Pre-processing for EEG and MEG

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 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 • 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, 127-152. • SPM Manual

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