EEG

Question 1

Strengths of EEG

Answer 1

1. Excellent time resolution
2. Cognitive, perceptual, linguistic, emotional processes are fast and dynamic – For example, consider theta band (4-8 Hz), a slow rhythm but quite fast for our conscious experience – Or consider gamma (30 -80 Hz)
3. Direct indicator of neuronal activity
4. Multidimensional (time, space, frequency, power, phase, connectivity etc)
5. Portability (observing brain in action)
6. Relatively inexpensive
7. Non-invasive

Question 2

Weaknesses of EEG

Answer 2

1. Poor spatial resolution- It is not well-suited for precise functional localization
2. It is not well-suited for measuring deep brain structures (e.g., putamen, thalamus, nucleus accumbens)
3. Sub-optimal method: where in the brain does process X occur or is information Y stored
4. It is also not very well-suited to study very slowly fluctuating process with uncertain and variable time course

Question 3

Two types of neuronal activity

Answer 3

1. Action potential (AP)

2. Postsynaptic potential (PSP)

Question 4

What is post synaptic potential

Answer 4

• Besides chemical synapses there are electrical synapses, or gap junctions. Ions flow directly through large channels into adjacent cells, with no time delay.
• PSP is an electrical potential initiated at a postsynpatic site that can vary in amplitude and spreads passively across the cell membrane, decreasing in strength with time and distance
• Generation of PSP – When AP reaches presynaptic axon end, a neurotransmitter is released into the synaptic cleft – The neurotransmitter binds to the receptor of the postsynaptic neuron by opening or closing an ion channels – This lead to a graded change in membrane potential which can cause an action potential o occur
• Two types of PSP
o Excitatory PSP (EPSP, for excitatory synapse)
o Inhibitory PSP (IPSP, for inhibitor synapse)

Question 5

What determines whether an action potential occurs following a post-synaptic potential

Answer 5

Two types of summation
1. Spatial summation
o is the summing of potentials that come from different parts of the cell. If the overall sum – of EPSPs and IPSPs – can depolarize the cell at the axon hillock, an action potential will occur.
2. Temporal summation
o is the summing of potentials that arrive at the axon hillock at different times. The closer together in time that they arrive, the greater the summation and possibility of an action potential.

Question 6

What are EEg signals based on?

Answer 6

EEG signals are primarily produced by summation of postsynaptic potentials of millions of neurons.

• EEG is considered to be a measure of the inputs to a group of neurons, rather than the outputs of that group. It is believed to measure postsynaptic potentials in the dendritic tree, and this is believed to be a measure of inputs arriving at the synapses on dendrites of neurons. But in general there is no one-to-one mapping between dendritic input and firing output, because (1) neurons are complicated nonlinear devices, and (2) neurons can receive excitation or inhibition not only through their dendrites, but also through synapses on the cell body. If input is very high it’s reasonable to assume the neuron will produce action potentials, but EEG cannot tell you whether this has in fact happened or not.

Question 7

Electrode naming and placement

Answer 7

Fp = Frontal pole
C = Central
O = Occipital
T = Temporal
P = Parietal
Larger the number, larger the distance from midline. Midline electrodes labelled with s z, i.e. Fpz, Fz, F3, F7
Odd ending: Left Hemisphere
Even ending: Right hemisphere

Question 8

How many electrodes?

Answer 8

• International 10-20 Electrode Placement System Jasper (1958) EEG Clin Neurophysiol
• Traditional 19
• Standard 32-64
• High density 128-256 (or more)
o Pros: Better spatial sampling, Source reconstructions
o Cons: Long preparation time, Electrolyte bridge, Poorer signal quality
** Rules of thumb: Unless you expect precisely localized brain activity, 64 electrodes will be sufficient.

Question 9

What do amplifiers do in EEG?

Answer 9

• The signal is amplified from a few microVolts to a few Volts.
• The amplification is done by Differential Amplifiers
• Three electrodes
1. Active Electrode (A) placed at the desired site
2. Reference Electrode (R) placed elsewhere on the scalp
3. Ground Electrode (G) placed elsewhere on the scalp/body
Amplifies AG – RG (where AG = A – G; RG = R – G)
• Elimination of ambient noise
• Works best when impedances are same (low) for A and R
• Amplifier gain: 5-10 K- Optimal gain depends on the input potential and output range

Question 10

Reference sites

Answer 10

• Avoid a site that is biased towards one hemisphere
o Choose a site over midline
o Choose two sites symmetrically placed over two hemispheres
o Choose offline or average referencing over linked referencing
• Avoid a site that is noisy (e.g., sites next to muscles)
• Avoid a site that is close to the expected effect’s location
• Choose a site that is used by other researchers using similar paradigm (for obvious comparison purpose)
• Use the same reference site for all your participants
• Most frequently used reference sites
o Average of two (left, right) earlobes
o Average of two (left, right) mastoids

Question 11

2 other referencing schemes in EEG

Answer 11

• Average reference
o Average of all of your scalp sites
o Needs high density recording (minimum 64, preferably 128-256)

• Current Source Density (CSD)/ Laplacian
o Current Source Density analysis (CSD) is a class of methods of analysis of extracellular electric potentials recorded at multiple sites leading to estimates of current sources generating the measured potentials.
o It is the flow of current out of the scalp at each point on the scalp
o Essentially reference-free

Question 12

What is the nyquist frequency?

Answer 12

: the minimum rate at which a signal can be sampled without introducing errors, which is twice the highest frequency present in the signal i.e. Sampling frequency (sf) > 2 f max

Question 13

What is aliasing?

Answer 13

When different signals that are sampled become indistinguishable. Occurs when samples are taken below the Nyquist frequency.

Question 14

What is sampling rate?

Answer 14

• The sampling rate is usually expressed in Hz, for example 240 Hz is 240 times per second.

Minimum sampling rate is nyqist frequency

Question 15

Four types of filters

Answer 15

1. Low pass- allows low frequencies to pass
2. High pass- allows high frequencies to pass
3. Band pass- allows only a specific band on frequencies to pass
4. Band stop / Notch- filters out/blocks a specific band of frequencies from passing.

Typically
 High pass: 0.5 Hz (or 0.1 Hz for slow brain responses)
 Low pass 100 Hz
 Notch 50 Hz (for removing power line noise; 60 Hz in USA)

Question 16

What is digitisation?

Answer 16

Analog to digital conversion (ADC)
• A digital EEG system converts the waveform into a series of numerical values. This process is known as Analogue-to-Digital conversion (ADC).
• Sampling at rates lower than the Nyquist value means that when the signal is converted back to analogue form, it will not resemble the original waveform.

Question 17

What is resolution in ADC?

Answer 17

• Resolution in this context refers to the conversion of an analog voltage to a digital value in a computer (and vice versa). A computer is a digital machine and thus stores a number as a series of ones and zeroes. If you are storing a digital 2-bit number you can store 4 different values: 00, 01, 10, or 11.
• A 3-bit digital value can represent 8 (2 to the power of 3) different numbers. A 12-bit digital value can represent 4096 (2 to the power of 12) different numbers. A 16-bit digital value can represent 65536 (2 to the power of 16) different numbers. It might occur to you at this point that a digital input could be thought of as a 1-bit analog to digital converter.
• ADC has 16/24 bit resolution.

Question 18

What is the resolution of ADC

Answer 18

ADC has 16/24 bit resolution.

Question 19

5 Artifacts in EEG

Answer 19

• Saccades (eye movement)
• EMG (electromyographic signal- originates from muscles movement)
• EKG (Electrocardiograph artifacts are defined as EKG abnormalities, which are a measurement of cardiac potentials on the body surface)
• Skin potential (If the individual’s body temperature or stress level increases, sweat will begin to fill the sweat glands, and this will increase the conductance and thereby decrease the impedance, even if no sweat is actually excreted from the sweat gland onto the surface of the skin. As the impedance between the inside and the outside of the skin changes, the electrical potential also changes, creating a very large artefact- often several hundred microvolts).
• Eyelid fluttering (often shows up in theta [4-7hz] and alpha [8-14 hz] ranges)

Question 20

2 kinds of Artifact elimination

Answer 20

1) Artefact rejection
• Essentially a ‘signal detection problem’
• ‘Brute force approach’: Reject if over threshold (75-100 mV)
o Artefacts usually have much larger amplitude
• Problems:
o Loss of significant portion of data
o Some participants are very prone to certain artefacts
o Some tasks essentially call for artefacts
2) Artefact correction
• Arithmetic algorithms are used to correct the data for the artefacts.
• Simple methods
o Subtraction method (variance based)
o Filtering

Also advances methods such as
o Dipole/Source modeling procedures
o Independent Component Analysis (ICA)

Question 21

How to minimise artifacts in EEG

Answer 21

• Electrical screening of the testing space (Faraday cage; an enclosure used to block electromagnetic fields)
• Careful instruction of partipants to minimize movement; blink pauses
• Ensuring the participants in relaxed condition (to reduce muscle activity)
• Careful electrode application to minimize impedance
• Maintaining cool temperature and low himidity level inside lab (to reduce slow drift- changes in skin potentials?)
• Filtering (e.g., high-pass filter to remove slow-shifts [i.e., low-frequency fluctuations in the EEG] and avoid aliasing, stop/notch filter to stop line noise.

Question 22

Different methods using EEG

Answer 22

Spontaneous EEG
• Measuring spontaneous oscillations in brain activity over a period of time.

Event Related/Evoked Potential
•	General class of potentials displaying stable time relationship to a definable reference event
•	Reference event
o	Onset/offset of a stimulus
o	Motor response
o	Decision moment

Question 23

Standard frequency bands in spontaneous EEG

Answer 23

o	Delta: < 4 Hz
o	Theta: 4 – 7 Hz
o	Alpha: 8 - 14 Hz
o	Beta: 15 - 30 Hz
o	Gamma > 30 Hz

Question 24

How is spontaneous EEG data analysed?

Answer 24

• Analysed using Fourier analysis
o Or decomposing a function into oscillatory components
o Describe EEG using sine/cosine functions
o The Fourier decomposes the EEG time series into a voltage by frequency spectral graph commonly called the “power spectrum”

Question 25

Applications of spontaneous EEG to clinical research

Answer 25

• Cognitive Research
o Experiments with longduration stimuli (i.e. task requiring sustained attention, ecologically appropriate stimuli) • Monitoring sleep stages
• Clinical Research
o Epilepsy
 Detection of seizures
 Localization of focus/foci
 Prediction of seizure onset
o Monitoring the level of anaesthesia
o Detection of brain death
o Measurement of drug effects
o Detection of cerebral pathology, e.g., through blood supply problems
o Sleep disorders
o Almost all neurological disorders have EEG correlates

Question 26

What are ERP’s?

Answer 26

• General class of potentials displaying stable time relationship to a definable reference event
•	Reference event
o	Onset/offset of a stimulus
o	Motor response
o	Decision moment
ERPs are waveforms characterized by a series of positive (P) or negative (N) deflections at different latencies (ERP components).
•	ERP parameters
o	Latency (ms)
o	Amplitude (mV)
o	Polarity (-/+)
o	Distribution over the scalp

Question 27

What is signal averaging?

Answer 27

• Averaging
“Baseline” period: In averaging, all trials are set (arithmetically) to have the same zero voltage at stimulus onset, so that only deviations from the baseline voltage are seen in the ERP, after stimulus presentation. [Baseline subtraction (mean of baseline period is subtracted)].

Question 28

Data analysis- 3 features of Measuring ERP amplitude

Answer 28

o Peak amplitude
 Maximum amplitude in a chosen time window
o Mean amplitude
 Mean amplitude in a chosen time window
( Both peak and mean amplitude measures are with respect to a baseline, assumed to be a zero potential)
o Peak-to-Peak
 Measure a peak relative to an adjacent peak (or trough) in the waveform

Question 29

Critique of analysing peak amplitude

Answer 29

o Peak amplitude
 Maximum amplitude in a chosen time window
 Problems
• Prone to (high frequency) noise contamination
• Estimation depends on the number of trials used for averaging
• Easily influenced by an overlapping component at the border of the measurement window
• Essentially a nonlinear measure prohibiting direct comparison between two grand-averages

Question 30

Critique of analysing mean amplitude

Answer 30

o Mean amplitude
 Mean potential in a chosen time window
 If the sum of the potential in a time window is computed, it is called an area amplitude measure.
 Advantages
• Narrower time window could be considered
• Less sensitive to high frequency noise because a range of time points is used rather than a single time point
• Allows comparisons with different number of trials
• It is a linear measure

Question 31

Critique of measuring peak to peak (amplitude?)

Answer 31

o Peak-to-Peak
 Measure a peak relative to an adjacent peak (or trough) in the waveform
 Advantages
• Useful for overlapping components
• Less prone to noise
 Disadvantages
• Difficult to interpret, we don’t know if change in ERP is because of change in peak change in Y ERP or peak change in X ERP that it is being compared to

Question 32

Data analysis- 3 ways of measuring ERP latencies

Answer 32

o Peak latency
 The time (latency) at which the component reaches its maximum (or minimum)
 Shares all limitations with peak amplitude
o Onset latency
 Relevant for response locked averaging component (e.g., to find when a preparatory motor activity has started
o Fractional area latency

Question 33

2 methods of ERP data analysis

Answer 33

o Exploratory ERP Analysis
 Only retain those time points which are successive for at least a certain period (usually 20-40 ms)
 At each sample point, run t-test to compare between two ERPs (i.e. two conditions)
 Useful for finding regions of interest for subsequent statistical analysis
o Global field power
 • It is the root mean square deviations between all electrodes in a potential field
 Using GFP to detect latency at which two conditions start differing globally
 Reflects time profile of the spatial standard deviation
 ERPs with peaks and troughs and steep gradients yield high GFP and flat fields yield small
 A reference free measure and helpful in detecting latencies of interest

Question 34

Limitations to ERP approach

Answer 34

• There are two principal limitations
o The first concerns interpretational issues, particularly with regard to interpreting null results
 ERP reveal little of EEG information
 ERP does not capture non-phase-locked responses (see below, evoked vs induced responses)
o The ERPs provide limited opportunities for linking results to actual neurophysiological dynamics
 ERPs are less understood compared to the neurophysiological mechanisms that produce neuronal oscillations and synchrony.

Question 35

Dynamic brain oscillations EEG

Answer 35

An oscillatory signal is characterised by its frequency (f), amplitude (A) and phase(θ)

Question 36

Evoked vs induced responses

Answer 36

Next to evoked activity, neural activity related to stimulus processing may result in induced activity. Induced activity refers to modulation in ongoing brain activity induced by processing of stimuli or movement preparation. Hence, they reflect an indirect response in contrast to evoked responses. A well-studied type of induced activity is amplitude change in oscillatory activity. (Oscillations can be evoked and induced, but for induced activity they are a particularly preferable measure). For instance, gamma activity often increases during increased mental activity such as during object representation. Because induced responses may have different phases across measurements and therefore would cancel out during averaging, they can only be obtained using time-frequency analysis.

Question 37

Invoked vs induced oscillations

Answer 37

Evoked oscillations have strict phase relationship with relation to the stimulus
Induced oscillations do not have strict phase relationship

Question 38

How are oscillations analysed?

Answer 38

Time-Frequency Representations/ time frequency analysis
A Time-Frequency Representation (TFR) of a signal provides some temporal information and some spectral information
Advantages
• Clear interpretations
o Neurophysiological mechanisms
o Ubiquitous oscillations
• Covers more comprehensive multidimensional space- time taken into account, useful for oscillations that aren’t steady over time i.e. frequency increases with time.

Question 39

One method of time frequency analysis

Answer 39

TFR by Wavelets
The TF representation of a signal is obtained by calculating a ‘moving average’ of the similarity between a wavelets and this signal

Question 40

Limitations to time frequency analysis

Answer 40

• Decreased temporal precision
• Complicated analysis strategies
• Fewer previous research for contextualization of findings
• Does not provide information on the co-operation between brain regions

Question 41

Neural synchrony approach to EEG

Answer 41

• Neural synchrony is the simultaneous / synchronous oscillations of membrane potentials in a network of neurons connected with electrical synapses (gap junctions).
• Cognition requires cooperation between neural populations within and across brain regions
• Synchronization between multiple and distant brain regions
• Often called phase synchronisation: the adjustment of brain waves from two or more neuronal groups within or across a brain structure to become uniform in EEG oscillation patterns in response to a stimulus. Interpreted as a sign of brain integration during many cognitive processes (such as learning and perception) and involves reciprocal neural connections.

Question 42

How is neural synchrony quantified

Answer 42

o Changes in spectral power
o Decrease of band powerDe-synchronization
o Increase of band powerSynchronization