1. Electroencephalogram (EEG) and Event Related Potentials (ERP) Lucy J. Troup 28 th January 2008 CSU Symposium on Imaging

2. Electroencephalography (EEG) What is measured? –electrical changes in groups of neurons How is it measured? –Difference between two electrodes What types of changes can be measured? –Sleep-related; Certain neurological disorders How are these changes measured? –Frequency, Amplitude, Specific Wave-Types

4. ERPs –“Electrical Potentials associated with specific sensory, perceptual, cognitive, or motor events” From EEG to ERP… –Time-locked average of EEG from many trials involving same ‘event’ –Signal/Noise Ratio reduction; what is left is ‘related to the event’ EEG = 20-50  v / ERP = 1-10  v What are Event-Related Potentials?

5. Filter & Amplify Average across Trials & Individuals Collapsed to form a “Grand Average” Or mean of means Electrical activity at the onset of a stimulus recorded

6. Time-locking + Signal/Noise Ratio Reduction = ERP derived from EEG Single Trial: 100ms visual stimulus Average of 200 trials to same stimulus

7. Where do potentials come from? Not action potentials… E xcitatory P ost S ynaptic P otential’s I nhibitory P ost S ynaptic P otential’s Most likely source

8. ERP Components –“Scalp-recorded neural activity that is generated in a given neuroanatomical module when a specific computational operation is performed” –Peaks are not necessarily the same as components; “peaks are not special” –Peaks are comprised of summation of latent components that are not observable, how we analyze our ERP data will relate to the validity and accuracy of our observations How do we analyze ERP waves?

9. Classic Approaches to Analysis Parametric Statistical comparisions –Peak to Peak –Mean Amplitude –Peak Latency Covariation –PCA Source Localization –BESA

10. Well-studied ERP components Visual –C1, P1, N1, N170 Auditory –BER, N1, MMN Cognitive –N2b, N2pc, P3, N400, ERN, FRN, RP, LRP

12. ERP precautions Can’t determine Where, only When –Scalp Topography vs Source Analysis Doesn’t measure all neural activity –Closed vs Open Fields Can only use when time-locking is practical –Not applicable for all areas of psychology

13. Artifacts –Eye blinks, mm. mvt, etc. –Lights, and other electrical sources Data Analysis Techniques –Artifact detection & rejection –Filtering –Reference electrodes (i.e. linked ears) –Time-locking (stim or resp?) –Segmenting (epochs, i.e. time windows) Some potential problems

14. What information does a “component” provide for us? Example from Troup, Pitts, Draper & Catellier (2007)

15. Electrical Geodesics Inc. 128 channel high density EEG

16. Example of a raw faceExample of a Gabor face Experiment 1 N=19 Raw and Gabor Faces Presented randomly 3 blocks of 66 faces

17. ISI 500ms Fixate 250ms Face 1 250ms Face 2 250ms Response Time 1000ms Example of Same Face Pair Example of Different Face Pair Experiment 2 N=19 7 Blocks 80 pairs per block S’s respond with key press to “Same Face pairs”

18. Questions Does N170 differ in amplitude and/or latency for Gabor-filtered versus Raw face images? Does N170 differ in amplitude and/or latency for Gabor-filtered versus Raw for Scalp location? Does N170 differ in amplitude and/or latency under the manipulation of same/diff face pairs in a rapid judgment task?

19. Raw- Gabor Raw Same-Different Scalp Location Frontal Left Temporal Right Temporal Central Center Occipital Behavioral Response Same Correct Different Correct Same Incorrect Stimulus type RawGabor Amplitude and Latency

20. Same/Different Grand Average Data Sig. Diff for Correct Vs Incorrect

21. Visual Sensory Gating Troup, Yadon, Pitts & Hafer-Zdral (2007/2008) Sensory Gating –The process of “gating out’ or not responding to a subsequent stimulus after the onset of the initial stimulus in rapid presentation –Auditory Stimuli “clicks” –Visual Stimuli “Flashes” Term used interchangeably with “Habituation” Are they fundamentally distinct or same process? Do people behave to visual stimuli in same way as auditory?

22. ISI 500ms Fixate 10s Flash 1 12ms Flash 2 12ms ISI 500ms Flash 6 80ms ISI 1200ms Flash 2 12ms ISI 1200ms Flash 6 80ms Flash 1 12ms

23. Channel 75 (Oz)

24. Stimulus Overlap Two differing Inter Stimulus Interval’s (ISI) Clearly show Stimulus overlap problems

25. ISI 500 ms Flash P100 P200 P300 ISI 1200 ms Flash P100 P200 P300

26. Areas for Potential Collaboration The Stimulus overlap Problem –ADJAR techniques Principal components analysis of ERP data Raw EEG and Spectral analysis –Something I am hoping to look at in relation to Gamma activity in the future

27. Thanks… Colorado State University –Dr Bruce Draper, Dr Ross Beveridge (Computer Science) –Carly Yadon, MS – Graduate Student in Psychology (Perceptual and Brain Science) –Dan Lopez, MS – Graduate Student in Psychology (Perceptual and Brain Science) Erin Catallier/Jessa Hafer-Zdral (REU Students) –Logan Keech (Undergraduate RA) University of California, San Diego –Dr Mike Pitts (Hillyard Lab)