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 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) Duracell

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 magnetic sensors called “SQuID”s can measure very small fields associated with current flowing through extracellular space Electric Current 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 These oscillations superpose in the raw data EEG is characterized by various patterns of oscillations These oscillations superpose in the raw data 4 Hz 4 Hz + 8 Hz + 15 Hz + 21 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 48 Hz % change From Pre-stimulus 24 Hz Frequency 16 Hz 8 Hz 4 Hz (onset) +200 +400 +600 Time

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? Duracell

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 Project “Forward Solution” This is most likely location of dipole Compare to actual data

Brain Electrical Source Analysis EEG data can now be coregistered with high-resolution MRI image