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