1. Electroencephalography and the Event-Related Potential
Voltage
Time
Place an electrode on the scalp and another one somewhere else on the body
Amplify the signal to record the voltage difference across these electrodes
Keep a running measurement of how that voltage changes over time
This is the human EEG

2. Electroencephalography
pyramidal cells span layers of cortex and have parallel cell bodies
their combined extracellular field is small but measurable at the scalp!

3. Electroencephalography
The field generated by a patch of cortex can be modeled as a single equivalent dipolar current source with some orientation (assumed to be perpendicular to cortical surface)
Duracell

4. Electroencephalography
Electrical potential is usually measured at many sites on the head surface
More is sometimes better

5. Magnetoencephalography
For any electric current, there is an associated magnetic field
Electric Current
Magnetic Field

6. Magnetoencephalography
For any electric current, there is an associated magnetic field
magnetic sensors called “SQuID”s can measure very small fields associated with current flowing through extracellular space
Electric Current
Magnetic Field
SQuID
Amplifier

7. Magnetoencephalography
MEG systems use many sensors to accomplish source analysis
MEG and EEG are complementary because they are sensitive to orthogonal current flows
MEG is very expensive

8. EEG/MEG
EEG changes with various states and in response to stimuli

9. EEG/MEG
Any complex waveform can be decomposed into component frequencies
E.g.
White light decomposes into the visible spectrum
Musical chords decompose into individual notes

10. EEG/MEG These oscillations superpose in the raw data
EEG is characterized by various patterns of oscillations
These oscillations superpose in the raw data
4 Hz
4 Hz + 8 Hz + 15 Hz + 21 Hz =
8 Hz
15 Hz
21 Hz

11. How can we visualize these oscillations?
The amount of energy at any frequency is expressed as % power change relative to pre-stimulus baseline
Power can change over time
48 Hz
% change
From
Pre-stimulus
24 Hz
Frequency
16 Hz
8 Hz
4 Hz
(onset)
+200
+400
+600
Time

12. Where in the brain are these oscillations coming from?
We can select and collapse any time/frequency window and plot relative power across all sensors
Win
Lose

13. Where in the brain are these oscillations coming from?
Can we do better than 2D plots on a flattened head?
As in ERP analysis we (often) want to know what cortical structures might have generated the signal of interest
One approach to finding those signal sources is Beamformer

14. Beamforming
Beamforming is a signal processing technique used in a variety of applications:
Sonar
Radar
Radio telescopes
Cellular transmision

15. Beamforming in EEG/MEG
It then adjusts the signal recorded at each sensor to tune the sensor array to each voxel in turn
Q = % signal change over baseline

16. Beamformer
Applying the Beamformer approach yields EEG or MEG data with fMRI-like imaging R L

17. The Event-Related Potential (ERP)
Embedded in the EEG signal is the small electrical response due to specific events such as stimulus or task onsets, motor actions, etc.

18. The Event-Related Potential (ERP)
Embedded in the EEG signal is the small electrical response due to specific events such as stimulus or task onsets, motor actions, etc.
Averaging all such events together isolates this event-related potential

19. The Event-Related Potential (ERP)
We have an ERP waveform for every electrode

20. The Event-Related Potential (ERP)
We have an ERP waveform for every electrode
Sometimes that isn’t very useful

21. The Event-Related Potential (ERP)
We have an ERP waveform for every electrode
Sometimes that isn’t very useful
Sometimes we want to know the overall pattern of potentials across the head surface
isopotential map

22. The Event-Related Potential (ERP)
We have an ERP waveform for every electrode
Sometimes that isn’t very useful
Sometimes we want to know the overall pattern of potentials across the head surface
isopotential map
Sometimes that isn’t very useful - we want to know the generator source in 3D

23. Brain Electrical Source Analysis
Given this pattern on the scalp, can you guess where the current generator was?

24. Brain Electrical Source Analysis
Given this pattern on the scalp, can you guess where the current generator was?
Duracell

25. Brain Electrical Source Analysis
Source Analysis models neural activity as one or more equivalent current dipoles inside a head-shaped volume with some set of electrical characteristics

26. Brain Electrical Source Analysis
Project “Forward
Solution”
This is most likely
location of dipole
Compare to actual data

27. Brain Electrical Source Analysis
EEG data can now be coregistered with high-resolution MRI image