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Preprocessing for EEG & MEG Tom Schofield & Ed Roberts.

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1 Preprocessing for EEG & MEG Tom Schofield & Ed Roberts

2 Data acquisition

3 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) ]);

4 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

5 Extracting ERP from EEG ERPs emerge from EEG as you average trials together

6 Overview Preprocessing steps Preprocessing with SPM What to be careful about What you need to know about filtering

7 mydata.mat

8 Epoching

9 Epoching - SPM Creates: e_mydata.mat

10 Downsampling Nyquist Theory – minimum digital sampling frequency must be > twice the maximum frequency in analogue signal Select ‘Downsample’ from the ‘Other’ menu

11 Downsample Creates: de_mydata.mat

12 Artefact rejection Blinks Eye-movements Muscle activity EKG Skin potentials Alpha waves

13 Artefact rejection Blinks Eye-movements Muscle activity EKG Skin potentials Alpha waves

14 Artefact rejection - SPM Creates: ade_mydata.mat

15 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

16 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 Weighting Value Outliers are given less weight Points close to median weighted ‘1’

17 Artefact correction - SPM Normal average Robust Weighted Average

18 Robust averaging - SPM Creates: ade_mydata.mat

19 Artefact Correction ICA Linear trend detection Electro-oculogram ‘No-stim’ trials to correct for overlapping waveforms

20 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

21 Averaging R = Noise on single trial N = Number of trials Noise in avg of N trials (1/√N) x R More trials = less noise Double S/N need 4 trials Quadruple need 16 trials

22 Averaging Creates: made_mydata.mat

23 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

24 Averaging: effects of variance Latency variation can be a significant problem

25 Latency variation solutions Don’t use a peak amplitude measure

26 Time Locked Spectral Averaging

27 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

28 Filtering Why would you want to filter?

29 Potential Artefacts Before Averaging… Remove non-neural voltages Sweating, fidgeting Patients, Children Avoid saturating the amplifier Filter at 0.01Hz

30 Potential Artefacts After Averaging… Filter Specific frequency bands Remove persistent artefacts Smooth data

31 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

32 Properties of Filters “Transfer function” 1. Effect on amplitude at each frequency 2. Effect on phase at each frequency “Half Amp. Cutoff” 1. Frequency at which amp is reduced by 50%

33 High-pass

34 Low-pass

35 Band-pass and Notch

36 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

37 Filtering Artefacts “Precision in the time domain is inversely related to precision in the frequency domain.”

38 Filtering in the Frequency Domain A BC DE

39 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

40 Filtering in the Time Domain Waveform progressively filtered by averaging the surrounding time points. Here x = ((x-1)+x+(x+1))/3

41 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

42 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

43 Summary 1. No substitute for good data 2. The recipe is only a guideline 3. Calibrate 4. Filter sparingly 5. Be prepared to get your hands dirty

44 References An Introduction to the Event-related Potential Technique, S. J. Luck SPM Manual

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