L9: MEG Preprocessing Flashcards
Preprocessing of MEG data involves - (3)
- Inspecting MEG data
- Epoching
- Dealing with noise (e.g., noise reduction, noise removal [filtering, automatically/manually rejecting noise trails], averaging)
Preprocessing and event-related analyses of MEG is similar to
EEG (apply similar steps)
What is MEG data measuring for each sensor?
magnetic field strength over time
An MEG system samples from lots of
sensors at lots of points in time
What is the typical sampling rate for MEG per second and millisecond - (2)
around 1000 Hz (1000 samples per second)
1 sample per millisecond
Why does MEG have a sampling rate?
MEG has amazing temporal resolution (when the activity has happened) so don’t need MEG’s data everytime time
Our MEG system in York has around how much sensors?
248
The EGG in York has approximately how many electrodes?
64 and 128 electordes
The raw data from MEG can be stored in a very large
Time x Sesnor matrix
What is shown in the rows and columns?
Along the top is the different sensors in MEG and along the bottom is millisecond
MEG’s Time x Sensor matrix in a 10-minute worth of data can have how many rows and columns with 248 sensors
600,000 rows and 248 columns
MEG’s Time x Sensor matrix , each entry in the matrix is
the magnetic field strength detected by a given sensor at that point in time, measured in femto-tesla (10 to the power -15 telsa)
In EEG, we can have a matrix of electrode x time in which each entry
EEG values which are the magnitude of the electrical (activity) potential in microVolts
MEG’s timecourse for all sensors can initally be
inspected
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)
plots the timecourse for all the sensors
y is the magnetic field strength (femto telsa [fT) and x axis is time in seconds (s)
First step of MEG- inspecting MEG data across time
What can you see in this example? - (2)
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)
Although inspecting MEG data across time is not usually informative, it can reveal
gross problems such as dead sensors or big artefacts
Inspecting MEG data such as one below is not enough to answer
our RQ
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?
event-related design
What does this diagram show? - (4)
- 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
Different conditions have triggers with different
numbers
Diagram of example of different conditions have triggers with different numbers - (2)
- 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
What is a trigger in MEG?
A trigger is indicating stimulus onset is stored as a number in MEG
Typically, the stimulus presentation PC sends the trigger signal to the
EEG/MEG recording PC