Voltage
Electroencephalography and the
Event-Related Potential
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
• 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
Electric
Current
Magnetic
Field
Magnetoencephalography
• For any electric current, there
is an associated magnetic field
Electric
Current
• magnetic sensors called
“SQuID”s can measure very
small fields associated with
current flowing through
extracellular space
Magnetic
Field
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
Frequency
48 Hz
% change
From
Pre-stimulus
24 Hz
16 Hz
8 Hz
4 Hz
0
(onset)
+200
+400
Time
+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
R
L
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 highresolution MRI image