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Electroencephalogram (EEG) and Event Related Potentials (ERP)

Electroencephalogram (EEG) and Event Related Potentials (ERP) . Lucy J. Troup 28 th January 2008 CSU Symposium on Imaging. Electroencephalography (EEG). What is measured? electrical changes in groups of neurons How is it measured? Difference between two electrodes

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Electroencephalogram (EEG) and Event Related Potentials (ERP)

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  1. Electroencephalogram (EEG) and Event Related Potentials (ERP) Lucy J. Troup 28th 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

  3. What are Event-Related Potentials? • 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

  4. 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

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

  6. Where do potentials come from? • Not action potentials… • ExcitatoryPostSynapticPotential’s • InhibitoryPostSynapticPotential’s • Most likely source

  7. How do we analyze ERP waves? • 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

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

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

  10. 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

  11. Some potential problems • 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)

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

  13. Electrical Geodesics Inc. 128 channel high density EEG

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

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

  16. 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?

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

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

  19. Visual Sensory GatingTroup, 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?

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

  21. Channel 75 (Oz)

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

  23. ISI500ms P100 P200 P300 Flash ISI 1200 ms P100 P300 P200 Flash

  24. 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

  25. 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)

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