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

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?

• 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

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

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?