EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

EEG Preprocessing for ICA

25th EEGLAB WorkshopJAIST Tokyo Satellite, Japan

Day 1John Iversen

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

• Start Matlab• Add the EEGLAB folder to your Matlab path:

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Installing EEGLAB and data folder

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 3

The EEGLAB Matlab software

main graphic interface

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

“Secrets” to a good ICA decomposition

Garbage in, garbage out (GIGO: it’s not magic)

Remove large, non-stereotyped artifacts

Do you have enough data? (based mostly on time, not frames)

High-pass filter to remove slow drifts (no low-pass filter needed)

Remove bad channels

Data must be in double precision (not single)

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

The Goal of Preprocessing

• Create a complete EEGLAB data set with– EEG time series signal– Channel Locations– Event information

• Applying singnal processings on EEG time series to help ICA decompositions– Data ‘cleaning’—artifact rejection.

• Removing noisy channels.• Removing noisy segments of data.

– Applying frequency filter.

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Pre-processing pipeline

Import time-seriesdata into EEGLAB

Import event markersand channel locations

High pass filter(~.5 – 1 Hz)

Examine raw data

Reject large artifact time points

Identify/rejectbad channels

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Remove line noise(if necessary)

Re-reference/down-sample(if necessary)

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 7

Importing a dataset

Tip for Biosemi users:

Use the ‘BDF plugin’ version

of the Biosemi BDF/EDF importer

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 8

Imported EEG data

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Comments and dataset history

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Also:>> EEG.comments

and>> EEG.history

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Pre-processing pipeline

Import time-seriesdata into EEGLAB

Import event markersand channel locations

High pass filter(~.5 – 1 Hz)

Examine raw data

Reject large artifact time points

Identify/rejectbad channels

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Remove line noise(if necessary)

Re-reference/down-sample(if necessary)

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 1111

• Import events from Matlab array or ASCII file• Import events from data channel• Import from Presentation event file• Import events from E-Prime event file• Import events from Neuroscan event file

Import data events

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(Often imported automatically

during data import)

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Appearance of an event channel in raw data

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Imported data events

If event import was

successful,

you will see an

appropriate

number here

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 14

Sample data: basic P300 paradigm

FileSimpleOddball.set

Data68 channel EEG, 256 Hz sampling rate, Biosemi system, re-referenced during import to averaged left and right mastoid electrodes

Taskspeeded button press response to star shape (noresponse to circle shape), 100 ms presentationduration, 200 trials

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Import channel locations

7 file formats supported (Polhemus, BESA, …)

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Import channel locations

EEG

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 17

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Imported channel locations

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Remove unwanted channels

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Pre-processing pipeline

Import time-seriesdata into EEGLAB

Import event markersand channel locations

High pass filter(~.5 – 1 Hz)

Examine raw data

Reject large artifact time points

Identify/rejectbad channels

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Remove line noise(if necessary)

Re-reference/down-sample(if necessary)

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

High-Pass Filter the data

Reason: To improve data stationarity.ICA is biased to amplitude, and EEG data has 1/f power specrum density.

0.5

High-pass

needed

for ICA

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Good resource for learning filters

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https://sccn.ucsd.edu/wiki/Firfilt_FAQ

https://cloud.github.com/downloads/widmann/firfilt/firfilt.pdf

Andreas Widmann from Leipzig

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Pre-processing pipeline

Import time-seriesdata into EEGLAB

Import event markersand channel locations

High pass filter(~.5 – 1 Hz)

Examine raw data

Reject large artifact time points

Identify/rejectbad channels

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Remove line noise(if necessary)

Re-reference/down-sample(if necessary)

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Cleanline

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Tim Mullen

Qusp CEO

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Remove line noise (Cleanline)

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check

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Filter comparisons

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0.5 Hz high-pass filter 0.5 Hz high-pass filter50 Hz low-pass filter

0.5 Hz high-pass filterCleanline

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 27

Data Cleaning for ICA

1. Continuous Data

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Pre-processing pipeline

Import time-seriesdata into EEGLAB

Import event markersand channel locations

High pass filter(~.5 – 1 Hz)

Examine raw data

Reject large artifact time points

Identify/rejectbad channels

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Remove line noise(if necessary)

Re-reference/down-sample(if necessary)

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Manually identifying bad channels

1) Identify bad channel

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Manually identifying bad channels

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Manually identifying bad channels

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Removing channel(s)

If not checked, will result

in dataset with one channel

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Pre-processing pipeline

Import time-seriesdata into EEGLAB

Import event markersand channel locations

High pass filter(~.5 – 1 Hz)

Examine raw data

Reject large artifact time points

Identify/rejectbad channels

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Remove line noise(if necessary)

Re-reference/down-sample(if necessary)

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Plot channel spectra

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 35

Scroll channel data

Alternate GUI option, same function

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 36

Scroll channel data

scaling

channels,time,

events

sec/epoch

Scroll buttons

Event markers

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Pre-processing pipeline

Import time-seriesdata into EEGLAB

Import event markersand channel locations

High pass filter(~.5 – 1 Hz)

Examine raw data

Reject large artifact time points

Identify/rejectbad channels

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Remove line noise(if necessary)

Re-reference/down-sample(if necessary)

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Reject continuous data

Equivalent

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Reject continuous data

Click and drag with mouse

over noisy data to reject

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Rejecting data for ICA

To prepare data for ICA:

KeepReject

... but keep stereotyped artifacts (like eye blinks)

Reject large muscle or otherwise strange events...

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Fast (but sloppy) artifact rejection

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 42

Fast (but sloppy) artifact rejection

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 43

Data Cleaning for ICA

Variant 2: Epoched Data

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 44

>> eeg_eventtypes(EEG)

1 140

2 60

201 60

Extract epochs

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 45

Extract epochs

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 46

Select a subset of epochs‘0’ because the subject

did not miss any targets

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 47

Save dataset (optional)

SimpleOddball target epochs

Or save later from menu

'Do not overwrite

current dataset'

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Scroll (epoched) channel data

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Reject epochs with artifact

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1.1 s epoch 1.1 s epoch 1.1 s epoch 1.1 s epoch 1.1 s epoch

Click anywhere within

an epoch to select it

for rejection

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 50

Reject data epochs

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing17th EEGLAB Workshop, Nov. 14-18, 2013, San Diego: Marissa Westerfield – ERP visualization 51

Reject data epochs

visual inspection

probability

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 52

Reject data epochs

Exceeds channel standard dev. max

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 53

Reject data epochs

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Visualize ERP in rectangular array

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Visualize ERP in topographic array

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Visualize ERP scalp distribution

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Visualize channel ERPs in 2D

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 58

Visualize channel ERPs in 3D

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Pre-processing pipeline

Import time-seriesdata into EEGLAB

Import event markersand channel locations

High pass filter(~.5 – 1 Hz)

Examine raw data

Reject large artifact time points

Identify/rejectbad channels

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Remove line noise(if necessary)

Re-reference/down-sample(if necessary)

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Re-reference data (if necessary/desired)

average reference

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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

On Average Referencing

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• In theory, positive and negative current across entire head should balance—no net current source or sink: Average referencing enforces this.

• ICA is invariant to re-referencing, except for

• Effect of rank deficiency

• DC difference.

• Average referencing reduces data rank by 1, so you must remove one channel (Cz often) See update below.

https://sccn.ucsd.edu/wiki/Makoto's_preprocessing_pipeline#Re-reference_the_data_to_average

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Resample data (if desired)

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256

Reason: Reduce space, time. But keep nyquist and

ICA data length requirements in mind…

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 63

END

EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing

Exercises (optional homework)

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• Preprocess data of your choice or load a previously filtered dataset e.g. faces_4.set•Identify and remove non-task portions of continuous data; see if the previously flagged channels are still identified as bad• Epoch on event of interest. Scroll the epoched data and perform visual rejection of epochs