HC 3

Question 1

What are the steps for data-analysis ERP?

Answer 1

1. Preprocessing
2. Main signal processing
3. Statistical testing

Question 2

What do we do when preprocessing?

Answer 2

-(Re-)reference of the EEG signals
-Preprocessing reduces noise signals and formats EEG data for the main signal processing
-filtering
-correction of eye movements
artefacts
-artefact rejection
-segmentation

Question 3

What are the two ways to do re-referencing (offline)?

Answer 3

1. Mastoid reference
   Cz - ((Lm+Rm)/2)
2. Average reference
   Cz - ((Fz+F3+F4…O1+Oz+O2)/nr of electrodes)

Question 4

Why do we have to take account for re-referencing?

Answer 4

ERPs can look very different with different references. This has to do with the distance between sensors. It affects ERP morphology, but not topography. Different references sensors are:

-Nose
-Average
-Earlobes
-Non-cephalic
-Mastoids

Question 5

What is noise?

Answer 5

Electric signals not from the brain.

Question 6

What are the common types of noise?

Answer 6

EMG= electromyogram for face movement

EOG= electrooculogram for eye movement. Blinking is the most occuring noise.

ECG= electrocardiogram for movement

Question 7

What is noise reduction?

Answer 7

It is a visual inspection and artefact detection/rejection. We have to inspect the data first, is it okay to use?

We use topographic interpolation to try to reduce noise. Then we use independent component analysis (ICA) for removing eye movement artefacts and then use filtering. So, we get rid of the noise.

You can either use manual or automatic rejection.

Question 8

What is the difference between automatic and manual rejection?

Answer 8

Manual: delete a segment from the data based on visual inspection.

Automatic: delete segment from the data if some criterion is met, for example if the amplitude from of a signal > 200uV

Question 9

What is artefact?

Answer 9

Another word for noise.

Question 10

What is the difference between liberal artifact- and conservative artifact rejection?

Answer 10

Liberal artifact rejection= many trials in the average, but contaminated with artifacts

Conservative artifact rejection= fewer trials in the average, though less artifacts

The more trials, the smoother the data.

Question 11

What is topographic interpolation?

Answer 11

When one or a few electrodes are very noise throughout the experiment, replace the electrode with the mean of the surrounding electrodes.

Question 12

What is ocular rejection?

Answer 12

If the participant keeps blinking before the stimulus, throw the data away.

Question 13

What is ocular correction?

Answer 13

Independent component analysis gets rid of the blinks without deleting data.

ICA can be used to remove eye movement and other artefacts.

Question 14

What is filtering?

Answer 14

It is removing high or low frequencies from the EEG signal. Why should we filter raw data? To remove non-neural voltages, such as the 50Hz AC noise(computer screen omits this noise regurarly).

Question 15

What are the different types of filtering?

Answer 15

-High-cut/low-pass filter= remove high-frequency noise

-Low-cut/high-pass filer= remove low-frequency noise

-Band-reject/Notch filter= filters out noise between the lower and upper thresholds

Different filtering can result in different ERPs. This is important to note in the method section of a paper.

Question 16

What is segmentation?

Answer 16

Cut the EEG into segments or epochs separately for both markers. Set a start and begin time epoch.

Average all epochs per condition.

Question 17

What is a problem occuring during segmentation? And what is the solution?

Answer 17

Problem: the baseline is different per epoch. Cannot compare ERPs, because the baseline starts high or low.

Solution: baseline correction. Separately per electrode: compute the average baseline activity. Then, subtract the average baseline activity from each sample in the epoch.

Question 18

What is a way to detect a peak?

Answer 18

Take the mean activity over some period. There are three results:

-When there is large lateny variation in peaks.

-When there is no peak, but more sustained activity

-When the difference between conditions lies between peaks

Question 19

How do we do statistical testing?

Answer 19

We take an average window and export it to SPSS. We then take the peak difference between two sensors and run some analysis with it.

We can do a t-test comparing GO with NoGo at certain electrodes. But a better way is to do a Repeated Measures ANOVA with the variables Condition (Go vs NoGo) and Electrode.

Question 20

What is multivariate pattern analysis (MVPA)?

Answer 20

It is a technique in neuro-imaging that can detect differences between conditions.

It is a machine learning technique that needs an algorythm. It needs to be fed data to make these.

We predict the type of stimuli it is, based on the EEG data we feed.

Question 21

Imagine we present blue circles and red squares, how does the brain code for these two stimuli? Can we differentiate the neural responses between these two stimuli using EEG?

Answer 21

Solution 1: Compare ERPs of blue circles with red squares

Solution 2: MVPA, predict the labels of stimuli, based on the EEG data

Question 22

How do we find the best decision boundary for seperate stimuli?

Answer 22

1. We train the data by feeding it EEG data
2. 80% of the data is getting used for data learning
3. Then we test it on 20% new fresh data

Question 23

What is the benefit of using MVPA compared to ERP analysis?

Answer 23

MVPA can extract any pattern from the data that ERP cannot and it does not have to be lateralized. It can identify differences, especially if the locus of the effect is unknown.

Question 24

What is the Fourier Transform?

Answer 24

It is a mathematical technique used to analyze brain activity data, typically collected through methods such as EEG or fMRI.

It allows researchers to decompose complex brain signals into simpler frequency components, revealing the underlying rhythmic patterns of neural activity. This can be useful for studying cognitive processes that are associated with specific frequency bands.

By applying Fourier transformation to brain activity data, researchers can identify oscillatory patterns or brain waves that are associated with different cognitive functions. This can help understand how neural networks are organized and how they function during various cognitive tasks or states.

Question 25

How does Fourier transformation work?

Answer 25

1. Decompose the signal into its frequency components
   -Oscillating signals like EEG consist of sine waves of different frequencies.
   -Fourier transformation: find the sine waves that make up the original (complex) wave
   -The amplitude (power) of these frequencies can be extracted from the EEG by a Fourier transformation
2. Fourier transformation
   -Shows different peaks in the ‘power spectrum’, corresponding to the frequencies that make up the signal.

Question 26

What is frontal alpha asymmetry (FAA)?

Answer 26

It is an indicator of assymmetric brain activity in the frontal cortex, which refers to asymmetrcial anterior activity (EEG) in the alpha band (8-13Hz).

Alpha power is inversely related to regional brain activity and decreased power values of the alpha band indicate an increase in cortisal or hemisperical activation.

The more alpha power, the less activity.

Question 27

What are the two predominant neurophysiological models accounting for the association between alpha asymmetric pattern and emotions?

Answer 27

1. Valence model
2. Approach-withdrawal model

Question 28

What is the valence model?

Answer 28

Negative vs positive emotions.

Greater left hemisphere activity (lower alpha power) is associated with positive emotion, whereas greater right hemisphere activity is associated with negative emotion.

Question 29

What is the approach-withdrawal model?

Answer 29

Postive emotion often correlates to approach-related motivation with more left frontal brain activity, whereas negative emotion correlates to withdrawal-related motivation with more right frontal brain activity.

Emotions with approach motivational tendencies are linked to a higher left frontal activity, whereas emotions with withdrawal motivational tendencies are linked to a higher right frontal activity.

Question 30

What is the time frequency analysis?

Answer 30

The principle of time-frequency analysis is to provide a detailed understanding of how the frequency content of a signal changes over time. Unlike traditional Fourier transformation, which represents a signal solely in the frequency domain, time-frequency analysis captures both temportal and spectral information simultaneously.

The key idea is to decompose a signal into its constituent frequency components at different points in time. This is achieved with techniques such as STFT, Wavelet transform or spectrogram analysis.

By applying time-frequency analysis, researchers can track dynamic changes in frequency content of a signal over time, which is useful for studying non-stationary signals or signals with rapidly changing spectral characteristics.

Question 31

When should you do time-frequency analysis?

Answer 31

Fourier analysis of EEG data is done over quite a long period (1 sec). You lose the timing information, which is the whole point of EEG. What if you want to know how the power of certain frequencies change over time?

Then you should do a time-frequency analysis.

Question 32

What is the binding problem?

Answer 32

We experience objects as whole, but many of the properties of objects are encoded and processed in different areas of the brain. How do coherent representations emerge?

Question 33

What is one theory for explaining the binding problem?

Answer 33

Rythmic synchronization of neural discharges in the gamma band (40Hz) may provide necessary spatial and temporal links that bind together the processing in different brain areas to build a coherent percept.

Tallon-Baudry reasoned that if gamma activity reflects a binding mechanism, it should be enhanced when a coherent percept is created in response to a given stimulus.

This hypothesis was tested by evaluating the strength of the gamma signal that is induced by stimuli that share the same physical properties, but do or do not lead to the perception of a coherent percept.