Electroencephalography. - Science topic

I am afraid that is not ideal. Using a single electrode to record in vivo local field potentials (LFPs) in the hippocampus is generally not recommended because: 1. Lack of Reference Point: LFP recordings measure the summed electrical activity of populations of neurons. Without a separate reference electrode, it’s hard to distinguish between true neural activity and background noise or artifact. A single electrode does not provide a way to differentiate local changes in electrical potential from broader shifts in the electrical field. 2. Volume Conduction: The hippocampus is embedded in a complex neural network, and the electrical signals recorded by a single electrode can include not just local activity but also signals that are conducted from other brain regions. This "volume conduction" effect makes it difficult to isolate hippocampal activity with a single electrode. 3. Spatial Resolution: A single electrode does not provide information about spatial variations in the neural activity across different regions of the hippocampus. This is especially problematic because the hippocampus has distinct anatomical and functional regions (e.g., CA1, CA3, dentate gyrus) that can have different activity patterns. Multi-electrode arrays or tetrodes allow for simultaneous recordings from multiple locations, improving spatial resolution and allowing for more precise mapping of hippocampal activity. 4. Difficulty in Signal Interpretation: LFPs represent a mixture of signals from excitatory and inhibitory neurons, synaptic potentials, and even glial cell activity. With only a single electrode, it is much harder to interpret which sources are contributing to the recorded signal, making the analysis less reliable. 5. Signal-to-Noise Ratio (SNR): Single electrodes are prone to picking up noise, which can be difficult to filter out when there is no comparison or control signal from nearby regions. Using multiple electrodes allows for better noise cancellation and more accurate isolation of the signal of interest. In summary, using a single electrode for LFP recordings in the hippocampus is limited because of issues with reference point clarity, signal contamination from volume conduction, reduced spatial resolution, and difficulties in interpreting the resulting signal. Multi-electrode setups help overcome many of these issues, providing a more accurate representation of hippocampal activity.

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However, it's still technically possible if necessary for specific experimental constraints. Here is a draft protocol: 1. Preparation - Anesthetize the animal with an appropriate anesthetic (e.g., isoflurane or ketamine/xylazine) and place it in a stereotaxic frame to secure the head. Ensure that the animal is fully anesthetized by monitoring vital signs (e.g., absence of the pedal reflex). - Shave the scalp and sterilize the area with ethanol and iodine. Make a midline incision to expose the skull. - Use a stereotaxic atlas to determine the precise coordinates for the hippocampus. Mark the area on the skull. 2. Craniotomy and Electrode Placement - Carefully drill a small hole in the skull over the target location using a micro-drill. - Insert the reference electrode in a distant brain region (e.g., the cerebellum or a peripheral muscle). Fix the ground/reference electrode securely. - Lower the recording electrode: Using the stereotaxic manipulator, slowly lower the single recording electrode into the hippocampus. Advance the electrode slowly to avoid damaging the tissue. 3. Signal Amplification and Recording - Attach the recording electrode to a pre-amplifier and connect the reference electrode to the ground of the system. - Adjust filtering parameters: Set up the amplifier to filter the signals between 1 Hz and 300 Hz (the typical frequency range of LFPs). Adjust the gain of the amplifier to ensure the signal is visible but not saturated. - Monitor the signal: Observe the incoming LFP signal in real-time on the data acquisition system. Adjust the electrode depth if necessary to optimize the quality of the signal. LFPs are typically visible as slow oscillations in the frequency range of 0.5 to 100 Hz, depending on brain state. 4. Recording - Once the electrode is properly placed, begin recording LFP data. Record for a suitable time period depending on your experimental question (e.g., several minutes to hours). - Monitor physiological parameters: Ensure the animal remains properly anesthetized throughout the procedure and check for any signs of discomfort or distress. Continuously monitor vital signs. 5. Post-recording - Electrode removal: Once recording is complete, carefully remove the electrode without damaging the tissue. - Close the craniotomy: Clean the exposed area of the skull and apply dental cement to cover the craniotomy. Use sutures to close the scalp. - Postoperative care: Administer analgesics (e.g., buprenorphine) and monitor the animal until it recovers from anesthesia. Place the animal in a warm recovery area. 6. Data Analysis - Filtering: Apply additional digital filtering if necessary to isolate LFPs from noise. - Artifact removal: Identify and remove artifacts from movement, heartbeat, or respiration. - Spectral analysis: Perform frequency-domain analysis (e.g., power spectral density) to examine oscillations like theta (4-8 Hz), gamma (30-80 Hz), or sharp wave-ripples (~100-200 Hz). - Signal-to-noise analysis: Evaluate the quality of the recording and signal-to-noise ratio.

Will you please read what I have written in the near past !

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.

Hi , 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

Maybe this could help:

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??

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.

Hi Darshil Shah

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.

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

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.

Please check this website and you can find some EEG dataset

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.

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

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.

EEG made easy

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.

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 :)

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

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.

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

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:

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

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

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

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.

No worries, thank you anyway!

Ciao!

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

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.

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...

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.

Update: Problem solved.

As Nikolay said, the error originated in Recorder (I had used the workspace from the previous experimenter), and the problem was solved by setting the label and position of each channel.

I tried editing the .vhdr file in raw (it seemed nice and quick to directly assign the channel names as labels) but i didn't quite find the way. Therefore, with a tip from the Brain Products team, I went about it within the program. First, I used the transform function 'Edit channels' to rename all labels and set each within their coordinates. I did that for just one subject (it doesn't take as long as it sounds). Afterwards, I created a 'History template' out of that process, and copied it to all other nodes.

At any rate, never getting out of the comfort workspace again... :D

Dear Sudhanshu Verma,

Look the link, please.

Regards, Shafagat

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 ?

 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

Hello, maybe you want to take a look at a previous discussion here on ResearchGate (see attached link).

Possibly sleep lab in our dept

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.

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 Patient:  http://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 !

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. 

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.

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..

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

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

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

I hope so.

Thank you Refik :-)

best wishes, Christina

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.

Dear Atef!  suggest you get acquainted with the detailed work in this direction. Please read the first one informative article in the application.

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!

Dear Benjamin, 

Check these papers out: 

We did two fMRI studies on self-monitoring including efference copies of speech output; maybe they're relevant - please see:

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.

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. 

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

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!

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).

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.

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.

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.

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.

See http://www.sciencedirect.com/science/article/pii/S1053811913010501

Let me pass you some manuscripts for evaluation this question. I hope, you will find a way...

Hi Wolf-Julian,

It is in the theta rhythm (4-8Hz), but its only for 40-60 microseconds.

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.

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, ....

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. 

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.

I have put my answer in the other topic with the same question here:

Kobayashi et al's study may perhaps help you. Detection of changes of high-frequency activity by statistical time-frequency analysis in epileptic spikes

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.

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:

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.

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