1. EEG / MEG: Experimental Design & Preprocessing Alexandra Hopkins Jennifer Jung

2. Outline Experimental Design fMRI M/EEG Analysis – Oscillatory activity – EP Design Inferences Limitations Combined Measures Preprocessing in SPM12 Data Conversion Montage Mapping Epoching Downsampling Filtering Artefact Removal Referencing

3. MEG vs. EEG ●Both EEG and MEG signals arise from direct neuronal activity -> postsynaptic dendritic potentials ●Electric field is distorted by changes in conductivity across different layers unlike magnetic field ●High temporal resolution ~ms.

4. Sources of M/EEG signals gyrus sulcus ●MEG sensors only detect tangential components of fields from cortical pyramidal neurons ●Less sensitive to deeper regions ●EEG signal consists of both tangential and radial components of fields

6. Two types of MEG/EEG analysis Event related changes (EP / ERP – ERF) Oscillatory activity – cortical rhythms (Time-frequency analysis) Otten, L. (2012, November 21). EEG/MEG Acquisition, Analysis and Interpretation, MSc Cognitive Neuroscience, UCL Time locked to stimulus

7. post-stim pre-stim evoked response Averaging Event Related Changes Repeats at same time When response is time locked - signal averages in!

8. Evoked vs. Induced ( Hermann et al. 2004) With jitter effect - signal averages out! Average trial by trial

9. resting state falling asleep sleep deep sleep coma 1 sec 50 uV ongoing rhythms active awake state Oscillatory activity

10. Non-averaged data collected during continuous stimulation or task performance (or during rest) lends itself to analysis of spectral power. Signals can be decomposed into a sum of pure frequency components which gives information on the signal power at each frequency. i.e. We can do Fourier analysis and look at spectra (not-event related – break data in arbitrary segments and do some averaging Oscillations

11. Cortical and behavioral deactivation or inhibition Closed eyes Alert, REM sleep Attention, and higher cognitive function Attentional and syntactic language processes Deep sleep Codes locations in space, navigation Declarative/episodic memory processes Successful memory encoding (8 – 12or 13 Hz) (12 – 30 Hz) (0 – 4 Hz) (4 – 8 Hz) (30 – 80 Hz) Visual awareness Binding of information Encoding, retention and

12. EP vs. ERP / ERF Evoked potential (EP) –sensory processes –short latencies (< 100ms) –small amplitudes (< 1μV) Event related potential (EEG) / event related field (ERF) –higher cognitive processes –longer latencies (100 – 600ms), –higher amplitudes (10 – 100μV) used interchangeably in general

13. Non-time locked activity(noise) lost via averaging over trials Averaging ERP/ ERF

14. Experimental design Number of trials – EP: 120 trials, 15-20% will be excluded – Oscillatory activity: 40-50 trials Duration of stimuli / task – Short: Averaged EP is fine – (Very) long: spectrotemporal analysis on averaged EP or non-averaged data Collecting Behavioral Responses

15. Inferences Not Based On Prior Knowledge Same ERP pattern Timing signals Distribution across scalp Differences in ERP across conditions and time Invariant patterns of neural activity from specific cognitive processes Timing of cognitive processes Degree of engagement Functional equivalence of underlying cognitive process Observation Inference

16. Observed vs Latent Components Latent componentsObserved waveform OR

17. Design Strategies Focus on specific, large and easily isolated component –E.g., P3, N400, LRP, N2pc… Use well-studied experimental manipulations -Similar conditions Component-independent experimental designs -Very hard to study anything interesting Luck, Ten Simple Rules for Designing and Interpreting ERP Experiments

18. How quickly can the visual system differentiate between different classes of object? Thorpe et al (1996) Component-independent experimental designs

19. Avoid confounds and misinterpretations – Physical stimulus confounds Side effect – What you manipulated indirectly influences other things Vary conditions within rather than between blocks - Fatigue effect ● Be cautious of behavioural confounds - Motor evoked potentials (MEPs) Design Strategies

20. Sources of Noise in M/EEG ●M/EEG activity not elicited by stimuli –e.g. alpha waves → relaxed but alert ●Trial-to-trial variability in the ERP components -variations in neural and cognitive activity → trial by trial consistency ●Artefactual bioelectric activity -eye blinks, eye movement, cardiac and muscular activity, skin potentials → keep electrode impedances low ●Environmental electrical activity -power lines, SQUID jumps, noisy, broken or saturated sensors → shielding

21. Signal-to-Noise Ratio Size of the noise in average = (1/√N) ×R Number of trials: –Large component: 30– 60 per condition –Medium component: 150– 200 per condition –Small component: 400– 800 per condition –Double with children or psychiatric patients

22. Limitations Ambiguous relation between observed ERP and latent components Signal distorted en route to scalp –arguably worse in EEG than MEG (head as “spherical conductor”) MEG: restrictions with magnetic implants Poor localization (cf. “inverse problem”)

23. Why? How? –Converging evidence, generative models – fMRI + EEG, fMRI + MEG Drawbacks –Signal interference –Complex experimental design Combining Techniques

24. Outline Experimental Design fMRI M/EEG Analysis – Oscillatory activity – EP Design Inferences Limitations Combined Measures Preprocessing in SPM12 Data Conversion Montage Mapping Epoching Downsampling Filtering Artefact Removal Referencing

25. PREPROCESSING IN SPM12 Goal: get from raw data to averaged ERP (EEG) or ERF (MEG) using SPM12

26. Conversion of data Convert data from its native machine-dependent format to MATLAB based SPM format *.mat (data) *.dat (other info) *.bdf *.bin *.eeg

27. Define settings: Read data as continuous or as trials (is raw data already divided into trials?) Select channels Define file name ‘just read’ option is a convenient way to look at all the data quickly Data Conversion

28. 128 channels selected Unusually flat because data contain very low frequencies and baseline shifts Viewing all channels only with a low gain *.mat (data) *.dat (other info) Data Conversion - Example

29. Sampling frequency is very high at acquisition (e.g. 2048 Hz) Downsampling is required for efficient data storage Sampling rate > 2 x highest frequency in the signal of interest = The Nyquist frequency Downsampling

31. Aliasing Sampling below Nyquist frequency will introduce artefacts known as aliases.

32. Downsampling reduces the file size and speeds up the subsequent processing steps At least 2x low pass filter e.g. 1000 to 200 Hz. Downsampling: SPM 12 Interface

33. Montage - representation of EEG channels Referential montage - have a reference electrode for each channel Identify vEOG and hEOG channels, remove several channels that don’t carry EEG data. Specify reference for remaining channels: Single electrode reference: free from neural activity of interest e.g. Cz Average reference: Output of all amplifiers are summed and averaged and the averaged signal is used as a common reference for each channel, like a virtual electrode and less biased Montaging & Referencing

34. RE-referencing

35. Montage & Referencing : SPM 12 Interface

36. Review channel mapping Montage & Referencing : SPM 12 Interface

37. Cut out chunks of continuous data (= single trials, referenced to stim onset) EEG1 EEG2 EEG3 Event 1 Event 2 Epoching

38. Specify time e.g. 100 ms prestimulus - 600 ms poststimulus = single epoch/trial Baseline-correction: automatic; mean of the pre-stimulus time is subtracted from the whole trial Padding: adds time points before and after each trial to avoid ‘edge effects’ when filtering Epoching

39. Epoching: SPM 12 Interface

40. M/EEG data consist of signal and noise Noise of different frequency; filter it out Any filter distorts at least some part of the signal but reduces file size Focus on signal of interest - boost signal to noise ratio SPM12: Butterworth filter High-, low-, band-pass or bandstop filter Filtering

41. High-pass – filters out low-frequency noise, removes the DC offset and slow drifts in the data e.g. sweat and non-neural physiological activity Low-pass – remove high-frequency noise. Similar to smoothing e.g. muscle activity, neck Notch (band-stop) – remove artefacts limited in frequency, most commonly electrical line noise and its harmonics. Usually around 50/60Hz. Band-pass – focus on the frequency of interest and remove the rest. More suitable for relatively narrow frequency ranges. Types of Filters

42. Examples of Filters

43. Bandpass Filter

44. Filtering: SPM 12 Interface

45. Artefacts

46. Removal Visual inspection - reject trials Automatic SPM functions: Thresholding (e.g. 200 μV) 1 st – bad channels, 2 nd – bad trials No change to data, just tagged Robust averaging: estimates weights (0-1) indicating how artefactual a trial is EASY Removing Artefacts

47. Robust Averaging

48. Use your EoG! Regress out of your signal Use Independent Component Analysis (ICA) Eyeblinks are very stereotyped and large Usually 1 st component HARDER Removing Artefacts

50. Special thanks to our experts Bernadette and Vladimir Litvak

51. References Ashburner, J. et al. (2010). SPM8 Manual. http://www.fil.ion.ucl.ac.uk/spm/http://www.fil.ion.ucl.ac.uk/spm/ Hansen, C.P., Kringelbach M.L., Salmelin, R. (2010) MEG: An Introduction to Methods. Oxford University Press, Hermann, C. et al. (2004). Cognitive functions of gammaband activity: memory match and utilization. Trends in Cognitive Science, 8(8), 347-355. Herrmann, C. S., Grigutsch, M., & Busch, N. A. (2005). EEG oscillations and wavelet analysis. In T. C. Handy (Ed.), Event-related potentials: A methods handbook (pp. 229-259). Cambridge, MA: MIT Press. Luck, S. J. (2005). Ten simple rules for designing ERP experiments. In T. C. Handy (Ed.), Event- related potentials: a methods handbook. Cambridge, MA: MIT Press. Luck, S. J. (2010). Powerpoint Slides from ERP Boot Camp Lectures. http://erpinfo.org/Members/ldtien/bootcamp-lecture-pptx http://erpinfo.org/Members/ldtien/bootcamp-lecture-pptx Otten, L. (2012, November 21). EEG/MEG Acquisition, Analysis and Interpretation, MSc Cognitive Neuroscience, UCL Otten, L. J. & Rugg, M. D. (2005). Interpreting event-related brain potentials. In T. C. Handy (Ed.), Event-related potentials: a methods handbook. Cambridge, MA: MIT Press.. Sauseng, P., & Klimesch, W. (2008). What does phase information of oscillatory brain activity tell us about cognitive processes? [Review]. Neuroscience and Biobehavioral Reviews, 32(5), 1001- 1013. doi: 10.1016/j.neubiorev.2008.03.014 http://sccn.ucsd.edu/~jung/artifact.html MfD slides from previous years