PREPROCESSING FOR ADVANCED DATA ANALYSIS

PSYC696BCHAP 7

JL SANGUINETTI

Collecting EEG

Data analysis

Preprocessing

• Reorganization• Transformation

Organize• Extracting epochs• Adjusting event codes

Removing Data• Removing bad electrodes• Removing artifact data• Eye blinks

Modifying Data• Re-referencing• Temporal filtering• Spatial Transformations

KEEP NOTES!

Need so you can replicate

But also to keep your analysis unbiased• keeps you consistent

from subject to subject

SIGNAL-TO-NOISE

• Signal and nose are often entangled• Thus, a tradeoff

• One scientists' noise is another's signal

• Some use low-pass filter of 30Hz (leave everything below 30)

• Others build careers on activity above 30

Sounds relativistic; is it?

Signal

Noise

Adapted from figure 7.1

T/f-based analyses increase signal-to-noise characteristics of your data – sweet.

EPOCHS

EEG data is continuous

To look at task-related changes in the EEG, you need to create time-locked epochs around events of interest.

Rugg et al., 1995

AVERAGING EPOCHS

When to make time = zero?

You can shift time = zero to another point in the epoch e.g., responses

In practice, I’ve found its best to create bigger epochs than you think you need. Trim later.

LARGER EPOCHS FOR T/FNoncontinuous breaks• First and last points go to zero

Figure 7.2

So…Make your epochs long for t/f• e.g.,1500 ms to 2500 ms

Edge artifacts last 2 or 3 cycles• Lower frequencies need more buffer

zone

Rule of thumb: three cycles at lowest freq you are interested in (1500 ms for 2-Hz activity) – explain. • Reflection method

MATLAB DEMO

Create epochs!

TRIAL COUNT

• Try to keep trial count similar across conditions

• Phase is more sensitive to trial count than power or the ERP – explain.• Phase: small trials introduces positive

bias• Power: raw power only positive, so

small bias introduced too• ERP: not biased since time-domain

values are negative and positive

FILTERINGHelps to remove: • High frequency artifact

• 60 or 50 Hz line nose• Low frequency drift

Not necessary for t/f approaches because temporal filters are applied• e.g., looking at 2 to 20 Hz• Discussed in later chapters• High-pass of 0.1 or 0.5 Hz on

continuous data to remove slow drift

TRIAL REJECTIONImportant, but open to interpretation

Manual vs automatic debate• What I think (if you care)

• Know your data!• Pick criteria for rejection before starting• Automatic rejection does not work for all subjects

• For t/f, sharp edges are an issue• Not as big a deal for ERP

SPATIAL FILTERS1) Localize a result

• Right visual field stimulation corresponds to left occipital2) Isolate topographical features

• Object semantics (anterior) vs word semantics (posterior)3) Preprocessing for connectivity analysis

• Surface Laplacian can help minimize volume conduction

When you filter depends on the type of filter and your goalse.g., PCA on t/f power single trials or trial-averages

REFERENCING• Voltage at one electrode is relative to voltage at another

• Beware: Any activity recorded in the reference will be reflected in all other electrodes.

• Many choices, but not close to where you expect your effect.

• No reference is perfect.

EEGLAB DEMO

Reference!

INTERPOLATION• Don’t let it get to that point.

• Why? Its not unique data – its weighted sum from data around.

• But if you do, keep it under 5 (some say 3).

INTERPOLATION

• Important for spatial filters• Important for averaged reference

• A bad channel will put noise in all channels

Maybe there is signal• Apply low pass filter does activity look like that

around? Keep it.

START WITH CLEAN DATA• No substitute for clean data.

• Fancy preprocessing methods will not save you.

• Preprocessing will make good data even better.

• Find ways to make your data clean from the start.• Going slow• Setting everything up beforehand• Testing before the study starts• Don’t be afraid to stop during a

session

You will make painful, costly mistakes that give you less than clean data. Learn from them!

End presentation