Pre-processing for EEG and MEG Przemek Tomalski & Kathrin Cohen Kadosh.
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Transcript of Pre-processing for EEG and MEG Przemek Tomalski & Kathrin Cohen Kadosh.
Pre-processing for EEG and MEG
Przemek Tomalski &
Kathrin Cohen Kadosh
Recording EEG
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Two crucial steps Activity caused by your stimulus (ERP) is
‘hidden’ within continuous EEG stream ERP is your ‘signal’, all else in EEG is ‘noise’ Event-related activity should not be random, we
assume all else is Epoching – cutting the data into chunks referenced
to stimulus presentation Averaging – calculating the mean value for each
time-point across all epochs
Extracting ERP from EEG
ERPs emerge from EEG as you average trials together
Overview Pre-processing
Converting the data Epoching/Segmentation Filtering Artifact Detection/Rejection Averaging Re-referencing
Convert the data
Overview Pre-processing
Converting the data Epoching /Segmentation Filtering Artifact Detection/Rejection Averaging Re-referencing
Epoching
Segmenting (Epoching)Segment length: at least 100 ms should precede the stimulus
onset (see baseline correction). The time - frequency analysis can distort the
signal at both ends of the segment, make sure you do not lose
important data and that the baseline segment is still long enough after
cutting off the affected portions. The affected segment length depends on the
frequency in an inverse manner (length ms ~ 2000/freq Hz)
The segment should not be too long nevertheless, the longer it is the
bigger the chance to include an artifact!
Epoching - SPM
Overview Pre-processing
Converting the data Epoching/Segmentation Filtering Artifact Detection/Rejection Averaging Re-referencing
Filtering Types of filters:
highpass lowpass notch (stopband filter) Butterworth (bandpass filter, backward and forward)! (require signal processing toolbox in Matlab)
Effects of filtering the raw dataLow
pass 3
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QuickTime™ and aTIFF (Uncompressed) decompressor
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QuickTime™ and aTIFF (Uncompressed) decompressor
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Raw
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Filtering in SPM
Overview Pre-processing
Converting the data Epoching/Segmentation Filtering Artifact Detection/Rejection Averaging Re-referencing
Artifacts in EEG signal
BlinksEye-movementsMuscle activityEKGSkin potentialsAlpha waves
Eye blinks
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Eye movements
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Sweat artifacts
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Artefact detection - SPM
Artifact correction
Rejecting ‘artifact’ epochs costs you data Using a simple artefact detection method will lead
to a high level of false-positive artifact detection Rejecting only trials in which artifact occurs might
bias your data Alternative methods of ‘Artifact Correction’ exist
Artifact correction - SPM
SPM uses a robust average procedure to weight each value according to how far away it is from the median value for that timepoint
WeightingValue
Outliers are given
less weight
Points close to median
weighted ‘1’
Artifact correction - SPM
Normal average
Robust Weighted Average
Robust averaging - SPM
Artifact avoidance
Blinking Avoid contact lenses Build ‘blink breaks’ into your paradigm If subject is blinking too much – tell them
EMG Ask subjects to relax, shift position, open mouth slightly
Alpha waves Ask subject to get a decent night’s sleep beforehand Have more runs of shorter length – talk to subject in between Vary ISI – alpha waves can become entrained to stimulus
Overview Pre-processing
Converting the data Epoching/Segmentation Filtering Artifact Detection/Rejection Averaging Re-referencing
Averaging
Averaging
S/N ratio increases as a function of the square root of the number of trials.
As a general rule, it’s always better to try to decrease sources of noise than to increase the number of trials.
Averaging
Averaging Assumes that only the EEG noise varies from trial
to trial But – amplitude and latency will vary Variable latency is usually a bigger problem than
variable amplitude
Averaging: effects of variance
Latency variation can be a significant problem
Overview Pre-processing
Converting the data Epoching/Segmentation Filtering Artifact Detection/Rejection Averaging Re-referencing
Re-referencing
It is important to re-reference the data in order to estimate a true, nonarbitrary zero value to which to reference the voltage measurements.
There are many different ways to re-reference, depending on the experimental question. Possibly the best solution: average reference, improves with increasing number of channelsOther option: linked mastoids, vertex, etc.
Re-referencing
Re-referencing
What comes next?
Visual inspection
of individual dataGrand meanStatistical Analysis
General Recommendations
Use short blocks, ca. 2 min with breaks Keep recording time under 45min Keep it small and simple Look for main effects and not for complex
interactions Don’t go fishing!
References Luck, S. J. (2005). An introduction to the
event-related potential technique. Cambridge, MA: MIT Press.
Picton, T. W., Bentin, S., Berg, P., Donchin, E., Hillyard, E., Johnson, J. R., et al. (2000). Guidelines for using human event-related potentials to study cognition: Recording standards and publication criteria, Psychophysiology, 37, 127-152.
SPM Manual
Thank you!
And many thanks to Dr. Vladimir Litvak for his advice!