Lecture 2 - CNS

Question 1

modern EEG device components

Answer 1

A/D converter
battery
USB relayer
acquisition computer
stimulus computer

Question 2

why is EEG with battery

Answer 2

main would create electrical noise

to exclude risk of electrical shock

Question 3

why two computers for EEG measurment

Answer 3

to ensure both have enough resources

less of a problem with modern computers

Question 4

Nyquist rate

Answer 4

you sampling rate has to be at least twice as high as the highest expected frequency
in practice it’s usually 3 to 5 times as high

Question 5

Nyquist frequeny

Answer 5

the maximal frequency you can reliably extract with your sampling rate
i. e. sampling rate / 2

Question 6

aliasing

Answer 6

when sampling rate is too low
high frequencies get recorded as low frequencies
sample connects peaks to minimums

Question 7

CD sampling rate

Answer 7

44 kHz

because lowest distinguishable frequency is 22 kHz

Question 8

greatest sources of noise in EEG

Answer 8

muscle movements, e. g. chewing
blinks
lateral eye movements

Question 9

drifts

Answer 9

noise from mains

50 hz in Europe, 60 in us

Question 10

slow voltage drifts

Answer 10

due to electrode polarisation, sweat etc.

Question 11

pre-processing pipeline

Answer 11

1. re-referencing
2. filtering
3. ocular correction with ICA
4. artifact rejection

Question 12

re-referencing

Answer 12

changing the location of the reference electrode

the artificial zero, that is never actually zero

Question 13

common reference electrode locations

Answer 13

most common is mastoids
also earlobes
or tip of the nose

Question 14

common mode noise

Answer 14

when impedance differs between active, ground and reference electrodes
the higher the impedance the more common mode noise
solution: high impedance input amplifiers

Question 15

getting rid of common mode noise

Answer 15

(A-G) - (R - G) = A - R

= active electrode - reference

Question 16

pattern decompostion

Answer 16

location of reference electrode does not matter

Question 17

high-pass filer

Answer 17

lets only high frequencies through

Question 18

low-pass filter

Answer 18

lets only low frequencies through

Question 19

notch filters

Answer 19

filter out specific frequencies

Question 20

band-pass

Answer 20

removes a range of frequencies

between two defined points

Question 21

band-stop filters

Answer 21

attenuated frequencies between x and y

Question 22

disadvantage filters

Answer 22

always change the signal
throw out data
can change shape of the ERP

Question 23

ocular correction

Answer 23

with ICA
or Gratton & Coles method
VEOG and HEOG

Question 24

VEOG

Answer 24

vertical electric oculogram

one electrode above, one below the eye

Question 25

HEOG

Answer 25

horizontal electro oculogram | one electrode lateral of each eye

Question 26

Gratton & Coles method

Answer 26

regressing out blinks with linear regression problem: might remove actual brain activity from VEOG

Question 27

ICA

Answer 27

independent component analysis for approximating solution to superposition problem blinks and eye movements are biggest contributors therefore ICA works good on them number of component = number of electrodes assumes normal distribution of random activity everything non-normal is something important rotates distribution to best fit the observation

Question 28

after ocular correction

Answer 28

components can be removed manually | ICA can be repeated and components can be thrown out as much as you want

Question 29

artifact rejection

Answer 29

removing whole trials containing artifacts throwing data out instead of correcting it can be done automatically or by visual inspection