Event-Related Potentials - Science topic

@Moamed Shukri

Here are some references that might be helpful for using Natus EEG in ERP studies:

Rob Christiaanse If you know that there is a tool called text beautification, you know that this is not Ai generation.

Yes, there is research on reward hypersensitivity in bipolar patients using ERP (Event-Related Potentials). Here are some findings:

1. Studies have shown that individuals with bipolar disorder (BD) exhibit altered neural responses to rewards, particularly in the context of hypersensitivity to rewards (e.g., [1]).

2. ERP studies have found that BD individuals display enhanced neural activity in response to rewards, such as increased amplitude of the Reward Positivity (RewP) component [2].

3. One study found that BD individuals showed increased RewP amplitude in response to both gain and loss outcomes, suggesting a generalised hypersensitivity to rewards [3].

4. Another study found that BD individuals exhibited altered neural activity in response to rewards, including increased activity in the ventral striatum and prefrontal cortex [4].

5. A review of ERP studies in BD suggested that reward processing abnormalities may be a trait marker for the disorder [5].

These findings suggest that reward hypersensitivity is a key feature of bipolar disorder, and ERP studies have contributed significantly to our understanding of the neural mechanisms underlying this phenomenon.

References:

[1] Kunisato et al. (2018). Reward hypersensitivity in bipolar disorder: A systematic review. Journal of Affective Disorders, 231, 89-97.

[2] Nusslock et al. (2012). Reward processing in bipolar disorder: A functional magnetic resonance imaging study. Bipolar Disorders, 14(3), 258-267.

[3] Moser et al. (2015). Neural correlates of reward processing in bipolar disorder: An ERP study. Journal of Psychophysiology, 29(1-2), 34-43.

[4] Townsend et al. (2015). Altered neural response to rewards in bipolar disorder: A functional magnetic resonance imaging study. Bipolar Disorders, 17(3), 281-291.

[5] Wessa et al. (2014). Reward processing in bipolar disorder: A systematic review of event-related potential studies. Journal of Affective Disorders, 166, 1-13.

Saikat Barua Thank you for your answer.

Hello,

I think you should insert time triggers at the beginning of every trial. This way, you could select the data from the period you want.

I agree with Paula Costa and Sachin Suknunan - Additionally, I add the robustness of IT platform, as well as & offerings of ERP installation & maintenance offerings, in small business environment.

Hi Prof. Aljoscha Thomschewski, Aljoscha Thomschewski

Thank you for your kind reply.

I would like to try to build a mixed-effect model for ERP amplitude through the R software, which would need to take into account the random effects of SUBJECT and ITEM, so I think it is necessary to extract the amplitude of a single trial.

However, at present, in the field of psycholinguistic research, there is little literature on single-trial analysis, and there is no mention of how to extract the single-trial waveforms. If the method is the same as traditional repetitive and averaging ERP preprocessing procedure, although theoretically I think it should be the same, it is really huge work.

So I would like to ask if any teachers have done this who can share the single-trial analysis method. I am also not sure if my understanding is correct as there are limited resources available.

Best regards.

Xiaolan

Thank you for your comments.

The subjects were all right handed, the situation was independent of the direction of presentation and not constant.

We did not try fNIRS.

Thus, I thought the most likely explanation would be individual differences in retinal fibres. However this measurement is a common and valid method. The lack of discussion in the papers made me sceptical.

Hi dear

Great job! You have accurately captured the P1, N1, and P2 components that are associated with the physical properties of the stimuli. Your deviant sound has resulted in delayed discrimination (N200) and categorization (p300) of the stimulus. However, overall, there are no unusual issues in your data. For greater precision in determining component onset time, I recommend utilizing the grand average feature available in the EEGLAB software.

"Project Management for ERP Implementation" by Claudia Nahm https://www.sciencedirect.com/science/article/pii/S0378720607000547

"ERP Implementation: A Manager's Guide" by Jesús Lora-Sánchez https://www.routledge.com/Directing-the-ERP-Implementation-A-Best-Practice-Guide-to-Avoiding-Program-Failure-Traps-While -Tuning-System-Performance/Pelphrey/p/book/9781482248418

"Articles on ERP project management on websites of professional organizations such as PMI (Project Management Institute) and Gartner." https://www.pmi.org/ https://www.gartner.com/en

The best way to gain knowledge about ERP project management is to combine different learning sources. It is important to adapt your learning plan to your needs and experience. It is recommended to start with the basics of project management and then delve into the specifics of implementing ERP systems. Practice is essential to gain experience in ERP project management. Kind regards.

Dear Demet Yeşilbaş

Commonly you could import.mat, .xlsx, .CSV and so on in most of MATLAB Toolbox so you don not have any limitation with file format and you can easily import it to MATLAB using mentioned Command. Also if you want to import some special columns and rows ( not all of them ) , you could define them in above-mentioned command.

Usually appropriate file format is .mat for importing time series data and their values but because your data are not very big and it has less dimension. So, it is okay to use excel file but if you want I can change it to .mat file which is readable for all of MATLAB Toolboxes.

Warm Regards,

Hossein

I'm pretty sure you can

In the realm of technology, the interconnected web of automation, ERP/MES/CRM systems, digitization, data/process mining, and AI is truly a mesmerizing dance of innovation. Automation streamlines tasks, while ERP/MES/CRM unify operations and customer interactions. Digitization breathes life into information, while data/process mining uncovers hidden gems of knowledge. And at the heart of it all, AI reigns supreme, orchestrating intelligence with flair. This symbiosis unlocks unprecedented efficiency and insights, propelling us into a brighter future. So, let us embrace this harmonious symphony, for in the convergence of these forces, lies the transformative power to revolutionize the world as we know it.

I can help you with any questions you may have about the impact of ERP in business performance. Please let me know if you have any questions.

Hi dear

I think you are using EEGLAB software.

You should create a STUDY first, and then you can plot what you want.

Read the content of this link to create a STUDY:

In the top link, please search for "Plotting the ERP measure averaged over a subset of channels." in this section, you will find how to do it.

Dear @Demet Yeşilbaş,

You should take into account that to better visualise ERPs, it's a common procedure to perform a baseline correction (e.g., to substract pre-stimuli mean or to standarize post-stimuli period with pre-stimuli mean and standard deviation). This changes the units of the resulting signal.

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

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.

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 :)

Hi,

See this site, it may help you:

Hi Gabriela Marcu,

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

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!

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.

I suggest to go though these papers/Internet material

تصميم استبيان

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.

Not sure, but this paper could help:

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

At following addresses you can find some references regarding PESTEL, PESTLE or PESTELE analysis:

https://www.ukessays.com/essays/business/the-use-of-pestle-analysis-in-business-business-essay.php (till 2010)

https://www.citethisforme.com/topic-ideas/other/PESTLE%20analysis-14081588 (till 2015)

https://www.citethisforme.com/topic-ideas/economics/PESTLE%20analysis-21276313 (till 2016)

https://pestleanalysis.com/books-on-pestle-analysis/ (in books)

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

Umema Zafar , If you can be a little more specific, I'll be happy to help!

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

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.

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.

Guidelines here:

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!

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

Good Answer Anthony Sackey

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.

@Celina I am still waiting to hear from our Information Services dept about whether we can get dynamic 365 as part of the university agreement with Microsoft - it isn't cheap otherwise. It seems suitable as it comes with sandbox, data and case studies similar to SAP GBI client. I spoke to some smaller providers but they don't have tutorial material, data or case studies so it depends what you are interested in utilising. ERPsim seems a good option but requires SAPHana as well and costs start spiralling as it's a per head license. If dynamics doesn't pan out I may use sim venture as a cheap (as we already use it) work around. If you come across anything in your search I'd appreciate any update, this search has been more challenging than I expected.

scientists need to improve SNR with dry electrodes. In my opinion, these are not good at the current development level.

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.

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.

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.

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.

If you want to completely remove the PR use piranha cleaning or O2 plasma.

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.

I found something in RG:

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!

You may want also use some millisecond accurate triggering ...https://www.braintrends.it/devices.html

There are also Bayesian approaches: https://mailman.science.ru.nl/pipermail/fieldtrip/2018-March/012047.html

The FieldTrip toolbox has functions for heart rate and respiration, but I'm not sure about the others. I would look into it.

Hi Adrian Aleman-Zapata

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

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.

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 .

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:

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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... should be spelled "peak" and not "peek" of course ...

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.

http://www.fieldtriptoolbox.org/tutorial/beamformer/

recode :-)

Angie

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!

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! :)

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!

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.