electroencephalography and the event-related potential
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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