EEG Analysis Sample Questions?

Question 1

In EEG, referencing is done by …….. the average activity at the reference electrodes from all other channels

Answer 1

subtracting

Question 2

During preprocessing EEG, high pass filtering is done usually on ….data instead of epoched data to avoid edge artefacts..

Answer 2

continuous

Question 3

Independent component analysis separates a set of signal mixtures into a corresponding set of statistically independent component signals of…… signals

Answer 3

source

Question 4

In the equivalent current dipole approach for EEG source reconstruction, the locations and ……. of dipoles remain fixed over time, but their magnitude may vary

Answer 4

orientations

Question 5

For an EEG signal to be considered chaotic at least one of its Lyapunov exponents should be….

Answer 5

positive

Question 6

In non linear dynamical system theory, a strange attractor is a complex object with….. geometry

Answer 6

fractal

Question 7

While using magnitude square coherence for analysing synchrony in the EEG data, the major problem is that it ……….. amplitude and phase information

Answer 7

mixes

Question 8

in brain the effective connectivity describes the …… effects of one brain region over another

Answer 8

casual

Question 9

A brain network with a ……. characteristic path length means that information, on average, be routed between distant brain regions using only a few intermediate regions

Answer 9

short/low

Question 10

Choose the INCORRECT statement in relation with muscular artifact in EEG recording

a. it contaminates high frequency brain activity
b. problematic for time frequency analysis
c. less problematic for ERP analysis
d. it is mostly found over the midline electrodes

Answer 10

d.it is mostly found over the midline electrodes

Question 11

Choose the INCORRECT statement in relation with global field power

a. relates to mean root square
b. it is dependant on the choice of EEG reference
c. it is visualised as a function of time
d. high values indicate a stable configuration of EEG scalp map

Answer 11

b.it is dependant on the choice of EEG reference

Question 12

In relation to scaling exponent a. using detrended fluctuation analysis, choose the incorrect statement

a. a=0 indicates white noise
b. a=1 indicates 1/f noise
c. 1<a></a>

Answer 12

a. a=0 indicates white noise

Question 13

In relation with EEG analysis, choose the odd one eout

a. correlation dimension
b. lyapunov eponent
c. entropy
d. fourier power spectrum

Answer 13

d. fourier power spectrum

Question 14

Choose the incorrect statement in the context of Granger causality analysis for EEG signal

a. it provides directional information
b. it indicates mainly nonlinear coupling
c. the estimation is biased for limited data
d. the formulation is parametric

Answer 14

b. it indicates mainly nonlinear coupling

Question 15

Choose the incorrect statement in the context of phase synchrony analysis for EEG signals

a. phase synchrony is a neurophysiologically meaningful analysis
b. it is sensitive to weak coupling
c. it requires fewer assumptions
d. phase is meaningful only for broadband EEG signals

Answer 15

d. phase is meaningful only for broadband EEG signals

Question 16

Discuss why mean amplitude measure may be preferred over peak amplitude measure in ERP analysis.

Answer 16

The peak amplitude measure is very sensitive to high frequency noise and it is a nonlinear measure and therefore the average of the peak amplitude cannot be compared to the grand average. IN contrast the mean amplitude represents the data in a better way as it characterizes as being extended over time, as the effect may be nowhere near the peak. It is also linear and can be compared to the grand average and can use a narrower measurement window

Question 17

What is a forward problem in the EEG source reconstruction? Discuss briefly the factors influencing the accuracy of the forward problem solution.

Answer 17

The forward problem is asking the question of what will be the source given the dipoles. It has a unique solution and can be solved analytically without EEG data. It accuracy depends on how is the head modeled. Therefore, the solution may differ based on whether the head is represented as anatomically correct or as a sphere. Also, it depends on whether data from each participants is measured precisely or standard electrode montage is assumed.

Question 18

Discuss briefly the common strategies used for the Equivalent Current Dipole approach for EEG source reconstruction.

Answer 18

One way would be to use PCA to find out the underlying spatial distributions of the activity.
Another way is to start with a few sensory dipoles to fit the early ERPs and then add more for later time windows.
Also, preexisting strategies can be used to decide the number of dipoles

Question 19

State the limitations of ECD approach for EEG source reconstruction.

Answer 19

It is not possible to know the priori number of dipoles
Most cortical activity is distributed over a large body of the cortex
The results are dependent on the researcher as there are many free parameters to choose and the result is dependent on these choices which can be biased by expectation
Cannot be completely sure about the accuracy of the solution as with so many free parameters per dipole, if one is wrong the rest will slightly change to reduce the residual variance. Therefore, if there is noise in the data, a wrong solution can have lower variance than the best fit solution

Question 20

According to the dynamical system theory, what is an attractor? Name the four common attractors.

Answer 20

An attractor is a geometrical figure, around which a dynamical system evolves over long enough time. There are 4 types of common attractors:

Fixed point – the system cannot change anymore without external force
Limit cycle – a closed loop in the state space
Torus – has complex donut shape
Strange attractor – complex object with a fractal geometry, associated with chaotic dynamics

Question 21

Name the two crucial parameters for state space reconstruction of an EEG signal. Name the methods used for selecting these parameters.

Answer 21

The two crucial parameters in reconstructing state space are time delay and embedding dimension. Some of the approaches that can be used to choose these parameters are:
Embedding – time series is converted into a sequence of vectors in an m-dimensional space. There are two types of embedding (time-delay and spatial embedding)
Correlation dimension, lyapunov exponent

Question 22

Discuss briefly why brain can be considered as a complex nonlinear system.

Answer 22

Simple nonlinear, dynamical systems can exhibit completely unpredictable behavior, which might seem to be random. This seemingly unpredictable behavior has been called ‘chaos’. Therefore for a system to be considered nonlinear, chaos needs to be present. Essentially, the human brain is spanning on multiple scales. For example, there is established connectivity between brain networks, so the whole works better than its parts. Also, it is difficult to make long term predictions when it comes to brain function which is a hallmark for chaotic system, and the brain is all about dynamic activity suggesting varying nature of the underlying system.

Question 23

What is neuronal synchronization? List two linear and two nonlinear measures of synchronization usually applied to EEG signals

Answer 23

Neural synchronization refers to the integration of distant brain networks to exchange information and mutually reinforce and influence one another. Therefore activity in one neural population influences another and their dynamics become mutually entrained.
Some linear measures of EEG signals are linear correlation, coherence, granger causality and multivariate modeling. Some nonlinear measures of neural synchronization are nonlinear correlation, phase-synchrony and generalized synchrony

Question 24

What are three types of connectivity in brain? Discuss briefly how they are related.

Answer 24

There are three types of connectivity in the brain
1. anatomical connectivity – describes the physical connectivity between local circuits and distant regions
2. functional connectivity – refers to the functional integration of brain structures in terms of their statistical interdependence
3. effective connectivity – describes the causal effects of one neural system over another
These are interconnected as such as the functional and effective connectivity are constrained by structural connectivity. The structural inputs and outputs of cortical region will determine the extent of its functional connectivity. Also, to some extend functional connectivity may influence the structural connectivity by the development of new synaptic connections especially during developmental stages.

Question 25

Name the three core measures to characterize a brain network. Discuss how these measures could distinguish major types of brain networks.

Answer 25

The three core measures to characterize a brain network are degree distribution, clustering coefficient and the characteristic path length. The degree distribution is an important quality of a network as it gives information about the distribution of local nodes, while the clustering coefficient gives information about the local structure (e.g. a measure of resilience if a node is lost), while the characteristic path length indicated the integration in brain networks and the efficiency of information transfer. Based on these features an interference can be drawn as to what type of network is being observed.

Question 26

List five common network features often associated with healthy human brain.

Answer 26

A healthy brain will exhibit: 
High clustering coefficient
Short-characteristic path length
Hierarchical modularity 
Scale-free degree distribution 
Presence of interconnected hubs