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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

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electroencephalography and the event related potential
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
electroencephalography
Electroencephalography
  • pyramidal cells span layers of cortex and have parallel cell bodies
  • their combined extracellular field is small but measurable at the scalp!
electroencephalography1
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

electroencephalography2
Electroencephalography
  • Electrical potential is usually measured at many sites on the head surface
  • More is sometimes better
magnetoencephalography
Magnetoencephalography
  • For any electric current, there is an associated magnetic field

Electric Current

Magnetic Field

magnetoencephalography1
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

magnetoencephalography2
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
eeg meg
EEG/MEG
  • EEG changes with various states and in response to stimuli
eeg meg1
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
eeg meg2
EEG/MEG
  • 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

how can we visualize these oscillations
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

16 Hz

Frequency

8 Hz

4 Hz

+200

+400

+600

0

(onset)

Time

where in the brain are these oscillations coming from
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

where in the brain are these oscillations coming from1
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
beamforming
Beamforming
  • Beamforming is a signal processing technique used in a variety of applications:
    • Sonar
    • Radar
    • Radio telescopes
    • Cellular transmision
beamforming in eeg meg
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

beamformer
Beamformer
  • Applying the Beamformer approach yields EEG or MEG data with fMRI-like imaging

R

L

the event related potential erp
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.
the event related potential erp1
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
the event related potential erp2
The Event-Related Potential (ERP)
  • We have an ERP waveform for every electrode
the event related potential erp3
The Event-Related Potential (ERP)
  • We have an ERP waveform for every electrode
  • Sometimes that isn’t very useful
the event related potential erp4
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
the event related potential erp5
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

brain electrical source analysis
Brain Electrical Source Analysis
  • Given this pattern on the scalp, can you guess where the current generator was?
brain electrical source analysis1
Brain Electrical Source Analysis
  • Given this pattern on the scalp, can you guess where the current generator was?

Duracell

brain electrical source analysis2
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
brain electrical source analysis3
Brain Electrical Source Analysis

Project “Forward

Solution”

This is most likely

location of dipole

Compare to actual data

brain electrical source analysis4
Brain Electrical Source Analysis
  • EEG data can now be coregistered with high-resolution MRI image
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