1. Electrophysiology

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

3. 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

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

5. 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

6. 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 =

7. 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 Frequency Time 0 (onset) +200+400 4 Hz 8 Hz 16 Hz 24 Hz 48 Hz % change From Pre-stimulus +600

8. 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 WinLose

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

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

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

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 Sometimes that isn’t very useful

16. 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

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 Sometimes that isn’t very useful - we want to know the generator source in 3D

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

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

20. 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

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

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

23. Intracranial and “single” Unit Single or multiple electrodes are inserted into the brain “chronic” implant may be left in place for long periods

24. Intracranial and “single” Unit Single electrodes may pick up action potentials from a single cell An electrode may pick up the combined activity from several nearby cells – spike-sorting attempts to isolate individual cells

25. Intracranial and “single” Unit Simultaneous recording from many electrodes allows recording of multiple cells

26. Intracranial and “single” Unit Output of unit recordings is often depicted as a “spike train” and measured in spikes/second Stimulus on Spikes

27. Intracranial and “single” Unit Output of unit recordings is often depicted as a “spike train” and measured in spikes/second Spike rate is almost never zero, even without sensory input – in visual cortex this gives rise to “cortical grey” Stimulus on Spikes

28. Intracranial and “single” Unit By carefully associating changes in spike rate with sensory stimuli or cognitive task, one can map the functional circuitry of one or more brain regions What are the advantages and limitations of this approach?