Lecture 4 - CNS

Question 1

induced power

Answer 1

from non phase-locked signals
needs time frequency analysis
can be isolated by subtracting the ERP of each trial

change in amplitude by stimulus

Question 2

evoked power

Answer 2

from phase-locked signals
easily analysed by ERPs

can either be onset of new oscillation
or phase-reset of current oscillation
by stimulus
power stays the same

Question 3

plotting oscillations

Answer 3

following a circle

Question 4

periodicity

Answer 4

two full cycles in a circle

zero to positive and negative to zero

Question 5

phase

Answer 5

from start to end of wave

Question 6

butterfly plots

Answer 6

for non-aligned / non phase-locked signals

Question 7

power

Answer 7

= amplitude of a frequency band squared
always positive
the envelope, independent of time
connects peaks of signal

Question 8

ERPs origin questions

Answer 8

can be the result of either single signal or oscillation

you cannot know

Question 9

oscillation examples in nature

Answer 9

light
sound
water
electromagnetism 
whales' tails

Question 10

relationship between frequency and power in nature

Answer 10

1 / f
negatively correlated
when frequency is high, power is low and vice versa

Question 11

frequency scaling

Answer 11

making frequencies more equal
as low frequencies tend to have more power
relative, based on change over time

Question 12

Fourier’s theorem

Answer 12

every wave can be decomposed into sine waves

Question 13

FFT

Answer 13

Fast Fourier Transformation
from time to frequency domain
amplitudes are respresented in histograms
x = frequency, y = power

Question 14

iFFT

Answer 14

taking out frequencies in frequency domain, thereby changing time domain
to filter out noise
you only know based on theory which frequencies you can take out and which you can’t

Question 15

non-stationary signals

Answer 15

oscillations that change over time

Question 16

convolving

Answer 16

= mulitplying

Question 17

Morlet wavelet

Answer 17

stationary oscillation that has been convolved with a Gaussian
looks like a wave within a normal distribution

Question 18

fewer cycles

Answer 18

more temporal resolution

Question 19

more cycles

Answer 19

better frequency resolution

Question 20

Heisenberg’s uncertainty principle in cycles

Answer 20

if you want better temporal resolution (fewer cycles), your frequency resolution goes down
and vice versa

Question 21

dot product

Answer 21

time series are represented as vectors of numbers
when they have the same length, they can be multiplied
product = dot product

Question 22

dot product meaning

Answer 22

the further from zero, the more similar
if zero, the cancelled each other out
is relative to range of amplitudes

Question 23

dot product in the signal

Answer 23

appears as its own wave

connecting all the positive peaks

Question 24

complex wavelet

Answer 24

three-dimensional wave
looks like a spiral 
has a real and an imaginary part
convolved with a sine wave 
characterises both power as well as wave

Question 25

complex Morlet wave

Answer 25

a three-dimensional spiral following a Gaussian

Question 26

Frequology

Answer 26

analogue to peakology

don’t over interpret waves and oscillations