2. By the end of today’s class you will be able to :
1. Describe the underlying biological basis of EEG
2. Identify patterns in waveforms in terms of frequency, period, and amplitude
3. Create some EEG recordings in the lab
4. Evaluate evidence for different power in certain frequency bands using your
own EEG recordings for data analysis
2
3. Electroencephalography (EEG) is an electrophysiological monitoring method to
record electrical activity of the brain. It uses electrodes placed along the scalp.
EEG measures voltage fluctuations resulting from ionic current within the neurons
of the brain.
3
6. EEG reflects postsynaptic potentials from large populations of neurons
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Excitatory Postsynaptic Potential
(EPSP)
• Often evoked by Glutamate
neurotransmitter
• Neurotransmitter raise voltage
of membrane to become more
positive.
• Moves neuron towards a
threshold where action potential
occurs
Inhibitory Postsynaptic Potential
(IPSP)
• Often evoked by GABA
neurotransmitter
• Neurotransmitter lowers voltage
of membrane to become more
positive.
• Moves neuron away from a
threshold where action potential
occurs
7. Work in pairs- Identify what processes have occurred at each
letter (A, B, and C)
If you are in disagreement, try to convince your partner why your response is accurate.
I need 1 pair to volunteer and report back on their conclusions.
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B
A
C
Pre-synaptic neuron
-
+
Post-synaptic neuron
synapse
8. 8
So far, we’ve described the electrical-chemical impulses from a single
neuron to another neuron.
You might be wondering:
How do large populations of neurons contribute to the EEG?
9. There are transmembrane currents
initiated by synaptic action or active
conductance
A difference in the net electrical charge
exists at each end.
Current flows in a circuit, first inward into
the dendrites and out through the soma.
When EPSP occurs, the polarity swaps.
Individual neurons in the brain act as dipoles.
Example of a cortical pyramidal cell
(basic input-output cell of cerebral cortex)
Positive ions
leave
Positive
charge
spreads down
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10. To be recorded at a distance, large numbers of neurons must have similar
voltage fields.
Equivalent Current Dipole
Local Field Potentials
Open Field Closed Field
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11. EEG most directly reflects activity between neural ensembles.
In two or more neurons, the difference in membrane voltage oscillations can become
synchronized.
We detect activity of large neural ensembles as macro-oscillations in the EEG.
Brain waves, like all oscillations, are patterns that move back in forth in time.
In EEG they are consistent, more or less, with a sinusoidal shape.
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14. The independence of frequency and amplitude
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Source: Ladefoged, 1996
Two sine waves with the same
frequency but different amplitudes.
Two sine waves with the same
amplitude but different frequencies.
15. Group activity- Learning about amplitude, frequency and phase
Divide into 3 groups.
Group 1 claps to the beat: 1-2-3-4.
Group 2 claps to the beat: 1&-2&-3&-4&
Group 3 claps to the beat: 1a&a-2a&a-3a&a-4a&a
What’s the relation between the groups and frequency?
Were the phases the same? How do you know?
How would we change amplitude?
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X X X X X X X X X X X X X X X X
V
V
V
V
V
V
V
V
16. 16
Beyond simple sinusoids
• EEG waveforms are not simple, pure sinusoids.
• Rather, they are complex, made up of sinusoids of
several frequencies.
17. Sample 1- EEG waveform
Sample 2- EEG waveform
What if we would like to know about frequency-specific amplitude?
What if we would like to know about whether different frequency waveforms are
differently concentrated in each sample?
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18. Waves representing potential changes consist of different frequencies
High Concentration;
Mental Effort; High level of
cognitive processing
Alert, actively
concentrating, anxious,
Awake and Relaxed; Eyes
closed; Reflecting
Drowsiness; Low arousal;
Light Sleep
Deep Sleep
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20. 20
Fourier Analysis
• Any waveform is equivalent to the sum of a set of sine waves of different
frequencies, amplitudes and phases
• Time domain: Amplitude as a function of time
• Frequency domain: Amplitude and phase as a function of frequency
• Fourier transform converts time domain to frequency domain
Fourier Transform
Inverse Fourier
Transform
Time Domain Frequency Domain
Amplitude (or Power)
(Phase, too!)
21. Fourier Analysis
Square-wave is the
original wave
Here is the signal with terms up to the
49th multiple.
At this point is seems that this process is
giving us a signal that is getting closer and
closer to a square wave signal
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This looks like a fairly lousy square
wave. Let's add a lot more terms and
see what happens.
23. What does the Fourier Transform do?
Given a smoothie, it finds the recipe.
How?
Run the smoothie through filters to extract each
ingredient. A mango filter for mangos, banana filter for
bananas, pineapple filter for pineapples, etc.
Why?
Recipes are easier to analyze, compare, and modify
than the smoothie itself.
How do we get the smoothie back? Blend the
ingredients.
Analogy: Recipes 23
24. Real World Scenario
Imagine you are doctors:
You have a group of three patients who report suffering from chronic insomnia.
You want to know if any of them suffer from a pathological sleep disorder.
You request they sleep in your clinic for two nights while their EEG is
monitored. You also give them a survey to fill out about sleep patterns.
The staff performs a power spectra analysis involving Fourier
transformation.
You expect that there will be changes to the delta and theta densities.
Because power spectra analysis must include all frequency bands, you get
a full picture of the changes in frequency that coincide with lack of sleep.
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25. Last class you received handouts for TODAY’s lab lesson. You should have read the handout for today’s lab lesson
and brought it back with you to class. You should have read chapter on artefacts from the Steven Luck An
Introduction to the Event-related potential Technique book.
You will work with a partner to complete Lesson 06 from the Biopac Student Software Package. I will go around
and assist your teams, but you are expected to work on these problems actively.
Tips:
• Listen to and exchange ideas with each other.
• Make sure you follow the instructions carefully .
I will be scoring written responses and inspecting the quality of your recordings
• Work together to resolve any concerns questions you have before moving to a further section of the handout.
Lab Lesson 25
26. During the recording, changes of alpha rhythm will be examined in different conditions:
1. relaxed state with eyes closed
2. performing mental math with eyes closed
3. recovering from a disruptive auditory sound with eyes closed
4. relaxed state with eyes open
Before starting, everyone will take 15 minutes to form hypotheses about changes in alpha power. Each handout has
been marked to indicate whether you will act as “observer” or “participant”. As explained by the handout, the
observer should have come up with experimental manipulations for conditions 2 and 3. This includes the math task
for the participant. The task should be neither too easy nor too difficult. It should contain operation with fractions. The
observer should have also downloaded a sound onto their laptop that they want to play. The sound should be alerting.
You must now, as a team, write out which manipulations were used in conditions 2 and 3 and your hypotheses. Please
be sure to explicitly reference how the power spectra will vary between conditions. I will collect you responses in 15
minutes. After I collect all of the responses, you may begin setting up and recording.
Start the recording *ONLY after 8 minutes of calibration.
**Record a 20 second long segment for each condition.
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