (also artifacts embedded in data) that are independent of each other, but of course different parameters of ICA may result in variations in your data. Good news is that blinks and saccades are way stronger than "the real brain signal", so it's easier to identify them. They also influence more the frontal electrodes than the central, parietal or occipital ones, so if you're interested in components in the latter locations, it's not such a big deal. I wouldn't worry about data loss caused by rejecting [artifact] channels with ICA, it's a risk the field takes, but if you're not convinced (and of course there are valid arguments for this scepticism), dig into the literature to understand the algorithm better and make an informed decision about how you want to treat your data. A brief search led me to this article, maybe you'd find it useful:
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Hi,
I will conduct an EEG study and I have the choice between a 32 or 64 channel EEG system. Which system would you recommand for event-related potentials (ERPs)?
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Dear Véronique,
the optimal number of channels in an EEG study depends on multiple factors, the most important ones are:
1. Research question(s) and type(s) of data analysis: If you are planning to run a simple ERP study on relatively well-known ERP components (MMN, P300, N400, etc.) and are not expecting to do fine-grained analyses of scalp topography (for which ERPs/EEG would not be the best method anyway), then 32 or even fewer electrodes may be perfectly sufficient. Often researchers use 64 or even 128 electrodes and then have no idea of how to analyze the data, so they group them in 6-15 'regions of interest' (ROIs), which ultimately gives you similar data as if they had run the study with 6 or 15 electrodes. Moreover, they may lose some channels during the experiment and will have to exclude or replace the data (e.g., by data interpolation). Preparing fewer electrodes is much easier and makes more sense (see point #2). However, if you want to do complex data analyses including source localization, more electrodes are generally an advantage. If data preprocessing involves artefact correction (e.g., eyeblinks) based on ICA, then you should not use less then 32 electrodes, and 64 would be better.
2. Preparation time (injecting gel to establish contact and lowering the impedance at each electrode): preparing and monitoring 64 channels takes much more time (30-60 minutes, depending on skills, scalp condition, and EEG/cap system), so some populations (kids, older people, patients) are not unlikely to get impatient and a little frustrated, which will affect their co-operation during the experiment and thus the EEG (and performance) data.
3. Comparability to similar previous research: Again, depending on your specific research question and approach, there may be certain standards in your field that you might want to respect to maximize comparability between your data and previous studies. A good idea might be to carefully read those papers (especially recent ones, as standards tend to change) and to discuss their specific methods - and their relevance to the findings - with your supervisor or collaborators. Ask yourself how you want do analyze the data (both in terms of processing steps and in terms of statistical models, including scalp topography, e.g., with factors hemisphere, laterality within each hemisphere, and position along the anterior-posterior axis).
A last comment: in addition to the number of EEG channels (and potential extra channels such as EOG etc), you might also want to think about the choice of reference electrode(s) and sampling rate. The average of left and right mastoid electrodes is a frequent choice for references, but you should use only one of them as reference online (and calculate the average off-line), i.e., do NOT connect the mastoid electrodes during the recording session. In some research areas, the nose tip is the best reference (e.g., for MMN studies, to distinguish between MMN and N2b components), while others use the average of all electrodes as an offline reference. The respective choice can have major impact on your data, so check out previous studies in your field. For example, with mastoid references you might see an N400 all over the scalp, whereas with an average reference you will always see a local N400 and a compensatory positivity on the scalp (because here the average of all electrodes has to be zero by definition). As to sampling rates, 250 - 500 Hz are often sufficient. Good intro books to EEG/ERPs are the ones by Steve Luck and by Todd Handy, also regarding filter selection etc. During the recording session, it's generally good to filter as little as possible (it's better to filter off-line).
I hope this helps - and wish you good luck with your study !!!
Cheers, Karsten
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Osgood et. al's SD method has been used extensively over 50 years.
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What direction are you looking for development? linguistics or technology?
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This chapter tells us how the ERP software works in relation to the basic business processes and functions and how it has been derived from traditional functions and processes that consumed a lot of time and the work was done manually and how it has changed from being a risky/ineffective mode of running a business to what it is today.
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Material master is considered the core functionality for any ERP system used in distribution or manufacturing type functions. The integration of all material data in a single materials database eliminates the problem of data redundancy. This permits the data to be used by various departments such as: => Accounting => Materials Planning and Control => Purchasing
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The evolution of ERP can be traced back to the era of 1960's when most of the software included inventory control features. Later on, in 1970's MRP (Material Requirement Planning) was added and further features such as Sales Planning, Cost Order Processing and Rough Cut Capacity Planning (also known as Closed-Loop MRP) were included. To further improve the software, the departments started to integrate and with new financial accounting systems in 1980's, MRPII was introduced. The new software could forecast the requirements for material and capacity planning and convert the information into financial requirements. It was in 1990 that all different Units of an organization integrated into one software such as supply chain accounts finance human resource etc. And the era of modern ERP system started.
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I have a dataset of an EEG recording referring to a task characterized by several "rest periods", during which the recording is not interrupted. For this reason, I was thinking that removing all these periods by epoching the recording and time-locking it to my ERPs of interest would facilitate the ICA by isolating only the artifacts occurring during my time-window of interest. However, I was warned to be careful with this operation since it can cause artifacts occurring along different epochs to overlap with each other. Any suggestion about the feasibility of this approach would be greatly appreciated. Thanks
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You can run ICA before or after epoching. But ICA is known to work best with more data. Your continuous raw data gives more information for the ICA to work on to get you more stable components (ICs). Your epoched data has already lost some information due to pre-processing (windowing etc). So, ICA can only work on so much. Always remember “garbage in garbage out” principle.
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What are the best features to classify schizophrenic vs normal using ERP dataset?
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... should be spelled "peak" and not "peek" of course ...
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I have processed EEG data with EEGLAB and ERP data with ERPLAB. Now I would like to estimate the source of the following ERPs: P1, N170/VPP, EPN, and LPP. After a lot of reading I've narrowed it down to source estimation techniques available in Cartool and Brainstorm. But I can't make up my mind. Which software do you recommend for ERP source estimation of these two? Where can I find a good tutorial? Brainstorm provides a nice tutorial, but couldn't find one for Cartool.
Thanks in advance!
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I'm not really familiar with Brainstorm, nor with Cartool, for source localization I use Fieldtrip toolbox. It's a Matlab-based toolbox, and it has very good documentation and hands-on tutorials as well.
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Hi all. I need some advice with creating eventlist  in ERPlab.
My study comprised 5 different type of blocks: 1. audio, 2. visual, 3. audiovisual-ordinary, 4. audiovisual-short, 5. audiovisual-long. Each of these blocks contained a standard stim with a fixed duration, and a deviant stim with six different duration (e.g. 10% less than standard; 20% - standard....). The presentation of the blocks were randomised.
The triggers were named 1 to 5 for the standard stim, and 21to 26 for the deviants. Also, in order to differentiate among the blocks, there were included markers (named from 31-60). So, for example:
- block 1: starts with marker 31 (which corresponds to deviant 21 for an audio block), and then the actual triggers: e.g. 1; 1; 1; 21; 1; 1;21....
- block 2: marker 37 (correspond deviant 21 for visual block), and then the triggers: 2; 21; 2; 2; 21; 2; 21....
So, I don't know how to create an eventlist that can identify/recode the deviant stim regarding the block they belong. If I create a simple eventlist, I'm unable to compare specific ERPs (e.g. differences between standard and deviant-21(duration 10%less than deviant) for the auditory block).  
Any ideas?
thanks in advance
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recode :-)
Angie
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I plan to experiment with ERP source localisation. However, some comments suggested that ERP source localisation is not reliable, so I am wondering what kind of situation is suitable for ERP source localisation.
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I would also agree with Riitta Hari's reply. There are also a number of papers specifically addressing the question of source localization error (e.g. Michel et al., 2004 Clinical Neurophysiology; Andino et al. 2005 Experimental Brain Research, and many others) as well as issues of data preprocessing prior to source estimation (e.g. Murray et al., 2008 Brain Topography; Tivadar and Murray 2019 Organizational Research Methods and many others). This is a non-trivial issue indeed. However, I would not forcibly restrict source reconstructions to fMRI activations, as there are a number of strong assumptions behind such a practice (though there are also situations where such assumptions may be warranted). I would also not personally advise splitting your electrode montage in half as source estimations can be strongly influenced by the number and distribution of electrodes. Again, these and many other issues are nicely covered in Riitta Hari's and Aina Puce's primer as well as in many review articles too (e.g. Michel and Murray 2012 Neuroimage).
Happy to continue discussing these and related points. Good luck!
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My experiment is regarding showing some visual stimulus to people while recording the EEG. The stimulus is of 3 types. One is facial recognition and other two is a simple one: Showing an object moving around. I want to extract the different ERP components from this experiment. I have BioRadio EEG device. It can record the voltages value.
I understand that ERP data requires performing an experiment by averaging it 30-50 times. How is this averaging done ? How do I extract the ERP components from the averaged value ? Please any help would be very helpful.
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Definitely read up on how it's done and what it even means to find a certain ERP. It's not so straight-forward and no software would show you a P3, or any other component, as such. Each component is identified based on its polarity (positive, negative), topography (where on the skull is the activation (of a certain polarity) detected), and latency (when (after the stimulus onset) is the activation visible). Identifying a component requires some experience in the first place. It also requires the researcher to be very much informed on what kind of ERPs they can expect in a certain experiment. P3, for example, can be confused with LPP. N200 may become N170 when presenting faces.
Make sure you inform yourself in the literature before you claim a certain wave is an ERP. Luck is definitely a brilliant source for getting well-informed.
Good luck! :)
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Hi everyone! I need to plot ERPs from an EEGLAB study with the mean error displayed as a shade around the waveforms. I'd like to ask if anybody knows the specific syntax for doing this, or if there is an eeglab version with this function built in the GUI.
Thank you so much!
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Thanks to Joaquin Menendez generosity, I was able to find a code that works and share it with everyone.
%Gráfico de ERP con desvío estandar[STUDY erpdata erptime] = std_erpplot(STUDY,ALLEEG,'channels',{'P4' 'C3'}); % std_plotcurve(erptime, erpdata, 'plotconditions', 'together', 'plotstderr', 'on', 'figure', 'on');std_plotcurve(erptime, erpdata, 'plotconditions', 'together', 'plotstderr', 'on', 'figure', 'on');
I already tried it and it works beautifully.
Thanks again Joaquin!
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Busque un ejemplo de aplicación en el Ecuador.
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sirve para integrar los procedimientos de negocios asociados con las operaciones de producción, los aspectos de distribución y los aspectos administrativos de una empresa de bienes o servicios. Los ERP ayudan a crear procesos más eficientes con lo que las empresas se pueden concentrar más en otros esfuerzos, como es el servir a sus clientes y maximizar los beneficios.
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We have have Enterprise Resource Planning in our university. The ERP composed of several modules to automate different routines ranging from administrative to academic.
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Hello Mohammed,
This is a very broad question. Please review this material to start your journey: https://www.sap.com/products/iot-platform-cloud.html.
Sincerely,
Doug
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Business processes and technology should go with, agreeing to each other harmoniously. Businesses and companies will benefit to the maximum extent. Pl comment
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Not surprisingly, SAP is working hard on Blockchain solutions. The main question is what should be on the Blockchain and what should be done off-chain. Additionally, many companies have legacy systems with existing processes. I know one specific example in the public sector in which an existing process was supplemented by the Blockchain such that at specific points in time information was put on the Blockchain that proves that a certain process has been completed. This makes sense only if external partners are involved. If the ERP is only for internal use, I do not see a compelling Blockchain use case.
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Hello everybody,
I have a study about how auditory evoked potentials can discriminate between healthy and mild cognitive impairment older adults. I have done a repeated measures ANOVA and I have performed ROC analyses under those signicant different variables between the groups.
I would like to perform a cross-validation of my model to be able to obtain an estimation of the prediction error. I have been said that a k-fold cross validation could be a good method. However, I don't know how to proceed with this analysis, not even if it is possible to do this with an ANOVA and the ROC curves, or if I need to do a new type of anlayses such a regression or discriminant analyses. Morevover, I don't know what kind of software I can use (SPSS, Matlab, ....).
I hope somebody could help me with this. I would really appreciate any input here.
Thank you in advance.
Ana
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Hello again, and thank you all of you for your answers. Siraj Muhammed Pandhiani , do you know how to perform this k-fold cross-validation in SPSS? I have been trying but I didn't find a way.....
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We use a 128 electrode array, but I would also be curious about 64. I am less curious about lower density arrays since bad electrodes should be relatively rare. I am also guessing the type of analyses being run are significant factors (ERP, Time-Frequency, etc.). Thank you!
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Agreeing with Trey Avery it really does depend on several factors. We’ve used both 124 and 64 Channel EGI Hyrodrocel GSN 30 nets. when Testing we try to make sure that the area surrounding the center of the reference, and the point of reference, have impedances below 50 mV. However it’s my understanding that impedances to 100mV are acceptable and have resulted in good data during analysis.
Throughout testing we try to check the impedances during breaks. We re-wet electrodes with high impedance and ensure their all making contact with the scalp (or the face).
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According to ERP article from Wikipedia, it is mentioned that "To see the brain's response to a stimulus the experimenter must conduct many trials and average the results together, causing random brain activity to be averaged out and the relevant waveform to remain, called the ERP". Does it mean we need to show the visual stimulus to the subjects a number of times, create epochs of the individual trials and then the averaging ?
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Thank you everyone. This will help me a lot.
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I am a beginner in EEG research. I have ERP data (obtained by using visual stimulus and EEG electrodes). I want to evaluate these features but i do not know where to begin from ? Is there some algorithm or software that would help to extract these different ERP components ?
Really need some good suggestions. Thank you
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Other often used softwares for EEG preprocessing are EEGlab (https://sccn.ucsd.edu/~scott/ica.html) and Brain Vision Analyzer (BVA) (https://www.brainproducts.com/downloads.php?kid=9). The first one is free (as long as you have Matlab), but a bit less "friendly" in terms of its GUI. A great and easy tool is BVA, but it's unfortunately quite expensive.
What I normally follow as pre-processing steps are:
1) Reference - your online and offline reference may be different, and different options can be used in the process. Some are referring the signal to electrodes on mastoids, some to Cz, some to the average of all the electrodes.
2) Filters - you probably already used some filters during data collection. Often, online filters are less restrict than what you may want to apply in the offline pre-processing. Consider something like 0.1 Hz high-pass, and 40 Hz low-pass filters.
3) Segmentation (or epoching, depending on software) - "cutting" the signal to pieces depending on your time-windows of interest and triggers (or markers, again - a choice of terminology). For example, if your trial consists of a visual stimulus presented for 1000ms and followed by 500ms ITI, you may want to "cut out" segments of -100ms to 1000ms from the onset of the stimulus (marked by a trigger).
4) Baseline correction - the "-100ms" from the previous point will serve now as baseline for the segments. Of course, you can consider a different time window for your baseline. 100 ms is quite standard, though.
5) Blink correction - this can be done in many ways. ICA (Independent Component Analysis) is often used for this purpose. There are plenty of sources online about how it works, and many softwares have implemented algorithms for using ICA on your data.
6) Artifact rejection - blink correction is already a part of artifact rejection, but there's more to this category. You may want to go through your signal and remove all the other artifacts (like movements, floating electrodes, sweat, etc).
7) Averaging - you may want to average the signal for each participant across conditions. This reduces noise. Or you may want to get into single trial EEG (again, many sources online).
8) Choosing ROIs - if you're planning to choose a Region of Interest, you can decide on a few electrodes that would serve as such. Again, there are different ways of how to make these decisions. Often people look at the topographies of their signal and compare that to the previous literature. Deciding on your ROI, you can average the signal from these electrodes and you end up with a datapoint per condition per participant.
It's a long way with many decisions to make, so reward yourself in the end of the process ;)
Good luck!
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My experiment includes two groups and two conditions, and I would like to compare between groups and conditions. To do so, I created a study design, uploaded the data sets and could make a comparison between groups and conditions at the amplitude level. My question is, can EEGLAB calculates the significant differences with ERP latency?
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You can give a try a Fieldtrip. As far as I remember it has the statistical analysis that you are looking for.
Also you can give a look to BESA Statistics (not free, but you can have fully working trial version for 1 month)
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Dear Colleagues,
Are cognitive control and cognitive effort the same thing?
I am confused. In the neuroscientific literature on second language learning, the neural organization of cognitive control and cognitive effort seems to overlap (e.g., the anterior cingulate, dorsolateral prefrontal cortex, inferior parietal regions). At the same time, I have come across some results that make me think that cognitive control and cognitive effort are not the same thing.
For instance, I have found fMRI studies that reported increased "cognitive effort" (and therefore more widespread fMRI activation) in less proficient speakers of a second language (e.g., Abutalebi et al. 2018). At the same time, several ERP studies have suggested more "cognitive/language control" in highly proficient second language users. For example, Fernandez et al. (2013) have shown that higher second language proficiency was associated with a greater mean N2 amplitude (greater inhibition) on an executive function test. Another example: Rossi et al (2018) concluded that individuals with high second language proficiency require more cognitive/language control for their first language, even before they speak their second language.
I will greatly appreciate your help.
With my best wishes,
Monika
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Hello,
I have a question regarding synchronization of artifact rejection in eeglab. I use the version: "eeglab 14_1_1b" (MATLAB plugin)
I work with epoched data. To find artifacts like blinks and eye movements/saccades I have used the built-in ICA function and followed the recommended guidelines from Chaumon et al. 2015 to pick out components. Furthermore I have used the "simple voltage treshold" til sort out any voltage below -75 microvolt and voltage above 75 microvolt.
My issue arises when I want to synchronize the artifact info in EEG and EVENTLIST. This is a must do step in order for me to compute the average ERPs. eeglab won't let me do this, and it brings me this exact message when i try to synchronize:
"It looks like you have deleted some epochs from your dataset. At the current version of ERPLAB, artifact info synchronization cannot be performed in this case. So, artifact info synchronization will be skipped, and the corrosponding averaged ERP will rely on the information at EEG.event only. Do you want to continue anyway
(yes, no)." I need to find a solution, so that every rejected trails and removed components will be removed from the dataset when averaging the ERPs.
I have not been able to find any version of eeglab which seems to solve the problem.
Any suggestions?
Best regards
Morten
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You're welcome Morten. Also, it seems from what you are saying that you segmented your data into epochs before you ran the ICA (and thus deleted the epochs containing bad data from your dataset before running the ICA).
My personal advice to you would be to clean your data and run the ICA, then segment it into epochs after you've removed the bad ICA components. This ensures that you get enough blinks/other artifacts so that the ICA can train on them, and increases the chance that you'll get a good ICA decomposition (this method is also recommended by the creators of EEGLAB). Using this method will also avoid the ERP/EEGLAB synchronization issues that you are having, as if you add a binlist after removing data then ERPLAB will only read the events left in the file.
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Hi everyone! I'm preparing an ERP study of written sentence processing in school aged children. My target population are children between 8 and 11 years old, and I'm aiming to examine N400 and P600 effects after semantic and syntax violations. Sentences will be displayed on the center of a computer screen, word by word (rapid serial visual presentation). I wanted to ask your opinion about what would be the optimal SOA (stimulus onset asynchrony) to maximize the probability of actually seeing the language-related ERP effects, as it has been shown that presentation rate may affect the magnitude of these potentials, at least in certain populations (like L2 and older adults).
Thanks for your kind attention!
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Hi Angel Tabullo --really neat study design! In our study of typically developing children this same age and slightly older, we had them "read sets of three sentences presented word-by-word with a target word, either real or novel, as the last word in each sentence. Sentences were presented word-by-word with each word appearing for 500 ms and a blank screen between words appearing for 300 ms. The blank screen directly preceding the target word was presented for 600 ms to establish a baseline for analysis of the novel word. The target word was presented for 600 ms" (Abel, Schneider & Maguire, 2018). I would also recommend Hagoort, 2003 "Interplay between Syntax and Semantics during Sentence Comprehension: ERP Effects of Combining Syntactic and Semantic Violations" for guidelines in study design (i.e. start-up and wrap-up effects) that I have found most useful for my own studies of semantic and syntactic processing in 8-12 year olds. Look forward to reading more of your work in the future!
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Dear all,
It seems my data are suffering from alpha distortion. It is possible to reduct it from some data with the help of ICA but for half of the participant ICA does not work that well.
So for now I tried to reject more components than usual (15-18 out of 64 ICs) and run the ICA again if it can catch the alpha. But it doesn't work.
In a nutshell, I wonder what are the best ways to reduce the alpha disturtion from data
Note: I also heart that PARAFAC seems to work better then ICA for reduction of Alpha but I cannot find a way to apply PARAFAC with EEGLAB GUI
Best,
Behcet
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I do not think it is a good idea to purposely delete ICs from your data that you believe are present due to brain activity. In most cases it is not a defensible assumption that this activity is 100% not relevant to what you are measuring, especially if it is consistent enough to occur during an ERP average. Also, it is not a good idea to re-run an ICA after you have removed ICs because you have reduced the number of dimensions in the data. In this case, you would have to run a PCA and enter the number of components that were left in the original dataset to obtain a valid decomposition.
I am not sure of your experimental design, but one of the best ways to reduce the amount of alpha activity in an ERP average is to use a baseline correction that is a multiple of 100ms. Because the dominant frequency of alpha oscillations is 10Hz, this value will ensure that there are an equal number of alpha oscillations in the baseline which will normally remove most of the alpha waves. If this does not work, you may be able to isolate the alpha waves in an ERP average by using Joe Dien's PCA toolkit to run a temporo-spatial PCA, which you can read your final EEGLAB/ERPLAB files into.
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- Typical maximum radiated power of each type of antennas is how ?
- Does it linearity with the power input of antennas ?
Let's say:
Effective Radiated Power (ERP)
ERP (dBm) = Power of transmitter (dBm) – loss in transmission line (dB) + antenna gain in dBd
For example, I search on internet someone said that:
–With a ceramic substrate, good heat resistant bonding of the copper layer, a low loss and a high impedance - it should survive way more than 70W for microstrip type antenna.
So, i need of a list of antenna types and its' typical maximum radiated power it can handle.
Thanks for any comments or advices
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And, one more question: At which level of microwave signal is harmful to human body ?
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Hello everyone,
I wonder how many ICA component do you generally eleminate aproximately? Do you think eleminating 25 out of 64 component sounds reasonable.
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Dear Behçet,
I think giving general recommendations for how many components to reject is not possible. This is foremost due to differing levels of noise –if noise is very low and stereotypical, few components might capture it sufficiently. If noise levels are high and / or if artifactual sources are diverse, most components likely include some noise.
Reducing noise before ICA is most crucial. As Marius already mentioned, data should be carefully inspected in order to remove non-stereotypical and large amplitude noise. If the gamma band is of interest, separating data into a low (e.g. <30 Hz) and a high band (e.g. > 30 Hz) can also be helpful.
In my opinion, however, one decision has to be made a priori. That is, how conservative or liberal do I want to handle the decision for rejection? The conservative perspective would be to reject components only if there is a very clear signature of noise (e.g. heart beat, eye movement etc.). After all, every component represents a mixture of signal and noise. The liberal perspective would keep components only if noise is apparently absent. While both approaches are legitimate, it is very important to be consistent across participants.
Cheers,
Jonas
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We know that:
We also know that:
However, I cannot find results suggesting that whilst holding and using a tool (e.g., a screwdriver) the action recipient (the object on which the tool is meant to be used; e.g., a screw) will be identified more easily than another object on which the tool isn't of any use (e.g., a pencil). Furthermore, I wonder whether visual features of the action recipient (e.g., its color) could be identified faster whilst holding and using the tool as compared to if that feature was on another object.
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In my opinion, yes.
But I think the subject's motor intention for which using the tool could be more important than holding the tool itself, or at least you have to control that variable someway. In our experiment, participants were less precise in identifying graspable objects as hand-related tools after having learnt to grasp with foot-pressing (by pressing a pedal in a virtual reality simulation); simply pressing the pedal not-to-grasp did not show the same effect.
By applying this to your scenario: I would hypothesize that participants would be able to identify more easily the screw if they are holding the screwdriver to actually use it to screw; if you prime them to using the screwdriver to stab something, or to point at something, or other, you will not find the same effect. ...conversely, if they are holding an object which is not a screwdriver but you prime them to use it to screw, you will find the facilitating effect.
In other words: it's not the tool per se which is important, but the motor intention you're representing about it prior to usage.
I would apply this hypothesis to your second question too: it depends whether the visual features are relevant to the intention, or not (e.g.: the same object can have multiple affordances, depending on how you want to use it).
Best!
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Recent updates in ERP Software's
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Could it be that in your question, ERP refers to Enterprise Resource Planning, not Event-Related Potential as Stephen assumes?
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I want to calculate the difference between sources in two conditions.
but which procedure is correct?
1- calculating the difference of sources in each subject and then grand averaging
2- calculating the sources of each condition from grand average ERPs and then subtracting one from the other.
Best
Shadi
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Hi Shadi,
are you planning to use individual head models for source estimation? In that case I think only option 1 is preferable as the you will receive individual source differences. Interpolating then across the subjects also gives you the opportunity to estimate a standard error for the grand-source difference.
I hope I got it right and the answer is helpful.
Best,
Aljoscha
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Dear all
After we acquisition EEG signal, How can ensure that the Data comply with relevant quality standards? and is reliable? And similar to it when we process a signal how can ensure that we correctly and completely do it? And on the other hand, didn't  we remove  Intrinsic property of data?
Also, After they record EEG/fMRI simultaneously, How perform QC?
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Hi Mohammad, I can only help with the first question (EEG QC). The main concern in that regard is the signal-noise ratio. With EEG recordings you will have various types of noise artifacts (e.g. blinks, muscle, sweat, etc). All of those are things that you should clean from your signal before making any averaging. This involves many steps, that usually are previously prepared as a pre-processing pipeline. For instance: 1. High-pass filter (and Low pass if your study only involves ERPs. You can do this afterwards as well though). 2. Eliminate the line noise (i.e. 50 or 60 Hz notch filter or Cleanline plugin in EEGLab). 3. Visual inspection of crazy artifacts in the entire continuous EEG recording (not considering here the blink artifacts) for rejection. 4. Identify Bad channels. 5. Run an Independent component analysis (ICA) to identify blinks or some other artifacts (not including the bad channels eventually detected). 6. Interpolate bad channels (a better choice if you have EEG system of 64 or more channels). To Run ICA and take good decisions about the components identified require some training. There is a good resource here: https://labeling.ucsd.edu/tutorial And also some useful material: http://esciedu.nctu.edu.tw/eeglab_workshop/ch/doc/2_ArtRej_RunningICA.pdf Please note that this is a short example of a preprocessing pipeline written as a very brief summary. All of these things should be made considering first your recording conditions (reference, number of channels, if you have EOG recorded, etc.). A very handy book about this subject, that I would absolutely recommend to you: https://us.sagepub.com/en-us/nam/eeg-methods-for-the-psychological-sciences/book238043 Chapter 4 of that book (Getting Started with Data Analysis: Data Pre-Processing) addresses extensively your query.
Best,
Boris Lucero
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Is there an actual proof that visual-spatial cues enhance early or late visual processing (as compared to uncued visual processing)?
Without saying that what is implied by this question is "true", we know that when it comes to response times (RT), peripheral (or exogenous) and central (or endogenous) cues will have a different impact (e.g., Doallo et al., 2004). However, I struggle to find any event-related potentials (ERP) study that demonstrates an enhancement of perceptual processes following a cue (preferably peripheral) when compared to "self-generated", or spontaneous, gazes (i.e., overt spatial attention).
For instance, say that you have to look out for forest fires all day long. You will probably end up doing something else to fight boredom, and hence end up looking for possible smoke from time to time.
Now the question is: Will you be able to report(RT) a smoke faster if you are spatially cued because the cue allowed you to perceive(ERP) it faster?
To summarize:
Endogenous Cue – Spontaneous = ?
Exogenous Cue – Endogenous Cue = ?
Exogenous Cue – Spontaneous = ?
Reference
Doallo, S., Lorenzo-Lopez, L., Vizoso, C., Holguı́n, S. R., Amenedo, E., Bara, S., & Cadaveira, F. (2004). The time course of the effects of central and peripheral cues on visual processing: an event-related potentials study. Clinical Neurophysiology, 115(1), 199-210.
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This is a great question because spatial cueing research is mainly about variations on a paradigm and it's important to stop and think again about what it all means. So, there is a big literature on ERP effects of spatial cueing, beginning (according to a quick search) with Eimer (1993). Many of these studies would however involve "self generated gazes" - or self-directed attention. For example, Nobre et al. (2000) performed an experiment in which the same (bicoloured, central) stimulus had two possible interpretations (i.e. target is probably right or probably left), according to prior instructions, and got early negative ERP enhancement contralateral to the cued hemifield.
However if we limit the question to exogenous cueing, a recent review by Slotnick (2017) concludes that early ERP effects in visual cortex (C1 component) are more likely to be observed for exogenous than endogenous cues, in upper visual fields, with distractors and with high attention load.
Presumably gamma enhancement and reaction time effects occur later than C1
Eimer, M. (1993) Spatial cueing, sensory gating and selective response preparation: an ERP study on visuo-spatial orienting Electroencephalography and Clinical Neurophysiology/ Evoked Potentials, 88 (5), pp. 408-420.
Nobre, A.C., Sebestyen, G.N., Miniussi, C. (2000) The dynamics of shifting visuospatial attention revealed by event-related potentials Neuropsychologia, 38 (7), pp. 964-974.
Scott D. Slotnick (2017) The experimental parameters that affect attentional modulation of the ERP C1 component, Cognitive Neuroscience, 9:1-2, 53-62, DOI: 10.1080/17588928.2017.1369021
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child psychiatry
neuroscience
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Many Thanks, It is great website
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The complexity and dynamic nature of current Information Communications Infrastructures ICT, Business Information Systems and large Enterprise Resource Planning ERP is often cause of potential Cybersecurity vulnerabilities and block chain weaknesses.
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The question may be "How can we ensure cyber Safety in today's global environment".
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Hi there!
I am doing ERP/EEG study with signal processing methods. My motivation is applying some machine learning methods, namely clustering, classification, and etc. on cognitive neurosciences experiments. I need to apply my methods on some ERP/EEG experiment data.
Can anyone suggest me how and where can I find EEG/ERP data to validate my methods? I need some free data sources to download and cite those if any acceptable results could be obtained. Since I have no access to EEG recording equipment to do experiment.
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Hi Reza,
You can find various data sets on this site.
There must be other similar repositories in other universities.
Hope this helps.
Joan