1. Electroencephalography The field generated by a patch of cortex can be modeled as a single equivalent dipolar current source with some orientation (assumed to be perpendicular to cortical surface)

2. Electroencephalography Electrical potential is usually measured at many sites on the head surface More is sometimes better

3. Magnetoencephalography For any electric current, there is an associated magnetic field Magnetic Field Electric Current

4. Magnetoencephalography For any electric current, there is an associated magnetic field magnetic sensors called “SQuID”s can measure very small fields associated with current flowing through extracellular space Magnetic Field Electric Current SQuID Amplifier

5. Magnetoencephalography MEG systems use many sensors to accomplish source analysis MEG and EEG are complementary because they are sensitive to orthogonal current flows MEG is very expensive

6. EEG/MEG EEG changes with various states and in response to stimuli

7. Two ways to approach EEG data The Event-Related Potential – Phase-locked or “evoked” – High inter-trial phase consistency – Retains polarity information at scalp – Rejects time-locked but not phase-locked changes Time/Spectral Analysis – Includes Non-phase- locked or “induced” plus “evoked” signal – Ignores inter-trial phase consistency (measured differently) – Rejects polarity at scalp

8. Time-Frequency Analysis of EEG/MEG Any complex waveform can be decomposed into component frequencies – E.g. White light decomposes into the visible spectrum Musical chords decompose into individual notes

9. Time-Frequency Analysis of EEG/MEG EEG is characterized by various patterns of oscillations These oscillations superpose in the raw data 4 Hz 8 Hz 15 Hz 21 Hz 4 Hz + 8 Hz + 15 Hz + 21 Hz =

10. Time-Frequency Analysis of EEG/MEG The amount of energy at any frequency is expressed as % power change relative to pre-stimulus baseline Power can change over time Frequency Time 0 (onset) +200+400 4 Hz 8 Hz 16 Hz 24 Hz 48 Hz % change From Pre-stimulus +600

11. Time-Frequency Analysis of EEG/MEG We can select and collapse any time/frequency window and plot relative power across all sensors WinLose

12. The Event-Related Potential (ERP) Embedded in the EEG signal is the small electrical response due to specific events such as stimulus or task onsets, motor actions, etc.

13. The Event-Related Potential (ERP) Embedded in the EEG signal is the small electrical response due to specific events such as stimulus or task onsets, motor actions, etc. Averaging all such events together isolates this event-related potential

14. The Event-Related Potential (ERP) We have an ERP waveform for every electrode

15. The Event-Related Potential (ERP) We have an ERP waveform for every electrode

16. The Event-Related Potential (ERP) We have an ERP waveform for every electrode Sometimes that isn’t very useful

17. The Event-Related Potential (ERP) We have an ERP waveform for every electrode Sometimes that isn’t very useful Sometimes we want to know the overall pattern of potentials across the head surface – isopotential map

18. The Event-Related Potential (ERP) We have an ERP waveform for every electrode Sometimes that isn’t very useful Sometimes we want to know the overall pattern of potentials across the head surface – isopotential map Sometimes that isn’t very useful - we want to know the generator source in 3D

19. Brain Electrical Source Analysis Given this pattern on the scalp, can you guess where the current generator was? Source Imaging in EEG/MEG attempts to model the intracranial space and “back out” the configuration of electrical generators that gave rise to a particular pattern of EEG on the scalp

20. Brain Electrical Source Analysis EEG data can be coregistered with high- resolution MRI image Source Imaging Result Structural MRI with EEG electrodes coregistered

21. CCBN Dense-Array EEG Netstation – records EEG and event triggers Digamize –records electrode locations BESA -post-processing -ERP averaging -voltage maps -source imaging MatLab Fieldtrip BrainVoyager SPSS -EEG spectral analysis - MRI coregistration MANUSCRIPT Stimuli Event Triggers Raw EEG.raw.sfp Data Files

22. Basic Elements of ERP Design EEG, therefore ERP, doesn’t provide interpretable absolute voltage The voltage is always relative to something else That something else may be: – The pre-stimulus baseline – A control condition

23. Basic Elements of ERP Design Thus a fundamental aspect of ERP design is not to plan to report voltages but rather a difference in voltage between two or more conditions What are some examples of conditions you might want to compare?

24. First Demo Contralaterality in Visual System – Hemifields project to contralateral cortex – Unrelated to which eye is stimulated! Occular Albinism – Eyes project contralaterally, irrespective of hemifield

25. Basic Elements of ERP Design The theory is that human visual cortex is organized contralaterally The prediction is that right hemifield stimuli will drive electrical activity in the left visual cortex and left hemifield stimuli will drive electrical activity in right visual cortex How do we test that prediction?

26. Basic Elements of ERP Design Experimental approach: Choices: – 1. you could compare ipsi to contra ERP waveforms with a trial E.g. O3 with O4 What’s the problem? O3 O4

27. Basic Elements of ERP Design Experimental approach: Choices: – 1. you could compare ipsi to contra ERP waveforms with a trial E.g. O3 with O4 What’s the problem? You would be comparing ERPs from different parts of the brain! How could you improve on that design?

28. Basic Elements of ERP Design Experimental approach: Choices: – 2. you could compare electrodes ipsi to stimulus on one side with electrodes contra to stimulus on the other side Notice those are the same electrode! Measure contralateral ERP magnitude O3

29. Basic Elements of ERP Design Experimental approach: Choices: – 2. you could compare electrodes ipsi to stimulus on one side with electrodes contra to stimulus on the other side Notice those are the same electrode! Measure ipsilateral ERP magnitude O3

30. Hands on agenda today: – Orientation to the EEG lab – Build your dipole models

31. Principals of Digital Signal Recording

32. How do we represent a continuously variable signal digitally? Sampling – Sampling rate – number of measurements per unit time – Sampling depth or quantization – number of gradations by which the measurement can be recorded

33. How do we represent a continuously variable signal digitally? Sampling – What would be the advantage to higher sampling rates?

34. How do we represent a continuously variable signal digitally? Sampling – What would be the advantage to higher sampling rates? Nyquist limit

35. How do we represent a continuously variable signal digitally? Sampling – What would be the advantage to higher sampling rates? Nyquist limit Aliasing – What would be the disadvantage? Data size Compute time

36. How do we represent a continuously variable signal digitally? Sampling – What would be the advantage to greater sampling depth? Finer resolution – What would be the disadvantage? Data size Possibly compute time

37. How do we represent a continuously variable signal digitally? Sampling – A note about data size and compute time: New data size = increase in quantization x number of samples x number of electrodes!

38. Filters used in EEG

39. What is a filter?

40. Filters let some “stuff” through and keep other “stuff” from getting through – What do we want to let through? – What do we want to filter out?

41. What is a filter? The goal of filtering is to improve the signal to noise ratio – Can the filter add signal?

42. Different Kinds of Filters Low-Pass (High-Cut-Off) High-Pass (Low-Cut-Off) Band-Pass Notch Each of these will have a certain “slope”

43. How do Filters Work? Notionally: – Transform to frequency domain – Mask some parts of the spectrum – Transform back to time domain

44. Are There Any Drawbacks? Yes Filters necessarily distort data – Amplitude distortion – Latency distortion Forward/backward/zero-phase

45. Recommendations Should you filter? – Yes, when necessary to reveal a real signal Problem: how do you know it’s “real” – No, always look at the unfiltered data first What filters should you use? – Depends on your situation (e.g. what EEG band are you interested in? Do you have 60Hz line noise?) – General rule: less aggressive filters are less distorting