electroencephalography and the event-related potential

Electroencephalography and the Event-Related Potential

Time

Volta

ge

-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

• pyramidal cells span layers of cortex and have parallel cell bodies

• their combined extracellular field is small but measurable at the scalp!

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)

Electroencephalography

• Electrical potential is usually measured at many sites on the head surface

• More is sometimes better

Magnetoencephalography• For any electric current, there

is an associated magnetic field

Magnetic Field

Electric Current

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

Magnetic Field

Electric Current

SQuID

Amplifier

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 changes with various

states and in response to stimuli

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/MEG• EEG is characterized by various

patterns of oscillations• These oscillations

superpose in the raw data

4 Hz

8 Hz

15 Hz

21 Hz

4 Hz + 8 Hz + 15 Hz + 21 Hz =

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

Freq

uenc

y

Time0

(onset)+200 +400

4 Hz

8 Hz

16 Hz

24 Hz

48 Hz

% changeFromPre-stimulus

+600

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 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 is a signal processing technique used in a variety of

applications:– Sonar– Radar– Radio telescopes– Cellular transmision

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

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

L

R

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 (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 (ERP)

• We have an ERP waveform for every electrode

The Event-Related Potential (ERP)

• We have an ERP waveform for every electrode

• Sometimes that isn’t very useful

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 (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• Given this pattern on the scalp,

can you guess where the current generator was?

Brain Electrical Source Analysis• Given this pattern on the scalp,

can you guess where the current generator was?

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 Analysis

This is most likely location of dipole

Project “Forward Solution”

Compare to actual data

Brain Electrical Source Analysis

• EEG data can now be coregistered with high-resolution MRI image