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Principles of CN Proper > L9: MEG Preprocessing > Flashcards

L9: MEG Preprocessing Flashcards

1
Q

Preprocessing of MEG data involves - (3)

A
  1. Inspecting MEG data
  2. Epoching
  3. Dealing with noise (e.g., noise reduction, noise removal [filtering, automatically/manually rejecting noise trails], averaging)
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2
Q

Preprocessing and event-related analyses of MEG is similar to

A

EEG (apply similar steps)

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3
Q

What is MEG data measuring for each sensor?

A

magnetic field strength over time

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4
Q

An MEG system samples from lots of

A

sensors at lots of points in time

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5
Q

What is the typical sampling rate for MEG per second and millisecond - (2)

A

around 1000 Hz (1000 samples per second)

1 sample per millisecond

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6
Q

Why does MEG have a sampling rate?

A

MEG has amazing temporal resolution (when the activity has happened) so don’t need MEG’s data everytime time

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7
Q

Our MEG system in York has around how much sensors?

A

248

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8
Q

The EGG in York has approximately how many electrodes?

A

64 and 128 electordes

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9
Q

The raw data from MEG can be stored in a very large

A

Time x Sesnor matrix

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10
Q

What is shown in the rows and columns?

A

Along the top is the different sensors in MEG and along the bottom is millisecond

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11
Q

MEG’s Time x Sensor matrix in a 10-minute worth of data can have how many rows and columns with 248 sensors

A

600,000 rows and 248 columns

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12
Q

MEG’s Time x Sensor matrix , each entry in the matrix is

A

the magnetic field strength detected by a given sensor at that point in time, measured in femto-tesla (10 to the power -15 telsa)

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13
Q

In EEG, we can have a matrix of electrode x time in which each entry

A

EEG values which are the magnitude of the electrical (activity) potential in microVolts

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14
Q

MEG’s timecourse for all sensors can initally be

A

inspected

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15
Q

First step of MEG - inspecting MEG data across time

Diagram of MEG time course for all sensors which plots.. and what are y and x axis - (2)

A

plots the timecourse for all the sensors

y is the magnetic field strength (femto telsa [fT) and x axis is time in seconds (s)

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16
Q

First step of MEG- inspecting MEG data across time

What can you see in this example? - (2)

A

6 sensors are very noisy (noisy lines underneath - broken sensors which should be removed)

others sensors are more stable showing a gradual drift (cone) of some picking up stronger magnetic field strength and others not so much –> some sort of artefact (e.g., changes in temperature/changes in magnetic noise)

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17
Q

Although inspecting MEG data across time is not usually informative, it can reveal

A

gross problems such as dead sensors or big artefacts

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18
Q

Inspecting MEG data such as one below is not enough to answer

A

our RQ

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19
Q

Usually in MEG studies, we would want to present stimuli at specific times and see how the brain responds

this is like what design in MRI?

A

event-related design

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20
Q

What does this diagram show? - (4)

A
  • Participant and their EEG/MEG recordings are being taken
  • Pps is looking at dog at stimulus PC
  • Then the stimulus PC sends a trigger (i.e., i showed image of puppy at this time) to EEG/MEG recording PC
  • Then EEG/MEG recording PC adds this to the data as the little 1s demonstrate when participants saw the puppy
  • This way know exactly when pps were shown something and look at the brain activity after they have done that to see when we show them a puppy
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21
Q

Different conditions have triggers with different

A

numbers

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22
Q

Diagram of example of different conditions have triggers with different numbers - (2)

A
  • Aside from recording when participant saw a puppy in MEG trace (e.g., 1)
  • We can also record when participants saw a cat (another condition) in MEG trace which is 2
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23
Q

What is a trigger in MEG?

A

A trigger is indicating stimulus onset is stored as a number in MEG

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24
Q

Typically, the stimulus presentation PC sends the trigger signal to the

A

EEG/MEG recording PC

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25
Q

In epoching, we can extract an ‘epoch’ of data around the

A

stimulus time [onset] (around trigger)

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26
Q

In epoching, we want some time before… and after… (2)

A

time (e.g., 500ms) before stimulus presentation as baseline and enough time after to see effects (e.g., 1500ms)

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27
Q

Why do we epoch some data (e.g., 500 ms) before stimulus presentation? - (2)

A
  • The effects we may see after stimulus presentation may be due to activity already going on in brain - lots of spontaneous activity
  • Acts as baseline and expect change to happen after that 500 ms
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28
Q

Diagram of epoching shows… (3)

A
  • Showed 100 puppies
  • After each time we showed them puppies (1s), we can look at the data after a 1 second or so and epoch it
  • This image does not include any baseline
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29
Q

In epoching we can extract the data before and after stimulus time (around trigger) in each.. - (2)

A

each trial in one condition

(e.g., epoch data for when pps seeing dog, epoch data when pps see cat)

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30
Q

In epoching, we can also have trial by trial

A

data

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31
Q

What does this trial of epoching of trial by trial data time course plot show? - (7)

A
  • 0 timepoint which is the stimulus presentation
  • baseline before 0
  • 0.5 and 1.0s (after 0) after stimulus presentation
  • Red is positive and blue is negative
  • Have different sensors at different trials
  • At 0.6s all sensors are consistently showing some change when showing some stimulus (e.g., puppy)
  • By looking at individual trials start to see effects consistent across sensors or across different trials (e.g., look at next trial showing puppy to see if we also have something [effect] at 0.6s)
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32
Q

In epcohing trial by trial data we can view data at one trial across

A

different sensors

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33
Q

In epoching trial by trial data, external noise such as… can often show up as …

A

subject movement and other artefacs (as well as real effects) often show up as correlated activity across sensors

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34
Q

In epoching trial by trial data the timings are now meaningful as 0

A

is stimulus presentation, see what changes

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35
Q

What is this diagram - (2)

A

This is a sensor montage which plots epoching trial by trial data in a different way (as compared to timeplot)

  • plots amplitude across different sensors in montage of head
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36
Q

It is very hard to distinguish EEG/MEG signal from

A

noise

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37
Q

The noisiness in EEG/MEG signals tend to be - (2)

A

much bigger than effects we are looking for (tiny magnetic strength/electrical potential effects)

problem for preprocessing EEG/MEG data

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38
Q

What does this diagram show? - (2)

A

brain’s magnetic fields we are looking for in an experiment are very small (down)

above it we have sources of magnetic effect that can reduce ability to see small experimental magnetic effects (e.g., earth’s magnetism, magnetic noise of town)

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39
Q

What are many noise sources in MEG (and EEG)? - (3)

A
  • electromagnetic interference from cars, mains power line, fans, MRI scanners etc..
  • Earth’s magnetic field
  • Participant movement, blinks and own heartbeats
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40
Q

Blinks of a participant show up on a

A

Electrooculography (EOG)

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41
Q

To detect and remove the blink artefacts in EEG/MEG

we use

A

electroculogram (EOG) sensors)

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42
Q

What does EOG stand for?

A

Electrooculography

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43
Q

EOG sensors is the

A

additional electordes placed on face to monitor blinks and eye movements

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44
Q

EOG may lead to

A

positive and negative voltage changes at some scalp electrodes

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45
Q

What does this diagram show? - (2)

A

Recordings of EOG is shown at the bottom two traces (circle)

These traces causes massive electrical /magnetic changes in EEG/MEG data when participant blinks

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46
Q

What does this diagram show in EEG? - (3)

A
  • The eye movements and blink have a specific topography sensors
  • Has effect where positive charge at the front of head and negative at back
  • If we see this kind of activity, we may see this as a blink
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47
Q

Can also use ECG sensors with EEG/MEG which record

A

electrical activity of the heart including rate and rhythm

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48
Q

What does ECG stand for?

A

electrocardiogram

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49
Q

Can also use ECG with EEG/MEG to remove heartbeat effects but less likely to - (2)

A

be correlated with task/stimuli presentation

(good thing! = enables us to do comparisons properly without worrying about heartbeat correlated unless comparing older vs younger which have different heartbeat rates)

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50
Q

What are common artefacts in MEG? - (3)

A
  1. Blinking
  2. Eye movements
  3. Heartbeat
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51
Q

Diagram of blinking in sensor space vs source space in MEG

A
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52
Q

What is this diagram showing of 3 different artefacts in MEG and why is its topgraphy of sensor space (eye movements) different to EEG? - (2)

A

We have different topography of different magnetic effects across MEG sensors when we blink, move eyes to the right and the heartbeat

This topography is different as whenever you have an electric charge you get a magnetic field that is perpendicular to it

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53
Q

The problem of noise (artefacts) is that it causes us to

A

not ‘see’ neural signals on a single trial

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54
Q

sa

We can deal with noise in the data by doing 3 things - (3)

A
  1. Noise reduction
  2. Noise removal (filtering, automatically/manually rejecting noisy trials
  3. Averaging
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55
Q

What is the solutions with noise reduction when doing the experiment? - (3)

A
  • study design should minimise movement, including eye movement and identify movement between conditions (i.e., no movement expected in one condition than another, get pp comfortable in scanner and get cushion and blanket)
  • Experiment taken place in magnetically shielded room (Faraday cage) –> earth’s magnetic field effect will reduce, no electrical machinery in scanner, de-mental the partticipants
  • Automatic comparison to reference channels
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56
Q

Reference channels in MEG systems is where their coils are positioned far away from

A

participants’ head

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57
Q

The purpose of reference channels is that

A

real MEGs sensors can be compared to the reference channels

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58
Q

Diagram of what inside of MEG scan looks like and what it shows… - (3)

A

SQUIDs stay cold so near liquid helium reservoir

Pink is signal coils picking up the magnetic signal and trying to send it near to SQUID

In between wehave green reference sensors which does not pick real brain activity but nearby to pick ambient things (e.g., traffic, earth’s magnestism

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59
Q

The reference electrodes are intended to pick up

A

ambient noise and interference not coming from the participant

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60
Q

The reference signals are then subtracted from the

A

real signals from rest of real MEG sensors = taking a lot of external sources of noise

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61
Q

How is subtraction of reference signals from real signals from MEG sensors is done? - (2)

A

done automatically (see ‘processed’ vs ‘raw’ folders)

OR

MaxFilter software for signal space separation has similar aim based on decomposing data to tell which are probably far from head

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62
Q

Noise reduction from reference electrodes does not help in detecting and removing

A

noise from the participant - reference electrode too far from the participant

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63
Q

After noise reduction, we will need to think about

A

noise removal

64
Q

What are the steps of noise removal - (2)?

A
  1. Filtering
  2. Automated removal of artefacts or Manual (or rule-based) removal of trails with artefacts
65
Q

After noise reduction and noise removal then we can think about

A

averaging over multiple ‘epoched’ trials

66
Q

For noise removal, ideally you want to do the same

A

preprocessing steps of noise removal for all participants

67
Q

To ensure noise removal is exactly the same (consistency and reproducibility) preprocessing noise removal steps for all participants,

we can

A

automated using a script, which also makes your analysis pipeline reproducible (i.e. someone else could run it and get the same results)

68
Q

There may be some sessions in which aggressive filtering of noise is necessary due to - (2)

A

idiosyncratic (unusal) conditions on the day

e.g., drilling noise in one day

69
Q

Any difference in analysis of noise removal between participants/sessions should be

A

reported

70
Q

With noise removal always want quality check data visually and calculate signal-to-noise ratio before preprocessing (manual or automated artificial artefact) and after

if we don’t then… - (2)

A

finding and remove/interpolate (e.g., broken) channels before statistical or automated artefact rejection as

will have to exclude many trials and may affect other sensors (automatic removal this signal may project to other sensors)

71
Q

Brain activity often happens at specific

A

frequencies (e.g.. alpha)

72
Q

Brain activity often happens at specific frequencies (e.g., alpha) which is the same of

A

noise

73
Q

Brain activity often happens at specific frequencies (e.g., alpha) which is the same of noise for example - (3)

A

the electrical sockets in UK have electrical polarity of 50Hz

So 50 times a second they switch direction

We get artefact in EEG/MEG at 50 Hz electrical sockets in other rooms

74
Q

What does filtering involve? - (3)

A
  • Using Fourier analysis to calculate the amount of activity of different frequencies
  • Plot frequency spectrum (also named Fourier spectrum, or sometimes amplitude/power spectrum)
  • Remove specific frequencies or frequency ranges to clean up signal that think might be noise (e.g., removing 50 Hz mains hum)
75
Q

Filtering is a recommended step in EEG/MEG preprocessing as

A

good for removing mains noise, muscle artefact and sensor noise

76
Q

Diagram of MEG frequency spectrum plot

what does y and x axis show and it is an output of.. - (3)

A

X axis is frequency from 0 -200 Hz

Y is strength of magnetic field in Log power (dB)

This is output of Fourier analysis

77
Q

What does this MEG frequency spectrum graph show? - (2)

A

Very prominent peaks around 50, 100, 150Hz, all coming from the mains voltage
(e.g., electrical sockets) - assume its noise

But we are not really interested in these frequencies!
So we can use a filter to get rid of them and clean up our data

78
Q

What are the 4 types of filters? - (4)

A
  1. low pass filter
  2. high pass filter
  3. bandpass filter
  4. notch filter
79
Q

A low pass filter lets through

A

signals below a particular frequency (low-frequencies)

80
Q

A low pass filter is used to remove

A

high-frequencies we are not interested in

81
Q

A high pass filter lets through

A

signals above a particular frequency (lets high frequencies pass)

82
Q

We use a high pass filter to remove low-frequencies such as

A

remove low-frequency drift in magnetic fields and DC component (cone shape)

83
Q

A low pass filter can be used to remove

A

50Hz mains effect ‘hum’

84
Q

A bandpass filter allows through signals between - (2)

A

two limits

used to combine a low and high pass filter

85
Q

A bandpass filter only lets through a particular

A

frequency band - that not very highest or lowest

86
Q

A bandpass filter gets rid of

A

some high and low frequencies

87
Q

What is a notch filter? - (2)

A

Removes a very tight range of frequencies e.g., around 50Hz to remove main hums

Not letting signals through a specific frequency , as compared to high , low, bandpass, but removing

88
Q

Example of notch filter

A

Want to get rid of anything between 49 and 51 Hz to remove main hums

89
Q

With different types of filters (e.g., high, low, bandpass, notch) you can

A

do multiple if useful

e.g., no point applying notch filter above the cutoff for a low pass (because signal will be 0 already)

90
Q

Diagram of showing effect of applying bandpass filter (0.5 - 48 Hz) of frequency plot of magnetic strength x frequency Hz - (2)

A

Scale changed as rid of anything above 48 Hz and removed spikes

Brain has 1/f property so low frequencies more relevant as shown in both graphs (more spikes in low frequencies) so less brain activity at higher frequencies

91
Q

Diagram of showing effect of applying bandpass filter (0.5 - 48 Hz) of magnetic field strength (fT) across time

A

Removed all the problematic sensors (dodgy lines) as well as got rid of gradual drift (cone) as slow changes of magnetic field over seconds have been removed (e.g., changes in temp has gone)

92
Q

What is another way to remove artefacts aside from filtering

A

automatic or manual removal of artefacts

93
Q

SSP and blind source separation is what type of removal of artefacts

A

automated

94
Q

If a type of artefact has usual or known topography (i.e., known spatial layout i in sensor space) then it can be identified and removed with

A

signal-space projection (SSP)

95
Q

What SSP require? - (2)

A

pre-defined topographies or spatial distributions (layout) for each artefact in sensor space

have a sense the type of artefact and where it is

96
Q

For each type of artefact in SSP, we can - (3)

A
  • work out how much of the data can be explained by this topography i.e., what number to multiply it by)
  • Remove the weighted version of each noise topography from the data
  • What is left is considered the signal
97
Q

In SSP we can remove or reduce real signal with a

A

similar spatial distribution e.g., frontal

98
Q

What does this diagram show - (3)

A

if blinking has this specific left-right topography in sensor space (left) then how much of data can be explained in each time point (e.g., over time we say does it look like we are blinking now)

weight spatial topography maps of sensor space of blinking, heartbeat and eye movement by multiplying by some number as regression with real signal to see what data is made up of

Then take the artefacts out if only want signal

99
Q

SSP automatic removal of artefacts works well for and okay for

A

well with eye movements &blinks and okay for heartbeat as consistent topography in sensor space

100
Q

In automatic removal of artefacts if we are not confident we know the - (2)

A

topography of artefacts we can instead separate data in data-driven way and identify which parts are likely artefacts

  • blind source separation
101
Q

Examples of blind source separation methods - (3)

A
  1. Principal Component Analysis
  2. Singular Value Decomposition
  3. Independent Component Analysis (ICA)
102
Q

What is the most popular blind source separation method?

A

Independent Component Analysis (ICA)

103
Q

The blind source separation methods does not require

A

pre-defined topographies in sensor/source space

104
Q

What is the difference between blind source separation and SSP?

A

In blind source separation, separate data in a driven way and identify which parts are likely artefacts based on mathematical assumption and not on spatial maps

105
Q

What are the steps of blind-source separation? - (3)

A
  1. Decompose data e.g., separate into components
  2. Choose which are likely artefacts and remove these
  3. Reconstruct the data
106
Q

The ICA separates sources

A

that are not dependent on each other i.e., are independent

107
Q

Diagram of ICA in ME - (3)

A
  • Have MEG data (x) on left
  • Spilt that data into time courses into 2 different sources on basis on being independent
  • One source could be heartbeat due to its spatial topgraphy in sensor space and it being rhythmic over time course which is different to brain signal so remove it
108
Q

In ICA they get

A

approximation of each source = time course and spatial distribution

109
Q

ICA in MEG ‘unmixes; the

A

signals assuming they are independent of each other and have separate sources

110
Q

The ICA explains as much of the data as

A

possible

111
Q

The ICA ‘unmixes’ the MEG signals by assuming they are independent of each other and separate sources so

A

they can recombine those signals that have not been removed (e.g., not heartbeat artefact)

112
Q

In ICA, some components of it wil mostly be.. and others will be..

A

mostly be artefact and others MEG signal

113
Q

Diagram of ICA

A
114
Q

Benefits of automatically removing artefacts - SSP or blind source separation (2)

A
  • Avoids having to reject data that are affected by an artefact (cleaning trials not removing trials - end up with same number of trials)
  • Can quickly and automatically clean up data - computer does it for you
115
Q

Can you do SSP and ICA together?

A

No , either one or the other

116
Q

Disadvantages of automatically removing artefacts - SSP or blind source separation (2)

A

Can’t detect all artefacts (e.g., idiosyncratic [strange] movements)

Not always accurate and can lose or distort signal-

117
Q

In automatic removing artefacts - SSP or blind source separation always check (2)

A

your results and excluded/included components

118
Q

In manual/statistical artifact removal most of times involve:

A

Go through each epoch and check it manually for obvious problems

119
Q

The disadvantage of manual/statistical artifact removal where researchers go through each epoch and check for obvious problems

A

very time-consuming but many researchers do this way

120
Q

Another way of manual/statistical artifact removal is when

A

removing outliers e.g., exceeding 3SD from mean of other trials

121
Q

The reason why other researchers don’t remove artefacts post-filtering at all via manual/statistical artefact removal as its - (3)

A

time consuming

could remove real effects

could be subjective

122
Q

Consider collecting other measures with MEG such as

for manual/statistical artefact removal

A

– ECG, EOG, EEG to help detect eye movement and cardiac effects (or MEG setup may have head position indicators)

123
Q

For manual/statistical artefact removal always quality check your data

A

before and after preprocessing steps

124
Q

Manual/statistical artefact removal approach may depend on participant grp e.g.,

A

more important with the high level of motion when testing children

125
Q

Averaging across multiple ‘epoched’ trials’ in which it has… and can average.. (2)

A
  • has preprocessed (epoch) per trial
  • can average the time course across sensors over multiple repetitions of the condition (e.g., of seeing a dog) and see if responses are consistent
126
Q

Averaging the time course over multiple repetitions of the condition

if responses are consistent across trials,

A

they are increased

127
Q

Diagram of averaging many ‘epoch’ trials of a condition - N is number of trials

A
128
Q

Averaging the time course over multiple conditions of the condition

if noise is different across trials the noise is (e.g., eye blinks/heart beat is random time)

A

decreased

129
Q

Averaging the time course multiple repetitions of condition improves - (2)

A

signal to noise ratio as hoping responses are consistent (e.g., same peak every 100 ms after seeing puppy)

but can lose real effects if they are not consistent across trials

130
Q

Which of these statements is true description of MEG compared to fMRI?

A. Better temporal but worse spatial resolution and more frequency info

B. Better temporal rbut wrose spatial resolution and less frequency info

C. Better spatial but wrse temporal resolution and quieter

D. Better spatial but worse temporal resolution and louder

A

A

131
Q

Which statements is TRUE?

A. We collect MEG data in source space and transform into sensor space

B. We collect MEG data in source or sensor space and transform it

C. We collect MEG data in sensor space and trasform into source space

D. We collect MEG data in outer space and transmit back to Earth

A

C

132
Q

MEG and EEG sources in brain are represneted by little battery like currents called what

A. Unipole

B. Bipoles

C. Dipoles

D. North poles

A

C

133
Q

Which of these statements is TRUE?

A. SQUIDS are further from the brain as they need to be extremely cold

B. SQUIDs are further from the brain as they need water to swim

C. OPM are further away from the brain so participant can move freely

D. OPMs are closer to the brain resulting in worse signal

A

A

134
Q

TRUE OR FALSE? - (3)

You can not predict orientation of magnetic effects from the electric current

MEG struggles with deep sources but EEG is fine

Activity must be synchronised over a few mms to be detected with EEG or MEG

A
  1. False - predict the orientation of magnetic effect from the electric current as its 90 degrees (perpendicular)
  2. False - both MEG and EEG struggle with deep sources as on the surface
  3. True
135
Q

Compared to EEG, MEG allows

A

monitoring of cortical activation sequences without severe disortion by the skull and other extracerebral tissues

136
Q

MEG as compared with fMRI directly reflects neuronal phemona whereas MRI does it

A

indirectly via hemodynamic response

137
Q

The main advantage of MEG is - (2)

A

excellent (sub)millisecond temporal resolution

insensitivity of the signals to the distorting effects of skull

138
Q

Although most MEG applications
remain in basic brain research, the use of MEG is increasing in

A

clinical medicine (epilespy etc..)

139
Q

The tiny cerebral magnetic fields can be detected by

A

sensors using SQUIDs that convert magnetic flux into recordable electric voltage

140
Q

SQUIDs in MEG is used in combination with

A

superconducting pick up coils that guide neuroimaging fields into SQUID loop

141
Q

The geometry of the pick up coil in MEG determines the

A

sensitivity pattern of a sensor

142
Q

MEG recordings are typically performed in magnetically shielded rooms constructed out of

A

mu metal and aluminum

143
Q

What are the main external artefacts and biological artefacts that contaminate MEG signal? - (2)

A

Main artefacts may arise from power lines, moving vehicles or magnetic stimulations and response devices

Biological artefacts: cardiac function, eye movement, blinks, muscular activity and artefacts related to participants’ articulation and movement

144
Q

The novel sigal space separation method (SSS) and temporal extension (tSSS) is type of what method in MEG

A

filtering

145
Q

The tSSS can

A

supress external interference

146
Q

MEG/EEG can give a grasp of dissociation between .. that can not be done with fMRI

A

early unconscious and later conscious processing

147
Q

Main focus of EEG was recording

A

spontaneous brain activity

148
Q

The brain’s spontaenous MEG/EEG activity contains both

A

rhythmic and irregular components

149
Q

The brain’s spontaneous MEG/EEG activity is usually below

A

30 Hz depending on participants’ vigilance, task, possible medications and disease

150
Q

Frequency tagging in magnetoencephalography (MEG) refers to a technique used to .. and example - (3)

A

selectively label or tag neural responses to specific frequencies of visual or auditory stimuli.

e.g, checkerboard pattern, that alternates between black and white at a specific flicker rate or frequency. For example, they might present the checkerboard with a flicker rate of 10 Hz (cycles per second).

ocusing on the frequency of interest (e.g., 10 Hz), researchers can isolate and analyze the neural responses specifically related to the flicker rate of the checkerboard.

151
Q

‘Life Time’ is

A

the recovery time of stimulus response (higher source is in processing hierarchy longer life time. quicket in primary sensory areas)

152
Q

Brain maturation is reflected in … - (2)

A

frequency content and distribution of the spontaneous MEG/EEG brain rhythms (same for child and adults - same region)

and in the time
lags and shapes of evoked responses to external
stimuli (timing of activations is delayed in child as compared to adults e.g. word perception)

153
Q

Which of the following statements about artefacts in MEG is FALSE?

A. Artefacts are signals in the data which are not related to the process which we want to measure.

B. EOG and ECG signals can provide useful information to assist with the artefact removal process.

C. The aim of artefact rejection is to remove both physiological and non-physiological noise from the dataset.

D. The use of Independent Components Analysis for artefact removal requires that the user mark individual epochs of data as “bad”.

A

D

154
Q

Which of these methods do NOT help reduce the amount of noise in MEG data?

A. Shielding the room

B. Avoiding metal near the scanner

C. Allowing the participant to move around

D. Comparing the sensor data to reference channels

A

C

155
Q

Question 3
In what ways are Magnetic Evoked Potentials (MEPs) different to other Event-Related Potentials?

A. They have inconsistent amplitudes and are labelled with an ‘M’

B. They have inconsistent directions and are labelled with an ‘M’

C. They have inconsistent timings and are labelled with an ‘E’

D. They have inconsistent frequencies and are labelled with an ‘E’

A

B

156
Q

Which of these would be the most important step to change in your pipeline because you think the data contains responses that are induced but not evoked?

A. Average the time courses across multiple trials

B. Filter the data

C. Perform a frequency-based analysis

D. Remove ill-fitting epochs

A

C

Principles of CN Proper (19 decks)

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