EEG Flashcards
Benefits of EEG…
• Excellent time resolution
– Cognitive, perceptual, linguistic, emotional and motor 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)
- Direct indicator of neuronal activity
- Multidimensional (time, space, frequency, power, phase (temporal), connectivity etc)
- Portability (observing brain in action)
- Relatively inexpensive
+ adanvaced analyis techniques on time series e.g. single trial classifcation methods using fourier transform.
Limitations of EEG…
• It is not well-suited for precise functional localization
• It is not well-suited for measuring deep brain structures (e.g., putamen, thalamus, nucleus accumbens)
Sub-optimal method: where in the brain does process X occur or is information Y stored
• It is also not very well-suited to study very slowly fluctuating process ‘infra-slow’ with uncertain and variable time course (but fMRI is)
from Joy
Interpretation issues:
1) suffers with interpretation of null results – absence of proof is not the proof of absence.
> ERP does not reveal all of EEG information (single trial)
> ERP does not capture non-phase locked responses
2) ERP limited opportunity for linking results to actual neurophysiological dynamics
> ERP less understood than oscillatory (is formed) and synchronous
What type of neuronal activity does EEG capture?
postsynaptic potentials
as opposed to action potential
what does EEG reflect?
EEG reflects the differences of electrical potential over time, created by the current flows originating from neuronal populations
what are chemical synapses?
Chemical synapses cause local changes in postsynaptic membrane potentials, through neurotransmitters. Information transmits with some delay on the order of a millisecond.
what are electrical synapses?
electrical synapses, or gap junctions. Ions flow directly through large channels into adjacent cells, with no time delay.
What is a Post-Synaptic Potential (PSP)?
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
how is a post-synaptic potential generated?
– 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
what two types of post-synaptic potential are there?
Two types of PSP
– Excitatory PSP (for excitatory synapse) – Inhibitory PSP (for inhibitor synapse)
what is required for a post-synaptic neuron to fire?
A postsynaptic neuron will fire an action potential if a depolarization that exceeds threshold reaches its axon hillock.
Generally the combined effect of many excitatory synapses is required for a post-synaptic neuron to fire.
What are the 2 types of summation ?
spatial and temporal
what is spatial summation?
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.
what is temporal summation?
Temporal summation 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.
what are EEG signals then?
EEG signals are primarily produced by summation of postsynaptic potentials of millions of neurons
summed millions of neurons/ firing in phase
aggregated millions of PSP’s
note: EEG does not measure action potential !!!!
how are the AP alligned?
geormetrically and in phase
EEG is less sensitive to….
It is less sensitive to deep brain structures
– Field strength decreases exponentially with distance
– Neuronal populations in deeper structures are not arranged in a geometrically parallel fashion
EEG cannot mesure …
It cannot measure individual molecular or synaptic events nor it can isolate events that are produced by a specific neurotransmitter or neuromodulator
It is not very suitable to measure to very slow (< 0.1 Hz) or very high (> 100 Hz) fluctuations
what are electrodes made from?
– Ag/AgCl Electrodes (Silver electrodes with a thin coating of silver- chloride
– Tin Electrodes
– Goldcap Electrodes
The conductivity should be good between the electrode and the scalp, how?
gel to reduce the impedance/resistance
– Impedance below 5 Kilo Ohms
– Scalp preparation (removal of dead skin cells)
also:
Active Electrodes
– Integrated pre-amplifier
– Faster preparation time
How many electrodes ?
Traditional 19
Standard 32-64 (sufficient)
High-density 128-256 (or more)
What are the pros to having more elctrodes?
better spatial sampling
better source reconstruction
What are the cons to having more elctrodes?
long prep time
electrolyte bridge
poorer signal quality
what is electrolyte bridge?
when the gel creates short circuit between closely placed electrodes
the signal is amplified, why, and using what?
- The signal is amplified from a few μVolts to a few Volts.
* The amplification is done by Differential Amplifiers
what electrodes are associated with amplification?
Three electrodes: Active Electrode (A) placed at the desired site Reference Electrode (R) placed elsewhere on the scalp Ground Electrode (G) placed elsewhere on the scalp/body
• 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
What are the usual reference sites?
Preferably a ‘neutral’ site (tip of the nose, the earlobes, the mastoids, the chin etc)
• Three practical criteria
– Choose a site that is convenient and comfortable
– Choose a site that does not induce hemispheric bias – Choose a site used by other researchers in your field
• Mostly used ‘neutral’ references: – average of two earlobes
– average of two mastoids
• Other referencing scheme:
– Average of all electrodes
– Current source density maps • Reference free method • Requires high density recording • Less accurate for boundary electrodes • Insensitive to deep sources
–Laplacian
what is aliasing ?
When we are sampling a system (brain) with a sampling freq less than twice the maximum freq of interest.
Because we monitor frame by frame - but what rate?
If it is not as fast as the original, then it is a POOR representation, as we haven’t sampled enough to capture the information in the actual sample.
Nyquist Criterion - sample at least twice as fast as the maximum freq
(Sampling frequency (fs) should satisfy Nyquist Criterion fs > 2 fmax (fmax = max. frequency of interest)
This can be something like x5 the maximum - if we wanna do alpha etc.
why would we need to filter the signal?
to reduce artifacts
what filters are applicable ?
Low pass
High Pass
Band pass
band stop, notch
what is high pass filter?
0.5 Hz (or 0.1 Hz for slow brain responses)
what is low pass filter?
100 Hz
what is a notch filter (band stop)
50 Hz (for removing power line noise; 60 Hz in USA)
We need to xxx analogue to xxxx
convert - digital
what resolution is EEG
16/24 bit Resolution (216 or 16192 different voltage values can be coded by the ADC)
what is aliasing?
When we are sampling a system (brain) with a sampling freq less than twice the maximum freq of interest.
Because we monitor frame by frame - but what rate?
If it is not as fast as the original, then it is a POOR representation, as we haven’t sampled enough to capture the information in the actual sample.
Nyquist Criterion - sample at least twice as fast as the maximum freq
(Sampling frequency (fs) should satisfy Nyquist Criterion fs > 2 fmax (fmax = max. frequency of interest)
This can be something like x5 the maximum - if we wanna do alpha etc.
whay should we avoid aliasing?
to get a faithful representation of our sample
What should the sampling frequency satisfy?
Sampling frequency (fs) should satisfy Nyquist Criterion fs > 2 fmax (fmax = max. frequency of interest)
For an EEG signal with maximum frequency of 70 Hz, aliasing occurs when:
A. fs=256Hz
B. fs=1024 Hz
C. fs=512Hz
D. fs=128 Hz
128
What are EEG artifacts?
Problems in the EEG signal that need reducing / eliminating
What are the 5 main artifacts?
1) Saccades
2) EMG (mastoid/jaw muscle/face muscles)
3) EKG (pulsation of the heart)
4) Skin potentials (leading to blocking)
5) Alpha waves (mind wandering etc)
what is the brute force approach to rejection?
‘Brute force approach’: Reject if over threshold (75-100 μV) as brain doesn’t create these freq
• artifacts usually have much larger amplitude
other factors in artifact rejection?
- Blink (Check vEOG, Topography, Polarity) -measure the diff
- Eye movement (Check hEOG, Step-like wave) - measure the diff
- Electrode shift (Shifting of potentials)
- Muscles (High frequency) beta - gamma
- Heart (Mostly in mastoid electrodes, Low frequency)
three issues with artifact rejection?
- Loss of significant portion of data
- Some participants are very prone to certain artifacts
- Some tasks essentially call for artifacts
TOO many trials are lost
what is the alternative to artifact rejection?
artifact correction
what are the simple methods to artifact correction ?
- Subtraction method (variance based)
* Filtering
what are the advanced methods to artifact correction ?
Mathematical approaches:
1) Dipole/Source modeling procedures
2) Independent Component Analysis (ICA)
brief description of Independent Component Analysis (ICA)?
a computational method to separate the sources of artifacts - identifying the troublesome parts by certain characteristics and individual weights - then reverse the weights (thereby reducing artifacts)
how to practically minimize of Artifacts?
• Electrical screening of the testing space (Faraday cage)
• Careful instruction of participants 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)
• Filtering (e.g., high-pass filter to remove slow-shifts [i.e.,
low-frequency fluctuations in the EEG], as well as low- pass filter to avoid aliasing=bandpass filter)
what are the 5 standard frequency bands?
- Delta: < 4 Hz
- Theta:4–7Hz
- Alpha: 8 - 14 Hz
- Beta:15-30Hz
- Gamma > 30 Hz
5 frequency bands (FRE BAND in THE GAY BED)
WHAT IS Fourier Analysis?
Transformation of the EEG into sine (sinusoidal) functions of various frequencies
like a freq histogram (strength of particular freq)
what does Fourier Analysis lead to?
leads to a power spectrum: power as a function of frequency
what are the applications of spontaneous EEG
• Cognitive Research
• Experiments with long- duration stimuli (i.e. task requiring sustained attention, ecologically appropriate stimuli)
> Perhaps Mind-wandering?
• Monitoring sleep stages
- Clinical Research
- Epilepsy
- Detection of seizures
- Localization of focus/foci
- Prediction of seizure onset
- Monitoring the level of anaesthesia
- Detection of brain death
- Measurement of drug effects
- Detection of cerebral pathology, e.g., through blood supply problems
- Sleep disorders
- Almost all neurological disorders have EEG correlates
What is an event related potential / evoked potential ?
General class of potentials displaying stable time relationship to a definable reference event
what is an ERP reference event?
Reference event
– Onset/offset of a stimulus
– Motor response
– Decision moment
who uses the term EP and who ERP?
Terminology
– EP: Perception and clinical research
– ERP: Experimental cognitive research
what characterises and ERP?
ERPs are waveform characterized by a series of positive (P) or negative (N) deflections at different latencies ERP Components
Exogenous Components: Modulated by external characteristics of stimuli Endogenous Components: Modulated by internal characteristics
What is the ERP hypothesis ?
ERP Hypothesis: ERP is a signal (s) that appears superimposed and without interaction on the background or ongoing EEG which is considered random noise (n).
Assumptions of ERP?
- ERP is uncorrelated with background EEG
- Background EEG is random
- ERP is invariant across trials (same ERP is repeated over trials - take average as invariant)
- Background EEG varies (randomly) from trial to trial
So how do we get an ERP?
Signal averaging
How does signal averaging work then?
After averaging across trials, noise will cancel out and
only the event related EEG response will remain.
Background signal cancels out and left with the ERPs which are invariant
What are the advantages of ERP?
- ERPs are simple, fast to compute
- ERPs require very few analysis or parameters
- ERP has high temporal precision and accuracy
- ERP literature is quite mature
- ERP provides a good quality check
What is an ERP component?
An ERP component can be simply defined as one of the component waves of the more complex ERP waveform. So one part could be the Ne and another the Pe.
An ERP component is a part of waveform with a circumscribed scalp distribution (physiological substrate) and a circumscribed relationship to experimental variables (functional substrate).
Example of an ERP component?
Examples:
MMN (mismatched negativity, 160-220 ms at central sites) N170 (face-related potential at occipital sites)
Why study ERP components?
- Common language linking diverse experiments, paradigms etc
- Base for integrating ERP with other measures of brain activity
- Structure-function information
What is the 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)
how many trials for EEG
as many as possible
The number of trial depends on signal-to-noise characteristics
the effect size
the type of analysis to be performed
SNR (signal-to-noise-ratio) increases as a function of the square root of the number of trials
Practical suggestions
– 50 trials / condition / participant
– Similar number of trials for all conditions
• Phase/power produce positive bias with fewer trials)
– If not possible, match trial count
• Select the first N trials from each condition (N = the number of trials in the smallest condition)
• Select N trials at random
• Select N trials based on some relevant behavioural or experiment variable (i.e. reaction time)
what is a VEP
visual evoked potential
what is an AEP
auditory evoked potential
what is an SEP
Somatosensory Evoked Potentials (SEP)
what is Contingency Negative Variation (CNV)
- Indicator of learning paired stimuli (Get Set – Go)
* Reflection of attention, concentration & readiness to S2 • Index of neuronal excitability
what ERP accompanies semantic violations ?
N400 in Semantic Violations
what accompanies violations in music
ERAN
What are the two main limitations of ERP?
• The first concerns interpretational issues, particularly with regard to interpreting null results - – absence of proof is not the proof of absence.
– ERP reveal little of EEG information (single-trial)
– ERP does not capture non-phase-locked responses
• 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
If ERP are evoked activity, what other type of activity is there?
Dynamic Brain Oscillations
And what two types of Dynamic Brain Oscillations are there?
Evoked oscillations have strict phase relationship with respect to the stimulus (every time brain respond same)
Induced oscillations do not have strict phase relationship (not always the same time - varying latency – e.g. (cognitive control / top-down / attention)
So what are limitation of ERP in regard to oscillations?
ERP takes the average (keep same latency) but induced response gets lost. Disadvantage as ERP only captures stimulus time-locked relationships.
x
x
Dynamic Brain Oscillations advantages
• Clear interpretations
– Neurophysiological mechanisms – Ubiquitous oscillations
- Neuronal oscillations are the most promising bridge linking findings from multiple disciplines
- Covers a more comprehensive multi dimensional space
methods for dynamic brain oscillation analysis?
short term Fourier transform of time-frequency time series
or wavelet
need freq + time!!!
Oscillations in complex cognition =
then give them a hint ….
brain oscillation structure - over occipital-parietal region - when higher (hint couldn’t help) but if gamma is lower… then the hint is successfully utilised. Only worked in specific brain state (receptive) characterised by oscillatory state
if alpha was high in temporal - then also was more likely to solve problem (when we focus on something, gamma increase, when diffuse attention, alpha is high. And for these types of problems, being too focused usually doesn;t work. need to be open (alpha) not fixed (gamma)
so posterior gamma = focused attention (fixation)
posterior alpha = diffuse (open to new solution)
Object Perception and feature binding
Gamma Band Synchrony
Visual binding in adults
in gamma band
comparing kaniza triangle
bind diff features - new perception represented by 40hz - and is phase locked
Attention to music in musicians
Gamma SYNCHRONY
Cognitive Insight =
Posterior Beta and anterior Gamma
Aha Moment! =
Posterior Gamma for sudden solutions
which ERP for Semantic Violations ?
N400
Music violations ?
MRAN
Early Evoked potentials: AEP –
SEP – N10
CEP – Chemosensory has no early ERP
later Attention P1 / n1 –p2
Early Evoked potentials: SEP
n10
Early Evoked potentials: CEP
Chemosensory has no early ERP
