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Electroencephalography and the Event-Related Potential

Electroencephalography and the Event-Related Potential

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Electroencephalography and the Event-Related Potential

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  1. Electroencephalography and the Event-Related Potential Voltage Time • Place an electrode on the scalp and another one somewhere else on the body • Amplify the signal to record the voltage difference across these electrodes • Keep a running measurement of how that voltage changes over time • This is the human EEG

  2. Electroencephalography • pyramidal cells span layers of cortex and have parallel cell bodies • their combined extracellular field is small but measurable at the scalp!

  3. 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) Duracell

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

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

  6. 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 Electric Current Magnetic Field SQuID Amplifier

  7. 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

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

  9. 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

  10. EEG/MEG • EEG is characterized by various patterns of oscillations • These oscillations superpose in the raw data 4 Hz 4 Hz + 8 Hz + 15 Hz + 21 Hz = 8 Hz 15 Hz 21 Hz

  11. How can we visualize these oscillations? • The amount of energy at any frequency is expressed as % power change relative to pre-stimulus baseline • Power can change over time 48 Hz % change From Pre-stimulus 24 Hz 16 Hz Frequency 8 Hz 4 Hz +200 +400 +600 0 (onset) Time

  12. Where in the brain are these oscillations coming from? • We can select and collapse any time/frequency window and plot relative power across all sensors Win Lose

  13. Where in the brain are these oscillations coming from? • Can we do better than 2D plots on a flattened head? • As in ERP analysis we (often) want to know what cortical structures might have generated the signal of interest • One approach to finding those signal sources is Beamformer

  14. Beamforming • Beamforming is a signal processing technique used in a variety of applications: • Sonar • Radar • Radio telescopes • Cellular transmision

  15. Beamforming in EEG/MEG • It then adjusts the signal recorded at each sensor to tune the sensor array to each voxel in turn Q = % signal change over baseline

  16. Beamformer • Applying the Beamformer approach yields EEG or MEG data with fMRI-like imaging R L

  17. 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.

  18. 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

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

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

  21. 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

  22. 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

  23. Brain Electrical Source Analysis • Given this pattern on the scalp, can you guess where the current generator was?

  24. Brain Electrical Source Analysis • Given this pattern on the scalp, can you guess where the current generator was? Duracell

  25. Brain Electrical Source Analysis • Source Analysis models neural activity as one or more equivalent current dipoles inside a head-shaped volume with some set of electrical characteristics

  26. Brain Electrical Source Analysis Project “Forward Solution” This is most likely location of dipole Compare to actual data

  27. Brain Electrical Source Analysis • EEG data can now be coregistered with high-resolution MRI image