ERPs in Cognitive Neuroscience

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ERPs in Cognitive Neuroscience

Charan Ranganath

Center for Neuroscience and Dept of Psychology, UC Davis

Center for Neuroscience

UNIVERSITY OF CALIFORNIA AT DAVIS

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EEG and MEG

• Neuronal activity generates extracellular electrical and magnetic fields that are measured by EEG and MEG, respectively

EEG system

MEG system

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What is an Event Related Potential (ERP)?

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What will/won’t produce an ERP

NO YES!

Example from Kutas & Dale (1997), in Cognitive Neuroscience, M. D. Rugg

(ed.),

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Sources of EEG and MEG signals

Primary currents

Courtesy Matti Hämäläinen Radial Source

Tangential Source

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Characteristics of EEG and MEG

EEG

• Sensitive to radial and tangential sources

• Electrical fields are distorted by skull, scalp

MEG

• Generally sensitive to tangential sources only

• Magnetic fields not distorted by skull

• Source localization is more tractable for MEG…

• But that does not mean that MEG is better than EEG

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EEG and FMRI

• EEG is related to synchronized synaptic potentials in cortical pyramidal cells

• BOLD signal (FMRI) is largely driven by metabolic demands related to synaptic activity

• So, if condition A elicits increased synchronous

synaptic potentials relative to condition B, there may be comparable neocortical sources for ERPs and FMRI signal in A-B contrast

• If ERP is related to phase-reset of ongoing oscillation then ERPs might be seen with no BOLD

• If synaptic activity changes are not synchronous then BOLD might be seen with no ERPs

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Typical Visual ERP Waveform

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WHAT IS AN ERP “COMPONENT”?

• A fuzzy concept with varying definitional criteria:

– Timing of positive/negative deflections or peaks – Scalp Topography

– Functional characteristics – Neural generators (sources)

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Sutton, S., Braren, M., Zubin, J., & John, E. R. (1965) © AAAS

Scenario:

Cue stimulus indicating whether click or flash was likely

Delay of 3-5 seconds: Subject guesses whether stimulus will be click or flash

Click or flash occurs P300

Sound- P300

Elicited ERPs

Light- Elicited

ERPs

Example: P300

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Different P300 components: P3a vs P3b

• Timing of

positive/negative deflections or peaks

– P3a: Positive peak

~200-300ms

– P3b: Positive peak

~300-400ms

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Different P300 components: P3a vs P3b

• Scalp Topography

– P3a: Fronto-central topography

– P3b: Parietal topography

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Different P300 components: P3a vs P3b

• Neural Generators

– P3a: Prefrontal Cortex, Hippocampus, Temporoparietal

– P3b: Temporoparietal

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WHAT IS AN ERP “COMPONENT”?

• A fuzzy concept with varying definitional criteria:

– Task-based definitions

• Error-related negativity

NOTE NEGATIVE PLOTTED UP!

Gehring et al., Psych. Science (1993) Cohen & Ranganath, J. Neurosci. (2007)

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WHAT IS AN ERP “COMPONENT”?

• A fuzzy concept with varying definitional criteria:

– Task-based definitions

• Error-related negativity

• Contingent Negative Variation

• Lateralized readiness potential

NOTE NEGATIVE PLOTTED UP!

Gehring et al., Psych. Science (1993) Cohen & Ranganath, J. Neurosci. (2007)

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WHAT IS AN ERP “COMPONENT”?

• Data-driven approaches to component identification

• Principal or Independent Component Analysis

Makeig et al. (1997)

PNAS

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Overlapping component problem

• ERP with one dorsomedial source

Example from Kutas & Dale (1997), in Cognitive Neuroscience, M. D. Rugg (ed.),

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Overlapping component problem

• Two simultanously active sources

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Overlapping Component Problem

• Which plot is from Source M and which is from Source L + R?

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Problems with the “component” approach

• Overlapping component problem

– Many important ERP modulations occur after 300ms post-stimulus

– By this time there is massively parallel processing in the brain

– A task manipulation might not affect the

component of interest but instead result in the generation of a new, temporally-overlapping component

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An MEG Study of Word Repetition

Dale et al., 2000

repeated words vs. novel words

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Experimental design tips for good ERP studies

• It is fine to capitalize on a known component, but try not to have your experimental interpretation hinge on a particular component

– Because of overlapping components, reverse inferences may be invalid

• Solution: Design experiments so critical result is a difference between two or more conditions

– For example, cognitive subtraction or parametric designs, analagous to imaging studies

• But, beware of known components that can confound your data

– Example: P300

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Experimental design tips for good ERP studies

• Eye movements and blinks create substantial EOG artifacts in EEG

– Artifacts can be corrected but better to avoid problems

• Solutions:

– Keep visual stimuli simple, foveal, and present for as little time as possible

– Include “blink breaks” in experiment

• Insert at least 2s of preparatory cue between blink break and trials

– Try to avoid contact lens wearers

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Experimental design tips for good ERP studies

•• SNR is a function SNR is a function of of sqrtsqrt(# of trials)(# of trials)

–– Doubling # trials Doubling # trials increases SNR by increases SNR by 41% [sqrt(2)=1.41]

41% [sqrt(2)=1.41]

–– Quadrupling # Quadrupling #

trials doubles SNR trials doubles SNR [sqrt(4)=2]

[sqrt(4)=2]

• Get LOTS of trials

Slide courtesy S.J. Luck

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Data acquisition

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Electrode Impedance

• Measured in Ohms

• Reduced by using conductive gel and abrading scalp

• For scalp electrodes <5 Ohms = low impedance

• Newer systems (EGI, Bio-Semi, etc.) have high input impedance so you don’t need to abrade scalp to get high quality recordings

• But with high electrode impedance, large, low frequency ‘skin potentials’ can occur

– Cephalic skin potentials are large, slow potentials that occur when the autonomic nerves and sweat glands in the skin are activated by heat or arousal (Picton & Hillyard, 1972). They are most prominently observed in the forehead, temples, neck, and mastoid regions.

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Luck & Kappenman (in press)

• High impedance does not globally attenuate ERP component amplitude

• Large, sudden changes in EEG were seen with high impedance recordings, esp. at high temperature.

Changes primarily in frequencies below 10Hz

• Removing voltage fluctuations >100 microvolts helps attenuate artifacts.

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Electrode Impedance and EEG Electrode Impedance and EEG

Frequency Content Frequency Content

Slide courtesy S.J. Luck

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High High - - Impedance & Noise Impedance & Noise

•• Direct comparison of high & low Z in Direct comparison of high & low Z in BiosemiBiosemi systemsystem

–– Oddball paradigm (N=12); cool/dry vs. warm/humidOddball paradigm (N=12); cool/dry vs. warm/humid –– Look at significance of rare vs. frequent P3 differenceLook at significance of rare vs. frequent P3 difference

Slide courtesy

S.J.

Luck

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Original International 10/20 System

Electrode cap and locations

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1994 Revised 10/20 System

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Do I need >128 channels?

• Pros:

– More accurate topographic, scalp current density maps – Necessary for source localization

– Makes average reference useful

– Not difficult with contemporary high impedance systems

• Cons:

– Takes longer to apply electrodes

• Subject fatigue more likely

–– Increased likelihood of bridgingIncreased likelihood of bridging

–– More electrodes -More electrodes -> More chances for problems> More chances for problems –– Data overload!Data overload!

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Tips on getting good ERP data (an incomplete list)

• Keep chamber cool

– Reduces skin potentials

– Note—you needn’t use a shielded chamber!

• Don’t use too much gel

– Reduces likelihood of electrode “bridging”

• Keep participants comfortable and relaxed

– Reduces muscle (EMG) artifact

• Use extra care in ensuring good data from ‘reference’

electrode

– Try to avoid EKG artifact in mastoids

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Data processing and analysis

1. Resampling 2. Re-referencing 3. Filtering

4. Artifact rejection/correction 5. Binning/Averaging

6. Normalization

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Data processing and analysis

1. Resampling 2. Re-referencing 3. Filtering

4. Artifact rejection/correction 5. Binning/Averaging

6. Normalization

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For your Reference

• EEG is a relative measure

– Data is usually collected relative to a reference electrode

• No matter what reference is used during

recording, you can algebraically re-reference the data offline

• Choice of reference site will alter observed

scalp topography

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Reference = Left Mastoid

Reference=

Average of Fz, Cz, Pz

Reference =

Average of Fz, Cz, Pz, O1/O2, and T5/T6

Slide courtesy S.J. Luck

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For your Reference

• Referencing strategies:

1. Relative to a electrically “dead” site

– Averaged/linked mastoid electrodes – Nose-tip, earlobe

If you use this approach make sure reference site is not close to regions that may generate your ERP effect!

2. Averaged reference

– Note that by definition, each positive effect will be accompanied by negative effects somewhere else – This method is closest to ‘reference free’ measures if

you have a lot of electrodes

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Scalp Current Density Scalp Current Density

•• Another option is to convert the data into current densityAnother option is to convert the data into current density

–– This reflects the current flowing outward at each point of the This reflects the current flowing outward at each point of the scalp; Reference

scalp; Reference--independentindependent

–– Calculated as the 2nd derivative over spaceCalculated as the 2nd derivative over space –– Emphasizes superficial sourcesEmphasizes superficial sources

–– Estimates are poor at edges of electrode arrayEstimates are poor at edges of electrode array

–– Requires large # of electrodes and high SNR (lots of trials)Requires large # of electrodes and high SNR (lots of trials)

Voltage Current Density

Courtesy S.J. Luck

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Filtering your data

• Filtering sacrifices the high temporal precision of ERPs, so avoid excessive offline (digital) filtering

• ERPs look ugly with high-frequency noise, but that’s reality

• Static, high-frequency noise will generally average out if you have enough trials

• Even short-duration components have some low- frequency contributions, so high-pass filtering may distort timing and amplitude of these components

• Necessary to high pass filter data if you are going to do independent component analysis (<.25 Hz)

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Artifacts: Blinks Artifacts: Blinks

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Artifacts: Eye movements Artifacts: Eye movements

Active: HEOG-L

Reference: HEOG-R

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Artifacts: C.R.A.P.

(Commonly Recorded Artifactual Potentials)

Slide and acronym courtesy S.J. Luck

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Dealing with artifacts

Artifact rejection

• Manual (Visual Inspection)

• Automated

– Fixed peak-to-peak amplitude thresholds – Slope

– Difference between frontal and vertical EOG electrode

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Artifact Correction: ICA Artifact Correction: ICA

http://www.sccn.ucsd.edu/~scott/tutorial/icatutorial8.html

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Averaging and Baselines

Baseline correction

• Usually done relative to average of activity during pre-stimulus period

– Duration of baseline is usually 100-200ms

• Watch out for pre-stimulus activity

– Artifacts, Alpha Oscillations – Meaningful pre-stimulus

activity may confound baseline-corrected ERPs

Urbach & Kutas (2006)

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You have ERPs, now what?

• Compute grand average

– Average of each subject’s average

• Examine

difference waves

– Sometimes timing of differences

differs from peaks in single-condition waveforms

Johnson et al. (1998)

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You have ERPs, now what?

• Closely examine maps of scalp topography

– Look for changes in topography over time and across conditions

• In general, indicates change in process/brain networks that are recruited

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You have ERPs, now what?

remember faces – forget faces

Paller, Bozic, Ranganath et al. (1999) Brain Res.

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You have ERPs, now what?

– Compare surface potential maps with scalp current density (SCD) maps

• Acts as a spatial filter to remove deep sources

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Measurement and Analysis

Different approaches, different assumptions 1. Peak amplitude and latency measures

– Used for ERP components

– Sometimes done relative to pre-stimulus baseline, sometimes relative to a preceding peak

– Problems:

• Peak ≠ Component: Peaks are NOT special

• May be excessively sensitive to noise

• Carries strong assumptions about components

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Measurement and Analysis

Different approaches, different assumptions 2. Mean amplitude measures

– Need to choose windows carefully

• Can choose windows that surround a component

• Can choose arbitrary measurement windows

– Example: consecutive 100ms windows

• Trade-off: wider windows can increase SNR but not if you are including time before or after effect of interest

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ERPs in Cognitive Neuroscience