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Brain Computer Interfaces - Science topic

Community for researchers who work with or are interested in BCIs and their applications.
Questions related to Brain Computer Interfaces
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Mario Beauregard from Montreal University is using real-time fMRI neurofeedback
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I would like to do fNIRS, so far I'm doing cortical oxygenation and aEEG assessment in the preterm population. ISS.Inc developed the "Imagent" a fNIRS system with good temporal resolution follow the next link for more info; http://www.iss.com/products/imagent/index.html
I am not working with the Canadians, however, I do know very well Mario Beauregard.
Regards
Leon
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Biological signals play an important role in providing correct motion intention of the users to the assitive robots. What roles will play by EEG and FNIRS when they used as a method to grasp the motion intention, individually and as a combination. Please comment your views.
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I think EEG is nowadays much more robust then fNRIS. EEG is extensively used in BCI appliacations. For sure that EEG will take place in this topic of robotics!
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I am struggulling with OpenVine for 3 weeks. I will use P300 magic for visual event related potantial BCI application. Images and backround can be changed easily for request.
1. a. In acquistion.xml data recorded in signal folder. Here subject ID must be exist in data file name. How can I add ID to filename?
b. Acquistion.xml trial and session number in gui are not same as application.
c. Why all images do not stimuli in aquiation.xml ? Only some of them flashes. In acquistion, I think data from all images must be recorded.
d. I tried to convert .ov file in to .csv or .edf as follows, generic file reader and csv/edf writer but it failed.
2. a. In train-classifier.xml file, defaultly generic stream reader reads signal/bcı-p300-signal.ov. Why only one file is used? For example, how can I data from 18 subjects?
b. How can MATLAB classifer be applied in this part?
Please help me.
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I agree that it may be frustrating when you don't get an answer to your problem, but you have to realize that this is a free and open-source software, meaning people are not making money out of it. So people who answer on the forum or write the documentation have to have the knowledge, motivation and time to do so, as they are doing it for free, purely out of good will...
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Looking for a simple technique/algorithm for artifact correction(not rejection) for analyzing oscillatory processes in EEG signals.
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There are a variety of ICA algorithms -- some more computationally demanding than others, which can be found here:
Hope this helps,
-Michael
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Where can I get an EEG signal database for emotion detection of neuro or behavioral disorders?
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Hope the following links will be useful
"In order to obtain the password to access the data please send an email to ashkan.yazdani@epfl.ch"
DEAP dataset:EEG (and other modalities) emotion recognition.
Enterface dataset: This EEG experiment was named "Emotion Detection in the Loop from Brain Signals and Facial Images".
(The file is attached)
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In EEG classification systems (for BCI), consider two scenarios where
1) Acc = 90%; T = 3 sec; (time window)
2) Acc = 70%; T = 1sec;
ITR for condition 2 is higher than condition 1 even though there is significant decrease in accuracy.
So, is ITR always a better option (than accuracy) to consider the efficiency of model ?
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If there are less than 3 classes, in this case model 1  is just  a bit better than random chosing.
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What had been used in the attached journal is the ICMS pattern for the CBI ( Computer Brain Interface). I would be grateful if you let me know is there any instruments or toolbox to generate this pattern. unfortunately I don't have an access to intracellular electrodes so is there any way to simulate BCI signals as invasive?
it might be useful that the experimented animal is rat.
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High-side digitally current controlled biphasic bipolar microstimulator
Authors
Timothy L Hanson, Björn Ómarsson, Joseph E O'Doherty, Ian D Peikon, Mikhail Lebedev, Miguel AL Nicolelis
Publication date
2012/5
Journal
Neural Systems and Rehabilitation Engineering, IEEE Transactions on
Volume
20
Issue
3
Pages
331-340
Publisher
IEEE
Description
Abstract—Electrical stimulation of nervous tissue has been extensively used as both a tool in
experimental neuroscience research and as a method for restoring of neural functions in
patients suffering from sensory and motor disabilities. In the central nervous system,
intracortical microstimulation (ICMS) has been shown to be an effective method for inducing
or biasing perception, including visual and tactile sensation. ICMS also holds promise for
enabling brain–machine–brain interfaces (BMBIs) by directly writing information into the ...
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Brain computer interface is the direct path between brain and computer
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Cognitive science started as an interdisciplinary field ~50 years ago, combining linguistics, information theory, computer science, and more. Since then fields such as augmented cognition, computational neuroscience, evolutionary psychology, and more have turned it into an umbrella of interdisciplinary sciences. I always recommend that people who are interested in this question simply check out the contributions to the conference proceedings to the HCII conferences (I was at HCII2013, so I can definitely recommend checking out the published proceedings from the ~30 volumes from that event).
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I mean testing of students with "BCI" such MindVave during training sessions and data collection?
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In BCI competition III: data set 2 there is 2 subject i.e. subject A and subject B.
In both case there is train data and test data. In training data set there is target character but in test data there is no target character. So, after classification of test data with which I should compare the result?
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Sure, I have the load_bci function that you need to load the recordings to matlab, it is a standard function of the BCI2000. Please send me a direct message or an e-mail and I'll send you back the info that you need.
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I have been trying to get some research questions on BCI, not quiet sure what to think about. Could anyone suggest some questions? 
Thanks you.
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Aiming for: game and behavior research in post PhD level.
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OpenBCI is is using the quite advanced TI ADS1299 A/D converter chip at it's heart. And is also Arduino compatible. We now support many research applications such as OpenViBE, EEGLAB, BCILAB, MATLAB, LabStreamingLayer, LabVIEW, VVVV, PureData; APIs for Python, C#, Java, Processing, etc.
As well as a very active Forum community, which I help moderate.
8 and 16 channel version available.
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I am working on Brain Computer Interface and would like to ask the scientific community if there is an existing mathematical model that can simulate neural adatation of the brain more importantly for application in Sensorimotor rythms based Brain Computer Interface.
Your Input will be of great help.
Thanks.
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Yves, this paper might be useful to you:
Eliasmith et al (2012) "A Large-Scale Model of the Functioning Brain"
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In a 2D environment, What is the difference between a 2D and 3D flicker (stimulus) in ssvep based bci experiments?
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I would guess that for SSVEP, shapes/meanings etc. of stimuli do not play an important role. SSVEPs appear as pre-cognitive evoked potentials, and are related to automatic retina response to rapid visual stimulation. You may want to verify, however, if e.g. changing from 2D to 3D can lead to an amplitude increase, or a similar effect.
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As there are several frequency bands associated with the EEG rhythms (e.g. alpha, beta, theta, delta, mu, gamma), I wonder do we need to analyze every band/rhythm or just some selective band(s) will be enough for BCI research, specially for Motor Imagery (MI) based BCI applications. Since every rhythm is associated with particular task/behavior that's why I suppose I don't need to analyze every bands. I would like to know what is the usual practice in BCI studies?
Thanks in advance! :)
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Hello
Dear friend
You can use this file, i hope be useful for you.
best regard
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Most of the BCI papers that used Canonical Correlation Analysis (CCA) technique, applied it only on SSVEP data. Why is it so? if not are there any papers that used CCA for MI or other oscillations?
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To the best of my knowledge, CCA is currently applied to SSVEP BCI. One main reason for this is that when CCA is used, some templates(e.g., 12 Hz signals and 13 Hz signals for SSVEP) first need to be built offline. Then, the collected EEG data online were matched with the templates to classify them into the  corresponding class. For MI,  imaging moving left or right hands cannot produce brain signals with different range of frequency. That is, such templates cannot be built. To better understand this issue, you need to figure out CCA and MI. 
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May I know what are the EEG channels that are considered as most important in BCI studies. I mean for example if the data is collected using the international standard 10-20 system, then for motor imagery based BCI, which channels we should look for classification, or for ERP  detection and classification? The answer would help us to decide for artifact detection and removal in a particular EEG channel.
Any suggestion or recommended link/paper would be highly appreciated. Thanks a lot!
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I would propose to look at some of the original neuroscience literature on imagined movement, e.g. by Pfurtscheller:
and then at some papers about BCI and motor imagery, e.g. by the Berlin group:
Short answer: C3 and C4 are the most commonly used sensors. The Berlin group in particular fancies a mathematical approach called common-spatial patterns, which extracts a spatial feature map that is then used for each subject. The purpose of your research, however, defines your approach. If you are an engineer, you might want to try the heavy machine learning approach. If you are a neuroscientist you might want to care more about the underlying brain region and related brain mechanism.
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gtec brain cap + Gammabox  + Nihon kodhen input box + Nihon kodhen amplifier. 
If you have experience on this combination please give your comments. (Functionality, Operation, Limitations and Advantages) 
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Dear Sanjaya Vipula Bandara, your question does not say wheteher you already use/ have this equipment or not. As in another thread, Nihon Kohden equipment is very reliable, but in the end, the utility of equipment lies in the user. You can see use of EEG equipment in references on my page for sleep studies, including paediatric sleep studies, studies of attention and also BCI. Hope this helps. Best wishes 
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Any recommended textbook about Brain Computer Interface? I already use Recent Advances in Brain-Computer Interface Systems by Reza Fazel-Rezai, any other easy understanding books?
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My main suggestion would be book by Prof. Wolpaw, who is one of the first to study BCI and his centre in USA is one of the best in BCI field. However, BCI is very dynamic field and in few years there can be new techniques or methods for better data processing.
I recommend this (fairly recent) book edited by Wolpaw:
"Brain-Computer Interfaces: Principles and Practice" ISBN-13: 978-0195388855.
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I just wanna to know effect of acoustic stimulation on visually stimulated brain-computer interface (BCI) devices.
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Theoretically cybernetics can be applied to all existing theories of aging.
However, I have not been able to find any research where a model of socio cybernetics has been applied to the evaluation of any intervention/observational research on healthy aging.
I would appreciate your suggestions. with thanks, chariklia
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Just in these year we try to develop the concept of socio-cybernetics.  Unfortunately as the aging  isn't our goal. However we are interested in using the cybernetics in social systems where we intent cu apply the control systems engineering. First attempts are encouraging our efforts.
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I have two question:
1)How to perform real time EEG signal processing?
2) How to create command from the real time processed data to any application(assume some android app)?
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In addition to the previous comments, check BCILAB, reported by Kothe, C.A., and Makeig, S. (2013). BCILAB: a platform for brain-computer interface development. J Neural Eng 10, 056014.
Also, see this paper that reviews BCI Software:
Brunner, C., Andreoni, G., Bianchi, L., Blankertz, B., Breitwieser, C., Kanoh, S.I., Kothe, C., Lécuyer, A., Makeig, S., Mellinger, J., Perego, P., Renard, Y., Schalk, G., Susila, I., Venthur, B., and Müller-Putz, G. (2013). "BCI Software Platforms," in Towards Practical Brain-Computer Interfaces, eds. B.Z. Allison, S. Dunne, R. Leeb, J. Del R. Millán & A. Nijholt. Springer Berlin Heidelberg), 303-331.
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I'm intend to buy this BCI head set. Please I need to know the pros and cons.
Also does it give an accurate enough EEG signal?
How about Ease of it's SDK?
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I've had an EPOC in my lab for about 14 months.  How functional it might be in your application depends on what you want to do with it.  The output is easily manipulated in Matlab, but unless you understand Matlab, you may have some problems getting data output in a way that it can be analyzed.  The biggest problem with the Epoc SDK is that it doesn't allow for any control inputs or outputs.  So, doing anything that is time correlated or looking at EPs is nearly impossible.  An Openvibe acquisition client is available, so you could use Openvibe and just ignore the Emotiv SDK, but, unfortunately, the Openvibe client works only with SDK 1.0.0.4, which is, pretty much, unavailable.  I see you are at CSU, talk to Tony Sahley in Audiology.  He may be able to help you.
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Dear Sir/Madam
As I want, how to implement the brain computer Interface, without using electrode? 
pls help me for our research
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In the last few years many consumer BCIs have come up with varying capabilities, it would be interesting to think of apps which can upload mood from such a BCI. This along with regular inputs can be used to design a small AI which can help us in increasing our productivity. I am working on such a project.
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Hi,
I am working on artifact removal from scalp EEG. I would like to see the effect of artifact removal on BCI performances. However, since I am completely new in BCI research, I would appreciate any kind of suggestion on this particular area, especially the requirement of artifact removal online for motor-imagery based BCI applications. What types of artifacts are more disturbing in this area, and which spectral band is analyzed for BCI classification?
Any insight on this topic is much appreciated. Thank you in advance.
Regards,
Kafiul 
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Hi Kafiul,
this pretty much depends on what you want to do. The most essential part is that you are dealing with a *BRAIN*-computer interface, therefore any feature you want to use has to be based on brain activity, and not on peripheral muscles. One big source of contamination are eye movements (including blinks) and facial muscles (including those on the scalp). If you want to go for imagined movement paradigms, take special care that participants are actually performing the imagined movement (i.e. not moving the respective muscles).These things are classically checked for by attaching additional electrodes to the respective muscles, or by using pressure-based pads, which can be more sensitive to micro-movements (there once was a paper about this, I forgot who wrote it). But as said, it all depends on the paradigm you want to use.
The paradigm also defines the spectral band for BCI classification. Most scalp EEG studies are done on imagined movement, which analyze the beta band (around 15-25 Hz; check papers from Pfurtscheller, Wolpaw and McFarland to start with). I myself conducted covert attention studies in the alpha band (around 10 Hz). There are also different (feature selection and classification) methods that can be used here, which brings me to the next point.
There is loads to read about BCI, but it all depends on your area of expertise, e.g. some groups are working on advanced machine learning algorithms to improve BCI (check the Berlin group around Klaus-Robert Muller), others working on integration with robotics (check Jose del R. Millan), others on application in locked-in patients (e.g. Niels Birbaumer) or on innovative paradigms like auditory BCIs (e.g. Benjamin Blankertz), other use fMRI instead of EEG (e.g. Rainer Goebel), or integrate both modalities to study effects on the brain itself (e.g. Tomas Ros) incl infamous neurofeedback applications (e.g. Martijn Arns edit: and John Gruzelier), and many more subodmains. I would propose that you read the method section of some papers to get a feel for what you really want and need. There are different approaches and opinions, and there is no one else but you (and your supervisor and co-authors) to decide what is the right choice for your endeavour. Good luck!
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References:
*Kalen et al. 'Age-related changes in the lipid composition...' Lipids 1989; 24: 579-85.
*Alehagen and Aaseth.'Selenium and Q10 interrelationship...' J Trace Elem Med Biol 2015; 31:157-62. 
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Dear Jan,
According to the free radical and mitochondrial theories of aging, oxidative damage of cell structures by reactive oxygen species (ROS) plays an important role in the functional declines that accompany aging. ROS are generated by mitochondria as a byproduct of ATP production. If not neutralized by antioxidants, ROS may damage mitochondria over time, causing them to function less efficiently and to generate more damaging ROS in a self-perpetuating cycle. Coenzyme Q10 plays an important role in mitochondrial ATP synthesis and functions as an antioxidant in mitochondrial membranes. Moreover, tissue levels of coenzyme Q10 have been reported to decline with age. One of the hallmarks of aging is a decline in energy metabolism in many tissues, especially liver, heart, and skeletal muscle. It has been proposed that age-associated declines in tissue coenzyme Q10 levels may play a role in this decline. In recent studies, lifelong dietary supplementation with coenzyme Q10 increased tissue concentrations of coenzyme Q10 but did not increase the lifespans of rats or mice; however, one study showed that coenzyme Q10 supplementation attenuates the age-related increase in DNA damage. Presently, there is no scientific evidence that coenzyme Q10 supplementation prolongs life or prevents age-related functional declines in humans.
References:
Kalen A, Appelkvist EL, Dallner G. Age-related changes in the lipid compositions of rat and human tissues. Lipids. 1989;24(7):579-584.  (PubMed)
Beckman KB, Ames BN. Mitochondrial aging: open questions. Ann N Y Acad Sci. 1998;854:118-127.  (PubMed)
Alho H, Lonnrot K. Coenzyme Q supplementation and longevity. In: Kagan VE, Quinn PJ,eds. Coenzyme Q: Molecular Mechanisms in Health and Disease. Boca Raton: CRC Press; 2001:371-380.
Singh RB, Niaz MA, Kumar A, Sindberg CD, Moesgaard S, Littarru GP. Effect on absorption and oxidative stress of different oral Coenzyme Q10 dosages and intake strategy in healthy men. Biofactors. 2005;25(1-4):219-224.  (PubMed)
Sohal RS, Kamzalov S, Sumien N, et al. Effect of coenzyme Q10 intake on endogenous coenzyme Q content, mitochondrial electron transport chain, antioxidative defenses, and life span of mice. Free Radic Biol Med. 2006;40(3):480-487.  (PubMed)
Quiles JL, Ochoa JJ, Battino M, et al. Life-long supplementation with a low dosage of coenzyme Q10 in the rat: effects on antioxidant status and DNA damage. Biofactors. 2005;25(1-4):73-86.  (PubMed)
Hoping this will be helpful,
Rafik
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In the complex system of the executive functions the basal ganglia are of great significance. These control cognitive activities such as spatial memories, the execution of motor actions in a specific context and motivational elements of learning. The cortex and the basal ganglia are closely linked and control, also through the cerebellum, the motivational aspects of a movement (the preparation for the action), the contextual aspects (the execution of the movement) and its state of execution. Now, in what way does this complex system generate rapid and unexpected actions?
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Thanks, Vsevolod! But the question is: how does our brain behave when faced with unforeseen situations, those not determined by innate motor skills? For a long time, little attention was paid to this problem. It was for the most part studied with regard to theories of the executive functions: the functions that allow an individual to design, plan objectives, carry out projects aimed at one purpose, monitor (and if necessary anticipate) his/her own actions to adapt to environmental changes. Numerous sub-skills are included in this sphere which are coordinated between themselves: for example, the inhibition of a response at an inappropriate moment (or its deferral to a more appropriate moment); the implementation of a plan of sequences of action useful for a purpose; the representation of the task that includes relevant information (memorised or perceived immediately) associated with the desired result. The associative cortex of the frontal lobe supervises these functions at three different levels of operation: a) the dorsomedial, assigned with functions of working memory necessary for the selection and maintenance in the memory of the aims of the behaviour; b) the mesial, assigned with the integration of the emotional and motivational aspects necessary for the continuation of the action and c) the orbital, assigned chiefly with the inhibition of behaviour and instinctual impulses. For the effective completion of the movement they collaborate with the frontal lobe, the cerebellum, the basal ganglia and the spinal cord for an effective execution of the movement.
 In this representation, the space for new sequences of action is reserved for lower levels of the motor control, where the procedures for the correction of the errors proceed from a superior to an inferior order and the individual movements are combined in routine and subroutine sequences that lead to the goal directed action. This hierarchical model renders the motor functions subordinate to the higher brain activities and drastically limits the understanding of new sequences of action. 
Catching a flying object, hitting a moving target, pronouncing words with great fluidity or playing a piano, call for such fast times that exclude the possibility of corrections by means of sensory feedback: an action may have success or failure, but due to the time constraints it cannot be correct. In what way, therefore, can the brain achieve such rapid muscle action sequences, to the point of overcoming the temporal limits of the neural circuits?
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Dear group, we're trying to connect 2X32 ch. EEG caps on 1 64ch. device. Does anyone have the experience in what way the ground electrodes should be placed? We can use NuAmps, Deymed or SynAmps EEG devices.
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You can either use 1 electrode and put a metal plate under the feet of the subjects or use 2 ground electrodes, one for both subjects.
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I like to work in BCI "Brain computer interface" please suggest me which is the best brain computer interface to do research (beginner) ?
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Hello!
I am not sure there is a definitive answer. It depends on what you want to do. Of course, if you're new at this and not sure yet whether to pursue BCI on a large scale, you could always start with an emotiv EPOC. It's comparatively cheap. Make sure you get the research edition. But I am pretty sure that there'd be strong limitations:
1. The electrode layout is limited. If you want to try something new that may pose problems.
2. I would also doubt that the equipment is appropriate for intensive use (i.e. lots of subjects and sessions in a short time).
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Hi,Hope you are fine. I am a student of Master's in Mechatronics Engineering (Air University "Islamabad Pakistan") and currently doing my research in fNIRS Based BCI (Brain Computer Interface) system. I have gone through Journal Paper “ A regularized discriminative framework for EEG analysis with application to brain–computer interface, Volume 49, Issue 1, pp 415-432,January 2010. In it a new algorithm that unifies feature extraction,feature selection,feature combination and classification is presented. Its very informative and i am trying to get through it but facing difficulty in understanding the algorithm.I only have raw data (which i got from brain). I am a bit confuse how above mentioned features can be unified. It's my humble request that kindly provide the regularized discriminative framework code and helping material which can be used for selection so that I could able to continue my research. I shall acknowledge your work properly and cite by my research papers .Thanks
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Dear Rayyan! Please try to evaluate some papers in the attachment ro my letter. I hope you will find your own way...
Vladimir
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Hi,
I want to analyze a size volume of intense spots in a MRI data file in FIJI (ImageJ)
I firstly project this file to 3D format using the Stacks -> 3D project tool.
Using the point tool, I can select which intensity should be used for calculating (for which I now only know 3D object counter, but this is a bit messy in my view, options? :) ).
Then using 3D object counter it calculates the amount of available spots.
But now I want only to use part of the 3D scan (which I compiled first) to be able to analyze only the intense spots in the frontal lobe and prefrontal cortex and in the hippocampus.
Is there an easy way to calculate this? (total volume of hyperintense spots in frontal lobe and hippocampus from MRI (.nii) data)
Thanks in advance,
A
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Hi Mr. Barthel,
Thank you for your answer. Unfortunately the project is already finished. I quantified the volumes manually or using 3D object counter per slice. But I will try your suggestion later.
Regards,
Alexander
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I'm currently thinking of buying the V-probe, but heard a rumor that they are not as reliable as the U-probe. Any comments or experiences with either of these? 
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Hi Edward,
I have some experience with the U-probe. It's really durable and perfect for repeatedly penetration. I'd say I love this probe if it's not that expensive. One thing I should point out is, due to the low impedance, the signal recorded from U-probe is not as good as regular single electrode (still all right for isolation). 
I have no idea about the V-probe. Presumably they should be similar except the shank. Hope someone else could share their experiences.
-Ji Dai 
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How to find out ERP component in Prospective memory task after stimulus presentation and which ERP component (Peak) is represent it?
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Pankaj,
The 2011 review paper from Neuropsychologia is probably the most complete review of the ERP and prospective memory literature. There are some newer studies that have appeared in the last couple of years; however, these have not really introduced new component but rather refined our understanding of components identified in previous research. I have attached a PDF of the Neuropsychologia paper, I hope that you find it useful in your research. Cheers, Rob
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I used labchart program v.5 for recording EEG signals in anethetized rats. Using lab chart reader v.8 i drew parameters such as maximum power, amplitude, duration, etc. I woul like o know for Quantitative EEG analysis which parameters are best o use?
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I think the mean amplitude would be the jest one (if I cirrectly underdtand what you mean in your question).
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Emotiv EPOC+ 14 channels 
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Definitely. If you have the softaware of Emotive then it is very easy to calibrate the machine before you have started the signal acquisition.
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is there any university teaching base or any application for Sci Fi as a field within information technology, Linking (these below concept with Sci Fi) :
- simulator 
- sci fi program (Academic)
- Beings ( Biological robot ,Robots and humanoid robots ,Artificial life ,Artificial intelligences , etc..)
- Body and mind alterations (X-ray vision , Artificial organs ,Remote sensing ,etc...)
- Technologies (Artificial gravity ,Emerging technologies ,Brain–computer interface , Asteroid mining, Robots ,Simulated reality , etc..)
etc..
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I would like to implement the approximated surface Laplacian (SL) estimated using Hjorth Algorithm but I can't find an open literature on the approximation of The SL at scalp edges as suggested in "Spatial Filter Selection for EEG Based Communication" by McFarland et al.
They mention Zhou's work for edge electrodes SL approximation but I do not have access to that literature.
Does anyone have relevant literature on that?
Or Does anyone Know How To Compute The Large Laplacian also called" next nearest neighbor SL" (up to the edge of the scalp)?
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Dear Yves,
I found the follwing statement in Mike X. Cohen - Analyzing Neural Time Series Data:
"A basic approximation to the surface Laplacian is to subtract from each electrode the
averaged activity of immediately surrounding electrodes (Hjorth 1975; Shepard 1968). This is not the most elegant solution, however: volume conduction does not spread only to nearest neighbors but rather to many electrodes up to tens of centimeters away, and the effect of volume conduction does not affect all neighboring electrodes equally but, rather, as a function of the distance between each electrode and the “source ” electrode (that is, the skull/surface source of the electrical activity). There are several algorithms to compute the surface Laplacian that are more accurate than nearest-neighbor subtraction."
This may not help directly with your question, but maybe you'll want to have a look at Cohen's book for some other calculations.
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Hi,
Has anyone got any reviews on gtec's g.nautilus dry electrode based system, g.USBamp over the g.MOBIlab+ ? I am currently using a 8-channel g.MOBIlab+ with active electrodes and am thinking of upgrading to a 16/32 channel system. I will be using the system for a BCI application.
Thanks.
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Hello. I have made some acquisitions with g.MOBIlab+ and dry SAHARA electrodes and the signal quality is good. The only problem that I found is that they are uncomfortable for the participants. They often complain after some time of using.
I'm currently using g.MOBIlab+ (16 channels version) with active electrodes and for me the signal is very good. I'm preparing a publication with the test I've made comaring these systems.
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Hi,
Is there a publication on dataset 1 describing the class label (original class label) for testing data of BCI competition 3?
Thanks.
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Dear Akshay Raj Gollahalli 
Goals of the organizers
The goal of the "BCI Competition III" is to validate signal processing and classification methods for Brain-Computer Interfaces (BCIs). Compared to the past BCI Competitions, new challanging problems are addressed that are highly relevant for practical BCI systems, such as
session-to-session transfer (data set I)
small training sets, maybe to be solved by subject-to-subject transfer (data set IVa),
non-stationarity problems (data set IIIb, data set IVc),
multi-class problems (data set IIIa, data set V, data set II,),
classification of continuous EEG without trial structure (data set IVb, data set V).
Also this BCI Competition includes for the first time ECoG data (data set I) and one data set for which preprocessed features are provided (data set V) for competitors that like to focus on the classification task rather than to dive into the depth of EEG analysis.
The organizers are aware of the fact that by such a competition it is impossible to validate BCI systems as a whole. But nevertheless we envision interesting contributions to ultimately improve the full BCI.
Goals for the participants
For each data set specific goals are given in the respective description. Technically speaking, each data set consists of single-trials of spontaneous brain activity, one part labeled (training data) and another part unlabeled (test data), and a performance measure. The goal is to infer labels (or their probabilities) for the test set from training data that maximize the performance measure for the true (but to the competitors unknown) test labels. Results will be announced at the Third International BCI Meeting in Rensselaerville, June 14-19, and on this web site. For each data set, the competition winner gets a chance to publish the algorithm in an article devoted to the competition that will appear in IEEE Transactions on Neural Systems and Rehabilitation Engineering.
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I've been doing some research with Emotiv Epoc, but it has a lot of disadvantages, for example it is almost impossible to evaluate female participants with long hair and small heads - the device does not get any signal from them. Furthermore Emotiv sometimes looses signal from some single electrodes and the quality of the data that it provides is not so great.
Do you know any other low-cost EEG devices that you would recommend for studies regarding emotions?
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Here's a recent paper evaluating OpenBCI performance relative to other commercial grade equipment. By Jeremy Frey, presented at the BCI Society 2016 meeting this week in Pacific Grove, CA (Asilomar Conference Center).
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I am planning to take up this research.
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I am doing some literature review on non parametric regression techniques.
I would like to ask those familiar with the topic if you may know the disadvantages and advantages of ANNs compared to other non parametric regression techniques like :
- MARS (Multiple Adaptive Regression Spline)
- Projection Pursuit Regression
- Gaussion Process Models (?)
- Additive Models
Is There Anyone who has a comparative literature on it?
Your Contribution will be of great help.
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Hi Yves,
Maybe this paper where ANN is compared among other models (MARS, PPR, SVR, RF) is useful to you.
Cheers,
Manuel
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I am working on Neurosky EEG heatset. I can get wave parameters like alpha beta gamma attention meditation levels. as per my survey , researchers work on Brain computer intration (BCI) with eeg signal. I need to know the impact on brain waves in real time wireless sensor network?. 
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Unfortunately I can not tell you much about the quality of the data sent, which I didn't test (if this is what you mean), but there are more info about the packages in the documentations on the developer website. 
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What the methods or transforms (other than CCA )can be used for SSVEP signal detection?
I am using SSVEP signal for BCI speller.
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I am working on BCI based SSVEP speller. I am acquiring SSVEP data with the help of flickering block (with a particular frequency) on the computer screen. CCA is the most common method to detect the frequency of the signal.  What can be the other possible classification methods or feature extraction transforms I can apply to detect the accurate frequency of the obtained signal?  and What are the features which can be used for the classification of biomedical  signals?
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So far I found the below equation to measure concentration level but the reference is not that solid and I can not relay on. Any one has other equation or solid reference that support this equation will be highly appreciated.
concentration level = ( (SMR + Beta) / Theta)
where SMR  = SensoriMotor Rhythm
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Dear Atef!  suggest you get acquainted with the detailed work in this direction. Please read the first one informative article in the application.
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Hello,
I have two volumes:  one structural MRI, and a functional NIFtii atlas. And I need to coregister these two volumes. 
What can be the suitable software to do this task (slicer,  freesurfer)? Slicer is a friendly software with a GUI, but I don't know how much the result is accurate. 
What are your suggestions? 
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Dear Aya,
The best and most flexible software for image co-registration is FSL. However, there are other softwares such as AFNI, or 
VISTA, Stanford) implemented in Matlab (Mathworks, Natick, MA).
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Has anyone got any reviews and practical recommendations about portable neuroimaging equipment that can be used to record during normal everyday activities?
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Hi Mitzi,
there are basically only two types of portable neuroimaging techniques, one being EEG (see e.g. products by TMSi, ANT neuro or gtec) the other is NIRS (near-infrared spectroscopy). While the former is measuring electrical brain activity, the latter measures hemodynamic responses. The stationary equivalents would roughly be MEG and fMRI respectively.
For more information about NIRS, see e.g. our website:
We happen to be the world leading company in creating portable NIRS devices, and of course offer great support prior and after sales. See http://www.artinis.com for our product portfolio. For some background literature and reviews, we gathered some relevant papers here: http://www.artinis.com/publications Should you require any of these or would like more information, feel free to contact me.
Disclaimer: Obvisouly, I am working for Artinis Medical Systems.
Edit: I could also tell a story or two about EEG, but I consider the reference to above mentioned companies to be a good starting point for further research.
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Ronald Fisher in his paper: The use of multiple measurements in taxonomic problems (http://rcs.chemometrics.ru/Tutorials/classification/Fisher.pdf) explains his approach to classification of measurements from two species.
He is looking for a function that will maximize the ratio of the difference between the means of species to the standard deviations within species.
Fisher uses label D for differences and label S for standard deviations. In the result his ratio should be D/S but instead he is maximizing (D^2)/S.
Could you explain me why he is taking square of D and not just D in his ratio?
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S is variance not standard deviation. So D/S is not appropriate but D^2/S is.
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We want to identify the differences in MRCP's and ERD's during dynamic wrist and hand movement. What parameters would be important to investigate when the differences will serve to the development of a platform for BCI- (neurofeedback) rehabilitation? We are already extractiong the time and amplitude of Peak negativity and could look at the rebound rate. What else would you recommend?
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You may look at three recent papers by C.Tangwiriyasakul et.al. Of course, the ERD pertains to a certain eeg frequency band (beta, mu, alfa); so, proper filtering and sampling to determine spectral contributions from the full time signal are paramount. 
Correcting for disturbing eeg contributions (eyes, muscles) is also most important.
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Does anybody have clinical experience with the OpenVibe generic software for BCI?
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Hi,
Lyon Neuroscience Research Center CNRS-INSERM is using OpenVibe for BCI applications. They are one of the partners who developed OpenVibe and therefore very experienced on how to use it or how to design scenarios for on line processing. The project that I am doing with them is on P300 Speller. I would recommend to contact Jeremie Mattout or Emmanuel Maby for more information on this software.
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There is a growing body of literature on ethical issues related to brain-computer interfacing. I'd like to know from neuro-engineers and neuro-ethicists and all others what they consider the most urgent or important ethical issues raised by the field of brain-computer interfacing. Just give the answer off the top of your head.
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as a clinician, instinctively we think first of "Do no harm" albeit the world has moved & Free Will seems to have trumped others pillers of ethics. Are we sure that the very disabled people who are most likely to have BCI, have full capacity (emotional & cognitive) to actually exercise their free will when BCI may look like reducing loving carers' workload. Are we sure about long term impact on the brain if BCI is used for long period ? Will it hinder neuroplasticity or even the social comfort of companionship with the hard working carer/ family ? Once, BCI is started , the existing support network will disappear & bringing them back will be near imposible. Trust me, I have to write business cases for new service ( what precisely it 'll be described at that future time. Which part of brain is BCI connecting to ?? Is it possible that that part was intact & was engaged in other adjustment work & because of BCI, may well be overwhelmed. I can go on on. But, we need to explore the potential for BCI as long as we are cautious & our endevours are underpinned by morality and intention to bring benefit to patients & their socio-personal context. Would love to read any recent ethical review on this question. If you have one, you send it to me please at msakel@nhs.net.
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I'm looking for brain and body sensors like "electronic noses", which have not yet been applied to human-computer interaction but offer unique physiological input once the technological barriers are broken. I'm less interested in devices like EMG,ECG,EEG, fNIRS, fMRI, GSR, etc. which have already been extensively researched.
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Well, I will mention pupils since they are not on your list of less interesting inputs. :) There was a surge in pupillometry in the 60's, and there has been some pupil-based applications in HCI, but so far it is not a very widely used sensor. If we consider the combination of gaze directed interaction + see-through displays any prediction of the future, I think we are likely to see more pupil measurements being used as an indicator of cognitive load and emotional response in the near future.
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Decision support system
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If those don't work for you, you may want to try BCI200 by Schalk's group:
However, I am also not sure exactly what you are looking for. There are a lot of BCI's and tools for neuroimaging. Perhaps these papers can help you understand a bit more and help you get started when considering BCI development/ tools for real-world neuroimaging:
General:
Zander TO, Kothe C Towards passive brain–computer interfaces: applying brain-computer interface technology to human–machine systems in general Journal of Neural Engineering, 8, 2011
Tools:
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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.
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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...
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Hello,
you can see in this link may be you find what you need :)
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Quite a few published researchers are using Amrex branded sponge electrodes with banana jack connections for 1x1 low resolution tDCS. We have attempted to use them & have encountered several problems including one serious safety problem.
The electrode in question is a 3" by 3" square non-conductive rubber frame containing conductive wire mesh overlaid with a removable coarse kitchen-type sponge that protrudes out of a 2" by 2" aperture when soaked in saline. The rubber frame is stiff & does not conform well to the curvature of the cranium, especially with smaller subjects. This in turn results in difficulty placing it accurately & reproducibly & also in making good & uniform electrical contact. Though the maximum contact area of the sponge on the scalp is ideally 4 in² (25 cm²), in practice it is considerably less & variable with only a central area of contact which can be approximated as a circular disc inscribed within the 2" by 2" square aperture. This leads in turn to the most serious problem:
Injected current levels up to 2.0 mA are routine in tDCS research. The research community generally accepts a current density limit of .08 mA/cm² for the safety of the subject's skin in contact with the electrode & also to minimize potential damage to the underlying brain tissue. Even if the 2" by 2" sponge made perfect contact with the skin, at the 2.0 mA injected current level the current density limit is reached, exactly, as bulk current density = current / cross-sectional contact area = 2.0 mA / 25 cm² = .08 mA/cm². But these electrodes do not make perfect contact even when the they are secured tightly because of the rigid frames enclosing the sponges. So the contact area is rather less, resulting in the denominator being smaller and the current density necessarily exceeding the safety limit. Even at somewhat lower levels of injected current, taking the variable contact area of the sponges into account, the current density could easily exceed the safety limit. Furthermore, this is a very coarse bulk analysis. Taking nonhomogeneity, edge & corner effects into account, local areas of unacceptably high current density are unavoidable & can be demonstrated convincibly with a more sophisticated analysis (one using finite element methods for example).
Yet another practical problem with these electrodes is they have a strong & pungent odor which research subjects find objectionable, penetrates their hair & endures on the electrodes even after successive washings. If one electrode is placed supraorbitally, as is a common position in tDCS, the obnoxious smell in close proximity to the subject's nose even has the potential to affect the outcome of the experiment because it induces stress & stress-related neurological activity that has the potential to confound results.
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Several issues here. Firstly i think that the 'safe' current density is much higher than you quoted in practice.
The smell is clearly disturbing. I have made my own electrodes, which is very easy and there is no smell associated with them. I would suggest at least trying that.
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In much of the tDCS literature I have reviewed so far, the position of M1 for anodal tDCS is given as coincident with C3/C4. Likewise, the positon of primary somatosensory cortex S1 for cathodal tDCS is given as 2 cm posterior or occipital to C3/C4. But now I am reading "the course of the central sulcus (rolandic fissure) which separates the frontal lobe from the parietal lobe corresponds to thin lines touching CPz-C2-C4 and CPz-C1-C3, respectively, [& actually courses through the centers of C4 & C3, respectively.] The two gyri immediately neighboring the central sulcus are the primary motor cortex (in frontal direction), and primary sensory cortex (in occipital direction)."
If it is true that it is the central sulcus itself that is coincident with the C3/C4 positions and that primary sensory cortex is estimated at approx. 2 cm occipital/posterior, then why is primary motor cortex not estimated as 2 cm frontal / anterior? I have not seen this discussed anywhere in the literature I've reviewed so far.
I am also trying to match up the M1 & S1 homoncular maps with their approximately corresponding electrode positions, understanding that only one electrode position each intended to stimulate all of M1 or S1 is much too coarse for the application we have in mind. Does anyone have a reference they would be willing to share which ideally would match up the 10-20 electrode positions in the vicinity of C3/C4 with their approximately corresponding somatosensory & somatomotor functional homunculi with higher resolution & greater specificity?
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Hi Mischa,
this is an interesting question. My concern is, that the 10-20 electrode positions and the relative gyrus localisation is very variable und would need individual control.
You could identify your regions of interest from cMRI/cCT imaging for each patient and subsequently calculate the relative distance in terms of ideal 10-20 electrode positions.
That would give you accurate results for each patient. Of course, this is only feasible if you have access to cMRI/cCT scans.
What is/will be the role of simulation in neurosurgery training and practice
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Currently simulation in neurosurgery is still at beginning stages
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Does anyone know any references for computational models on the effects of plasticity on the brain and brain mapping techniques? How does plasticity impact structure with regards to function mapping and how does it impact brain mapping? How does the model gets modified as the individual ages?
While I have found a lot on the stability plasticity dilemma, I could not find reference to any simple models that may account for plasticity. Any references to this topic either computationally oriented or biologically oriented are welcome.
classification in BCI
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Classification techniques for motor imagery tasks
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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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I would like to gather a list of all the BCI related journals with some important highlights. Here, is the journals I know about:
Frontiers in Human Neuroscience (IF: 1.940), open access, publication fee
Frontiers in Neuroprosthetics, open access, publication fee
Journal of Neural Engineering (IF: 3.837), open access, NO publication fee
IEEE Transactions on Neural Systems and Rehabilitation Engineering (IF: 2.4), NOT open access, NO publication feejavascript:
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This year Taylor and Francis starts publishing the new "Brain-Computer Interfaces" journal. http://www.tandfonline.com/loi/tbci20#.UcsCD_lM-dc
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Brain Chip
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For what purpose ?
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Specifications of an EEG
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The first thing to do is check the website or the manufacturer's manual.
For example I use in my laboratory a BrainVision equipment. The entire equipment consists of a cap, an amplifier, a connection to a computer, a program to record the signal (Brainvision recorder in my case) and another aplication that analyze the data (Brainvision analyzer).
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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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Can someone tell me if there are any Brain Computer Interface Research happening in any Indian Institutes? Also please tell me what is the scope of Brain Computer Interface apart from Brain study and Brain controlled devices.
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CDAC Noida has some work considering BCI -- using EEG -- to control desktop, etc. Non-invasive BCI is good for people with severe physical disabilities...
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I think the startup company that was manufacturing something like this went bankrupt. We are searching for a product similar to EPOC Emotiv, but using fNIRS.
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Have a look at http://www.artinis.com. Their NIRS system is used in the http://www.braingain.nl BCI project and their software includes an open real-time interface that is compatible with matlab, python, java, c and c++ (see http://fieldtrip.fcdonders.nl/development/realtime/implementation, although I just now notice that it is not listed yet).
Disclaimer: I am coordinating the infrastructure part of the BrainGain project and Artinis is one of the commercial partners.
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We are seeking to create simple yes/no responses to questions addressed to people with severe brain trauma who can not speak or otherwise indicate discriminative responses.
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I would suggest you to have a look at the DECODER European project which addresses this specific question: http://www.decoderproject.eu/
They are indeed designing and using BCI systems for this purpose.
Concerning LORETA analysis, inverse solutions (including sLORETA) have been successfully used to design BCI, see e.g., the following papers:
Besserve, M.; Martinerie, J. & Garnero, L.
Improving quantification of functional networks with EEG inverse problem: Evidence from a decoding point of view
Neuroimage, 2011
Congedo, M.; Lotte, F. & Lécuyer, A.
Classification of Movement Intention by Spatially Filtered Electromagnetic Inverse Solutions
Physics in Medicine and Biology, 2006, 51, 1971-1989
Lotte, F.; Lécuyer, A. & Arnaldi, B.
FuRIA: An inverse Solution based Feature Extraction Algorithm using Fuzzy Set Theory for Brain-Computer Interfaces
IEEE transactions on Signal Processing, 2009, 57, 3253-3263
Qin, L.; Ding, L. & He, B.
Motor Imagery Classification by Means of Source Analysis for Brain Computer Interface Applications
Journal of Neural Engineering, 2004, 1, 135-141
Noirhomme, Q.; Kitney, R. & Macq, B.
Single Trial EEG Source Reconstruction for Brain-Computer Interface
IEEE Transactions on Biomedical Engineering, 2008, 55, 1592-1601
de Peralta Menendez, R. G.; Andino, S. G.; Perez, L.; Ferrez, P. & Millán, J.
Non-Invasive Estimation of Local Field Potentials for Neuroprosthesis Control
Cognitive Processing, Special Issue on Motor Planning in Humans and Neuroprosthesis Control, 2005, 6, 59-64
So in principle the answer to your question would be yes!
hope this helps,
Regards,
Fabien
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In BCI we use a lot of tools and techniques from MVA but sometimes we need to move a little bit deeper on the topic. It will be great to know which books the BCI research community is using.
Thanks!
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I can very much recommend the book "Modern Multivariate Statistical Techniques - Regression, Classification and Manifold Learning" by Alan Julian Izenman
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I am doing a project on brain computer interface for cursor movement . I wanted to create a matlab GUI to display the results. If anybody has any ideas about it please do suggest.
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Do you want it to be in Matlab only, or are you open to other languages or software? Indeed, there are already a number of free and open source BCI platforms available, most of which coming with existing tools to provide feedback to the user, such as cursor movement. These platforms include OpenVIBE, BCI2000, BCILab, Biosig, FieldTrip, etc. (the last 3 are based on Matlab by the way). You can find more info about these platforms in this paper: sccn.ucsd.edu/~scott/pdf/Brunner_BCI11.pdf
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The BCIs can be categorized into (i) dependent or independent (ii) exogenous or endogenous.
From my point of view, both criteria are very similar. The advantages of dependent BCI and exogneous BCI are the same. The same consideration applies in the case of independent BCI and endogenous BCI
Then, what is the difference betweeen independent BCI and endogenous BCI? Likewise, what is the difference betweeen dependent BCI and exogenous BCI?
I hope someone can give me an answer. Thank in advance.
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In my understanding, dependent means that the BCI does require some control over peripheral nerves and muscles (e.g., eye gaze control), whether independent BCI solely rely on brain activity. Endogenous means the BCI is based only on spontaneously generated brain patterns (e.g., motor imagery) whereas exogeneous means the BCI is based on brain responses to external stimulus (e.g., P300). If I am not wrong, by definition, endogenous BCI are necessarily independent. However, exogenous BCI can be either independent or dependent. For instance an SSVEP-based BCI that does not require the user to gaze at the target would be independent (see, e.g., the work of Brendan Allison on this topic) whereas an SSVEP-based BCI that requires the user to gaze at the target would be dependent, since it requires gaze control (many, if not most SSVEP-based BCI are dependent). There are also some recent work by Peter Brunner which suggested that P300-speller may not be independent.
I hope this answers your question.
Best regards,
Fabien Lotte
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I'm a fresh student in Neruosciences and interested in the connection between neuro and computers that are created by humans. Is there any progress in this field recently?
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Yes of course, using both the invasive (Implanted electrodes) and noninvasive (EEG, fNIRS) techniques. The following papers are good for a starter
1) Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M., 2002. Brain–computer interfaces for communication and control. Clin. Neurophysiol. 113, 767-791
2) Coyle, S.M., Ward, T.E., Markham, C.M., McDarby G., 2004. On the suitability of near-infrared (NIR) systems for next generation brain-computer interfaces. Physiol. Meas. 25, 815-822.
3) Coyle, S.M., Ward, T.E., Markham, C.M., 2007. Brain-computer interface using a simplified functional near-infrared spectroscopy system. J. Neural Eng. 4, 219-226.
4) Hochberg, L.R., Bacher, D., Jarosiewicz, B., Masse, N.Y., Simeral, J.D., Vogel, J., Haddadin, S., Liu, J., Cash, S.S., van der Smagt, P., Donoghue, J.P., 2012. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485 372-375.
5) Pfurtscheller, G., Neuper, C., Schlogl, A., Lugger, K., 1998. Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Trans. Rehabil. Eng. 6, 316-325.
6) Pfurtscheller, G., Neuper, C., Guger, C., Harkam, W., Ramoser, H., Schlogl, A., Obermaier, B., Pregenzer, M., 2000. Current trends in Graz brain–computer interface (BCI) research. IEEE Trans. Rehabil. Eng. 8, 216-219.
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I'm trying to conduct a fmri experiment using a device as a response system (Resonance Technology-EvokeInterface) that has an specific software in which I can assign a number to each key (5, one for each finger). On the other hand, I use e-prime as the visual stimulation system and I need these two programs (e-prime and Resonance Technology-EvokeInterface) to interact. However, eprime do not understand the previous assignation of number/keys programmed in Resonance Technology-EvokeInterface. As a consequence of it, eprime always provides the response '1' regardless of which key is pressed. Any idea as to a potential solution?
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hello
If you email the support team from Eprime they turn to be super helpful until you solve the issue. So, they will ask you for the file and anything they need to see what you see on your problematic. Try with them. good luck!
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We have performed group ICA in two groups of patients (BOLD fMRI data). One group is controls, one is with very severe developmental brain abnormalities (mixed group). In controls, ICA revealed 6 components (mostly bilateral), while in the diseased group, we get 30-40 components on the group level, and the components are small and focal. Data preprocessing is the same, data quality is also the same (although the brains themselves might show some anatomical heterogeneity as well).
I am very curious how this excessive number of IC can be interpreted. We may assume that the diseased group have impaired brain functioning, even no brain functioning (e.g. due to neural migration disorders) at the respective areas.
Thank you.
András
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There is very little that the number of components found could tell you about brain functioning or connectivity, since many of the components could be related to noise, artefacts or be vascular components. You should take some considerations into account. Did you perform group ICA with temporal concatenation? Did you use automatic estimation of the number of ICs?
If you really want to compare the integrity of the resting state networks in a group of patients vs. controls your approach is not adequate.  I suggest that you go for a entire sample group analysis, and after identification of the real resting state networks you apply a two-groups differences analysis. Take into account structural differences into account. In the end, differences found would reflect differences in network integrity/pattern. 
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I am confused between choosing suitable eeg electrode for my project.The active dry electrodes like g.SAHARA are costly and not easily available.How about making some DIY active electrodes?Is it appropriate to design active electrodes for a system that itself needs to be tested?
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I have the usual answer: It depends! 
It depends what do you want to achieve: Improvement in signal processing methods? Improvement in usability? Improvement for a particular patient? The need for hundreds of volunteers to be measured? ??  It depends! 
When you only want to repeat existing experiments, you better keep, what earlier researchers stated in their papers.
When you are really in research, you are free to do what you want: In case you are developing some new "active dry electrodes", use them in comparison to standard gelled AgAgCl cups first - and then improve yours or replace theirs. I have seen some quite fancy DIY dry electrodes based on gold rod arrays - which are no worse than the commercial dry electrodes (which seem to be sensitive to noise...). We built ourselves capacitive electrodes and did get some nifty results. We now do the same with a new NIRS sensor....
No matter what the building blocks, you have to prove they are working with an existing BCI paradigm - which may lead with novel electrodes to novel signal processing algorithms to novel use case to.... I hope you get the point: Start your own path and you will see where that leads! 
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Is it because of the high-precision electronic components? Or maybe it's due to strict regulatory requirements that end up increasing the overall cost of bio-medical devices in general? Another possible explanation would be the low demand of these devices, forcing developers to increase the end costumer price, what are your thoughts on the matter?
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An EEG system is pretty straightforward. A set of electrodes connected to an amplifier. The EEG amplifier tends to be more complex than a regular amplifier because, as with all physiological data, it is very noisy. EEG signals at the scalp are in the order of microVolts, which is why they need amplification. They immediately have noise introduced through bad coupling due to dead skin cells, scalp oils, hair, air gaps, etc. The data collected is then transferred to the amplifier through wires which act as antennas collecting all sorts of noise. To improve on this data lots of filters and systems are introduced within the amplifier to try to minimize the errors. Then you have to think about the electrodes, which aim at having the best coupling: some are even coated in gold or have pre-amplifiers in site to improve connection. And if you consider that they have around 30 electrodes each, the cost is driven up significantly. But even with the cost of the electronics and head probes and electrodes and such, most medical device companies that make them don't make most of their profit on the system itself, but on the data acquisition and signal processing software that accompanies them. They tend to be pretty fancy software packages that help even the most untrained of users do something useful with the data collected from the system. Add intellectual property costs, company overhead costs (which are driven very high by having to be certified by all sorts of gubernatorial agencies around the world (FDA for example)), and that's what yields systems in the order of 50k. If you actually consider other medical devices (MRI PET CT, etc), EEGs are actually quite inexpensive, and the demand is quite high, so that isn't an issue. These systems are being sold at these prices all over the world, Germany and Netherlands come to mind for having several EEG sellers, so the USA isn't the problem. However, still, to the non-medical user it is very high. There have been multiple attempts at lowering these costs through companies that have "open" hardware and no fancy software packages to sell low cost systems. (check out http://www.openbci.com/ for an example). Ultimately, if you are interested in cheap EEG, try BCI hardware or try finding systems that don't come bundled with expensive software packages and do the signal processing yourself. I hope this helps.
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I`m studying brain computer interface, and trying to use FieldTrip toolbox. (http://fieldtrip.fcdonders.nl/).
But I can't understand "indata".
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The correct answer will of course dependent on which kind of data you have, and how you will want to analyse it.
A good start would be:
and some reading for general info:
And if you have any specific questions I greatly recommend to post directly to the forum: http://fieldtrip.fcdonders.nl/discussion_list there ir normally some expert that helps with every doubt.
Cheers
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how to separate those signal form raw wave and also need some article about it.
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Hi Thiyagarajan,
What type of system are you using for recording? And analysis? There are numerous programs out there, many of them freeware. One that is pretty handy (and comes with loads of tutorials on the website) is EEGLAB (http://www.sciencedirect.com/science/article/pii/S0165027003003479). A general information website may be found here (http://www.neuroelectrics.com/about_eeg/how-is-EEG-studied).
The frequency bands comprising of alpha, beta, gamma and so on are not of "fixed" bandwidths as they were once conceived, and are highly susceptible to individual effects. I personally work with alpha waves, and have found the recommendations of Klimesch 1999 (http://www.ncbi.nlm.nih.gov/pubmed/10209231) very helpful. The methodology he recommends regarding situating an individual's alpha bandwidth along the transition frequency has moved the field forward immensely (http://www.ncbi.nlm.nih.gov/pubmed/23701947). Some of these papers may help you get started.
If your question was more methodological, then here's a simplified process for at least observing some spectra:
i) remove artefacts (e.g., ocular, mandible, etc)
ii) Use the app. filters (e.g., 0.53 low pass, 50 high pass for non-american power supplies)
iii) Re-reference to Cz (though using current source densities may be more viable for some - see http://www.sciencedirect.com/science/article/pii/S138824570500324X)
iv) Separate data into (at least) 1200 ms epochs
v) Perform Fast Fourier Transforms along the pre-specified bandwidths (try 8-12 Hz for traditional alpha) 
vi) The power/amplitude values are interchangeable (P=V*C, after all). 
Hope that gets you started!
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The most of data are related to disorders and disabilities, i want the perfect one of them. can any one help me? thanks.
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Dear Roy! Your search for indicators of children's psychological disorders with EEG-diagnostic is a very interesting topic. However, the EEG method is informative only if it has an analytical approach to understanding problems neurodynamics disorders at the local and systemic levels. Our 18 years of experience in rehabilitation and treatment of patients with psychoneurological disorders in children and adults allowed to understand the ideology of the brain and abnormalities in psychoneurological status under certain failures in the brain. For the understanding of EEG curve needs the wide spectr of multidiscipline knowledges and deep analytics. only such approach provides unprecedented results of treatment of hyperactivity, cognitive impairment and cognitive decline, depression, autism and others. Today we are ready to realise a project to produce a complete technologies for diagnosis and correction of the brain/ This project needs the availability of financing.http://lushchyk.org/en/portfolio-view/menedzhment-mozku-dlya-likariv/
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I am new to EEG Signal processing. I downloaded DEAP dataset (preprocessed) in .mat format. Once we load them to workspace (a metrix with channel numbers and data), how can we proceed up to a feature space? I read several papers but methodology is bit confusing. specially I have following sub questions.
1. Are we framing the stream? if so what is the time duration for a chunk?
2. How we load features of a single frame to matlab?
I found EEG lab but I would like to start this from scratch to understand the process.
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Hi
It 's dependent on which type of features you need.
e.g. PSD, HOC, Coherence, etc 
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The discussion of ethical issues brain-computer interface
For an example of  brain-computer interface
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Since Brain-Computer Interfaces are not a distant prophecy but an upcoming technology in rehabilitation, gaming and other fields, they might be used in educational settings as well. Think of virtual trips and tactics training for athletes, or direct communication for teaching and learning (as indicated in the paper linked below).
What will happen if humans and computers are intimately joined? Will it only free human mind from keyboards, mice and other devices, or will it in the end limit students' thought processes to computer capabilities?
Is it something desirable for the broad population or should it be used only for special applications?
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Dear Michael
Who does not know the most famous case of Stephen Hawking?
People affected by severe motor disorders  need alternative methods for communication and control. They may not be able to use even the most basic conventional assistive technologies, which all rely in one way or another on muscles. Studies from this and other laboratories have shown that humans, including those with severe motor disabilities, can learn to control sensorimotor rhythms and other features of scalp‐recorded electroencephalographic  activity and that they can use this control to select letters or icons, or move a cursor in up to three dimensions.
Brain-Computer Interfaces are a chance for them and further developments are close.
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I am trying to design a speller paradigm which can have better acceptability and accuracy. At a same time I need to know what are the critical factors that should be met during the design of a paradigm.
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A very important factor is the ease of operation from the point of view of the user. For example, the P300 speller is very reliable but the flashing stimuli are annoying for most users and it is not easy to use this speller for an extended period. Some users actually prefer a slow rate of seplling (e.g., 20 sec per letter) to allow for prolonged usage. The important questions here are what is the typical duration of a session going to be and what is the required spelling rate? These are competing factors and the design may involve some trade-off between them.
Other factors are:
  • The number of electrodes to be used - is there a limitation?
  • Training - is there a constraint on the duration of the training period before actual use?
  • Reliability of the relevant EEG features - are the EEG features to be used easy to extract in different subjects? are they consistent for the same subject across different sessions?
  • Individual differences among users - what modifications can be made to best adapt the paradigm for each individual user?
Obviously, there are many more factors. There are quite a few good review papers on BCI as well as books that might be useful for you. They discuss these issues in much detail.
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 and how we apply machine learning for classification and training of brain signals...
epoc is uesd for brain computer interface
if anybody knows...let me know as soon as possible 
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With EPOC interfacing, I am assuming that you refer to e-motiv system and how to obtain signals from that device. The e-motiv developers’ license comes with an SDK that provides function that enable you to access to raw signal. Alternatively, there is an open source library which enables you to access the raw signal from e-motiv (i.e. google emokit).
Now it terms of how to apply machine learning algorithms for brain computer intrefaces … well that is a very broad topic. There is a huge literature on how to pre-process EEG signals, remove artifacts, define a classification problem, solve the optimization etc. You could google any of the terms Common Spatial Patterns, Bilinear Discriminant Analysis, Second Order Bilinear Discriminant, eeglab to you find few references take it from there.
If you are just interested in building a brain computer interface, the easy way around would be be to use an open source package such as OpenVibe with EPOC system.
A note of caution, e-motiv was intended to be used as a gaming interface, and is not necessarily accepted as reliable EEG signals measuring device.
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I've been using Curry 7 extensively and very much favor its capability in EEG source analysis (with different methods available for solving inverse problems). However it seems to be fabulously expensive to have it for personal use. 
Now, I am looking for open source toolboxes that come with methods for solving forward/inverse methods with high precision.
I am aware of some toolboxes such as FieldTrip, LORETA, NFT, SPM8, but I don't know their pros and cons. 
My research will focus on localizing sources associated with EEG oscillatory responses (rhythms not ERPs) for BCIs. 
Any response is highly appreciated. 
Berdakh. 
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if you don't restrict yourself to matlab you can have a look at MNE.
it supports many inverse methods (MNE, dSPM, sLORETA, LCMV, DICS, MxNE, single dipole fit and soon RAP-MUSIC)
The language used for scripting is Python.
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I'm especially interested in the apps that improve attentional abilities.
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Dear Alessandra, I am using the mindwave from Neurosky to test the attention in sport sponsorship. I need help with the software. Someone interested in a collaboration?
Thank you
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Which is responsible for memory saving mechanism: Neural network of brain or molecular quantum transition in definite locations in brain or selective chemical reactions all over the brain or none of them? How and where does the brain store such a tremendous amount of data? What are read and write mechanism of this data? Is the chemical -base processing dominant or physical -base one?
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Good question. You may read Eric Kandel's book and work on the topic.
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I want to develope an ERP-based speller for locked-in patient. I have used classic BCI2000 P300Speller with Enobio adquisition system, but it doesn't work for patients with no gaze control. What platform is easy to use to build an RSVP speller presentation system? Does some open free script or program that's ready to use exist?
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Thank you for your attention. I saw the paper that  you point out. But those speller require gaze control and my patient has no gaze control. I will search for Psych Toolbox.Thank you.
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I incorporated the color as an element into the 6×6 character speller matrix. The results reveal that a sharp negative deflection (N200) peaked at 200-300 ms stimulus-onset for target-stimulus over occipito-temporal region. I have no idea about reason. I really wish some peers can give some suggestion or provide some related paper to me.
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There is an ERP component with similar latency and topography (typically referred to as the N1) that has been attributed to discrimination processing. It is of greater amplitude for discrimination tasks as compared to simple detection tasks. It has also been shown to be modulated by the difficulty of stimulus discrimination. This could be what you are observing.
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There are certainly arguments for and against specific recording technologies and critiques against the lower-level controllers within robot handling the details of the movement, but I don't see a conflict here. He is providing a polished demonstration of what can be done right now in humans. I don't think this suggests that EEG is the only way to move forward; in fact that would go against more than a decade of his own lab's work. Instead, it is a prototype. Hopefully it inspires more funding, interest in the field, and development of more advanced neuroprosthetics.
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OpenBCI is also pursuing an interesting alternative thought. 3D printed headsets that cling to the wearer’s head. Along with size, the placement of electrodes can be customized–similar to how alternatives like the EPOC work so and so.
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EEG signal analysis using MATLAB-EEGLAB, OpenVIBE, and some other tools can be possible. I need to know whether the analysing of EEG signals using Cloud computing through web application is available?
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This might be a useful place to start:
-Cloud-Based Analysis of EEG Signals for BCI Applications; Ericson et al
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Dear All,
I want a standard data set of EEG signals for the intent of movements. I want the standard data sets for left, right, front, back, start, and stop movements of alpha, beta, and gamma signals. Please let me know, where can I find the standard data set.
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Hi, Shumit. You may find something here: https://www.bbci.de/competition/.
Best wishes, Ren
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NIRS to detect sleep patterns.
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Thank you Brian for answer and your time. They will not be direct parallels means we have more avenues for research I guess.
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Phase-Amplitude cross-frequency coupling. I am trying to analyze the phase-amplitude cross frequency coupling and I have no basic knowledge of MatLab.
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Hi Dear Dania
I think DIgSILENT PowerFactory (14.1.3) is the best for your simulation
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Technical support of every day clinical practice in neurorehabilitation increases with each day. Rehabilitation robots, virtual environments, telerehabilitation, eye-trackers, brain computer interfaces, neuroprostheses, exoskeletons becomes useful and effective tools for members of multidisciplinary therapeutic teams (especially physiotherapists). Where is the place of engineers within it? Will be there possibility (or even necessity) to incorporate them into therapeutic teams in the future?
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Multispeciality hospitals employ a wide variety of engineers to guarantee the smooth day-to-day running of patient care, diagnostics and treatment - from building services engineers to electrical engineers, IT engineers, medical technology engineers, and so on.The biomedical engineer is to form an interdisciplinary link between the physician and his respective technical environment.
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I would like to develop an entry-level understanding of the neurological diagnostic methods used in Western TBI treatment.
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Hi Phil,
most hospitals in the US will probably only perform a CT scan (depends also on your health insurance and how a patient presents clinically). It will show skull fractures or bleedings, for instance. A CT scan is not very informative in most cases of mild TBI though. More advanced imaging techniques such as T1 or T2* weighted anatomical images can also show microbleeds, for example.
Research seems to prefer diffusion weighted imaging (DWI) to study TBI, as it can map white matter. This is particularly true for mild TBI. (Diffusion Tensor Imaging DTI is one type of DWI - there are others such as diffusion kurtosis imaging DKI as well.)
Note that the DoD, for example, still considers diffusion weighted imaging an experimental method that is not fit for regular use in diagnosing (mild) TBI. This evaluation is based on the inconsistency in research findings RE DWI metrics following (mild) TBI (fractional anisotropy FA and mean diffusivity MD as the most frequently used metrics) and their associations with behavior (e.g., cognition, symptoms).
Hope his helps!
-- Mareen
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Is there any intracranial EEG system available, which is fully implantable - i.e. like the NeuroPace system, but without the responsive stimulation part?
If you are aware of such a product, or of research groups doing advanced research on such a device, I would strongly appreciate your information.
best regards
KS
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Hello Kaspar Schindler
please check pdfs