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Hi,
I am looking for a good EEG amplifier (preferably with more than 16 channels) for general neuroscience and BCI research with a budget of 10000 USD. After some research, Mitsar EEGs seem to be suitable for my budget. However I am having a hard time finding people using Mitsar EEGs for neuroscience and BCI research - majority of the papers I have found were using Mitsar EEGs for qEEG and neurofeedback which is not an area I am interested in.
Are there many universities which use Mitsar's EEGs for neuroscience and BCI research? Would you recommend a Mitsar EEG amplifier or are there better alternatives for that price?
Thanks in advance.
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Our Mitsar-EEG amplifiers are compatible with a few EEG caps. Mitsar-EEG-BT and Mitsar-EEG-202-24 are equipped with D-sub DB25 connector that is used in most EEG caps on the market.
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I am trying to use new emotiv epoch+ headset which was bought in 2018. I am having half of the electrode as green Like this picture. But my question is why the over all contact quality is 0% (written in Red color).
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Hi
Chi-Kuang Sun
, ensure the electrode id okay, I mean no rust in the gold plate. Then soak the foam properly put 4-8 drops of saline. And must ensure you have proper connection of CMS and DRL. Thanks
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What is the protocol/method to identify or classify different emotions from a EEG data set using EEGLAB?
Is there any other software or any plugins for classifying Emotions?
Thank you
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Please share your knowledge on using Nihon Kohden system for EEG analysis. 
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can analyze the data on matlab in real time ?? and what is the tool used to open the data coming on usb port?? do any one have the usb port drive of the junction box for windows 64??
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Hi everyone,
This is just a 'out-of-curiosity' question, but why is the cerebellum used as the reference point? What is the reasoning? I was always told is it because it is 'silent' compared to the cortical regions, but obviously the cerebellum is also active. Is there any paper that explains the choice, or if a better reference region or method is available?
Thank you!
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Dear Haram,
I have been always heavily involved in this past discussions when we measured with telemetry EEG. The answer has been always that the cerebellum is obviously active but the neurons fires at very high frequencies whereas the most interesting frequencies for a "normal" EEG are much lower (0.5-100 Hz). That is why most likely the cerebellum is taken as reference and additionally if you have differential electrodes you get greater signal/noise ratios. Hope this helps.
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I am working EEG signal classification. I am using an EEG cap which is having standard 10-10 electrodes placement system. I am not able to find 3D location for the same. .elc file is available for 10-20 but not for 10-10. Has anyone worked with 10-10 electrodes placement system?
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Have you tried different coding snippets in EEGLAB or FieldTrip...?
1. In EEGLAB:
"EEGLAB will automatically read these channel labels. When you then call the channel editing window, the function will look up 10-10 channel locations in a database of 385 defined channel labels, the file “Standard-10-5-Cap385.sfp” in the “function/resources” sub-folder of the EEGLAB distribution. You may add additional standard channel locations to this file if you wish. As of 2021, the default channel location file for electrode position is the MNI file, which is best suited for source localization. Before 2021, it was the BESA spherical location file.
To load or edit channel location information contained in a dataset, select Edit → Channel locations. A dialog box (shown below) will appear, asking you if you want to use standard channel locations based on the imported electrode position labels (for example, ‘Fz’) from a channel locations file using an extended International 10-20 System."
2. In FieldTrip:
3. If I got your other question correctly, for .elc as Cartesian 3D electrode coordinates: EETrak software may help.
See this code:
and
Hope this helps.
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Thresholding is a common step in the preparation of a connectivity matrix for graph-theoretical analysis. This is done by applying an absolute or a proportional weight threshold, but this threshold is often arbitrarily determined. Most papers I found do use a threshold, but I saw at least a few that didn't.
  • Given a functional connectivity matrix based on EEG data (the measure used to compute connectivity is the phase-lag index (PLI), an index of phase synchronization), is thresholding an essential step? Or can graph-theoretical measures be computed also on the completely connected, weighted graph?
Some papers used a range of proportional thresholds (e.g. 10% -90% of the strongest connections, in steps of 2.5%), obtaining a graph for each threshold, then computed a graph-theoretical measure for each graph and then averaged these measures across the different graphs to get a single measure. Others, instead of a threshold, used a minimally spanning tree (MST) to retain the strongest connections.
  • If thresholding is the best practice, what is the best approach to do so? What are the pros and cons of each?
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Hi, Marta
It's very interesting topic about the threshold choice during the brain network analysis. I was also concerned this threshold problem before. But now I have read a lot of papers and got some inspirations, here I share with you!
(1) First, as Avraam answered, minimum spanning tree (MST) is an unbiased choice to avoid the threshold as the MST extract the backbone of the network and it generates the non-cycle graph with all nodes connected (also with total smallest edge distance). CJ Stam and his team have proved the MST is another alternative choice to avoid the threshold. Maybe you can search the google scholoar by using the key words "CJ Stam", "minimum spanning tree", "unbiased" or other related key words. I have also focused on the minimum spanning tree in EEG brain network and recent I am ready to submit a paper about this topic, if someday the paper was accepted and published, I can send it to your email. Rather than traditional MST, I have also given some new try in this paper.
(2) Second, maybe you can try the fully connected weighted network (rather than binary), that is no threshold used. Although fully connected network may not represent the true brain network connectivity state, however, it provides very robust classification features to distinguish the EEG resting and task states for both healthy subjects and patients. you can read my recent published paper " Changes in Brain Functional Network Connectivity in Adult Moyamoya Disease". In this paper, we adopted the fully connected weighed network rather than chose a fixed threshold, maybe you can try this way.
(3) Third, the another automatic threshold choice strategy is based on the statistical significance. I read this paper "A novel index of functional connectivity: phase lag index based on Wikcoxon signed rank test". Although it's a method related paper, it inspired me to consider using the statistical test to automatically avoid the threshold. You can download it and hope it be helpful to you.
If you still have any question about the brain network analysis, you can send your puzzle by email "gxzheng16@fudan.edu.cn". If I have spare time, and I will give your reply.
Gaoxing Zheng
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I want to plot average Headplot or topoplot for specific time range. Is there any way of doing doing that in eeglab. Here is a video of that. Is it possible in eeglab?
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I hope it will help you, follow the tutorial for your analysis with EEG LAB.
Creating a STUDY
This part of the tutorial will demonstrate how to create an EEGLAB STUDY and perform simple plotting. An EEGLAB STUDY (or study) contains descriptions of and links to data contained in many epoched or continuous datasets, for example, a set of datasets from a group of subjects in one or more conditions of the same task or performing different tasks in the same or different sessions. We use a STUDY to manage and process data recorded from multiple subjects, sessions, and/or conditions of an experimental study.
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I would be thankful if you could list the ones you know.
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Please check this website and you can find some EEG dataset
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When working with a single electrode EEG device, how should identified outliers (>100 μV) be handled in preprocessing before analysis? Should they simply be removed and nulled, interpolated, or something else?
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a single channel EEG is useless, you need the space/frequency distribution to get a usefull apraisal. To check awake/drowsy transition you need eye movements plus EEG data.
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I am interested in knowing:
1) Whether Individual Alpha Frequency (IAF) obtained for an individual varies from day to day, and also within a day (Obtained using eyes closed resting phase peak alpha power value). What I know is that it varies slowly with age.
2) How do we decide the lower and upper alpha bands based on IAF?
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Hi Ganne,
I want to assess, say mental effort, of the participants in doing a task or a group of tasks. From what I have read, that IAF (as measured by eyes closing peak alpha frequency) varies from person to person. I'm afraid if I choose fixed frequency bands, especially for alpha (and theta), my results will get affected.
Now, my concern is how do I choose the bands:
1) IAF divides alpha band into lower and upper alpha band, while lower and upper limits are defined as +- some value of IAF (as is usually reported in literature)?
2) IAF divides divides alpha band into lower and upper alpha band, while lower limit of alpha is defined as transition frequency (TF) as calculated by, say a minute of eyes closed and a minute of eyes open? From my understanding this TF varies a lot for an individual.
3) If IAF ( and also TF) varies a lot, what value of IAF ( and TF, if I follow number 2 strategy) should I consider? Just before the task eyes closed-eyes open and then perform the task?
Thanks,
Umair
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EEG (Electroencephalogram) is a technique for recording electrical activity of brain. Traditionally Ag/AgCl, Ag, Stainless Steel are used as the electrode material. Can copper be used for dry EEG electrodes ?
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I am afraid copper would start to electrochemically interact with the skin (not really dry - especially after cleaning it from fat to lower the impedance) very soon (minutes) which would change the impedance of your setup or even introduce a potential like a battery (skin pH<>0). Ag/AgCl in combination with a NaCl gel remains very stable during the period of EEG measurement.
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I am very interested in studying signal processing, but I don't know which book would best suit my needs.
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EEG made easy
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Sometimes, the nature of the tasks is such that, it doesn't allow us to take more than one trials e.g. we may be interested in way of response when a stimuli appears for the first time to a participant.
Are there any specific approaches that we need to follow in those circumstances? Further, is it appropriate to compare EEG for single trials across different participants.
Thanks.
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I quote below from the article that I posted in the Attachment.
"The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuroscience, and methods to analyse such time-varying networks in EEG/MEG data are required".
Please read the article to understand the solution to your question.
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Most of researchers are using fMRI for analyzing dynamic functional connectivity networks, I want to know if it is possible to use EEG as well and if so, what s the advantageous and disadvantageous of using EEG in comparison to fMRI, except temporal resolution of EEG
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Hi, EEG is already used for dynamic connectivity analysis and in my opinion it is much better tool for this (because of temporal resolution). The neuronal connectivity in EEG is also known for much longer than fMRI (however named differently, as coherence or DTF). There are few things that you have keep in mind to do this correctly:
1. You need high density EEG - at minimum 32 channels, recommended 64 and above
2. Very good data quality (i.e. high SNR).
3. There are a lot of papers suggesting that EEG connectivity analysis should be performed using source space and not sensor space. So first data has to be recomputed to source space.
I
think fieldtrip has some solution for EEG connectivity analysis if you need freeware tool. You might also check BESA Connectivity
PM me in case you need any more info about Besa Connectivity (I do not want to make advertisements here :)
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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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We found high synchronization on the EEG in creative people, which confirms your facts about the 3 zones of the brain
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You are scrupulously analyzing the problem of creativity. When you try to find similarities in activity on EEG and MRI, please do not forget about the differences in activity. Differences give rise to individuality.
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I'm Working on clustering task on Event-related potentials(ERP) data, I used many Clustering algorithms such as (K-means, Hierarchical, SOM, FCM and etc.)to do that. The problem is, except using "correlation" based similarity measurement the other measurements find different objects (different from the view of neurosciences) in same cluster or similar objects in different clusters, How to deal which this problem, any experience or suggestion?
(Enclosed sample is by using SOM for clustering)
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Dear Reza
We often calculate the coherence in their phase and wavelet-based methods to study dynamical interrelations between brains signals on rest-state EEG data. Bicoherence is a method to measure the related between two frequency on single channels or different channels. I am not sure whether bicoherence can be used to calculated ERP data. you can search some article to find that. By the way , we often use wavelet bicoherence to get a similarity matrix. the picture is a example of bicoherence matrix.
Hope a little help for you.
best
Zhou Tianyi
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My university lab has BIOPAC-MP35 student version, and I have to do a study on children. Unfortunately, EEG cap is not available and I will have to use single electrodes for my research. Moreover, I have to do neurofeedback on children, for which BIOPAC doesn't seem much useful. So I am thinking of using Emotiv Insight as it is cheap, affordable, portable, and has 5 electrodes (I am planning to purchase it).
So what would be the value of research, in terms of quality and possibilities for publication later? Is BIOPAC-MP35 better than Emotiv Insight?
Thanks in advance!!
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Hi,
I think that Emotiv along other "low cost" systems have good advantage for neurofeedback, especially because there many applications (hence documentation) that have been developed in throughout these years. Also, the availability of compatible environments make it suitable for a research tool. I would like to add some literature and Emotiv compatible development environments. I hope my answer was useful.
Emotiv based neurofeedback research examples:
Emotiv compatible development environments:
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Studies have been accumulated more and more evidence regarding the beneficial effects of EEG neurofeedback training in sport performance. However, little insights come up from the perspective of sport trainers, concerning how to apply EEG neurofeedback training with some efficient and effective protocols. I'm appreciated to get to know some practical experiences from the field.
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Dear Cheng, 
I was working with athletes from tennis players to sharpshooters.
First step for me is good anamnesis (what is a problem)
Then EEG. After that I'm looking correlations.
I don't like word "protocol". I'm trying to work individuallly.
Please, write my some more details in private message, I will take a look.
Best wishes,
Pawel
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We all know that the human eye can percept only pictures with duration higher than 13 miliseconds. I have an 8 milisecond flicker picture that repeats 12 times in a second.(I have 120 black frames in a second (so each lasts 8ms) and every 10 other of them is my stimuli frame, so I will get 12 of them in a second).
I know that the operational range of SSVEP is 3Hz to 75Hz; much like to have strong relations with that famouse 13 miliseconds(1sec/75=13 ms).
So my main question is if SSVEP can show some stimuli wich our eye cant percept it?
In my specific case I have 12hz stimuli(in ssvep operational range)  with 8 milisecond(out of perception range). I'am eager to know if I will be able to see this 12hz in my ssvep or not? 
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Hi Mohammad,
I think you should see a 12Hz SSVEP but its power will greatly depend on stimulus energy. The latter can be defined by a wide range of stimulus parameters such as luminance, and immediately related, contrast to the pre- and succeeding frames, stimulus position in the visual field, size, spatial frequencies etc.
If you make your stimulus small enough, dim and present it far in the visual periphery it may happen though that your 12 Hz SSVEP is indistinguishable from noise.
By the way, I think that most of the hard limits you list above may actually depend on these parameters. Even a 100Hz flicker can drive a response (see Herrmann 2001 Exp Brain Res).
Best,
Christian
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Hello,
I am working on my masters that involves an exploratory analysis of EEG signal during motor tasks. I am attempting to find differences between the trials and baseline periods. I have taken the fft and plotted, but I am worried that differences I am seeing is just due to the change in resistance over time of the electrodes. So I am looking for ways to normalize for this. I attempted to first divide by the integral of the total amount of power from the beginning of the delta band to the end of the gamma band. Is this a valid way to analyze data?
Later I divided each power spectra power value by the power in each band (delta through gamma) to see if it improved the results, shown below. Attached I have shown the differences between the average +/- SEM of the trials (blue) compared to the average +/- SEM of an equal time length of baseline (red). It seems that at each frequency band limit, the graph is distorted (4Hz , 8Hz, 12Hz, 30Hz).
My main question is if this is even a valid way to perform analysis? It seems all a bit arbitrary as I can choose to divide out by certain power bands and change the results to my desire. In the attachment it would appear there may be an elevation of signal around 11 Hz during tasks. Is this a fair assumption? Also, any other tips for a beginner like me to perform EEG analysis is greatly appreciated!
Thank you!
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Hi!
I think the best way to normalize is to take the fft both on baseline and on the trials.
Then, you should whiten the power spectra to avoid pink-noise of the brain. (whitening means that you multiply the power value in the given frequency with the given frequency).
After that you can divide your power-spectra of the trials and the baseline into chossen frequency bands. Althought it's not compulsory, if you would like to interpret your results in each frequency, it's ok.
So, you have the whitened power spectra in the given frequency (or frequency band) of trials (Pt) and the baseline (Pb). You can then normalize them in each frequency in two ways: logarythmic and percentile-way. I will show you the percentle-way:
N=100+100*(mean(Pb)-mean(Pt)/mean(Pb))
This will give you the percentile-changes compared to the baseline. N=100 means that Pb was equal to Pt (no changes occured, N=200 means that Pt was 2*Pb.
Also, make sure that the length of baseline and the trilas are equal in order to avoid mismatches of the frequency resolution of the power-spectra. (If they aren't equal, make sure that you calculated the frequency resolution properly) 
You can find pretty good lecturelets here: http://mikexcohen.com/lectures.html, and I reccomend Cohen's book (https://www.amazon.com/Analyzing-Neural-Time-Series-Data/dp/0262019876/)  if you would like to work with EEG-analysis.
Hope I helped!
András Puszta MD
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I am new to sleep analysis using REMLogic before. I am now hoping to use MATLAB for my analysis as my respiratory measures have been acquired using a different headbox and program to my EEG measures. I have been looking at PRANA software and sleepsmg but was wondering if anyone had any advice regarding the best MATLAB toolboxes to use for sleep analysis. Also, an added issue is that my work computer is Windows and my home laptop is a Mac so I want to be able to use both of these to continue analyses. Thank you. 
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i don't have any information about sleep analysis, but MATLAB signal processing toolbox includes hundreds of useful functions for analyzing any kind of 1-D signals  including biophysical signals.  Also, EEGLAB provides useful tools to analyze EEG signals.
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I should peform some recordings from cortical areas in the rat brain and from muscles.
For what concerns the cortical recording, I'm using gold screws and I would like to know which is the best material that can be soldered to it (or at least the best compromise between material's cost and signal's quality).
For what concerns the EMG, I wanted to used copper at first but then realized it is toxic.
Do you have any suggestions?
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No worries, thank you anyway!
Ciao!
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Hi there!
I'm having some trouble understanding how “TruScan Explorer” extracts EEG data. After exporting there is no information regarding channel location on the ASCII text file exported.
Thereby, each data column represents one channel but I'm not able to tell which is which. I searched in the company’s website and several other places but I did not found any solution. I need to know this so I can work the file on EEGLab afterwards.
Does anyone know how “TrueScan” exports data? Which channel sequence is defined by the software when exporting?
Best regards!
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You can use the Matlab scripts (read_deymed_ini.m and read_deymed_dat.m) from the fieldtrip repository: https://github.com/fieldtrip/fieldtrip/tree/master/fileio/private
Then you'll have information about the channel order
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We will need the Neuroscans to conduct P300 and MMN ERPs on a couple of upcoming clinical trials.  Any help will be much appreciated.
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Stephen, their problem is that they probably need an EEG amp for recordings inside the MRI scanner, right? And not so many companies produce those to my knowledge. I think I know only about Neuroscan, Brainproducts and Mega. We used Brainproducts when I was a PhD student. They worked pretty well, so you may try to find those as well.
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Hello,
I have heard a lot about the application of EEG signals and one of them is about sleeping process. I would like to know that how researchers in this field can get information from this complicated signals that are often with noise and realize the sleeping deepness?
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Hi Atefeh,
Normally specialists use the Rechtschaffen and Kales (R&K, 1968) criteria to classify the sleep states in EEG signals... I'm not sure whether there exists some automatic method to do it...
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I know this is not advised, but I would like your opinion on this. I am talking about stimuli during between 16-32 ms (like frames in a video). Has anyone done this or is anyone aware of experiments using similar designs? I have only found examples in single cell recordings.
A more precise example: Let's say I present on every trial a very rapid succession of 5 stimuli A-B-C-D-E in a random order and I want to analyze the response to stimulus A exclusively when it is presented in 5th position, would it be feasible to regress out the variance due to the other stimuli in other time frames (either using linear regression or perhaps more a nonlinear technique such as mutual information)? If not, why? Maybe it would not work because of too much nonlinear interaction between the different stimuli such that they would not be linearly separable? Other potential caveats?
Thank you!
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Hello again. "Proof of the pudding is eating", and thus running a pilot experiment and looking at the results is always a good idea, whatever the skeptics say. Does not sound too feasible to "uncover the whole ~1000 ms signal due to each stimulus" at stimulus repetition rates as high as 30–120 Hz, but of course only the experimental data will finally tell ;-)
A related paper just came out by Bernd Lütkenhöner: Estimation of a transient response from steady-state responses by deconvolution with built-in constraints. J Theor Biol. 2016 Sep 7;404:143-59. doi: 10.1016/j.jtbi.2016.05.032. Epub 2016 May 24.
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Hi everyone, 
If you could please give me a hand with this error, I would be very grateful. I have EEG from a psychological experiment, recorded with BrainVision Recorder, and being analyzed with BrainVision Analyzer 2. Most of the recordings are perfectly fine, but a few present a big error. Out of 64 original electrodes, only two appear. These are the right mastoid (RM) and the left eye sensor (LEOG). Both are bipolar electrodes. RM is to be re-referenced to the online reference electrode, while LEOG is to be re-referenced to the right eye electrode.
I just can't fathom the error because all electrodes worked fine during the recording. Also, the data sets with the error are quite as heavy in terms of bytes as those without the error. Further, why should the RM and LEOG channels remain perfectly well as they do?
This issue might seem like a simple zoom I've bypassed, or similar... But unfortunately the channels are just not there. I've confirmed it as I tried to copy the pipeline from the good data sets onto the faulty ones, where I got the error 'No channels enabled.' In case you had access to the BVA analysis software, please find the raw files for one of the faulty data sets here: https://www.dropbox.com/sh/2bpoh4coi2z5y6a/AAAaAipH2RuD2RXUlX6pRd5Ea?dl=0
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Hi Pablo,
I have opened your data on my computer and all 64 channels can be seen (see the figure), but all except RM and LEOG lack labels. Is then the lack of labels that wories you or you don't see the waveforms either? The latter would suggest that your Analyzer somehow omits the channels without labels. This is very strange, but seems like in either case bringing back the labels should solve the problem.
The lack of labels must have originated in your Recorder workspace. The Name column in the amplifier setup should contain electrodes names and not numbers as it is now. The best is to upload the ready-made workspace for your cap from the manufacturer before experiment. You can also correct it after recording by editing your .vhdr file as ChXX=electrodelabel, but be careful with those and make sure you keep the backup of the data.
Greetings, Nikolay
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Dear experts
can anyone help me to find out a software for mind wave mobile EEG headset, which can analyze the raw data of brain wave and give me some numeric score.
thanks
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Are the electrode locations same for a particular international system or does it vary with the number of electrodes present in the acquisition cap ? Because the standard 10-20 electrode location files in bcilab toolbox (97 electrode ) and the standard 10-20 electrode location files in eeglab toolbox (81 electrode) are different ?
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thank you for the responses . But my doubt was when a location file is provided along with the data set for a particular standard ( say 10-20 ) , are the coordinates in the location file person specific or are they same for any data set which is acquired using 10-20 standard ?
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The advent of mobile eye tracking technology enables measurements of eye fixation locations (relative to head) while subjects engage in everyday tasks, such as walking, driving, working at the computer, making things with their hands, etc. I would like to get raw or processed data from such measurements.
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 Beau,
You will no doubt find more recent and better references than these, but I am confident that you will find a use for them – and it seems possible that they may be been lost to contemporary workers:
Mackworth, J. F., & Mackworth, N. H. (1958). Eye fixations recorded on changing visual scenes by the television eye-marker. Journal of the Optical Society of America, 48, 439-445.
Llewellyn-Thomas, E. (1968). Movements of the Eye. Scientific American, 219, 88-95.
 Best regards.
Walt
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Any model is available for generation of synthetic EEG signal?
Any formula which we can use to generate a synthetic EEG signal.
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I am trying to record motor evoked potential from Wistar rat by ADI stimulating and recording unit by stimulating brain area and record from hind limb. I have to average 512 response. but in this system, I am unable to average. They have that option but response are getting dampened whenever we are doing average.
And second issue with the co-ordinates of hind limb area in cortex. The area I am stimulating is not consistently giving sweeping movement.
Please suggest stimulation parameters and co-ordinates.  
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Possibly sleep lab in our dept
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Please see the attached image for the channel data recorded using neuroscan. All the channel data seems following similar trend. 100% sure that there is no extra gel spreading across channels. Any solutions ?
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Where is the reference electrode in this Neuroscan 32 electrode cap? All channels will be influenced equally by electrocortical activity near the reference electrode.  One way to mitigate this effect is to re-reference to the average reference (i.e. average across channels and subtract that average potential from each electrode, see the attached book chapter). Or you could calculate a spline-Laplacian/"current source density" estimate that is not influenced by reference choice.
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I'm interested in pain perception during pregnancy, and wondering if there are any safe and acceptable methods of functional neuroimaging in this population? Thanks. 
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Hi Rhiannon,
in my experience, MEG and EEG are friendly techniques in case of pregnacy because they both only sense the electromagnetical activity emerging from the brain. As Stephen mentioned MEG have been perfomed already in fetal studies. Personally I worked with Magnetocardiography in pregnet women to record the cardiac activity of multiple fetus (twins and triplets) during weeks without secondary efects (Processing the magnetocardiographic signal in the identification of fetal and maternal heart beats in a triplet pregnancy).
However, in case of CT, fMRI, PET and NIRS I have my doubts. 
Klein and  Hsu wrote an interesting article touching this topic. This can be a useful reference for you. Take a look: Neuroimaging during pregnancy (http://www.ncbi.nlm.nih.gov/pubmed/22113508).
Here you have another reference about fetal MRI: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4515352/ .
There are studies and guidelines already for some neuroimaging techniques applied during pregnacy:
Compendium of National Guidelines for Imaging the Pregnant Patienthttp://www.ajronline.org/doi/abs/10.2214/AJR.10.6351
Imaging of Pregnant and Lactating Patients: Part 2, Evidence-Based Review and Recommendations: http://www.ajronline.org/doi/abs/10.2214/AJR.11.8223
On the other hand, here is an rticle about side effects:
I hope these information can be useful to you. Probably you already read those articles.
Have a nice day. All the best for your research !
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I think the difference should be in frequency component but i don't know the exact difference?
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Power spectral density function (PSD) shows the strength of the variations(energy) as a function of frequency. In other words, it shows at which frequencies variations are strong and at which frequencies variations are weak. The unit of PSD is energy per frequency(width) and you can obtain energy within a specific frequency range by integrating PSD within that frequency range. Computation of PSD is done directly by the method called FFT or computing autocorrelation function and then transforming it.
electrical power shows total energy of a power waveform. 
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Does anyone have any experience? The discovery that electroencephalography (EEG) contains useful information at frequencies above the traditional 80Hz limit has had a profound impact on our understanding of brain function. In epilepsy, high-frequency oscillations (HFOs, >80Hz) have proven particularly important and useful. But can I register the HFO routinely?
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Several challenges exist to seeing HFOs with standard equipment and software.  First, the sampling rate should be at least 1 kHz to resolve the frequencies (though the HFO detection sensitivity is quite low at 1 kHz.  2 kHz is OK, but it is best to have 5 kHz).  Secondly, the anti-aliasing filter position of the equipment must be set higher than the frequencies of interest--i.e., >500 Hz so that you can resolve the shape of the HFO.  Third, the viewing software often puts another anti-aliasing filter in place, as they assume that the screen resolution will be too low to see the higher frequencies anyhow.  Fourth, even with all the rest in place, you have to zoom in (in both time and amplitude) or they will not be observable due to screen resolution.  For more info about screen resolution, see Schevon, et al., (2004) "Inadequacy of Standard Screen Resolution for Localization of Seizures Recorded from Intracranial Electrodes".
The questions you did not ask are that even if you could see them, how will you detect them?  And once you detect them, how will you interpret them clinically?  There are many methods to detect HFOs, and there is not an accepted method by the HFO community to prospectively interpret the HFO data.  Note many physiologic processes also produce HFOs (both ripples and fast ripples).  See, for example, Blanco, et al., (2011) "Data mining neocortical high-frequency oscillations in epilepsy and controls" where HFOs were recorded in control patients without epilepsy.  In short, HFOs are a promising biomarker, but are not yet ready for clinical translation.
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Dear all,
I recorded local field potential in different brain areas and tried to analyze the coherence between those brain areas. I am using filter amplifier from multichannel system with a bandpass filter 0.1-300Hz. I sampled the local field potential at 1000Hz. When I use matlab mscohere function to analyze those local field potential data, [Cxy F]=mscohere(x,y,hamming(2048),1024,2048,1000), I got a linear increase in coherence from 100 Hz to 300 Hz and get a very high coherence from 300Hz to 500Hz. I knew something was wrong here, since my data is filtered by a bandpass filter (passband 0.3 to 100Hz, low cutoff frequency 0.1, and high cutoff frequency 300Hz) and there should not have any high coherence after 300Hz. Can any one explain what may lead to a linear increase in coherence from 100 Hz to 300 Hz and why there is coherence after 300 Hz?
Thanks.
Ding
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Hey Ding,
Just an increase in spectral power in a particular frequency band would lead to elevated coherence in that range. Therefore, it makes sense to analyze coherence between two signals only when there is a reasonable power in both signals in the corresponding frequency band. For this reason, in your case, coherence at >300Hz is spurious. I would suggest first to compute PSD for your signals and identify frequency ranges with increased power, then compute coherence for these specific ranges. Then,you will see whether your linear increase in coherence s still there..
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Philosopher Merleau-Ponty claimed that the feeling Self is the living body or its flesh. In this case, a conjecture can be made that siamese brothers or sisters would not have opposite feelings simultaneously (as one being happy and the other sad). Do you know any data that could help to support or disconfirm this conjecture?
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Dear Alfredo,
There are no many EEG studies on Siamese twins.
We find the following:
H.G. LENARD AND F.J. SCHULTE. Polygraphic sleep study in craniopagus twins. Journal of Neurology, Neurosurgery, and Psychiatry, 1972, 35, 756-762 (however it is sleep, and twins are very small - may be self is not developed yet...)
To check your hypothesis it is interesting to contrast EEG of twins joint at somewhere in the body with EEG of twins joined at the head (craniopagus). Then again the effects may be different depending on which part of the brain is shared...
Check please the following link for the popular article and the video on twins joined at the head.
Several years ago we watched British documentary about adult twins (females) joined at the head (if we remember correctly they sheered frontal lobe). Nothing was told in this film about EEG... But what was interesting that they had different hair stiles and very different personalities. Each of them had her own room to rest with different stiles...
If you will find something please share it with us.
Best,
Alex & Andrew
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actually i know R tool so first que is how to convert edf file into csv or textfile .
2 que is is it possible to analyse this eeg data in R tool,or weka?
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Usually it is better to import EEG data directly into R, MATLAB or Python from its native file.
For .edf files I found this utility with a quick Google search:
from
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Hello. I'm currently doing a final project about biomedical engineering. I'm looking sample EEG dataset from stroke patients. I'm planning to clasification of stroke using eeg signal. But I am having difficulty on finding dataset related with stroke. Does anyone have EEG data for stroke? Please help me. Thank you.
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BNCI Horizon has some datasets publicly available. One of them involves modulation of slow cortical potential in chronic stroke patients. You can find the databases in the following link: http://bnci-horizon-2020.eu/database/data-sets
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Has anybody knowledge of an open source program, to measure neurite outgrowth and which is able to distinguish between several neurite branches?
I cultivate SG-explants which generate neurites with many branches, so that its hard to distinguish between neurite and branches of different origins.
I already tried NeuronJ but it doesn't workt for my SG-Explants.
Any suggestions?
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I hope so.
Thank you Refik :-)
best wishes, Christina
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I want to extract EOG and eye blinking artifacts from EEG signal. I know that ICA can perform this task, however, I have only one channel EEG recording (fp1 electrode). So I want to know what is the best algorithm to apply for this task.
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Assuming there is only a single EEG channel and no EOG to correlate the signal to; I would think that a simple thresholding procedure to reject bad epochs would be sufficient to remove eye blinks. This is however not an algorithmic procedure, if that is what you specifically is asking for.
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So far I found the below equation to measure concentration level but the reference is not that solid and I can not relay on. Any one has other equation or solid reference that support this equation will be highly appreciated.
concentration level = ( (SMR + Beta) / Theta)
where SMR  = SensoriMotor Rhythm
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Dear Atef!  suggest you get acquainted with the detailed work in this direction. Please read the first one informative article in the application.
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i am interested in Electroencephalography (EEG) and have by an article(Classification of human emotion from EEG using discrete wavelet transform) related to EEG , unfortunately i do not have its data base. tank you
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These are the only two I know, you need to make a request, sign an EULA (End-User License Agreement) and then you will be authorized to download them! Among EEG you have some other signals too if you are interested in multi-modal systems! Good luck!
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I was hoping somebody could direct me to a paper or two that seem reliable and suggest that cerebellar measurements are possible with a standard 10-10 electrode placement EEG system. Many thanks in advance, Ben
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Efference copy is an internal copy of a motor task/imagery. Are there any studies which show the existence of efference copy, in terms of a brain task. 
Or are there any EEG studies which distinguish the efference and the afferent ?
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We did two fMRI studies on self-monitoring including efference copies of speech output; maybe they're relevant - please see:
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Recent research has enabled EEG readings with a sensor 4mm away from the skin:
In 2013, an extremely sensitive atomic magnetometer was designed that does not require stringent shielding from the Earth's own field:
I have discovered super sensitive sensors that can be flown in airplanes and can detect submarines, their screws turning and inside machinery:
One would think that this would support the possibility of extracting EEG using this technology. 
If one could remotely extract biological signals using non-invasive technologies that allow for a full range of mobility and free-ranging activities in an un-shielded open environment, then there are a tremendous number of opportunities that would become available in the rescue, policing, medical, commercial, and military arenas, such as; neuroprosthetics, exoskeletons, sleep/hibernation systems, pain reduction systems, threat detection systems, interrogation devices, dictation devices, secure/silent speech, games/simulations, advanced security devices, monitoring EKG/EEG readings of those in critical care units, increasing mobility of those suffering from limiting or confining ailments or seizures, etc.
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What impedes the operation of a capacitance electrode beyond 4mm?  I suggest 4mm as the extraction of EEG readings at a distance of 4mm has already been done.
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Recently I’ve started collecting EEG data by using a 10-20 array of electrodes.My experiment, in general is conducted in front of a computer. The subject is presented with a stimulus, he is supposed to respond, and then an effect appears. I’m looking for changes in the LRP, but I’m concerned about overlapping between trails.
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The LRP can onset almost a second before the actual movement, so the overlap is a concern. You may find the following papers helpful for optimizing your paradigm design:
Smulders, F. & Miller, J.  (2012). The lateralized readiness potential. In The Oxford Handbook of Event-Related Potential Components edited by S. Luck & E. Kappenman.
Eimer, M. (1998).The lateralized readiness potential as an on-line measure of central response activation processes. Behavior Research Methods, Instruments, & Computers, 30(1), 146-156.
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See reference: J Clin Neurophysiol 2015;32: 87–95.
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SO,  you toughts are in line with my idea. I suggested to add this specific indication in the recently published guideline about this theme. Thank you, colleagues. 
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Hi, I've collected BDF data using a Biosemi and Eprime. My data looks great in EEGLab, but it just looks like noise in Brain Vision Analyzer 2.1. Other projects' files collected with the Brain Vision cap look fine in Analyzer, so presumably Analyzer requires different/additional steps for BDF files. Others in the lab are having the same problem. Any tricks or tips would be greatly appreciated.
Thanks so much in advance!
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Dear Diana,
I'll try to do my best to answer you because I am not an expert.
.bdf files are raw files that needed to be converted first in .edf files before being used. .edf files are smaller that .bdf files. For conversion, you will have to specify a low pass and a high pass filters. These filters will change according to what you are interested in (i.e. EEG signals, EMH signals...).
.edf files are editable by brain vision. You can define as many marks as you want.
I' m not sure because last time I used Brain vision was one year ago, but the conversion was performed by Actiview, a soflware available on biosemi website (link: http://www.biosemi.com/download_actiview.htm )
AS for myself I used Brainvision only for EMG signals and the filters were 0.16Hz for the high-pass filters and 100 Hz for the low-pass filters.
Hope it will help
Best regards
Emmanuel
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I assume many of you have done a similar task like this: I would like to present a video clip of recorded behaviors along with the corresponding polygraph simultaneously. I am thinking of creating a simple vertical line that moves from left to right along the time axis on the EEG at a speed that synchronized with the video. Perhaps the EEG traces behind this vertical line can turn darker from gray to help visualization. What software(s) do I need to create this moving line (or moving boundary of areas with different transparency) and to superimpose to the EEG? Thank you.
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Brainstorm is a free, open-source application that can read almost any EEG file and now, synchronized videos as well. It can do the screen capture you need, including the vertical bar moving over the EEG traces. For more info and full documentation (incl. tutorial data) see link. Cheers!
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I have read many studies which are describing that evoked activity change amplitude spectrum profile (absolute amplitude, normalized amplitued, heuristic model, etc.) But I have not found yet, how the phase synchrony behaves. 
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You may take a look at the case study published in International Journal of Clinical and Health Psychology, 2010, Vol. 10, No 1, pp. 167-179 (you can freely download it from the journal website).
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Some children need to be medicated to sleep on the EEG lab. I need to find a safe alternative to chloral hydrate, even though that has a slightly greater interference on the trace.
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Chloral hydrate, chloral hydrate--promethazine and chloral hydrate -hydroxyzine efficacy in electroencephalography sedation. Indian J Pediatr. 2014 Jun;81(6):541-6.
Efficacy of Chloral Hydrate and Promethazine for Sedation during Electroencephalography in Children; a Randomised Clinical Trial. Iran J Pediatr. 2013 Feb;23(1):27-31.
The mechanism responsible for the drowsiness caused by first generation H1 antagonists on the EEG pattern. Methods Find Exp Clin Pharmacol. 2000 Apr;22(3):163-8.
Sedation in pediatric patients undergoing diagnostic procedures. Drug Intell Clin Pharm. 1988 Sep;22(9):711-5.
The hypnotic effects of an antihistamine: promethazine. Br J Clin Pharmacol. 1986 Dec;22(6):715-7.
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In a 2D environment, What is the difference between a 2D and 3D flicker (stimulus) in ssvep based bci experiments?
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I agree with Lukas, there sholud be no difference between using for example a virtual (2D) or using a real LED (3D). Something that could improve the performance of the 3D stimuli is the realistic environment, the subject could be excited because the experiment and its entusiasm should improve your results.
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Its a 32 channel EEG cap and I am wondering if a single broken electrode could cause all the other channels to say they have an impedance of zero with the ground electrode...
Perhaps if the electrode was to short to the shield?
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The ANT waveguard caps usually come with a test electrode that snaps into an electrode position (in case it is broken). Can you try connecting that electrode to the amplifier at the reference position but not snap the electrode tip to the cap? Do you get the same result? Another way to check shorting would be to use a multimeter on two separate electrodes and between an electrode and the reference (all on the cap side). If they are showing connected then something is shorted somewhere. Switching among different electrodes might help you figure out which one is to blame.
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I've been using Curry 7 extensively and very much favor its capability in EEG source analysis (with different methods available for solving inverse problems). However it seems to be fabulously expensive to have it for personal use. 
Now, I am looking for open source toolboxes that come with methods for solving forward/inverse methods with high precision.
I am aware of some toolboxes such as FieldTrip, LORETA, NFT, SPM8, but I don't know their pros and cons. 
My research will focus on localizing sources associated with EEG oscillatory responses (rhythms not ERPs) for BCIs. 
Any response is highly appreciated. 
Berdakh. 
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if you don't restrict yourself to matlab you can have a look at MNE.
it supports many inverse methods (MNE, dSPM, sLORETA, LCMV, DICS, MxNE, single dipole fit and soon RAP-MUSIC)
The language used for scripting is Python.
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What has been proved without doubt?
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Let me pass you some manuscripts for evaluation this question. I hope, you will find a way...
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The event appears to exist only for 40-60 micro seconds in the recorded EEG signal sampled at 2KHz.
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Hi Wolf-Julian,
It is in the theta rhythm (4-8Hz), but its only for 40-60 microseconds.
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Just sat in on a presentation arguing that neurofeedback could affect positive clinical responses in patients who made little progress with other short-term therapies. Even suggesting that these modalities have shown results with persistently suicidal patients treated on an outpatient basis. 
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For reported research, there are various studies regarding "neurofeedback" or "EEG biofeedback" at the NIH's Entrez PubMed Website. The best bet may be to enter an advanced search with the specific condition, e. g. depression, PTSD using each of the terms in quotes above. Though, they may not specifically refer to Acute Inpatient Psychiatric participants. Specific studies include the Peniston Protocol. I think his original research was in a VA hospital. You may also want to try the Clinical Trials Network for NIH for current or recent sutdies.
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Wiggers diagram show relationship in the cardiac cycle. How to get ECG signal and heart sound together? Some papers use Biopac, any other tools are able to get them together?
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you can download many ECG signals from the website http://www.physionet.org/.  Heart sound signals can be obtained via (1) http://www.dundee.ac.uk/medther/Cardiology/, (2)  http://www.medstudents.com.br/cardio/heartsounds/heartsou.htm, (3)http://depts.washington.edu/physdx/heart/demo.html, ....
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I'm looking for cognitively interesting event-related potentials at FP1 and FP2 that can be measured with consumer-grade EEG hardware. Right now I have an Interaxon Muse EEG and want to see if it can be used to capture ERPs. Does anyone have any experience/recommendations?
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Have you tried using BCI2000(downloadable for free within academic institutions) to capture and process the information? When I last used it, it could be configured to work with Neurosky, Emotiv(there are papers that show Emotiv picks up P300 ERPs) and any consumer EEG system via a configuration file. 
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 How many electrodes that should we us to control the movement of the right hand joints (shoulder, elbow and wrist) by EEG signal,e ? What are these electrodes?
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I'm not sure what you exactly mean by "determine this electrodes"?
Usually people use caps to fit electrodes to the scalp, and the layouts of the caps are usually provided by the manufacturer and comply with the 10-20 system I linked in the original answer. If you have a cap, look up the layout for it and it should have positions C3, C1 and Cz.
If you don't have a cap, you can determine the positions using a measuring tape and few anatomical landmarks (see the link).
The C1 position is not defined in the original 10-20 system, but it is halfway between Cz and C3.
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How many electrodes that should we us to control the movement of the right hand joints (shoulder, elbow and wrist) by EEG signal,e ?
What are these electrodes?
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I want to simulate EEG data for different experimental conditions, and different sampling rates (from 200 Hz up to 2 kHz). Which mathematical model is used for their simulation?
Thank you very much in advance.
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Kobayashi et al's study may perhaps help you. Detection of changes of high-frequency activity by statistical time-frequency analysis in epileptic spikes
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How can I precisely synchronize a digital writing system with BCI2000 and Matlab/Simulink, respectively? I have a Wacom Intuos Pro and I would like to record simultaneous data from EEG, EMG and digital writing i.e.pressure and position of the pen on the tablet.
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Thank you Jason! I will have a look of your suggestions and your software.
In our experiment we planned to record together EEG and EMG signals and digital writing samples on the same pc. 
By the way, yes, BCI2000 allows you to record EEG, EMG and other kind of bio-signals and give a feedback based on them to the user in real-time forming one of the most used platform for the so-called Brain-Computer Interface applications.
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I am trying to design a speller paradigm which can have better acceptability and accuracy. At a same time I need to know what are the critical factors that should be met during the design of a paradigm.
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A very important factor is the ease of operation from the point of view of the user. For example, the P300 speller is very reliable but the flashing stimuli are annoying for most users and it is not easy to use this speller for an extended period. Some users actually prefer a slow rate of seplling (e.g., 20 sec per letter) to allow for prolonged usage. The important questions here are what is the typical duration of a session going to be and what is the required spelling rate? These are competing factors and the design may involve some trade-off between them.
Other factors are:
  • The number of electrodes to be used - is there a limitation?
  • Training - is there a constraint on the duration of the training period before actual use?
  • Reliability of the relevant EEG features - are the EEG features to be used easy to extract in different subjects? are they consistent for the same subject across different sessions?
  • Individual differences among users - what modifications can be made to best adapt the paradigm for each individual user?
Obviously, there are many more factors. There are quite a few good review papers on BCI as well as books that might be useful for you. They discuss these issues in much detail.
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Do you know which the best method (formula) is to compute inter-hemispheric asymmetries?
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The details will depend a very a bit depending on things like what analysis software you're using, what frequency range you're measuring and what population you're interested in (children, patients, unselected adults, etc.); but, in general, the steps go something like this:
1) Do a little bit of  filtering - although less than you might do for a time domain study. A notch filter will probably be enough (use 50 or 60 Hz depending on what the line frequency is where you live).
2) Reject artifacts. In frequency domain studies, it's often better to remove ocular artifacts rather than correct for them. So choose some criteria (the criteria will likely depend on your population - e.g. children typically have noisier data than adults) for detecting artifacts and remove those data.
3) Segment your data into segments lasting for something like 2 seconds and overlap them by about 1.5 seconds to correct for issues that arise when you impose the Hamming window.
4) Rereference your data. Mastoids and average are typical - however, there are some data that suggest that the choice of reference can really make a difference for asymmetry. I would recommend using the reference scheme that is most appropriate for your field/research questions.
5) Conduct a fast fourier transformation with a Hamming window. Taper the most distal data of each epoch (the most distal 10% should do). Convert to power spectra and average across epochs.
6) Compute power in the spectrum of interest and then asymmetry scores (typically right minus left). The details will depend a bit on which spectrum you're interested in. For example, it's common to compute alpha asymmetry as LN(right) minus LN(left).
Hope that helps