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

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

3. Brain Electrical Source Analysis EEG data can now be coregistered with high- resolution MRI image Anatomical MRI 3D volume is rendered and electrode locations are superimposed

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

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

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 Magnetic Field Electric Current 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. MEG/EEG 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. MEG/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. 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

11. 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

12. 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

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

14. 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

15. Beamformer To apply Beamformer to EEG or MEG data we first select the band and time window of interest – in this case theta between about 175 and 375 ms

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

17. Your Research Proposal Project A research proposal attempts to persuade the reader that: –The underlying question is highly important –The proposed methodology and experimental design is the best approach –That you have the knowledge and know-how to do the proposed research L R

18. Your Research Proposal Project A research proposal is therefore similar to many other situations in which you will try to persuade someone of something –The skill is portable L

19. Your Research Proposal Project As in other situations, your reader should be assumed to be unconvinced and thus unwilling to spend much time and energy entertaining your argument! You must make your argument easy and fast The key to that is organization L

20. Research Proposals Should be “Theory Driven” Most proposals are organized around a specific theory What is the difference between a theory and a question? L

21. The Parts of a Research Proposal Background Statement of the theory Prediction(s) that follow from the theory Experimental Method and Design Timeline Budget References L

22. The Parts of a Research Proposal Background Statement of the theory Prediction(s) that follow from the theory Experimental Method and Design Timeline Budget References L These aren’t necessary for your project