IVE 2024 Short Course - Lecture 8 - Electroencephalography (EEG) Basics

Lecture 8 of the IVE 2024 short course on the Pscyhology of XR.

This lecture introduced the basics of Electroencephalography (EEG).

It was taught by Ina and Matthias Schlesewsky on July 16th 2024 at the University of South Australia.

1. Electroencephalography (EEG) basics
IVE Winter School 2024
Ina Bornkessel-Schlesewsky, Matthias Schlesewsky
July 16th 2024
1

4. Physiological basis of the EEG
The EEG reflects summed dipole
moments, i.e. voltage differences
between higher and lower cortical layers
originating primarily between pyramidal
cells. These result from synchronous
postsynaptic activity.
4
Bornkessel-Schlesewsky & Schlesewsky (2009)
IBS & MS - IVE Winter School 2024

5. Distance between electrode and
neural activity 5
Rösler (2005)
the distance between the
source of the current and
the electrode determines
scalp distribution
under certain circumstances,
the potentials of distinct
sources may merge at the
surface
IBS & MS - IVE Winter School 2024

6. The inverse problem
•Scalp-recorded EEG activity does not
allow any unique conclusions as to its
underlying sources
•There are mathematical models to
address the inverse problem, but all
solutions remain approximations!
6 IBS & MS - IVE Winter School 2024

7. EEG and different states of
consciousness
EEG activity occurs in different frequency
ranges
7
Gazzaniga et al. (2014)
IBS & MS - IVE Winter School 2024

8. Linking EEG activity to
cognition
The human EEG was discovered by Hans
Berger (1873-1941), a German psychiatrist
1929: Über das Elektrenkephalogramm
des Menschen
(On the human electroencephalogram)
8
http://upload.wikimedia.org/wikipedia/commons/6/69/
HansBerger_Univ_Jena.jpeg
IBS & MS - IVE Winter School 2024

9. Berger’s EEG lab
9
Berger (1929)
IBS & MS - IVE Winter School 2024

10. An early EEG trace
10
Berger (1929)
EEG
ECG
time
(0.1s)
Berger applied electrodes made of silver foil
to the scalp. Today, most EEG labs use
electrodes made of silver/silver chloride ...
IBS & MS - IVE Winter School 2024

11. The first sleep EEG
a = deep sleep;
b = after waking from two hours of sleep
THE FIRST SLEEP-EEG
a = deep sleep & b = after waking from two hours of sleep
(top line = EEG; bottom line = metronome for timekeeping)
Berger (1929)

12. EEG & chloroform
•same participant without (top) and with
(bottom) chloroform
12 IBS & MS - IVE Winter School 2024EEG & CHLOROFORM
same participant without (top) and with (bottom) chloroform
first line = EEG, second line = EKG, third line = metronome
Berger (1929)

13. EEG & problem solving
•Berger’s daughter’s EEG while solving a
maths problem
•top: EEG at rest (alpha waves, ~10 Hz)
•middle: during the task (beta waves; faster,
lower amplitudes)
•bottom: transition from task to rest
13 IBS & MS - IVE Winter School 2024
EEG & PROBLEM
SOLVING
EEG from Berger’s daughter solving a
maths problem:
Figure 1 (top) = rest EEG
Figure 2 (middle) = during the task
Figure 3 (bottom) = transition from
task to rest
Rest: alpha waves (large waves at
around 10 Hz, i.e. 10 per second)
Task: beta waves (smaller, faster waves)
Berger (1929)

14. Berger’s achievements
•Discovered the α (~8-12 Hz) and β (~12-30 Hz) rhythms
•Observed changes in the rhythmical activity of the EEG depending on changes in
cognitive state
•α decreases during problem solving (e.g. mental arithmetic)
•increases again during relaxed wakefulness
14IBS & MS - IVE Winter School 2024

15. Event-related brain potentials
(ERPs)
Bornkessel-Schlesewsky & Schlesewsky (2009)
ERPs are small changes in the spontaneous
electrical activity of the brain that are time-
locked to certain sensory or cognitive events
15IBS & MS - IVE Winter School 2024

16. ERPs
16
•Very small potential changes (in
cognition, often between
approximately ca. 2-8 μV) in
comparison to the background
activity (often in the range of
100s of μV)
•Low signal-to-noise ratio
•present a larger number of
stimuli of each type (approx.
30-40)
•average over a number of
participants (approx. 20-24)Kutas & Federmeier (2000)
N400
IBS & MS - IVE Winter School 2024

17. ERPs
Kutas & Federmeier (2000)
17
•An ERP component is an ERP
response that occurs with a
certain:
•latency (time after stimulus onset)
•polarity (negative or positive
relative to a control)
•topography (which electrodes?)
•Typical nomenclature: N (negativity)
or P (positivity) + peak latency, e.g.
N400
•Instances of a component with
different amplitudes (“strengths”):
effects
N400
IBS & MS - IVE Winter School 2024

18. Selected ERP components
IBS & MS - IVE Winter School 2024

19. The P300 (P3)
19
The first “endogenous” ERP component to
be discovered (tied to internal processing
rather than sensory stimuli)
IBS & MS - IVE Winter School 2024
Sutton et al. (1965, Science)

20. The P300 (P3)
20
•Aprominent positive deflection after approx. 300
ms (topography and latency vary according to
various parameters) Observable in response to
salient (e.g. unexpected — oddball), task-relevant
stimuli
•need not be unexpected; also found for self-
relevant stimuli (one’s own name)
•“motivationally significant stimuli”
•Longer latency for more complex stimuli,
amplitude varies e.g. with attention
•For discussion see Picton (1992, J Clin
Neurophys 9, 456-479), Verleger (1988, Behav
Brain Sci 11, 343-427), Nieuwenhuis et al. (2005,
Psych Bulletin)
IBS & MS - IVE Winter School 2024

21. The first language-related ERP component
Kutas & Hillyard (1980, Science)
IBS & MS - IVE Winter School 2024

22. Measuring prediction errors
in the brain: EEG
the Mismatch
Negativity (MMN)
Oddball paradigm:
standarddeviant
MMN:
early negativity
(between ~100 and
200 ms post stimulus
onset) for deviants vs.
standards; increasing
amplitude with
increasing physical
deviation between
standard and deviant
thought to reflect a
mismatch with a
transient sensory
memory trace Duncan et al. (2009, Clin
Neurophysiol)
IBS & MS - IVE Winter School 2024

23. e.g. to measure the effects of
predictive cues in spatial augmented
reality (SAR)
Vollmer et al. (2023, IEEE: TVCG)
IBS & MS - IVE Winter School 2024
An oddball task as a secondary
task to measure “cognitive load”

24. Scalp EEG / ERPs: data acquisition
IBS & MS - IVE Winter School 2024

25. An EEG session
25

26. Electrode placement
Bornkessel-Schlesewsky & Schlesewsky (2009)
extended 20-20 system
26
Electrooculogram
(EOG) monitors
vertical (V) and
horizontal (H) eye
movements
Reference electrode(s)
IBS & MS - IVE Winter School 2024

27. The reference electrode
•The EEG is measured as a voltage difference between two electrodes, e.g. between the
electrode of interest and a reference electrode
•The choice of reference electrode determines the resulting signal
•Options:
27
common reference
average reference
active reference
inactive reference
(e.g. mastoid, nose)
IBS & MS - IVE Winter School 2024

28. The reference electrode
•Common choice in experiments on cognition:
•left or right mastoid
•+ re-referencing to the average of both mastoids offline to avoid topographical
distortions
28IBS & MS - IVE Winter School 2024

29. Why the choice of reference is
important
•oddball paradigm; active common
reference (close to PZ)
29 IBS & MS - IVE Winter School 2024

30. Why the choice of reference is
important
•oddball paradigm; same data
rereferenced to the left mastoid
30 IBS & MS - IVE Winter School 2024

31. 31
Scalp EEG / ERPs: data analysis
IBS & MS - IVE Winter School 2024

32. Raw EEG data
32IBS & MS - IVE Winter School 2024

33. Steps in data analysis
•Preprocessing
•Re-referencing
•Raw data filtering
•Artefact rejection (automatic + manual)
•Averaging
•single subject average (per electrode, condition and time window)
•grand average (average over single subject averages)
•Statistical analysis
33IBS & MS - IVE Winter School 2024

34. Why filter?
34
before after
IBS & MS - IVE Winter School 2024

35. Filtering
•High pass
•removes frequencies lower than a specified frequency
•good for removing signal drifts from raw data (see last slide)
•generally between 0.1 and 0.3 Hz (not higher!!)
•must be applied to raw (not epoched) data!
•Low pass
•removes frequencies higher than a specified frequency
•good for “smoothing” averages for data presentation (typically 8-10 Hz)
35IBS & MS - IVE Winter School 2024

36. Filtering
•Band pass
•combination of high and low pass filters, i.e. selects a frequency band
•Notch
•removes a specific frequency range from the data
•most common application: removal of 50 or 60 Hz (mains power frequency)
36IBS & MS - IVE Winter School 2024

37. Artefacts I: ECGvirtually eliminated by rereferencing
37IBS & MS - IVE Winter School 2024

38. Artefacts II: eye movements
(blinks)
must either be excluded or corrected
38IBS & MS - IVE Winter School 2024

39. Artefacts II: eye movements
(saccades)
not as disruptive as blinks
39IBS & MS - IVE Winter School 2024

40. Artefacts II: eye movements
(saccades)
not as disruptive as blinks
40IBS & MS - IVE Winter School 2024

41. Averaging
•Single subject average (per electrode, condition and time window)
•segments the raw data into stimulus-related epochs (length depends on question, but
usually between 800 and 2000 ms)
•options:
•absolute values
•relative to a baseline (e.g. -200-0 ms “pre-stimulus” or 0-100 within stimulus)
•excludes trials that contain artefacts (optionally: trials for which the control task was not
performed correctly)
•excludes noise and background activity
41IBS & MS - IVE Winter School 2024

42. Averagingsingle subject average
42IBS & MS - IVE Winter School 2024

43. Averaging II
•Grand average
•average of the single subject averages
•again per electrode, time window and condition
43IBS & MS - IVE Winter School 2024

44. Averaginggrand average
44IBS & MS - IVE Winter School 2024

45. Averaging considerations
•Single-subject averages are (mostly) not interpretable
•signal-to-noise ratio too poor
•only evaluate data quality (no. of trials, ocular or movement artefacts, problems with
single electrodes, drifts ...)
•Grand averages
•the fact that a particular participant does not show the expected effect (or the effect
observed in the grand average) is not a sufficient reason for exclusion from the data
analysis!
45IBS & MS - IVE Winter School 2024

46. Statistical analysis
46
•Average amplitude (in a specific time window)
•repeated-measures ANOVAs including
condition factors and topographical factors
•topographical factors: regions of interest
(ROIs)
•Peak-to-peak analysis
•amplitude differences may lead to shifts in
the time window
•analysis of the difference between the last
“common” peak and the critical peak
example ROIs
IBS & MS - IVE Winter School 2024

47. And always keep in mind
47
•ERPs are relative measures between a critical
condition and a control condition
•Absolute potential shifts cannot be
interpreted!!
two negative deflections
IBS & MS - IVE Winter School 2024

48. The “finished product”
48
•An ERP component that can be described in
terms of
•latency (time to component onset/peak
after critical stimulus onset)
•polarity (negative or positive deflection
relative to control)
•topography (electrode positions (ROIs) at
which the effect is observable
•(amplitude - “strength” of the effect)
•Nomenclature typically reflects polarity and
latency (e.g. N400)
IBS & MS - IVE Winter School 2024

49. Component versus effect
49
•An ERP component is typically associated
with a functional interpretation
•Components may also appear in control
conditions (e.g. N400 for every word)
•Differences between two instances of the
same component (and more generally
between two conditions): ERP effects
IBS & MS - IVE Winter School 2024

50. An alternative approach:
time-frequency analyses
Recall Berger’s (1929) observation:
frequency characteristics of the
human EEG can reflect higher
cognitive processes
50
-
+
theta
alpha 1
alpha 2
...
IBS & MS - IVE Winter School 2024

51. Classic EEG frequency bands
•Delta: 0.5 - 4 Hz
•Theta: 4 - 8 Hz
•Alpha: 8 - 12 Hz
•Beta: 12 - 30 Hz
•Gamma: > 30 Hz
51
NB: individual differences apply!
IBS & MS - IVE Winter School 2024

52. EEG frequency and higher cognition
•Independently of (and in parallel to) ERP-based research, a frequency-based research
tradition has identified correlates of different cognitive domains / processes within
different frequency bands, e.g.:
•alpha band
•attention (Ray & Cole, 1985, Science, 228, 750-752)
•memory performance (Klimesch, 1997, J Psychophysiol, 26, 319-340)
•intelligence (Doppelmayr et al., 2002, Intelligence, 30, 289-302)
•theta band
•episodic memory (Miller, 1991. Cortico-hippocampal interplay ... Berlin: Springer.)
52IBS & MS - IVE Winter School 2024

53. Frequency bands:
The measures
53
Induced activity
(jitter in latency)
Evoked activity
(fixed latency)
Averaged evoked potential
•evoked versus induced activity
IBS & MS - IVE Winter
School 2024

54. Isolating frequencies of
interest: filtering
•Filters remove certain frequencies from the EEG
•Different types of filters:
•High pass
•removes frequencies lower than a specified
frequency
•Low pass
•removes frequencies higher than a specified
frequency
•Band pass
•combination of high and low pass filters, i.e. selects
a frequency band
•Notch
•removes a specific frequency range from the data
(most common application: removal of 50 or 60 Hz
mains power frequency)
54
Example from Widmann et al. (2014)
IBS & MS - IVE Winter School 2024

55. Event-related
(de-)synchronisation (ERD/S)
•Application of a bandpass-filter (frequency
band of interest) to each trial
•Squaring of each amplitude sample to
obtain power values
•Averaging over all trials of interest
•Conversion to relative power by defining
the power of a reference interval as 100%
=> “event-related”
•ERBP (event-related band power): z-
transformed ERD
55 IBS & MS - IVE Winter School 2024

56. Frequency bands: The measures
56
Relation between ERPs and
measures in the frequency domain
IBS & MS - IVE Winter School 2024

59. Time-frequency measures of
performance
in complex operational environments
59IBS & MS - IVE Winter School 2024
Cross et al. (2022, Scientific Reports)

60. EEG analysis
60
•We use MNE (open-source Python package)
•https://mne.tools/stable/install/index.html
•Useful tutorials:
•Overview: https://mne.tools/stable/
auto_tutorials/intro/10_overview.html
•ERPs: https://mne.tools/stable/
auto_tutorials/evoked/30_eeg_erp.html
•Time frequency: https://mne.tools/stable/
auto_tutorials/time-freq/
20_sensors_time_frequency.html
IBS & MS - IVE Winter School 2024