eeg preprocessing for ica - sccn.ucsd.edu
TRANSCRIPT
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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
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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 3
The EEGLAB Matlab software
main graphic interface
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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
6
Remove line noise(if necessary)
Re-reference/down-sample(if necessary)
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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
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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 8
Imported EEG data
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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
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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
10
Remove line noise(if necessary)
Re-reference/down-sample(if necessary)
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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)
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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
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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
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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)
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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
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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)
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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing
Cleanline
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Tim Mullen
Qusp CEO
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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing
Remove line noise (Cleanline)
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check
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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
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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 27
Data Cleaning for ICA
1. Continuous Data
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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)
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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
33
Remove line noise(if necessary)
Re-reference/down-sample(if necessary)
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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
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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
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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
37
Remove line noise(if necessary)
Re-reference/down-sample(if necessary)
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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
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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 43
Data Cleaning for ICA
Variant 2: Epoched Data
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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 44
>> eeg_eventtypes(EEG)
1 140
2 60
201 60
Extract epochs
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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 45
Extract epochs
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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
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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'
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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
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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 50
Reject data epochs
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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
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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 52
Reject data epochs
Exceeds channel standard dev. max
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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 53
Reject data epochs
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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
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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)
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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
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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…
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EEGLAB Workshop XXV, Sep 26-29, 2017, Tokyo, Japan – John Iversen – Preprocessing 63
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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