Electrophysiology
Electroencephalography
• Electrical potential is
usually measured at
many sites on the head
surface
• More is sometimes
better
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?
•
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
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
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
Stimulus on
Spikes
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
• By carefully associating changes in spike rate
with sensory stimuli or cognitive task, one can
map the functional circuitry of one or more
brain regions
• What are the advantages and limitations of
this approach?