Electroencephalography The field generated by a patch of cortex can be modeled as a single...

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


• 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


• 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

Two ways to approach EEG data

• The Event-Related Potential– Phase-locked or

“evoked”– High inter-trial phase

consistency– Retains polarity

information at scalp– Rejects time-locked but

not phase-locked changes

• Time/Spectral Analysis– Includes Non-phase-

locked or “induced” plus “evoked” signal

– Ignores inter-trial phase consistency (measured differently)

– Rejects polarity at scalp

Time-Frequency Analysis of 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 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 =


• 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


• 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.


• 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


• 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 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?

• 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 high-resolution MRI image

Source ImagingResult

Structural MRI with EEG electrodes coregistered

CCBN Dense-Array EEG

Netstation – records EEG and event triggers

Digamize –records electrode locations

BESA-post-processing-ERP averaging-voltage maps-source imaging

MatLabFieldtripBrainVoyagerSPSS-EEG spectral analysis- MRI coregistration

MANUSCRIPT

Stimuli

Event Triggers

Raw EEG .raw

.sfp

Data Files

Basic Elements of ERP Design

• EEG, therefore ERP, doesn’t provide interpretable absolute voltage

• The voltage is always relative to something else

• That something else may be:– The pre-stimulus baseline– A control condition


• Thus a fundamental aspect of ERP design is not to plan to report voltages but rather a difference in voltage between two or more conditions

• What are some examples of conditions you might want to compare?

First Demo

• Contralaterality in Visual System– Hemifields project to

contralateral cortex– Unrelated to which eye is

stimulated!

• Occular Albinism– Eyes project

contralaterally, irrespective of hemifield


• The theory is that human visual cortex is organized contralaterally

• The prediction is that right hemifield stimuli will drive electrical activity in the left visual cortex and left hemifield stimuli will drive electrical activity in right visual cortex

• How do we test that prediction?


• Experimental approach:

• Choices: – 1. you could compare ipsi to contra ERP waveforms with a trial

• E.g. O3 with O4• What’s the problem?

O3O4



• Choices: – 1. you could compare ipsi to contra ERP waveforms with a trial

• E.g. O3 with O4• What’s the problem?• You would be comparing ERPs from different parts of the brain!• How could you improve on that design?



• Choices: – 2. you could compare electrodes ipsi to stimulus on one side with

electrodes contra to stimulus on the other side• Notice those are the same electrode!

Measure contralateral ERP magnitude

O3



• Choices: – 2. you could compare electrodes ipsi to stimulus on one side with

electrodes contra to stimulus on the other side• Notice those are the same electrode!

Measure ipsilateral ERP magnitude

O3

• Hands on agenda today:

– Orientation to the EEG lab

– Build your dipole models

Principals of Digital Signal Recording

How do we represent a continuously variable signal digitally?

• Sampling– Sampling rate – number of measurements per unit

time– Sampling depth or quantization – number of

gradations by which the measurement can be recorded


• Sampling– What would be the advantage to higher sampling

rates?



rates?• Nyquist limit



rates?• Nyquist limit• Aliasing

– What would be the disadvantage?• Data size• Compute time


• Sampling– What would be the advantage to greater sampling

depth?• Finer resolution

– What would be the disadvantage?• Data size• Possibly compute time


• Sampling– A note about data size and compute time:

• New data size = increase in quantization x number of samples x number of electrodes!

Filters used in EEG

What is a filter?

What is a filter?

• Filters let some “stuff” through and keep other “stuff” from getting through– What do we want to let through?– What do we want to filter out?

What is a filter?

• The goal of filtering is to improve the signal to noise ratio– Can the filter add signal?

Different Kinds of Filters

• Low-Pass (High-Cut-Off)• High-Pass (Low-Cut-Off)• Band-Pass• Notch

• Each of these will have a certain “slope”

How do Filters Work?

• Notionally:– Transform to frequency domain– Mask some parts of the spectrum– Transform back to time domain

Are There Any Drawbacks?

• Yes• Filters necessarily distort data– Amplitude distortion– Latency distortion• Forward/backward/zero-phase

Recommendations

• Should you filter?– Yes, when necessary to reveal a real signal

• Problem: how do you know it’s “real”

– No, always look at the unfiltered data first• What filters should you use?– Depends on your situation (e.g. what EEG band are

you interested in? Do you have 60Hz line noise?)– General rule: less aggressive filters are less

distorting

Date post:	19-Dec-2015
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Electroencephalography The field generated by a patch of cortex can be modeled as a single...

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