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Science method

EEG - Science method

Explore the latest questions and answers in EEG, and find EEG experts.
Questions related to EEG
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Though many researchers use video for emotion arousal( stating that they are more effective), i haven't yet seen any available datasets for the same purpose. I am in search for any standard collection. Any help will be highly appreciated.
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Thanks for answering. But the second link is not found.The pitts university database is not video collection. They seem to be image sequences of recorded facial expressions. That would be based on mirror neuron system, am i right?
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I am estimating the power spectra of EEG data in 600ms long window. In my opinion, the power of frequency which is larger than 2Hz can be calculated by FFT with bandwidth 1 Hz. For example, I can obtain the powers of 8Hz, 9Hz and 10Hz individually. But some one argue that the resolution of FFT is about 2Hz, that is only the powers in 8Hz and 10Hz can be estimated. Can anyone confirm this for me?
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No, you can't, the resolution is too limited to allow this, apart from the fact that 8 and 9Hz are not on the "grid" of your FFT frequencies. In fact the FFT calculates the output of a linear filtering operation. The main lobe of this filter is too wide to make the difference between 8Hz and 9Hz reliably. It is possible to calculate the energy centered around 9Hz, but this will also contain contributions of the energy around 8Hz and 10Hz.
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We are going to use motor imagery based BCI for stroke rehabilitation and currently I am going through some papers that discuss the EEG-EMG coherence and time lag. While the results of different studies are not quite consistent and you can see values around 50 MS for LFP-EEG time lag in monkeys (Morrow and Miller, 2003; Rivera-Alvidrez et al., 2010), you will see conduction time measured by TMS around 20 ms(Samii et al., 1998), and finally EEG-EMG lag time of around 26 ms (Whitham et al., 2011).
Could someone please let me know how to interpret these differences?
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I would repeat the above comments, that the TMS latency is the fastest available conduction pathway. The advantage to my mind of coherence type measures is that it records what is actually used physiologically. This can be slower pathways than the fastest possible. The lag may also have contributions from the whole pathway, including the feedback portion.
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I want to measure sample Entropy from EEG signal. IS it possible to use wavelet transform?
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Of course!
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During my researches on sleep spindles and their correlates, I encountered many methodological difficulties that seem to plague this field of investigation. I concluded that a concerted effort is needed for overcoming these difficulties and accelerating the pace of discoveries in this area. Along this line, I am preparing a research proposal on the topic of improving spindle analysis methodology. A proposal for a Frontiers Research Topic has also been submitted on this topic to provide a discussion forum for all experts interested by the study of sleep spindle (invitations to contribute will be sent but those not receiving such an invitation can contact me (Christian.oreilly@umontreal.ca) and I’ll send them one). To fuel this topic I invite everyone investigating sleep spindles correlates to share what they think are the most limiting methodological problems regarding sleep spindle studies, what are the technical/methodological problems that they would like to see being solved for their research to produce better results. I start this Q&A by providing some limitations I noted:
• A fuzzy definition (variable frequency range from studies to studies, one ore more spindle classes [slow, fast, ultra-fast, …], etc.);
• Low inter-agreement rate on expert scoring;
• Unaivalability of an automatic detection algorithm that is commonly aggred upon;
• Low reproductibility of many reported observations;
• Unavailability of a large, high-quality, reference polysomnographic database.
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The availability of clear definitions for a variety of specific EEG (especially sleep related) events, such as spindles, K complexes, alpha activity, etc., is a theme that goes back many years.  I recall chairing a Workshop on "Techniques in Measuring Normal and Abnormal Sleep" with Wilse Webb & Ismet Karacan as major participants at the 1984 Sleep Research Society.  One of the major aims of the Workshop was to develop definitions of sleep EEG events in order to facilitate more quantitative approaches to "scoring" sleep.  After about 15 years of focusing on just the "alpha" sleep EEG activity I cannot say I was any more optimistic in having a clear definition of the sleep EEG alpha activity to facility reliable inter-rated agreement, automatic detection, and quality reference database. i have since ceased to be actively engaged in sleep research. 
What s the rationale for focusing on the sleep spindle? 
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Different types of anesthesia can be used depending on functional state and psychophysiological characteristics of subject. Is it possible to use EEG for actualization of such characteristics?
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I'm working on the attentional selection and I wish to create a visual search task with two salient distractors, but different in saliency intensity (dark red vs. light red) surrounded with green stimuli. However, I need an equiluminant display (I'm working with EEG). How can I deal with this problem?
Is there any technique in order to change the saliency without changing the luminance intensity?
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Although your question is several years old, I would be curious to see how you dealt with this problem. I can only imagine using saturation as a variable since a dark red and a light red would not really be equal in luminance, but you could mix pure red with gray of the same luminance to de-saturate the red and obtain (perhaps) a lower salience.
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I have an Emotiv EPOC to play around with and I am wondering what are the limitations of this headset for research settings, specifically for application to the disabled and the elderly? Also, what can I do to mitigate the limitations of this low-cost headset such that I can get data comparable to more high-end setups they would have in neuroscience departments?
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Since you want to use it for disabled people, I don’t think the EPOC is good for acquiring signals over the somatosensory cortex for mu-rhythms etc. Within the 10-20 system, electrodes C3, C4, P3 and P4 (Anderson, Devulapalli and Stolz 1995) or FC3, FC4, CP3 and CP4 (Wolpaw et al. 1991) are mostly used. In the EPOC there are no electrodes (AF3, AF4, F3, F4, F7, F8, FC5, FC6, P7, P8, T7, T8, O1, O2) that can be used for a typical Sensory-Motor Rhythm (SMR) analysis as there’s no electrodes positioned over that area. In general, for motor imaginary channels C3 and C4 are necessary for motor classification or the adjacent electrodes (with Laplacian filtering), i.e., FC3, CP3, C5, C1 for C3 ; FCz C1, C2, CPz for Cz and FC4, CP4, C6, C2 for C4.
Others have tried to place the electrodes over the somatosensory cortex by rotating the headset (I cannot find the link now) but I don’t think will be a safe or descent montage for elderly/disabled people.
In general (excluding motor imaginary) for a 300USD headset the price/quality ration is good.
Hopefully, within the next few months we’re going to run a test with the EPOC vs a Texas Instruments ADS1299EEG-FE (8 channels) and see what we can get. I’ll keep you posted.
I hope this helped a bit. People with more experience might have to add more. I'm relatively new in using this,
-Anderson, C. W., Devulapalli, S. V., and Stolz, E. A. (eds.) (1995) Neural Networks for Signal Processing.
-Wolpaw, J. R., McFarland, D. J., Neat, G. W., and Forneris, C. A. (1991) 'An EEG-Based Brain-Computer Interface for Cursor Control'.
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For example, for ECG signal, R peaks are considered, and ECG time series are constructed by RR intervals.
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Brain activity is not inherently periodic and regular like cardiac, so signals are usually analyzed relative to some exogenous signal. This is called ERP, "evoked response potentials" (or "event-related potentials"). The classic approach is to just average the epoch surrounding an event (for repeats of the same event type). It is common practice to use a small pre-stimulus period (say 150ms) which is thought of as a baseline. There is a substantial literature, dating back many decades, about the features that appear from this kind of averaged, stimulus-locked EEG and how these features are distributed over the scalp, and how they change in response to manipulation of the stimulus. Of course, ERPs differ greatly depending on the sensory modality of the stimulus.
This whole body of practice is tied to the hypothesis-testing approach and, although it can sometimes encompass analysis of single trials, it does not provide much insight into the spontaneous wiggles of non-stim-locked free-running EEG. The classic example of this is to epileptic seizures, though they are pretty much impossible to miss. There is a body of literature, more from a clinical perspective (not as much experimental or hypothesis-driven) that studies "rhythm" or frequency (and sometimes phase) components of ongoing "spontaneous" activity.
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I'm currently having problems attempting to use FieldTrip for the pre-processing of TMS-EEG data. Until recently I've been using the ANT EEPimport functions and then transforming the data using functions written in MATLAB, with some elements of EEGLab.
When I attempt to create a cfg structure for FT I am presented with problems related to reading the data and the event structure.
Does anybody have any suggestions? I've tried the tutorials on the Donder's site and found them largely unhelpful.
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Perhaps you could clarify where you are having the problem in your processing stream. Are you trying to move your data from EEGLAB into FT and getting a problem, or are you trying to read the data from the ANT formatted files into matlab using the Fieldtrip code? In either case, if you could be more explicit about what steps you have done successfully and when you have the error, that would be helpful. Posting the actual cfg you are using for calls to FT functions, as well as the error itself within matlab, could also be helpful. I'd suggest posting this on the FT discussion list as well. I've found the FT developers and community very responsive to my questions in the past.
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For example: used to distinguish emotional states or responses of two groups of subjects?
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There are mixed reports from what I've found:
Some show that the low-cost systems are much poorer quality than standard medical/lab systems: e.g. P300: http://www.actapress.com/PaperInfo.aspx?PaperID=453213&reason=500
Others show that low-cost systems do quite well, but their needs are different to the standard set-ups, such as deign, as Mark mentioned above: http://www.univie.ac.at/meicogsci/php/ocs/index.php/meicog/meicog2011/paper/view/210
Google Scholar is quite useful for this, if you put in the name of the system, e.g. for the above I used emotiv, there are quite a number of studies that could be used to support/weaken your reasoning for using the low-cost system. As mentioned by others, it would depend on the usage scenario, and perhaps once the drawbacks are acknowledged it might work well. This happened in the same way in eye tracking, where lower spec systems were made available in the last decade that allowed for their usage in many fields outside the lab. Obviously there are sacrafices, but for a limited budget, a lower quality system might be a good starting point in my opinion.
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Is there any EEG database for distraction or attention in driving? I've searched for nearly one week but find none.
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Hi,
I suggest you contact Benjamin Blankertz team. Have a look at their paper entitled 'EEG potentials predict upcoming emergency brakings during simulated driving'.
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In mental rotation tasks, researcher already measures the subject's response time and answer's correctness. So, why do we need EEG?
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I suppose that in these studies you refer they´re trying to find neural basis to a specific cognitive process, which is the mental rotation. Knowing which parts of the brain are involved in the phenomenon is important to provide us a whole understanding of it.
Furthermore, as Tyler just said, to judge de relevance of using EGG techniques we need to know what question is being answered.
Hope it helps.
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It is normally 15% but is this a standard? And why?
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I would like to ask your valuable suggestion on which seizure analysis software I can use for analysing different parameters. I have already recorded EEG files for several days. This I have done by EEG telemetry method, and for the same I used the software provided by the company who provided us the electrode transmitters. But I find the software limited for analysis purpose. My target is to separate seizures and analyze different parameters like frequency distribution, inter-spike intervals, number of spikes. I am seeking any good software that I can analyze seizures and would achieve my purpose. I would be grateful to have your suggestions in this case.
Thank you,
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EpiScan is a seizure detection software developed by the Austrian Institute of Technology. The detection performance of EpiScan will be much higher than Persyst, a publication including a large prospective study (228 patients) will be published soon. The software will be available from this side: www.eeg-vienna.com
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What do you think is the best method for doing this (without anesthesia)?
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I'm agree with Gedeon about anesthesia. We are registering resting EEG asking children to play with us, for example like we are austronauts and we are in a dark cosmos that's why we need to close eyes and not to move before we reach our station. It takes a lot of time to make a child feel free, not everyone wants to play,etc. Better to meet 2 times and more. It is painstaking way, but if you manage to do it you feel happy, belive me. Play with a child and ask mother to help you.
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Please tell me how could I import triggers/markers from E-Prime to actiChamp Brain Vision Analyser?
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Dear Narendra Kumar,
I would like to advise you to contact the Technical Support Team at Brain Products (techsup@brainproducts.com). They will be happy to assist you.
Best regards,
Filipa Viola
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I'm looking for EMG/EEG amplifiers and filters for research rather than clinical use as I am setting up a satellite lab across town for a project.
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From my experience, many labs have old equipment that they no longer use collecting dust in storage rooms, likely because if a repository for used neurophysiology equipment exists, it's not well known. So, I'd recommend actually contacting other neurophysiology groups directly to see whether a deal can be made. I did find one company that sells used EEG equipment, here: http://www.emgneedleelectrode.com/used-eeg-equipment.asp
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I want to analyse my data using the individual alpha frequency to determine the theta and alpha frequencies, but I am not so sure about how I can do that. How could I do that using EEGLAB/MATLAB? Is that possible?
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The individual alpha frequency (IAF) peak can be defined as the frequency associated to the strongest EEG power within the extended alpha range (Klimesch, 1999).
In our paper about EEG rhythm sources in patients affected by Alzheimer and Frontotemporal dementia (Caso et al. 2012), we calculated the IAF as follows:
- Spectral estimation for each EEG channel, using an FFT based method
- Global power spectrum calculation, as average of all individual channel spectra
- Selection of the IAF as the frequency showing a power peak within the extended alpha range (7-13 Hz)
As suggested in a recent paper (Babiloni et al 2012), referencing to the IAF, you can calculate the edges of bands of your interest, i.e. delta (IAF-8 to IAF-6 Hz), theta (IAF-6 to IAF-4 Hz), alpha 1 (IAF-4 to IAF-2 Hz), alpha 2 (IAF-2 to IAF Hz), and alpha 3 (IAF to IAF+2 Hz). For example, if power peak in the extended alpha range was observed at 10 Hz (IAF), the frequency bands of interest were as follows: 2–4 Hz (delta), 4–6 Hz (theta), 6–8 Hz (alpha 1), 8–10 Hz (alpha 2), 10–12 Hz (alpha 3).
Babiloni C, Stella G, Buffo P, Vecchio F, Onorati P, Muratori C, Miano S, Gheller F, Antonaci L, Albertini G, Rossini PM. Cortical sources of resting state EEG rhythms are abnormal in dyslexic children. Clin Neurophysiol. 2012 Dec;123(12):2384-91.
Caso F, Cursi M, Magnani G, Fanelli G, Falautano M, Comi G, Leocani L, Minicucci F. Quantitative EEG and LORETA: valuable tools in discerning FTD from AD? Neurobiol Aging. 2012 Oct;33(10):2343-56
Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Brain Res Rev. 1999 Apr;29(2-3):169-95. Review.
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I am trying to write-up a note on this but can't find clear text
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There's a nice chapter, 'From neuronal activity to scalp potential fields' by Daniel Brandeis, Christoph M. Michel and Florin Amzica, in the book 'Electrical neuroimaging' edited by Michel and Brandeis together with Thomas Koenig, Lorena R.R. Gianotti and Jiri Wackermann.
Otherwise, Niedermeyer's Electroencephalography has several well-written introductory chapters on the physiological basis of EEG signals, brain rhythms, as well as the biophysical aspects of EEG/MEG and principles of EEG localization.
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For example a pop-up window that make the user able to select which cannels need to be repaired, or stuff like this.
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Hi, Aleksandra,
thanks a lot for your answer. Yes, I gave a glance at every page of documentation. Though, I was wondering if someone has built some kind of personal software, just to know if there are advantages or not. I found a little "dangerous" to manually change parameters inside the code for every analysis. Every time I fear that something changes and doesn't work anymore. (or... nevermore ! :-D )
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AD and Normal EEG signals.
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There is a similar post and since February it did not receive any answer: https://www.researchgate.net/post/Is_there_any_EEG_signal_database_of_Alzheimers_available
Far from neuroimaging databases, i did not find any 'disease database' with EEG data. Since there are some works on Alzheimer and EEG, you can ask the authors for info on that. Perhaps they can help you more rapidly.
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Can we make the display time and response together as long as we want?
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In designing any ERP experiment, you have three primary considerations:
1) The total length of the experiment
2) The number of stimuli to present (and responses to collect) per condition to get stable averages
3) The duration of each stimulus and response window to make the task doable
If you assume that the total length of the experiment is fixed, then the main balance is between the number of stimuli to present and the duration of each stimulus-response window. For example, let's assume an experiment that's 40 minutes long with 4 separate conditions of trials, or 10 minutes of stimulus-response window time total per condition.
Thus, if your primary components of interest are large (e.g., the P3b or LPP), or have a large signal-to-noise ratio, then you can have very long stimulus presentations and response windows. This can allow you to collect other psychophysiological data (like skin conductance) that has long onset, response, and reset times. If you need 20 trials in the experiment described above to get an acceptable average per condition, that means each stimulus-response combined time could be as long as 30 seconds.
However, if you have small components (e.g., auditory brainstem responses), you may need thousands of trials to get a usable ERP. Consequently, your stimuli will have to be short, and you may not have much room for any sort of response at all. In the experiment described above, if you need 1000 trials per condition, you would have a maximum of 600 ms for a stimulus-response combination, which is likely barely enough time for more than a simple reaction time measure to be collected validly.
For a Go/Nogo paradigm, you may want around 100 trials per condition (and ideally, the more the better). So in the experiment described above, let's assume the 4 conditions are Go-easy, Go-hard, Nogo-easy, Nogo-hard. That means in each condition, you could have a stimulus presentation-response window combination lasting up to 6 s per trial. You might choose to have the stimulus on for only 500 ms and allow a response window for the whole 6000 ms, or you may decide to do something different.
At that point, you may need to tweak the durations of the stimuli to make them appropriately easy or difficult for your participants. You might want the "easy" conditions to have stimuli last 2000 or 3000 ms, whereas the "hard" conditions to have stimuli lasting only 500 ms. Or in both types of conditions, the total stimulus duration might be 2000 ms, but in the "easy" condition the stimulus is unmasked, whereas in the "hard" condition it's presented for 350 ms then backward masked for 1650 ms. However, if you find that participants are committing too many errors with short stimulus durations (or too few with long duration), you might tweak the durations to give you the desired number of errors.
Hope this helps you think about future paradigm design!
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Research on biomechanical (kinematic and kinetic) and electromyographic patterns of fast whole body movements (e.g. kicks and punches).
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We have tried OpenBCI and Emotiv, and gotten results from both. When used carefully, Emotiv EPOC's raw data is surprisingly good and free of artifacts, while the raw signal in OpenBCI required significant preprocessing. For example, we easily recorded SSVEPs from Emotiv but they were much harder to get from OpenBCI. Unfortunately, Emotiv's licensing system to get the raw data is getting increasingly expensive (and hard to hack). 
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I am only starting my work in the field and wanted to ask for an advice to look for the "guru" key papers in the field. Mostly interested in error-related negativity in BCI context and the influence of various testing conditions (reward, stress) on it.
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Are you after Error Related Negativity or Error Potential? They are related but not the same thing.
Best paper I've found so far is from W. J. Gehring, B. Goss, M. G. H. Coles, D. E. Meyer, and E. Donchin, “a Neural System for Error Detection and Compensation,” Psychol. Sci., vol. 4, no. 6, pp. 385–390, Nov. 1993.
However I've also found someone in NL who did a fairly thorough review (good base to start, the experiment unconventional) J. Teeuw, “Comparison of Error-Related EEG Potentials,” in 13th Twente Student Conference on IT, 2010, p. 10.
I'm also doing a PhD research in ERR Potential. If you're interested, we can always compare notes.
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The recording of human EEG remains one of the most powerful tools to assess functional aspects of brain activity. Two recently launched projects, namely the European Union funded Human Brain Project and the US-funded BRAIN Initiative aim at increasing our understanding of how the brain works. While the focus of the Human Brain Project is on the computational simulation of human brain, the BRAIN Initiative targets the development of new technologies. In this light I wonder how findings from EEG research could possibly contribute to a generalized model of brain function?
Do you think that such mega projects provide the right plattform for relevant progress in neuroscience at all?
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Hi Sebastian. I think this is a very interesting topic. My knowledge on this is very limited (evoked potentials in fish), but I would like to propose the use of comparative animal models to further explore the relationships between brain structure and -functions and EEG responses. There is considerable variation among animal species spanning from animals with very simple or specialized brains to the very complex, thus providing possible basis for identifying links between EEG, brain structure and brain function.
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I'm doing source analysis. Does anyone know a software for source analysis like FD VARETA?
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I don't have any experience with VARETA, but I can suggest you to try LORETA or, better, sLORETA. I used it in different occasions and find it a very useful tool. You can perform source analysis both in time and frequency domain with a very simple user interface. You can also carry out group comparisons. Image quality is really nice and single voxel values in the Talairach space are available for further home-made processing. Take a look here: http://www.uzh.ch/keyinst/loreta.htm
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I am planning to conduct an auditory EEG study in which I present spoken sentences. I currently do not have headphones available that are optimized for our recording system (Brain Products actiCAPs), so I am wondering if I would be better off using normal speakers or in-ear headphones.
Does anyone have experience testing with commercially available in-ear headphones?
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Copy of paper presented at 13th International Conference on BioMedical Engineering December 3 –6, 2008, Singapore.
Google book link below captures all but the last few references of the article.
mschier}{swin.edu.au (replace }{ with @ to email) if you want a complete copy as I am unable to upload article for copyright reasons.
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I have extracted the S-T segment from an ECG signal. Now I want to retain only T-waves.
For that I want to detect curved lines and discard the S segment.
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You can try looking at the zero crossings of the second derivative of the S-T segment to find the border between the S and T waves.
Alternatively you might be able to use shape matching by using a small annotated set of reference T-waves and aligning them with the T-wave in the S-T segment by finding the position with maximum cross correlation.
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If we have the Fp1 electrode (with reference to ear electrode). What kind of research can we do (with only Fp1 electrode). The frontal lobe of a brain is exposed to EOG artifacts. Where I can find information on brain waves from this area? Can we use paradigm SSVEP, P300 or ERD/ERS?
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Wojciech, you can compare the two spectra in this simple way.
1. extract the power of the band of your interest, say alpha band. You will have Power_rest and Power_mov
2. normalize in this way: 100*(Power_mov - Power_rest)/Power_rest.
This is the percentage of power decrease (if negative - task related desynchronization) or increase (if positive - task related synchronization) of your "mov" condition compared to the "rest" one.
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EEG: Electroencephalography
pdf: probability density function
Entropy: H(x)=E{log(p)}=sum{p*log(p)}
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Well i think electrical activity of a current source (brain) spreads out in space before it is measured with EEG electrodes. So modeling can only be done if measured with varying distances of electrodes from the brain. Approximate models could be derived because of haphazard construction of brain and structure. Please the paper
"A theoretical model of the spatial distribution of EEG/MEG for correlated and uncorrelated sources "
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I have written a procedure to pre-process combined TMS-EEG data, using independent component analysis to reduce the extent of the tms artefact in the eeg signals. I have been able to reduce the noise in the data, though I am convinced that this could be done more efficiently and more effectively. Is it possible to apply a method that can quantify which signals are most likely to be strong representations of the artefact. Although this would not act as a substitute for visual inspection and studying of the components it would be a useful starting point. Thanks.
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do you want to use the ICA to remove the TMS artifact or to improve the signal after you remove the artifact with after methods?
I don't believe using ica really help in the first case. It will likely remove part of the magnetic artifact but you'll also remove most of the brain activity around the stimulus and this is quite evident if you look at the data from the electrodes surrounding the hot spot...and you'll still have artifact.
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I wonder if EEG patterns of activity, both during wakefulness or sleep phases, have an intrinsic genetic basis, and therefore are heritable.
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Although the best person to answer this question is Dr. Tobler, I shall give it a try
Studies in rodents reveal that there is 3-4h difference between straines. However, when it comes to individuals, there is 'negligible' difference.
Please refer the following papers:
Franken P, Malafosse A, Tafti M. Genetic determinants of sleep regulation in inbred mice. Sleep. 1999 Mar 15;22(2):155-69.
Huber R, Deboer T, Tobler I. Effects of sleep deprivation on sleep and sleep EEG in three mouse strains: empirical data and simulations. Brain Res. 2000 Feb 28;857(1-2):8-19.
Additionally,
With regards to reported sleep duration (total sleep/REM sleep) for individual species by some studies might not be very accurate. There are many confounds:
a. age of the animal could be different. younger animals tend to sleep more than older animals
b. animal sleep less in naturalistic habitat than in laboratory environment.
c. use of different strains of animals.
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Ece or eeg data sets
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I tried to create a localization file for SEEG data in order to perform treatment on a EEGLAB, unfortunately I am unable to do to that: the software transforms the coordinate I created. Has anyone already created such a file?
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The dreams that an individual is able to recollect can be synthesized into a visual using computer graphics. The EEG recorded overnight can then be compared to an EEG that is being recorded when the visual is re shown to the individual.
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I am not sure what is going to be possible in the future, but even if dreams can be visualized EEG is not going to be appropriate tool for such a purpose. EEG is a time and space averaged electrical signal of the neuronal activity in the cortex with a fairly good temporal but a very bad spatial resolution, therefore important spatial detailes are lost (remain hidden) for the electrodes. No way.
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Working with the EDF+ standard for polysomnographic analyses, I’ve noticed some limitations of the format such as the low standardization of annotation labeling scheme and the fact that adding annotations sometime can requires rewriting the whole data file, which is quite inefficient. I’m very interested in knowing what the limitations that researchers using this standard encountered are and how they have get around these limitations. For example, for the second limitation I gave as example, I spited the recordings and the annotations in two separate EDF+ files such that modifications to the annotations never require changing the recording file.
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Thanks, Diego Alvarez-Estevez, for informing me about these questions of Christian and Antoinette.
1. Christian, have you seen the list of standard texts at http://www.edfplus.info/specs/edftexts.html? What kind of extra standardization would you suggest?
2. Christian, in my sleep lab, the PSG file contains the recorded signals and only the on-line annotations, either on-line typed by the technician or coming from a marker button. We do not allow any change to that file, so it is never rewritten. Any annotations made off-line after the recording session (possibly made by a different technician), such as sleep scores but also corrections of the on-line annotations, are put in a separate file. If a third technician would also score the PSG, she produces her own annotations file.
We typically record PSGs with 13 EXG signals sampled at 256Hz, 11 polygraphic signals (resp, SaO2, sound, light, body position etc) sampled at different frequencies of 4 - 256Hz. The file has datablocks that are each about 10kByte in size and 2 seconds in duration. The annotations have 120 samples, that is 240 bytes, reserved in each datablock. Not ever in a history of many thousands of EDF+ recordings did we need more than 240 bytes in 2 seconds. No technician types that fast. Rewriting the big PSG files (typically 500MByte) is never necessary.
The off-line file contains only annotations including the sleep scores, respiratory events, leg movement events and so on. These files are typically 50kByte till 100kByte, so rewriting those is no problem. In fact they are rewritten as a backup several times during the scoring procedure without the technician noticing that.
Your suggestion to also save the on-line annotations separate from the PSG signals is interesting. It makes life for the programmers more easy, at the rather small cost of having to organize the coupling between those two files. We did not choose this option because we wanted to disable changing the on-line obtained annotations. Having the on-line annotations in a separate file (just like the off-line annotations) would make it more easy to change those unless the on-line file is protected in another way.
3. Antoinette, can you provide info about the AANEM standards? The standardization of EEG terms has until now mainly been limited to electrode names and polarization rules. The EDF+ article explicitly notes the possibility to add other standard texts.
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Particularly in terms of TLE (both SP and CP), there are numerous articles describing pre-ictal, post-ictal, and inter-ictal EEG changes/manifestations. At the same time, there are similar--and often almost identical--descriptions of these changes on EEG in migraine. Is it possible to distinguish between the two (migraine vs. seizure), or are some migraines starting to be seen as seizure variants, particularly in clients with focal neurological signs? Thank you!
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1. The changes in migraines are RARE because they are only DURING the attack, whereas in seizures there are (often) interictal abnormalities.
2. Interictal abnormalities that are epileptiform (sharp waves, spikes) are specific for seizures and are not seen in migraines.
3. Careful what you read, EEGs are misread (over-interpreted) all the time.
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I'm planning a study using one of these techniques.
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Thank you for this, it looks very interesting.
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Is there any algorithm or procedure for how to prove the frequency band (alpha, delta, theta, beta @ gamma) that we choose is the best selection for cognitive tasks? Does it depend on the power spectrum density or region of the brain?
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you may check power spectral density
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I have a data set of EEG data acquired during the performance of multi-joint self-paced elbow extension movements, and I would like to know whether it is possible to quantify the amount of noisy information during the onset and further movement performance in channels C3, C4 and Cz, possibly produced by head or shoulder movement. I did not measure the head location or the EMG of the neck muscles during recording, so I only have the EEG data to do that.
I wonder if there is any measure, maybe based on power-spectrum or entropy to do that?
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Maybe you can try using passive movements, anyway EMG noise has much higher frequency than EEG...
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We are registering ERPs and RTs with a prime image and a simple visual task. We will study differences between prime images. Should we include in the ERP analysis all the trials or only with right answers? As I see, some remove incorrect answer trials, some do not write about it in articles.
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Dear Vladimir,
you should definitely remove the trials with erroneous responses since they contain the error (related) negativity which may strongly influence your correct ERP pattern.
Greetings,
Michael falkenstein
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Is it possible for automatic identification of artifacts in EEG signal? I'm particularly interested about artifacts in EEG signal from Fp1 electrode (with reference to an ear electrode). There should be a lot of artifacts related with vision. What is the simplest way to identify them? How can I delete them?
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The most common way to identify the artifacts in Fp1-Fp2 ,f7-F8 is to register the EOG. The signals in frontal elctrodes usually sinchronous with vertical eyes movements(same polarity), f7-f8- with horizontal movements(opposite polarity). Using EOG helps you to identify these artifacts and usually you can eliminate them with any EEG software. Also you can indicate the limit of amplitude for non-artifact signals. Some software use ICA algorithm for artifacts. Anyway I dont like absolutely automatic artifacts deletion - you must "know the enemy by sight".
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My professor is looking forward to collecting his own data set for EEG signals of patients with temporal lobe epilepsy in an attempt to analyze epileptic seizures. He wants to know the setup/apparatus for this (Hardware and Software)? Please give us suggestions.
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I would suggest the following three EEG amplifiers:
1) Gtec 2) Brainproduct 3) Neuroscan.
For software, I think matlab should work.
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I am interested in understanding creativity from the point of view of psychophysics. Apart from the EEG, have other psychophysiological methods been applied to study creativity?
Here, creativity is defined in the research sense of the generation of acts that are original and useful.
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Adhering to the often used criteria for creativity (original plus appliccable in reality) like Campbell and others have done seems to me a good starting point. On the other hand, low level psychophysical correlates of creativity may be difficult to find compared to the two big groups of variables -- (1) effects of arousal level on intellectual activities and (2) the dynamics and varieties of cognitive processes characteristic to individuals with known high level of creativity. As known already for long time and nicely summarised, reviewed and studied by the early and late Colin Martindale, the state of arousal allowing the so-called flat association hierarchies is beneficial for creativity (especially if related to the right-hemisphere processes compared to left-hemisphere processes). Overly focused type of attending and thinking, slowness of associations and small span of the associative chain, as well as too much emphasis on symbolic instead of imagery-based mentation are counter to being likely create a novel idea or theoretical vision. Thus, any recording means (EEG plus whatever else) allowing to objectively measure correlates of associative chaining and creating remote associations would do nicely. Additionally, comparative studies of brain dynamics of individuals with proven high creativity and those with known low creativity are advisable. (Caveat: IQ and creativity scores need not have high level of correlation as some would expect to be somewhere near r=0.8-0.95. Mental ability testing results of highly creative individuals may not be necessarily very high, although often they are. Better use Mednick's, Baron's et al. approahces and tests of alternate uses of objects and concepts, tolerance of ambiguity, ability to mentally take the position of anorther person depicted on a picture, tests of imaginary manipulation with mechanical or other devices and performing imaginary construction tasks etc could do.) All this means that we deal with relatively high level cognitive representations and processes.
In my tens of years long experience as an examiner of university students I have noticed that high levels of creativity correlate with musical training and musical aptitude and also good capabilities of visualization relatively exceeding the skills based on having learned the verbal concepts. (But this is nothing new and original, simply my observations are consistent with what has been known for some time.)
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In carrying out experiments related to imagine the movement of a hand using C3 and C4 electrodes.
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Wojciech, there is a difference between imagination of self movements and mentally observed movements of other person. The differents related to mirror system activity, as far as I remember.
try this new paper, may be it'll be use ful
Goal or movement? Action representation within the primary motor cortex (pages 3507–3512)
Andrea Cavallo, Giulia Bucchioni, Umberto Castiello and Cristina Becchio
Article first published online: 20 AUG 2013 | DOI: 10.1111/ejn.12343
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Does this device provide the raw EEG signal or alpha, beta, etc. waves? Does it collaborate with MATLAB?
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My colleague tries read an EEG signal, but in my opinion she is making a mistake. Her experiments fail. This device is using wet EEG electrodes (not reinforced, AgCl). In my opinion, the reliable measurement we can get only for bald places of a skull. What is your opinion?
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Unusually thick hair can physically interfere with good coupling between the electrode and scalp when using gels and other electrolytes. But normally hair is not a problem for EEG. It is easier to place electrodes on bald patches but in my experience the impedance are often higher there.
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I have several recordings from a Grass Technologies Comet EEG system similar to http://www.mfimedical.com/comet-portable-eeg.html. I would like to import them into EEGLAB and for that I am looking for accurate coordinate values in Cartesian and spherical systems. Any help is greatly appreciated.
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I you want to create your own coordinate values, you can do it the way I did.
Use one of 10-20 head models you can find online, in literature or manuals.
1. Import that model into the drawing/imaging software you are familiar with.
2. On a second layer create a square. Centre both to the position of Cz.
The height of the square is the maximum length on the head model.
3. Then create a grid into the square. For my purpose, I used 96x96. But if you desire more accuracy, you can increase the number of grids.
4. At this point you can read the x,y values of the centre of each electrode position.
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I got some EEG (electroencephalography) data from my epileptic mice, I want to know how to calculate the amount and lasting time of sharp wave during a period of time. Is there any software I can use to achieve the aim?
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Contact Prof Dr Michel (MJAM) van Putten at Medisch Spectrum Twente (MST) , Enschede, The Netherlands, dept Neurofysiology. see e.g. J Clin Neurophysiol 25; 77-82,2008
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Should we not treat IEDs in patients with cerebral palsy who display IEDs in their EEGs despite absence of clinical epilepsy?
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Dear Colleague Dr. Jaseia, as we all know the Cerebral Palsy conditions can be considered as "non-progressive" disorders just from the "lesional" and "temporal" point of view ( noxae acting in a specific developmental period : from early peri-natal to infancy) .
Viceversa, from the "functional", "developmental" and "psychomotor/behavioral" points of view, acting the noxae in a developing organism (from neonatal to childhood), CP must be considered in any case a "progressive" disorder. That's why, by definition, a "non-progressive" lesion can interfere with a "developmental status" in many ways. In addition, this "progressive" condition (from a developmental point of view !) can be even worsened due to concomitant and additional negative effect on the cognitive performance (which are sleep-related) caused by EEG-abnormalities on sleep physiology (see for example the ESES conditions).
So I completely agree with You, but we have to be carefull why this message can be misunderstood and lead to overtreatment.
In conclusion, as I told You before, I think that we should perform cognitive tests (before and during the AEDs treatment) in order to see if it is worth continuing.
Pasquale Parisi
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There is quite wide agreement on the fact that the peak frequency of the EEG power spectrum (which generally corresponds with "alpha" oscillations) can vary slightly from subject to subject, and therefore the traditional definition of frequency bands (such as taking alpha from 8 to 12 Hz) could overlook more subtle differences between subjects. Moreover, lower and upper alpha band display distinct properties in some situations, which makes the distinction even more necessary (see for example several studies by Klimesch et al.)
This is particularly relevant when working with patients with Parkinson's (as is my case), who have on average a lower individual alpha frequency (some of them as low as 7 Hz!). However, in performing data analysis, I found out that it is not always so straight-forward to compute the individual peak: 1) because a good number of subjects do not show a clear peak, or they show more than one; 2) because the peak might differ slightly in different areas of the brain - and so what do I choose? 3) because the peak might differ in different conditions (during task performance vs resting state, or eyes open vs closed).
I would like to hear some opinions/comments from the community. Have you dealt with such issue, and how?
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I have to agree with Eric Harris that this is really an important issue.Besides the different alpha band activities related to cognition, the EEG-alpha rhythm is one of the "hallmarks" of the resting brain. Not only the interindividual differences (i.e. differences of alpha peak frequency) have impact on analyis of EEG-data, but also do have the intraindividual changes that take place during the transition from wakefulness to more drowsy states. After closing the eyes, a dominant alpha rhythm over the occipital leads will apear. With increasing drowsiness during the resting state, a transition to more parietal and finally anterior brain areas will take place along with a decrease of the individual frequency peak. Therefore, when using resting state data, it is important what one wants to analyze: if you are interested in vigilance shifts, you should take into account all spatial and frequency distributions of the alpha band (see for example the VIGALL-algorithm of our research group; Hegerl et al. World J Biol Psychiatry. 2012). On the other side, if you want to process EEG data that is restricted to more anterior sites, you should look at the specific peak frequency at the frontal leads. Some interesting work on EEG band distribution and changes during rest have been done by e.g. De Gennaro (e.g. Clin Neurophysiol. 2001) or by Tsuno et al. (2002).
I also added a pic of a clearly distinct EEG alpha peak at frontal sites (9Hz) and posterior sites (10 Hz) during a 15 minute resting state EEG-recording.
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I am looking for a database containing EEG data (not ERP) which is related to visual perceptual tasks (for example face/ non face discrimination or any other similar work). I would be grateful if anyone could help.
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thank you dear Pouya Ahmadian for your help,
In fact I had seen these databases before, but I need the pictures (which has been shown to people) too.
also in the next step, I need EEG data of some patients who have disabilities in their visual perceptual aspects, for example Autistic people whose discrimination between face/non-face images is impaired ...
I am trying to find some data, otherwise I should record my own database, which is a really hard work for me, I am trying to avoid this as much as possible.
any way, many thanks for your answer again
best wishes
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I am looking for a mathematical model for video specifically for EEG/ECG applications that provides analysis with Power-Rate-Distortion.
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During wake/sleep transitions in animal/birds, various circuitry in the brain undergoes change/modification, citing the role of various types of neurotransmitters in the regulation of sleep-wake transition. Nucleus coeruleus (LC), a small nuclei in the pons of the brain stem region, has been shown to regulate the above transition as it is involved in maintaining waking and alertness by supplying norepinephrine to various parts of the brain like the pre-frontal cortex, hypothalamus etc. Increases in NE concentration in the brain drastically activate the cortical EEG and thus keep the animal awake. It has been shown that during sleep, LC firing subsides and in REM sleep it almost ceases its firing. I want to monitor the in-vivo changes of the NE concentration in the rat brain during the EEG recording.
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Wise answers below. I do recommend the combined EEG/EMG and microdialysis technique in freely moving animals, and not head restraint due to a non-stressed recording of NE in the different sleep stages. Good luck!
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I´d like to include in my clinical practice any neuroimage technique such as neurofeedback (EEG) or relatives but not sure about the methodical even epistemological inconvenients of that intend. Is there any pionner contribution on that field?
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Allan Schore's work is very interesting from this perspective.His latest book is The Scence of the Art of Psychotherapy. In fact take a look at all the WW Norton series on this topic.
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I am trying to use the evoked potentials method to investigate efficient frontal lobe functions.
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If you are new to the evoked potential methodology, I highly recommend Steve Luck's book on event-related potentials. It does a great job of introducing you to the method, provides lots of practical tips on designing and executing experiments, and useful guidance on how to analyze and interpret your results. Here's a link to the book:
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To use in research/look for improvement in treatment in EEG.
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I have EEG Data which I extracted a few features from like energy, entropy and PSD using the wavelet approach. I need to classify the resulted matrix with GMM (Gaussian Mixture Model) which have 3 categories, cat1, cat 2 and cat3. Let me know about some software which have a GMM GUI facility. Currently I'm using MATLAB with the following commands for GMM.
mix1 = gmminit(mix1, data(label==1, :),
options(1) = 1; % Print error values
options(5) = 1; % Prevent collapse of variances
options(14) = 1000; % Number of iterations
mix1 = gmmem(mix1, data(label==1, :),
Generate Test data
test = wstest8(:,1:8);
test_label = wstest8(:,9);
test_probs = [gmmprob (mix1, test) gmmprob(mix2, test)
gmmprob(mix3, test)];%
target = [ test_label==1 test_label==2 test_label==3]; %No.
of outputs
fh1 = conffig (test_probs, target);
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Hi
If you want to have an idea of what could be done with more complex GMM-based tookit, look at what was done in speaker recognition.
We are providing a complete C++ toolkit see alize.univ-avignon.fr
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It is well known that the power of the scalp EEG can vary between different subjects due to several factors, including also anatomical characteristics. For this reason, it is necessary to have a way to account for differences in broadband power across subjects. This can be achieved with different normalization approaches.
I would like to hear your opinion regarding how to approach a resting-state data set, where a baseline is not available. My goal is to be able to compare the resting scalp power before and after a task and further comparing a patient group and a control group. From what I have seen in other studies, some people normalize the power of each channel by subtracting the average power on the scalp (in each frequency bin). Others standardize the channels voltage by transforming it into z-score.
What do you think would be most appropriate?
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Hi Clara,
Normalization by dividing by total power is tricky indeed, because total power both contains power due to unspecific (and for instance gender specific) 'noise', but also power due to the very brain activity your interested in (for example alpha power with eyes closed has a large contribution). So by dividing by total power you can end up 'mixing' in band specific brain results to all other bands. So what you end up with after normalization depends on the activity (power spectrum) you have, and can be different per experiment (and conditions like eyes open or eyes closed or cognitive task). I would advice you to make topographical and power spectrum plots of the absolute and relative power to see if all looks ok after normalization. You can also try using for instance only the power in higher frequencies (where the power is more due to noise and less to brain) to normalize by to avoid mixing in alpha effects. Hope it helps and good luck. And I'm following this thread with interest since I'm struggling with the same thing at the moment :)
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It can be a bit difficult to understand whether a scientific manuscript is worthy or not. Can please everyone state the points that they look for when reviewing an article for publication?
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I have done quite a few reviews. Dinesh has quite a good starting list but my concern is that reviewers can reject work because it doesn't fit with what they believe is the direction that research in an area should be taking.
1) I look to see whether the authors have provided a solid reason for their research question. How have they supported the research with literature (preferably fairly recent literature)?
2) Is the research method described and reasonable for the problem that they are trying to address?
3) Have they provided sufficient data to support their analysis and conclusions?
4) Does the whole paper develop a sound argument or is it fragmented and lacking flow?
I usually also comment on ease of reading as I find some authors seem to have difficulty writing good sentences or they use words that are poorly defined and possibly not known to their readers. I have had cases where an author has invented words. It sounds scientific if I use big words is one line of argument. I agree with Einstein that "Things should be made as simple as possible, but not any simpler." or "If you can't explain something to a six year-old, you really don't understand it yourself" also stated as "If you can't explain something simply, you don't understand it well." So I am critical of people who force me to go hunting for information to understand the basics of their argument. Even if I am a novice in their field (which I am usually not for review assignments), I should be able to understand enough to know whether there is a valid argument in what they have written.
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I am wondering if it is possible to (partially) synchronize the EEG with external EM signal
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A good mechanistic paper about the effectos of electric fields on neuronal activity (very much related to ephaptic interactions):
Endogenous electric fields may guide neocortical network activity.
Fröhlich F, McCormick DA.
Neuron. 2010 Jul 15;67(1):129-43.
A couple of nice papers about EEG synchronization with TMS:
Synchronization of neuronal activity in the human primary motor cortex by transcranial magnetic stimulation: an EEG study.
Paus T, Sipila PK, Strafella AP.
J Neurophysiol. 2001 Oct;86(4):1983-90.
Rhythmic TMS causes local entrainment of natural oscillatory signatures.
Thut G, Veniero D, Romei V, Miniussi C, Schyns P, Gross J.
Curr Biol. 2011 Jul 26;21(14):1176-85.
And if you are interest to “reversible lesions”, take also a look at the literature on transcranial direct current stimulation (tDCS) and to our own work with transcranial static magnetic field stimulation (tSMS):
Transcranial static magnetic field stimulation of the human motor cortex.
Oliviero A, Mordillo-Mateos L, Arias P, Panyavin I, Foffani G, Aguilar J.
J Physiol. 2011 Oct 15;589(Pt 20):4949-58.
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Does anyone have experience with whether to send an EEG BrainAmp amplifier from the EU to the U.S.? Are there reported problems with the customs duty (e.g. that it gets caught there for a longer time period).
The amplifier will be sent to the US only to get calibrated and should be returned as soon as possible. Does it make sense to leave a letter of the US centre inside the postal package to confirm that it is sent in a scientific manner?
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It seems that there are many benefits of rTMS treatment for Depression and Anxiety, yet many people are unaware of the option.
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It appears that Dr. Giulio Tononi's use of TMS is only in the area of his earlier studies on sleep.
More recently, the AHRQ (Agency for Healthcare Research & Quality; U.S. Dept of Health & Human Services) published in September 2011:
"Comparative Effectiveness Review: Number 33, Nonpharmacologic Interventions for Treatment-Resistant Depression in Adults", in which ECT, rTMS, VNS, and CBT/IBT were investigated.
"This comparative effectiveness review (CER) is intended to help various decisionmakers come to informed choices about the use of nonpharmacologic interventions for TRD in adults. Our principal goal is to summarize comparative data on the efficacy, effectiveness, and harms of electroconvulsive therapy (ECT), repetitive transcranial magnetic stimulation (rTMS), vagus nerve stimulation (VNS), and cognitive behavioral therapy (CBT) or interpersonal psychotherapy (IPT) in patients with TRD." (p. 3).
There are many active clinical trials on rTMS sponsored by various manufacturers, as without additional proof of long-term efficacy, most insurance providers will continue to refuse coverage, thereby killing the future market for the devices. But it seems that little has yet been published.
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I would like to hear the opinion of the community about software to perform group analysis on EEG data, possibly including group x condition interactions. I usually write my own scripts but it would be good to have some automatic routine. I tried out the STUDY feature of EEGlab but I find it a bit cumbersome. I am also in the process of trying the free package Brainstorm. Would love to hear if there are other suggestions.
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Dear Clara,
the Fieldtrip toolbox provides a rigorous framework for statistical testing of EEG data. For a long time it wasn't possible though to do a group x condition analysis. Recently Saskia helpling from the Institute for Medical Psychology of the Goethe University Frankfurt has contributed the necessary code. If it's not in Fieldtrip yet, you may contact her directly. Another alternative is SPM8 for EEG/MEG which has a different statistical aproach but should also work well for a group x condition design.
Best,
Michael
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My lab is in the process of building a high-density in-vivo epidural recording setup for use in mouse KO models. Our intention is to try to place 32-64 electrodes on the epidural surface with maximal coverage of the whole brain. We hope to be able to make recordings in awake moving animals. I’d be interested in any advice people might have regarding setting up such a preparation, and any related experiences. I’d also appreciate hearing about any multichannel grid-type electrode arrays that people have found particularly useful. The same question applies to amplifier systems that might be considered ideal for recording field potentials in such a preparation.
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Hi John, I'm building a similar system to record HD-sEMG, it depends how much money you want to invest and how knowledgeable you are with electronics. If you can do some integration yourself, you can try the analog-front ends (AFE) from Texas Instruments and Intan (http://www.intantech.com/). Intan has AFE for 32 and soon 64 channels. If you want something completely integrated you can check out Triangle BioSystems (http://www.trianglebiosystems.com/). Good luck!
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I computed a cross-frequency coupling diagram (phases and amplitudes) based on the methods described in Tort et. al. (Dec. 2008). It seems to work out and the values seem to fit but I´m not sure about the noise matrix, which should be normal distributed. I used a time lag and 200 surrogates so I assume normal distribution but how can I proof it´s statistical signifance noise? oO
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I want to explore brain activity (EEG) for verbal reflection, in verbal therapy.
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how would you suggest to measure empathy behaviorally? i want to compare between conditions by eeg activity, by its correlation to mirror neurons (MNS). i am looking for researches that measured eeg activity ( mu waves as indication for MNS) while verbal reflecting
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Frequency, spread, morphology, other findings, all of them?
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The very occurrence of PLEDs (periodic lateralized epileptiform discharges) especially in Frontal region is suggestive of an epileptic state.
However, epilepsy is a clinical diagnosis; hence, clinical history is important in labeling the EEG findings as epileptogenic, otherwise in absence of clinical history of epileptic attacks, epileptiform discharges is a better term in my opinion.
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Our research team is considering purchasing an EEG system compatible with MR. We were wondering which one is recommended? We would be grateful for any opinion or advises about the equipment.
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We use BrainProducts Equipment (64 Channel Brain Vision setup) which works excellent in our scanner environment (TIM TRIO Siemens).
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In general, the preprocessing methods used in EEG are very dependent on the goal of the applications. Having said that, there are some methods that are used very commonly to improve the quality of Signal to Noise ratio, such as Common Average Referencing (CAR) or filtering. It would be interesting to summarize the effective signal preprocessing methods since they usually can be similar in different applications. What are the methods that you have found effective on your data?
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Adham provided some suggestions re dimension reduction, but sometimes it is also possible to multiply the data, as in our series of papers such as:
You can take existing epochs and use different subsamplings or subperiods as new epochs, and it is even useful for these to be overlapped, or biased to the central and more consistent part of the epoch.
However, you asked specifically about Laplacian, and we have also shown that this is the best method we have found in terms of getting rid of unrecognized muscle (EMG):
This work is very interesting because the demonstration of the elimination of EMG is based on a unique paralysis dataset in which subjects are paralysed to eliminate it!
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I want to calculate the alpha/theta ratio for our EEG datas. Do I have to use BrainVision for this or can I do it by myself?
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About Alpha and Theta frequency bands, if you are doing experiments on different subjects, the frequency bands may not be the same for all of them.
You can take a look at this paper for more information:
"EEG alpha and theta oscillations reflect cognitive and memory
performance: a review and analysis" by Wolfgang Klimesch
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I need a dataset of EEG signals recorded with high frequency, to study with BCI. Can someone help me?
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I would recommend the BCI competition (http://www.bbci.de/competition/) data sets. Some are recorded with 1000 Hz sampling frequency (e.g. BCI competition IV, data set 1 and BCI cometition III, data set IVa,b) and the paradigms covers a variety of applications from P300 speller to imaginary data. Just read the description of each data set and if you have any questions, email the contacts of each data set.
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I work on environmental artfacts during EEG recordings. I'm studying the light, the influence of the conductor equipment linked, or not linked, to the ground and the use or non-use of the electrodes which record EKG and EMG. I would like to know if there are other artifacts, but only non-physiological artifacts.
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I have developed an open source matlab toolbox for the automatic EEG artifact rejection, as published in: http://www.mdpi.com/1099-4300/16/12/6553
If you are interested, please visit: http://nadiamam.wix.com/eawica
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I want to determine the AR model coefficients for EEG data and it is quite large so I am not able to figure out how to use that data for the AR model coefficient.
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Use arburg. Very simple to use.
See help or the doc.
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I have worked on many aspects of EEG signal in neurocognition. I want to work in clinical detection and improvement in Encephalitis by EEG signals. Can anyone provide me with clinical EEG data of Encephalitis.
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Have you tried UCIML repository?
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Is drowsiness a synonym of sleepiness? Are they differ in term of intensity? How about in term of brain wave?
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Dear Rabihah,
the terms drowsyness, sleepiness and hypnagogic period are oftenly used as synonims in the literature, but there are important differences that should be underlined.
"Hypnagogic period" is the term that comprizes and tries to explain both neurophysiological and psychophysiological phenomena of the transition period from the wake to sleep state.
"Drowsyness" is the term that defines the state of the transition period from wake to sleep state that can be described by objective measures , usually by EEG parameters (please see Hori T et al., 1990), EOG, reaction time etc. Typical tests that describe the quality and quantity of the drowsyness are Multiple Sleep Latency Test and Maintenance of Wakefulness test.
"Sleepiness" is the term that describes the subjective state of the subject/patient. It is also qualified and quantified, but by the rating scales: Stanford sleepiness scale, Epworth sleepiness scale, Karolinska sleepiness scale etc.
You will agree that there is no net distinction between these terms, as there is no clear border between neuro-psycho-physiological sciences. Still, it is always better to be as precise as possible when one speaks about certain phenomena.
Bests,
Tijana
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Suppose there is one movement task lasting for 1s. There are total 10 subjects and for each subjects 60 trials. EEG data is captured during each task. We want to see the scalp topography of the beta band (13-30Hz). Normally for each electrode, one beta band power for each trial will be calculated and then average all trials for one subject to get the scalp topography. In this case, there will be one scalp topography for each subject. Is it reasonable to average across the subjects and get one scalp topography?
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Hi,
both methods (orders of processing) might be of interest. The first - computing the ERP and then the spectral power per subject - is producing the evoked oscillation, in which you only see the frequency response that is locked to the stimulus onset (mostly early components of the ERP or big ones like the P300 if it is not 'jittering' too much). The second - computing the spectral power of your trial and then averaging over all these spectograms - produces the induced oscillations. They also reflect oscillatory activity that is not stimulus locked - often later responses that are not immediately caused by the stimulus but some higher processes/feedback from regions further down the processing stream. Have a look at Tallon-Baudry & Bertrand 1999 for a nice introduction to this topic: Oscillatory gamma activity in humans and its role in object representation.
C Tallon-Baudry, O Bertrand - Trends in cognitive sciences, 1999 - Elsevier
In the end your choice depends probably on the processes you are most interested in :)
cheers,
C.
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Can you suggest good material about the functions of different brain waves (delta, theta, alpha, beta)? In which processes different rhythms are involved? I have found some material of course, but these describe functions as if, for example, during eyes-closed resting state there would be only alpha waves. That isn't so as I have seen from the EEG recordings. There is still a pretty fine amount of delta activity, as well as some theta and beta. Is it true that delta rhythm reflects basic physiological processes such as breathing, heart beats? What are theta and beta reflecting during resting state?
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Brain oscillations are still a mystery. The human dominant rhythm is in the alpha band, with a peak at 10 Hz, although each of us has an individual alpha peak frequency, usually referred to as IAF. Among the quantitative EEG parameters, IAF is regarded to be the best indicator of maturation, has the highest heritability level, and can serve as an indicator of pathology. It is further assumed that different narrow alpha band frequencies are associated with attention—suppression of irrelevant stimuli and selection of relevant information. Below alpha is theta – dominant in animals, and thought to be related to memory processes with a frontal/parietal distinction for attention and storage. Another aspect of theta and the lower delta is in cross frequency coupling, where the lower frequencies act as a gating mechanism, providing optimal neural conditions for specific processing assumed involved in binding. The association delta-beta and theta gamma, are assumed to be related to topographically more distant and/or more narrow coupling activity. The topic is extreme complex. Some up to date articles might give you a better understanding:
Arnal LH and Giraud AL (2012). Cortical oscillations and sensory predictions. Trends in Cognitive Sciences July, Vol. 16, No. 7. 390-398
Klimesch, W (2012). Alpha-band oscillations, attention, and controlled access to stored information. Trends in Cognitive Sciences December, Vol. 16, No. 12, 607-617
Sauseng et al., (2010). Control mechanisms in working memory: A possible function of EEG theta oscillations. Neuroscience and Biobehavioral Reviews 34 1015–1022
Canolty, RT and Knight RT (2010). The functional role of cross-frequency coupling. Trends in Cognitive Sciences, November, Vol. 14, No. 11, 506-515.
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Until today, EEG has been widely used in research into activity of the human brain. However, attempts to use EEG techniques in clinical diagnostic conditions have proved to be unsuccessful; as far as I'm aware, no specific patterns have been discovered relating to any specific nosology, including in particular epilepsy.
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Yes, you are totally right in this regard. It´s impossible to make any clinical diagnosis based merely on the EEG aspects of an individual patient. Indeed, I think that EEG is an auxiliary test, and as with other exams, it is absolutely necessary to include its features in a patient´s clinical setting to extract the its best performance.
On the other hand, it is practically impossible to manage some medical conditions without the use of an EEG. As a neurophysiological method, it has the peculiar property to identify normal and pathologic findings on brain electrical activity.
In this regard, EEG is undoubtedly the best assay to study and manage patients presenting with paroxysmal events, and it is central for the diagnosis of an epileptic disorder and its classification, and for the differential diagnosis of epilepsy with its imitators. It also provides valuable information for the clinical management of patients with epilepsy.
In another relevant set, individuals with acute decrease in level of consciousness, EEG is considered the gold standard test for the diagnosis of a potentially fatal disorder called nonconvulsive status epilepticus. It also assists diagnosing the severity and providing elements to establish a prognosis for comatose patients.
For neurological conditons like headache, dementia, movement disorders, autism, ADHD, and cognitive-behavioral disorders of children, the role of EEG remains rather limited. But still, it is the only one which can identify epileptiform discharges, often useful in neurological settings.
We cannot deny that Hans Berger´s original goal of distinguishing the different psychopathologies by their electrical expression was not achieved. However, 90 years after its invention, EEG still has a role in the management of patients with neuropsychiatric conditions, despite being just a drop in the Medicine´s ocean.
Best regards
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This is a great article and its findings are consistent with our experiences in the lab. We use the EPOC to take BCI systems, that have proven to be robust in a lab setting, out into the world and into the hands of the audience at events.
One of the difficulties we notice is that the wireless EPOC does not seem to have any reliable way of synchronizing what happens on the computer screen with the EEG data. Usually, research systems have a wire running from the computer (for example the parallel port) to the amplifier, through which we can send a short pulse whenever we present something on the display. This pulse is interleaved with the EEG data by the amplifier and can later during the analysis be detected to determine the exact moment of a stimulus onset in the EEG stream. The EPOC has no such facility, so the best we can do is mark the EEG data as soon as it arrives with timestamps. There are multiple buffers in the hardware/software stack, so the timing is unreliable.
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Hi Marijn
I suggest you talk to Dave Hairston, he might be able to help you out with that. He has addressed this issue before and you can find links to his articles through scholar.google.com (if you can't find him, find me and link through me). For example, I just was able to download the following article that includes discussions about the Emotiv EPOC: Hairston, W.D., (2012) Accounting for Timing Drift and Variability in Contemporary Electroencepholography (EEG) Systems. http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA561715
He should be able to not only give you this ins and outs of the issue but may have an answer.
Whitaker, K.W., Hairston, W.D. (2012). Assessing the Minimum Number of Synchronization Triggers Necessary for Temporal Variance Compensation in Commercial Electroencephalography (EEG) Systems. http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA568650
Kaleb
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I've been undertaking EEG recordings in the GAERS model of absence seizures which display typical spike-and-wave discharges @ ~6-9Hz interspike frequency. My recordings typically last 30-60 mins during which time there are on average 0.5-2 seizures per minute. The seizures vary in duration from ~1s to ~60s.
Since analyzing by hand (I've just been using Clampfit) is rather time consuming I'd like to run these data through some automated software to tell me (a) what time the seizures occur (b) how long the duration of the seizures and (c) what interspike frequency the spiking occurs at.
Can anyone provide any recommendations for software to perform this that is either free or commercially available?
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Stefanie - Thanks for your suggestions. I've been in touch with Dr Van Luijtelaar regarding Spike-and-wave finder; is that the same software as you mention in your second bullet?
Maxine - Thank you also. Yes I remember you from Alpbach too! I'll try the free trial of Neuroscore.
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Is it possible to create an ERP study using 4 positions?
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You pose a tricky question as there are big differences between ideally, good enough, and what you can get away with. What this really boils down to is your specific question of interest. If you are focusing on uncovering the neural processes underlying a specific task then you might require a lot of post processing and even source localization. In this case a lot (>64) of channels would be ideal. However, if you are trying to create and improve a BCI for use in a clinical population that tires easily, then one of the systems with just a few channels (~16-24) that sets-up quickly and easily might be good enough. However, if you just want to make a demo BCI off of a large and commonly found signal like a P300, then you might get away with a system with less than 8 total electrodes.
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From the MRI segmentation of the brain.
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Hello Medani Takarinas, in order to generate an FE mesh you either need to have a closed area description (2D) or a closed soild / volume (3D) in which an FE modeler builds in the FE elements. Another opportunity to start from is if you have surface points in which, via surfaces and volumes, FE meshes can be generated, or , if you have a sufficiently large Point density it might be possible to intoduce surface elements directly and volume elements subsequently. However what you probably Need to do first is that your MR Image has to be converted into a dataset of surface Points, a surface or volume description (shape) in 3D space. From what I have seen, I believe, this Information should be available by your MRI "machine" as it uses this Information for ist own Images already. The question might be if it has the right Format or it Needs to be converted to the one of an FE modeler like PATRAN and hypermesh.
Is there an example of a finite element mesh head with several layers! and the methods used to obtain this model?
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To simulate the model in FE method
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thank you, i got it, i use some software, like freesurfer, brainVisa or brainSuite, we can get any surface of the brain using the IRM
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The interaction of rhythms in different EEG bands is commonly called “cross-frequency coupling” (CFC) and has been reported in continuous electrophysiological signals obtained at different levels. Several methods exist for assessing phase-amplitude coupling and no single method has been chosen as the gold standard for detecting the phenomenon.
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EEGLab. Completely modifiable, as opposed to some other software where you can't get "under the hood" and optimize/customize for your own data set. It spits out ITC quite simply, but you can do other things too. You can check out a plug-in called PACT, http://sccn.ucsd.edu/wiki/EEGLAB_Plugins. Depending on the nature/format of your data set you might need another conversion plug-in (same URL). However, it is not difficult to use plug-ins and increasingly they are object-oriented. There is also a new toolkit/plugin, SIFT, which does all kinds of analyses & visualization for multivariate causality and information flow analyses (same URL or http://sccn.ucsd.edu/wiki/SIFT).
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I have already identified the necessary ERP components FN400 and LPC from cued recall-recognition paradigm, but need to use time frequency analysis to determine if I get different patterns of EEG data (i.e., more betta or less beta) for behavioral responses of the participants in terms of hits, misses, false alarms and correct rejections. From my ERP analysis, I found greater parietal amplitude at 600-800ms for misses followed by hits compared to correct rejections. Unfortunately I don't get the standard frontal old/new effect, most likely because I didn't look at the correct electrodes. I am looking at topography to see if this is the case. Time frequency analysis of the EEG data could help support my findings.
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Hello Ahmed,
Fieldtrip is all code (but its website has many examples and in-depth documentation). EEGLAB has a graphic user interface.
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Mice aged 3-5 weeks at time of electrode placement.
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I would like to use Eprime file to import maker in EEG file (Brain Vision). Data in the Eprime files correspond to good and wrong answers and I want to use it for ERPs analysis. Can someone give me some advice?
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Dear Amit and Valia,
I finally solved my problem, easy when you modify the BrainVision marker file indeed.
Thank you anyway for your answers!
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I am not too familiar with power analysis techniques. Is my understanding correct that the power analyses typically reported in EEG research papers is the power AVERAGED across a segment of the waveform/EEG data?
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You can do power analysis by averaging over some segment of the waveform using an FFT. This will give you power for whatever frequency bins you choose. However, it is generally considered better to analyze the power using something like wavelet analysis which lets you look at power and still keep the time axis. Using wavelets you end up with a plot where the Y-axis is frequency, X-axis is time, and Z-axis is power. I have used EEGLab in the past to do this type of analysis. It's available free. They have excellent documentation and a very good online community for support. Good luck.
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How are people going about this? We have SMI Red eye-tracking and would like to integrate EEG recording during cognitive computer tasks. SMI and Emotiv (EEG neuroheadset and software) have partnered to provide simultaneous eye-tracking and EEG, so we are considering that.
My question is: is it necessary to use a package that integrates that data, or could we record them separately, but at the same time (i.e. not purchasing all the software updates etc. that make the systems compatible) and then "manually" integrate the two components? Would this, however, cause big problems in the data analysis (e.g. artifacts in ERP due to eye blinks) that would have been accounted for with the integrated software packages?
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Hello,
In my opinion, you would not need such package to integrate both signals during the recording. The only thing is that, you need to know the exact time in which both apparatus start recording. I assume you will use some kind of software to present stimuli that is able to send triggers through a port. If so, one possibility might be to send a trigger to both apparatus at the same time. Another possibility could be to initiate the recording from the eye-tracker using this trigger, and at the same time this trigger appear in the EEG recording.
I did something similar in my lab, to synchronize the EEG signal with a movement analyzer to study markers of brain activity associated with the kinematic of the movement. Using an 'in-house' parallel cable that we built, we could send the same trigger to both apparatus, and it worked fine.
If you need any kind of help, do not hesitate to contact me through mail or here!
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I want to understand the difference.
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Dear colleagues,
One remark specifying the difference between EPs and ERPs is necessary. Indeed, as Juliane pointed out, they are time-locked to certain event, but while often EP = ERP, there are cases where we have to differentiate ERPs from EPs. 1. For example, readiness potentials (Bereitschaftpotenzial, Kornhuber et al.) and contingent negative variation (CNV, Gray Walter et al., Tecce) emerge before the stimulus and can be considered as a signature of motor preparation or expectation of a stimulus. So literally they are not evoked, but are related to certain events and therefore it is more precise not to call them as evoked potentials but regard them as event-related potentials. 2. There is a strong tradition to make difference between evoked and induced brain potentials. Evoked ones are time-locked to the stimulus that evokes them and thus, for example, traditional simple averaging of ERPs across single trials leads to a conspicuous waveform of an averaged EP. However, induced potential changes may vary in terms of their temporal relation to the stimulus to which they are related. Which means that when we do simple traditional averaging over single trials the signature we look for may become hidden because components may cancel out or seriously distort each other due to their phase differences. In these cases procedures like wavelet analysis (with a temporally „travelling window“ of a filter) etc are advisable.
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I am looking for a free / open source software.
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Hi
Please see osdoc.cogsci.nl/
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I am doing an EEG study. I am using a one-channel EEG headset and it sends the EEG recordings as csv files. I am at the very beginning of the research but I already have a lot of csv files and nested folders. I am thinking about creating a database with mySQL but I want to learn about the pros and cons of it? Are there any other ways, or an organized environment, to keep the recordings?
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Dear Melis,
Interesting question. For the NBT toolbox (Matlab EEG toolbox), we decided to use a very strict fileformat namely: <ProjectID>.<SubjectID>.<Date of recording>.<Condition> , (see http://www.nbtwiki.net/doku.php?id=tutorial:importing_data_into_nbt_format).
Having this standard file format makes it very easy to identify a file, even if it by mistake ended up in the wrong project folder, and it further makes it possible to , e.g., quickly search for a specific subject (via the <SubjectID>)
We further store all the additional subject data (e.g. age, gender etc.) in another file with the format <ProjectID>.<SubjectID>.<Date of recording>.<Condition>_info. This makes it very easy to read in this data during analysis + makes it easy to share the data with colleagues (you simply copy-paste all your files).
We have previously used (we still do for some data) either excel sheets or a mySQL database. But, my experience is that this approach is less flexible. The core issue with excel sheets or mySQL databases, is that people forget to document what the different columns means (what does GY_Z2 mean?).
Best wishes,
Simon-Shlomo Poil
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Me and my colleagues are just starting EEG research and we are looking for some good traditions and practice from experienced labs. What software to manage such a research would you recommended? In particular I would like to hear the recommendation of the community about software to manage:
- research plan,
- information about research participants,
- research results.
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In my deparment we are using the ICM+ software, for monitoring & data analysis of various signals: http://www.neurosurg.cam.ac.uk/pages/ICM/
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I am trying to create a STUDYset in EEGLAB, but when I load my datasets, it returns a looping never-ending message at command window: "Duplicate entry detected in new design, reinitializing design with new file names". I am following the instructions in EEGLAB tutorial. Does anyone know what I am doing wrong? Tks!
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Hello Nastassja!
What script are you using to create the study? Here's the one that worked for me:
[STUDY ALLEEG] = std_editset( STUDY, [], 'commands', { ...
{ 'index' 1 'load' 'D:/S1.set' 'subject' 'subj1' 'condition' 'Eng1' }, ...
{ 'index' 2 'load' 'D:/S2.set' 'subject' 'subj2' 'condition' 'Eng2' }, ...
{ 'index' 3 'load' 'D:/S3.set' 'subject' 'subj3' 'condition' 'Ger1' }, ...
{ 'index' 4 'load' 'D:/S4.set' 'subject' 'subj4' 'condition' 'Ger2' }, ...
{ 'dipselect' 0.15 } });
STUDY = pop_savestudy( STUDY, EEG );
Just change the .set files path, subject codes, and condition names, and the script will create a study. It will pop up a dialog box allowing you to choose a save location, etc.
Hope this works for you,
Nikola
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I am working on nonlinear time series analysis of EEG signals. Can anybody tell me if there is any difference between fractal dimension and correlation dimension? If I have the values of fractal dimension and approximate entropy then is there any formula to calculate correlation dimension?
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The correlation dimension is one member of the big family of fractal dimensions.
1) The most popular, probably because this is the first one introduce by B.Mandelbrot, is the capacity dimension (also known as box counting dimension). It consists in computing the number of small cubes needed to cover the whole fractal object and to compare the result with the boxes size on a log scale. The limit of the ration when cubes size tends toward 0 defines the box counting dimension (denoted D0)
* The drawback of the capacity dimension is that it is a purely geometric notion and that is not sensitive to the density of points. If the object is a dynamical trajectory (as it is probable for your EGG) or a sampled data series issued from a dynamical process, this remark means that Boxe counting dimension do not take into account the dynamic behavior. This fractal is not sensitive to time spent in a cube.
2) Since points are not spread out with a uniform density, some regions are more often visited than others. This fact is taken into account by the entropy notion which evolves the probability of occupancy of each cover cube. From the entropy, the information dimension is defined. This second fractal dimension is denoted (D1)
** If you use some toolboxes to perform estimations of fractals, be very aware with the consistency of results because they are very sensitive to the algorithm parameters. One way to check the consistency is to verify D0 > D1 (D0 = D1 means a uniform density of points).
3) Now, take a look to another fractal dimension: the correlation dimension (denoted D2). If you are interesting in fractal matter, I suppose that you EGG is dealing with chaotic dynamic. Then, through a proximity property of two random points on the chaotic attractor, the correlation dimension takes into account the fact that the attractor is a dense set of points. The correlation integral is first computed versus the cube size and then the correlation dimension by a limit when the cube size tends toward 0.
*** Because your data set is not infinite, you will not be able to compute the limit when the cube size tends towards 0. Also, the statistical properties of noise become prevailing for very small boxes. Then the slope of the correlation integral versus the cube size is a sufficient estimator of the correlation dimension.
**** An other important remark is the necessity to not perform fractal estimations on original attractor but only on an embedded attractor using the embedding theory but it's an other topic (see at the end of my answer).
***** You have to compute several estimations of the correlation dimension by increasing the embedding dimension. When it is high enough, the correlation dimension converges to a constant value.
****** An other checkpoint for the consistency is D0 > D1 > D2
4) Reny's spectrum
The previous fractal dimensions can be considered as the first elements of the Rényi dimensions spectrum.
The Rényi’s dimensions are defined from the notion of generalized entropy. The principle is to weight the probability of the most often visited cubes according to the order of the dimension.
******* A relationship has to to be check by Renyi's dimensions: D_q+1 < D_q.
EMBEDDING THEORY
Embedding process have to precede any estimations of fractals from a data series. First of all, the question is "why do we embed"?
Generally speaking, one is not able to sense all state variables. Theoretically, according to the Takens theorem, any state variable can be used to calculate the invariants of the dynamics. But practically, it is not a calculation that is made, but only an estimation performed by an algorithm. This raises the problem of convergence and it is directly related to the more or less good conditioning of the useful information into the variable.
The main practical task are:
A) The choice of the embedding variable.
B) The choice of the embedding delay.
After having chosen the embedding variable, the next step is the determination of the right embedding delay. It can be estimated by the first zero crossing of the autocorrelation function or, better, by the first local minimum of the mutual information.
A way is to program the Fraser and Swinney algorithm.
C) After that the determination of the embedding dimension (it's not a fractal but an integer usually denoted De).
The embedding dimension is the minimal dimension of the state space that allows the attractor reconstruction without topological ambiguity and able to represents all dynamic behaviors.
In order to find the right embedding dimension, you can use the False Nearest Neighbors algorithm. The good dimensions are the values with the number of false neighbors near to zero. You will see that the result depend on the size of the data set (the biggest is the best) but also of your initial choice for the embedding variable... So be very careful...
Once again, remember that algorithms are useful but the expertise is essential for interpreting results and doing the good choices. It can be seen the importance to have a critical glance in order to align the results of various estimations with the other invariants like fractal dimensions.
Fore example, base on a mathematical conjecture, it is expected that De >= 2Df, where Df denotes the fractal dimension.
Pr. B.G.M. ROBERT - University of Reims Champagne-Ardenne, France.
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Preliminary evidence suggests that meditation improves stress, depression and chronic pain. Is there sufficient evidence to indicate that brain changes occur over time?
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The research team of Prof Richard Davidson, from the University of Wisconsin (Center for Investigating Healthy Minds, Waisman Center), has produced numerous publications concerning this topic. Here are some of the publications: http://www.investigatinghealthyminds.org/cihmScientificPub.html
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I need some Parkinson's Patient's EEGs to carry out my project. I found a few on Physionet but that is not enough. Can anyone help me out please?
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I am new in EEG signal analysis and processing topic. My aim is to remove motion artifacts from ambulatory EEG. Now, for EEG, I understand that there are mainly two types of signal component (originated from brain source): one is EEG rhythm (alpha, beta, theta, gamma, delta, mu, etc.) and another type is transient (epileptic/non-epileptic spikes). My question is how to get the ground truth data for EEG? Is there any similar database available online? If I don't have or don't know what is the ground truth EEG signal, then I can't be sure about the other non-brain originated signals picked up by the electrode (e.g. artifacts, interferences, etc.) to be detected. And hence the performance of artifact removal algorithm can't be evaluated. Can anyone please help me? Thanks in advance
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Artefacts are still better recognized and 'filtered' by experienced technicians than by algorithms. Therefore, artefact removal algorithms can be compared to technicians.
Artifact removal is not a goal in itself: it should improve the final analysis result. Any evaluation should address that result, including the contribution of artifact removal.
Close to ground truth data are intracerebral recordings, as made for instance in patients considered for epilepsy surgery.