1. Pre-processing for EEG and MEG
Przemek Tomalski & Kathrin Cohen Kadosh

2. Recording EEG

3. 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

4. Extracting ERP from EEG
ERPs emerge from EEG as you average trials together

5. Overview Pre-processing
Converting the data
Epoching/Segmentation
Filtering
Artifact Detection/Rejection
Averaging
Re-referencing

6. Convert the data

8. Overview Pre-processing
Converting the data
Epoching /Segmentation
Filtering
Artifact Detection/Rejection
Averaging
Re-referencing

9. Epoching

10. 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!

12. Epoching - SPM

13. Overview Pre-processing
Converting the data
Epoching/Segmentation
Filtering
Artifact Detection/Rejection
Averaging
Re-referencing

14. Filtering Types of filters: highpass lowpass notch (stopband filter)
Butterworth (bandpass filter, backward and forward)
! (require signal processing toolbox in Matlab)

15. Effects of filtering the raw data
Lowpass 30 Hz
highpass 0.3 Hz

16. Filtering in SPM

17. Overview Pre-processing
Converting the data
Epoching/Segmentation
Filtering
Artifact Detection/Rejection
Averaging
Re-referencing

18. Artifacts in EEG signal
Blinks
Eye-movements
Muscle activity
EKG
Skin potentials
Alpha waves

19. Eye blinks

20. Eye movements

21. Sweat artifacts

22. Artefact detection - SPM

23. 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

24. 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
Weighting
Value
Outliers are given less weight
Points close to median weighted ‘1’

25. Artifact correction - SPM
Normal average
Robust Weighted Average

26. Robust averaging - SPM

27. Artifact avoidance Blinking EMG Alpha waves 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

28. Overview Pre-processing
Converting the data
Epoching/Segmentation
Filtering
Artifact Detection/Rejection
Averaging
Re-referencing

29. Averaging

30. 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.

31. Averaging

32. 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

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

34. Overview Pre-processing
Converting the data
Epoching/Segmentation
Filtering
Artifact Detection/Rejection
Averaging
Re-referencing

35. 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 channels
Other option: linked mastoids, vertex, etc.

36. Re-referencing

37. Re-referencing

38. What comes next? Visual inspection of individual data Grand mean
Statistical Analysis

39. 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!

40. 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,
SPM Manual

41. Thank you!
And many thanks to Dr. Vladimir Litvak for his advice!