As long as our brain is a mystery,
the universe, the reflection of the
structure [function] of the brain will
also be a mystery.
—Ramon y Cajal
Using EEG to Solvethe Mystery… Plus Some Other Tidbits
Alexander J. Shackman
Postle Lab Meeting
September 2009
The Big Picture
Ψ = f ( )
Use brain activity to characterize the mechanisms underlying psychological constructs
e.g., working memory, anxiety, etc.
Acquisition Pipeline
- Anti-aliasing Filters (e.g., 0.1 – 200Hz)- Amplification (e.g., 10, 000)- Analog/Digital conversion- Sampling (e.g., 500 Hz)- Calibration (μV)
Luck, 2005
128-chan ‘net’
raw EEG
raw EEG
3 Kinds of Analysis
[1] Event-Related Potentials (ERPs)- time-domain- event-locked average voltage, μV- Metric = component amp, latency
raw EEG
3 Kinds of Analysis[1] Event-Related Potentials (ERPs)- time-domain- event-locked average voltage, μV-Metric = Amp, latency of “components,”
e.g., P300
Tidbit 1: Teaser for My Next Talk
Shackman et al., in prep. A; Maxwell, Shackman et al., in prep.
raw EEG SafeThreat of Shock
Impact of Threat-Evoked Anxiety on WM
Tidbit 2, cont’d: A Preliminary Pitch…
+ +
WM-RelatedERP (CDA)
SS fMRI Stereo-TMS
= Nature!
Happy Brad
raw EEG
[2] Spectral Analyses (FFTs)- frequency-domain- use Fast Fourier Transform to
decompose complex signal (EEG)into constituent frequencies (sine waves) of different phases and amplitudes
- Metric = power density (μV2/Hz)for “bands,” eg, alpha (8-13Hz)
3 Kinds of Analysis
3 Kinds of AnalysisTypical Power Spectra: Gray Bar Indicates Alpha Band (8-13Hz)
Shackman et al., under review A
3 Kinds of AnalysisTypical Power Spectra: Gray Bar Indicates Alpha Band (8-13Hz)
Shackman et al., under review A
3 Kinds of AnalysisTypical Power Spectra: Topography of Factor Analytically Derived Bands
Shackman et al., under review A
Alpha Bands, α
raw EEG
[3] Event-Related Spectral Perturbations- time + frequency domain- event-locked spectral power, μV2/Hz/ms
3 Kinds of Analysis
Ψ = f ( )EEG + Artifact
Separating neural signals from biological artifacts is a ubiquitous problem for any neurophysiological investigation (cf. motion correction).
Biological artifacts can compromise sensitivity or masquerade as genuine effects, particularly when they are confounded with statistical contrasts.
Inferential issues — Did activation go up or down? How much? Where?
When such artifacts are markedly smaller than neural signals, they can be safely ignored. When they are rare, they may be rejected with trivial consequences.
Shackman et al., Brain Topog., 2009
Inferential Impact of Artifacts
Electromyogenic (EMG) Artifacts
McMenamin, Shackman et al, under review, Psychophys, 2009; Shackman et al., BT, 2009
Scalp Musculature EMG, Time Domain
- Widespread on the scalp
- Big: potentially 1-2 orders of magnitude larger than EEG effects …So even modest residual contamination can distort inference
- Widespread in the frequencydomain … detectable into the thetaband (~4-7 Hz)
-Worst of all, it’s confounded withcognitive load, emotion, stimulus processing
Electromyogenic (EMG) Artifacts
Shackman et al, Brain Topog, 2009
Realistic example: Residual EMG at right “dlPFC” sensor (F4) after art. rejection
Electromyogenic (EMG) Artifacts
Shackman et al, Brain Topog, 2009
RelaxedTense
Realistic example: Residual EMG at right “dlPFC” sensor (F4) after art. rejection
So Now What— Is Correction Possible?
• Need to assess validity
• Factorial design– Independently vary presence/absence of EEG vs. EMG– Instruct participants to …
• Close/Open Eyes (“Berger maneuver”) Modulates activity in the alpha band: Close > Open
• Tense/Relax MusclesModulates myogenic activity: Tense > Relaxed
– Assess sensitivity and specificity in the EEG (alpha) band• Sensitivity: Correction should render EMG-corrupted data
statistically identical to EMG-free data
• Specificity: Correction should not change EMG-free data
Validation: No Correction
Shackman et al, Brain Topog, 2009; McMenamin, Shackman et al., Psychophys, 2009
Validating Correction: Regression-Based Correction
Shackman et al, Brain Topog, 2009; McMenamin, Shackman et al., Psychophys, 2009
EEG Source Modeling—Can Also Assess Validity in the Source Space
McMenamin, Shackman et al., Psychophys, 2009, under review; see also Shackman et al., Psych Sci, in press
128-Channel EEG Low Resolution Electromagnetic Tomography LORETA
Application of ICA to EMG Correction
• Independent Component Analysis (ICA)– Means of blindly separating statistically
unrelated temporal components (time-series)
– “Fancy” version of PCA or Factor Analysis
Langers, Neuroimage, 2009
Application of ICA to EMG Correction
• Independent Component Analysis (ICA)– Rapidly become one of the most popular
techniques for correcting biological artifacts in EEG
– To date, evidence that ICA is a sensitive and specific tool for correcting EMG specifically is semi-quantitative (e.g., before/after pix for ‘representative’ subject, dB reduction)
McMenamin, Shackman et al., under review; Shackman et al., BT, 2009
ICA Pipeline
McMenamin, Shackman et al., under review
• Raw data
• Principal components analysis to reduce dimensionality (64 PCs)
• ICA 64 ICs / participant
• Manual classification – Big Time Investment!– 64 ICs per subject @ 2 min** per IC + initial inter-rater reliability training
• Remove “bad” ICs and reconstruct the “corrected” time-series
• Assess sensitivity and specificity on the scalp and in the intracerebral(LORETA) source space using ROIs (peak EEG/EMG)
The Conundrum: What to Drop?• 4 Kinds of ICs
– Neurogenic ICs – Keep!
– Myogenic ICs – Drop!
Neurogenic/EEG Myogenic/EMG
The Conundrum: What to Drop?
McMenamin, Shackman et al., under review
• 4 Kinds of ICs– Neurogenic ICs – Keep!
– Myogenic ICs – Drop!
– Other Biological/Physical Artifacts – Drop!
The Conundrum: What to Drop?
McMenamin, Shackman et al., under review
• 4 Kinds of ICs– Neurogenic ICs – Keep!
– Myogenic ICs – Drop!
– Other Biological/Physical Artifacts – Drop!
– Everything Else - ????
The Conundrum: Everything Else?
McMenamin, Shackman et al., under review
Mixed EMG + EEG Components – Should We Throw the Baby w/ the Bathwater?
The Conundrum: Everything Else?
McMenamin, Shackman et al., under review
What About Unclassifiable “Noise” Components? Or Components that account for trivial amounts of variance?
Noise
The Conundrum: Everything Else?
McMenamin, Shackman et al., under review
This ain’t just a couple of ICs…
The Conundrum: Everything Else?
McMenamin, Shackman et al., under review
…or a percentage or two of variance
EMG
McMenamin, Shackman et al., under review
“Pure” EMG ICs…
…and EMG-dominant mixed ICs
…and EEG-dominant mixed ICs
Scalp Sensitivity / Specificity
McMenamin, Shackman et al., under review
Max EMG / Max ‘NNNM’ shows good (but not great) performance on the scalp
++ Excellent; + Adequate; ? Questionable; — Poor
But …
McMenamin, Shackman et al., under review
• None of the protocols proved adequate in the source space … Max/Max overcorrected the data
• Replicating what we saw using regression-based correction (McMenamin et al., 2009)
• Why the discrepancy?
But …
McMenamin, Shackman et al., under review
1. Life on the OutsideROIs don’t capture the quality of correction at channels lying outside … but those channels areused to compute the LORETA solution
1. + vs. ++None of the protocols showed consistently excellent sensitivity/specificity within the ROIs… oftentimes, one or more “worst case” channels showed evidence of less than perfect validity
More Fundamentally—ICA Doesn’t Perfectly Separate
EEG/EMG
McMenamin, Shackman et al., under review
Mixed ICs—Early sign of trouble?
2 Plausible Explanations
1. Model order- 64 ICs is too many, leading to ‘splitting’
of ‘pure’ ICs into mixed ICs- Ss vary in the ‘right’ number?
2. EMG violates assumptions of ICA- too Gaussian?- too temporally correlated with EEG?
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