okay so um the next speaker is and robert rubber smith oh she will be presenting a a a a method for robust minimum variance beamformer and its application to um and E G and a little potential i can start a the um that top like a to see it um okay a i it that's that some work which we do doing in oxford between a of us um so i i mean miss amy is the map rotation any he's done most of the work on the details the beamforming so i look a fast for that we also working with um the pulse like a tree um i an engineering that's made and speaker i Z is is one of the D sections work in they uh that if possible source just or not in the brain stimulation this is a the based in a sense we've got a clinical problem um which is to do with the brain stimulation and we are trying to image it's using a make and and stuff a so i'll start with a base discussion about um and then all want to the beamforming and then out of use some results a just a fish that that stimulation is technology web i oh they plan electrodes into the brain so that the here you can see a that that coming in a top coming coming down it's used white and not your the sources in particular we you it's not with problems see for the most common uh those that eating with M are that is press it's channel and eating with a a a a a motion problems slide stand here like can easy easier the case not can be talking about that is pain and we say that a uh is that you've a person and well pain is for the the perception of pain is all sixteen is subjective some people see it could very well with pain or not find a right handle um it's also use some of the um areas such as much as its is not in your so no one yeah stands for it works what you do is you plot the election in the brain and will be here brain stimulation the of signals actually coming out of N be very small the electrodes implant they then put on a five vote sine wave normally five votes like very bits at a frequency to about fifty hz and the functions that a um and then a a a go through period of this a a a lecture it's by search that's then a period where the external eyes well you might access to the stimulates to and S the signals coming off the implanted electrodes the local field potentials and during that time they tried rate so they one the site that's signal of the patient and then off to that they employ a battery and everything missing the was down inside everything becomes internal the what we do is very trying to get a of that text last period so we can actually the feel potential of the electrodes and use the to try to improve a so mad ninety two and stuff lot of a a thirteen new technology um picking up a magnetic fields of you were have to um gauss so that that's about three more three um eight or the magnitude smaller than the spec feel so essentially put a number of my comment isn't but you might as rather here got doesn't a she you can see what here and for these very small signals then we tried to um we can sort the sources within the brain and a point of that may is to so uh one is to improve the surgery so they it that what in four and the other one is to try to understand more about the you it's so that's what are difficult to use with D B S one is way looking at that very small signals we expect most of the excitation of the brains to come from the a region around the a lecture well fortunately that's quite good separation between the frequency at which are the electrodes and the frequency that which principle so it is possible to try to access that's small signals want to me like sing so we looking at about say fifty to a hundred thirty hz stimulation and the low cell um a a range between about five and fifteen a for the range from yeah that that quite big it's you is when you bring the wires out they are have to do a lot and skull uh the wires a magnetic you got cost of wires are yeah the whole of the skull than not done inside but those double for distortion a that region in terms of your source or so that think in this paper a is where um got two things it using a placement of an now now that a whole we a a well on that show how to improves the recovery of the of the spatial source what we think to take that in this paper it's it's used in the idea that want all simulation is on then it's you would expect most of your signal so yeah the stimulation can see be coming back from a region and which stimulating and hence by splicing the cross correlation between the signal you putting in and the signal you're thing at my comment is then any fact you can improve your beamformer especially one region of interest which is found the region whether you like to tune so the idea here is way looking at stimulation on and trying to get the best stuff beam as we can and then but using time and stimulation is all to try to verify that so what i do i'm elements is they stimulate the time um people don't seem to get a uh used to that's T where king but there are it's use a at that time so that just cost about ten thousand pounds um and they lost from a couple of years that's what made thing not to take infection the um put in you new battery yeah that is you which is coming more to like that does seem some evidence that stimulation at that some of the of the range emotional areas and that's some showing that actually and not to stimulate send march because of things that a strange so a of my and fast which should welcome foundations support is trying to actually um find an adaptive method where we actually you know way to simulate when and once tried in former to you very quickly um Z say that's very high meets work and i don't think that yeah that you and then began to demonstrate using training data from a a a a a patient pain with P B S i mean have them so a techniques yeah shows um is look something like so that that's uh how a bit of each and to um a few centimetres long they have a number or electrodes or may have about four electrodes on where you can stimulate and these are the to that the field of getting around each one is in fact a let's approximately so we can to assume the N F P data comes from a small cool volume a bound the implant electrode we know that where that is from looking at um ct a oh i want to implement you can use see um we can we that now a a a a a whole of skull and then make a to use a robust minimum variance beamformer um what's the to work by element set out in two thousand and five but the obvious didn't use the particular aspects which relevant to um make imaging yes or forward model so the first time here so so white state wide C R be of measurements at skull um S is the stimulation so that's from the electrode and a and is the forward model leave but a vector then we have a second term which includes the uh oh S to try to now but i that's a N as T and we assuming a um but the N F P data is going to a small volume only got here i can the location of the whole is known so we can deal with that and then got some noise there's also in fact times day going from be um lot because the instrumentation between the um excitation signal and am signals with a so just think they are a problem formulation is to optimize are beam for um so we've got a why we want to estimate S use optimisation on it or or first time here is the difference between that's essentially thank expectations um with a value you out for a penalty factor um which there is we be and one so you put note you want you take in and that is of of the product of a correlation between the source i'm the measurements um and then subject to all source vector so that's well and it to it's all so we know we got source that is the only condition okay and then we find that so that if we take the um the variances of the and take the expectations then a first term does becomes this one down here so a why S yeah is the first good that is between the source the excitation we put on and the measurement it's a a a a solution essentially well we're trying to maximise or or correlations so something to minimizing constraints such that constraints you can find exact use the W this for increments as work that E which comes to stop one yeah yes one of the parameters which says what which is the described in that where you putting on the excitation now to me a of once month i constant um if we substitute are right if W back into the a constraint and in fact we find you this is but we then that no as that's using the normalization low so be yeah so i put in this i um we oh of the um diarization is with that i've the factor are a it's a half sheet a a heart sounds pose so that's the vector which later the region of what we know the excitation is to the um close correlation between the measurements and the excitation we can do that i was that get a secular equation yeah which late a a a a me we found this relates number of than our on unknown to the eigenvalues yeah i of all normalized matrix or a of how cute so the details of that are in the paper um and the was in the paper and so i am of a fast of it that that's time is and results um and i have got some papers is here no once but a big advantage the way that actually be normalized this so we've normalized here um talk about V before so E and uh i with the to some of those covariance matrices and also the parameters of the of is what we put X i is actually uses as a nice solution which then amenable able to an effective solution to have find um and uh or as some previous work has a a few a given bounds for them the this kind of normalization as she allows as to optimize and you know yeah a out a which allows us to do that which are not been to go to i think time that that used to some themselves so we've done this in simulation one of the problems of course as we all know made doing things well mel by medically um it is very hard to try to validate um so we done validation using a simple simulation of a spherical head model put in the deep source simulates the excitation station we put see in the source of the but of interference um and with that's noise and to sure that we can to look up on and all conditions we've allowed to all source um which would only be i dominate dominated by the stimulation we on or we should be seen was once the brain when off a different frequencies you sign "'cause" on way so yeah that was with the start of wiener filter a and we've compared them with the filter so in both whole now without correlation so yeah it is just the interference and their noise i i mean is um because it's that we've simulate interference long that thing else be be estimated it um we can see the wiener filter is that you not um a well um um as well improves things the the S I but that but using the be so if that the S noise as well as a fair comparison that we can see a method that she does give us advanced just oh the the other two men said putting in this post correlation so um um between of the source and the measurements since we know sources is doing that frequency as she does hell a technical data is most interesting um of the forty or or one with a body pain wanting pain had separate yes um in but in the pack which is close down to them oh and fifty hz so the it and sell them on magnetic so they are a problem for the mac uh but in fact the say whether whether wow clusters then you can still do you have an all i can just and that's before four we assessed the lectures for sequences like to um so i are you can use for but not often you got inside you and that just can't of is that um using the he was first right this my the things we do first of all we recall yeah that P data in all conditions so that's that she recording straight of the electrodes the that are too long used a beamformer um and then we were what happened all so we use the beam forming one number you know we then be constructed using a P point how much is what we would expect to see from the yeah it is maybe a filter yeah it is using a a a a as well a use of the um i three not very much and here it is using you a B and you be so you can see you press that i think that that i um i think this is a much better the U B finally the a point of this try to find out a in the brain so this is rate just the fit it interest as suppose and this is a get the difference between the stimulation of one so this woman that a once it was turned off and you didn't on on um not an entirely goods um experiment because she stopped yes you knows this awful do you a you do get some differences between the of a young condition and the way in which are response this a you looking at part should known to be sensitive to pain um um rate standard high a i'm the A C C is the and tear single call yeah right and the three and i is the um of the email so this technique can see that point a spatial layers grain um where we getting sports to be um nipple stimulation so we we've also a of using correlations that all closely and identify more closely spatial regions in right um i i a and the results if five using elliptical volume of the yeah that's P to improve things of using circular volume still provide solution and we've shown some results um improvements using simulation i mean and you and you yeah and yeah well G as much so or or a more you are still going on or you are you two more yeah well and i oh or like to call or yes may just the problem say that's that for my my kinetic model of the brand such a simple "'cause" you can assume that is a of a two D is is one is what to so the oh well as for a um but it's not a context could you what a much more complex model right so my a standard are not experts what they don't E G my understanding is make use gives you what oh that is to discuss that with you for are actually that if anyone and any information on that that's a my hands and you i mean it is a recent technology is come about lost five to ten years well a you also to machine and not let me oh what should you signals tricks of what was is are you sure from your speech and so or but for a the S T N was a packet is next to it is but pain yeah the use S the end from it oh so you're right trying to be your with peace for four what what you saying for the final are we measure yeah that peaks as X to i and then we tried to estimate as well yes using the mac in a compared so you for point well yep so what is right for she a all the marks is i five to i i mean that sense that to the signal we receiving see in the of or you a frequency a point where we stephen it is that you want place that is quite hard to know how you try to optimize world i mean would really strong so i one Q one so still from and estimate um right which still what we are we are doing a spectral analysis as well so we looking so a beamformer is that you i i think that if for so we all by some that to look at maybe to right the B two of what's it's yeah was either and yeah the source you right hmmm part to ooh yeah a just that even that's one but that's what if you they get sure one uh_huh four do we would you hmmm it's a that's not in a sense that are not quite so what you're looking for seven seven sense i mean it is that's what we try to estimate we getting and then measure we get "'em" and i suppose way we can possibly indicate still one thing to get a good results or but it's good question and very hard to know how about it from see you try to the um so i mean okay well thank you everyone uh i think you again maybe