Subdural Grid
• Intracranial electrodes typically cannot be used in
human studies
• It is possible to record from the cortical surface
Subdural grid on surface of Human cortex
Electroencephalography and the
Event-Related Potential
• Could you measure these electric fields
without inserting electrodes through the
skull?
Electroencephalography and the
Event-Related Potential
• 1929 – first measurement of brain electrical
activity from scalp electrodes (Berger, 1929)
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 and the
Event-Related Potential
• 1929 – first measurement of brain electrical
activity from scalp electrodes (Berger, 1929)
– Initially believed to be artifactual and/or of no
significance
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
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/MEG changes with various
states and in response to
stimuli
Electroencephalogram
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
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
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?
• Source Imaging in EEG/MEG
attempts to model the
intracranial space and “back
out” the configuration of
electrical generators that gave
rise to a particular pattern of
EEG on the scalp
Brain Electrical Source Analysis
• EEG data can be coregistered with highresolution MRI image
Source
Imaging
Result
Structural
MRI with EEG
electrodes
coregistered
Intracranial and “single” Unit
• Single or multiple electrodes
are inserted into the brain
• “chronic” implant may be left in
place for long periods
Intracranial and “single” Unit
• Single electrodes may pick up
action potentials from a single
cell
• An electrode may pick up the
combined activity from several
nearby cells
– spike-sorting attempts to
isolate individual cells
Intracranial and “single” Unit
• Simultaneous recording from
many electrodes allows
recording of multiple cells
Intracranial and “single” Unit
• Output of unit recordings is
often depicted as a “spike
train” and measured in
spikes/second
• Spike rate is almost never zero,
even without sensory input
– in visual cortex this gives rise
to “cortical grey”
Stimulus on
Spikes
Intracranial and “single” Unit
• Local Field Potential reflects
summed currents from many
nearby cells
Stimulus on
Spikes
Relationship between EEG / LFP /
spike trains
• All three probably reflect
related activities but probably
don’t share a 1-to-1 mapping
– For example: there could be
some LFP or EEG signal that
isn’t associated with a change
in spike rates.
– WHY?
Whittingstall & Logothetis (2009)
Synthesize the Big Picture
Metabolic
Imaging
• fMRI/PET
Extracranial
electrophysiology
Intracranial
• LFP/single-unit
• EEG/MEG
Understanding
Brain-wide
neural circuits