Preprocessing for EEG & MEG Tom Schofield & Ed Roberts.
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Transcript of Preprocessing for EEG & MEG Tom Schofield & Ed Roberts.
Preprocessing for EEG & MEG
Tom Schofield & Ed Roberts
Data acquisition
Data acquisition
Using Cogent to a generate marker pulse..
drawpict(2);
outportb(888,2); tport=time;
waituntil(tport+100); outportb(888,0);
logstring( [‘displayed ‘O’ at time ' num2str(time) ]);
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
Preprocessing steps Preprocessing with SPM What to be careful about What you need to know about
filtering
mydata.mat
Epoching
Epoching - SPM
Creates: e_mydata.mat
Downsampling
Nyquist Theory – minimum digital sampling frequency must be > twice the maximum frequency in analogue signal
Select ‘Downsample’ from the ‘Other’ menu
Downsample
Creates: de_mydata.mat
Artefact rejection
BlinksEye-movementsMuscle activityEKGSkin potentialsAlpha waves
Artefact rejection
BlinksEye-movementsMuscle activityEKGSkin potentialsAlpha waves
Artefact rejection - SPM
Creates: ade_mydata.mat
Artefact correction Rejecting ‘artefact’ epochs costs you
data Using a simple artefact detection
method will lead to a high level of false-positive artefact detection
Rejecting only trials in which artefact occurs might bias your data
High levels of artefact associated with some populations
Alternative methods of ‘Artefact Correction’ exist
Artefact 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’
Artefact correction - SPM
Normal average
Robust Weighted Average
Robust averaging - SPM
Creates: ade_mydata.mat
Artefact Correction
ICA Linear trend detection Electro-oculogram ‘No-stim’ trials to correct for
overlapping waveforms
Artefact 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 Jitter ISI – alpha waves can become entrained to stimulus
Averaging
R = Noise on single trialN = Number of trials
Noise in avg of N trials (1/√N) x R
More trials = less noiseDouble S/N need 4 trialsQuadruple need 16 trials
Averaging
Creates: made_mydata.mat
Averaging
Assumes that only the EEG noise varies from trial to trial
But – amplitude will vary But – latency will vary Variable latency is usually a bigger
problem than variable amplitude
Averaging: effects of variance
Latency variation can be a significant problem
Latency variation solutions
Don’t use a peak amplitude measure
Time Locked Spectral Averaging
Other stuff you can do – all under ‘Other’ in GUI
Merge data sessions together Calculate a ‘grand mean’ across
subjects Rereference to a different
electrode FILTER
Filtering
Why would you want to filter?
Potential Artefacts
Before Averaging… Remove non-neural voltages Sweating, fidgeting Patients, Children Avoid saturating the amplifier Filter at 0.01Hz
Potential Artefacts
After Averaging…
Filter Specific frequency bands Remove persistent artefacts Smooth data
Types of Filter
1. Low-pass – attenuate high frequencies
2. High-pass – attenuate low frequencies
3. Band-pass – attenuate both
4. Notch – attenuate a narrow band
Properties of Filters
“Transfer function”1. Effect on amplitude at each frequency2. Effect on phase at each frequency
“Half Amp. Cutoff”1. Frequency at which amp is reduced by
50%
High-pass
Low-pass
Band-pass and Notch
Problems with Filters
Original waveform, band pass of .01 – 80Hz
Low-pass filtered, half-amp cutofff = ~40Hz
Low-pass filtered, half-amp cutofff = ~20Hz
Low-pass filtered, half-amp cutofff = ~10Hz
Filtering Artefacts “Precision in the time domain is inversely related to
precision in the frequency domain.”
Filtering in the Frequency Domain
AB C
D E
Filtering in the Time Domain
Filtering in the time domain is analogous to smoothing
At a given point an average is calculated in relation to two nearest neighbours or more
X+1
X-1
X
Filtering in the Time Domain
Waveform progressively filtered by averaging the surrounding time points.
Here x = ((x-1)+x+(x+1))/3
Recipe for Preprocessing
1. Band-pass filter e.g.0.1 – 40Hz
2. Epoch
3. Check/View
4. Merge
5. Downsample?
6. Artefacts; Correction/Rejection
7. Filter
8. Average
Recommendations
1. Prevention is better than the cure
2. During amplification and digitization minimize filtering
3. Keep offline filtering minimal, use a low-pass
4. Avoid high-pass filtering
Summary
1. No substitute for good data2. The recipe is only a guideline3. Calibrate4. Filter sparingly5. Be prepared to get your hands
dirty
References
An Introduction to the Event-related Potential Technique, S. J. Luck
SPM Manual