1. All slides © S. J. Luck, except as indicated in the notes sections of individual slides Slides may be used for nonprofit educational purposes if this copyright notice is included, except as noted Permission must be obtained from the copyright holder(s) for any other use The ERP Boot Camp Averaging

2. Segmenting & Baselining Prior to averaging, we must extract segments (epochs) of the EEG surrounding the relevant events Prior to averaging, we must extract segments (epochs) of the EEG surrounding the relevant events Baseline correction is usually performed at this point Baseline correction is usually performed at this point -This is important for some types of artifact rejection procedures (e.g., absolute voltage thresholds) -For most purposes, baseline correction can be performed at any time

3. Reason 1: DC Offset 115 µV

4. Reason 2: Baseline Drift

5. How to Correct Baseline Goal: Subtract estimate of DC offset from the waveform Mean prestimulus voltage is usually a reasonable estimate Subtract this value from each point in the waveform Note: Anything that messes up the baseline (e.g., noise, overlap) will be propagated to your amplitude measurements

6. Baseline Distortion Example 1 Entire waveform shifted down (negative) because of positive noise blip

7. Baseline Distortion Example 2

8. How to Correct Baseline What to use for response-locked averages? What to use for response-locked averages? -A period that is equivalent across conditions -Often, only the prestimulus period is guaranteed to be equivalent Simple option- Simple option- -Average using a long pre-response interval and use a time range that is prior to the stimulus for every response Complex option- Complex option- -Use the prestimulus period for each individual trial

9. Averaging and S/N Ratio S/N ratio = (signal size) ÷ (noise size) S/N ratio = (signal size) ÷ (noise size) -0.5 µV effect, 10 µV EEG noise -> 0.5:10 = 0.05:1 -Acceptable S/N ratio depends on number of subjects Averaging increases S/N according to sqrt(N) Averaging increases S/N according to sqrt(N) -Doubling N multiplies S/N by a factor of 1.41 -Quadrupling N doubles S/N (because sqrt(4) = 2) -If S/N is.05:1 on a single trial, 1024 trials gives us a S/N ratio of 1.6:1 Because sqrt(1024) = 32 and.05 x 32 = 1.6 Because sqrt(1024) = 32 and.05 x 32 = 1.6 -Ouch!!!

10. Individual TrialsAveraged Data Look at prestimulus baseline to see noise level

11. Individual Differences

12. Good reproducibility across sessions (assuming adequate # of trials)

13. Individual Differences Grand average of any 10 subjects usually looks much like the grand average of any other 10 subjects

14. Individual Differences www.beautycheck.de

15. Assumptions of Averaging Assumption 1: All sources of voltage are random with respect to time-locking event except the ERP Assumption 1: All sources of voltage are random with respect to time-locking event except the ERP -This should be true for a well-designed experiment with no time- locked artifacts Assumption 2: The amplitude of the ERP signal is the same on each trial Assumption 2: The amplitude of the ERP signal is the same on each trial -Violations of this don’t matter very much -We don’t usually care if a component varies in amplitude from trial to trial -However, two components in the average might never occur together on a single trial -Techniques such as PCA & ICA can take advantage of less- than-perfect correlations between components

16. Assumptions of Averaging Assumption 3: The timing of the ERP signal is the same on each trial Assumption 3: The timing of the ERP signal is the same on each trial -Violations of this matter a lot -The stimulus might elicit oscillations that vary in phase or onset time from trial to trial These will disappear from the average These will disappear from the average -The timing of a component may vary from trial to trial This is called “latency jitter” This is called “latency jitter” The average will contain a “smeared out” version of the component with a reduced peak amplitude The average will contain a “smeared out” version of the component with a reduced peak amplitude The average will be equal to the convolution of the single-trial waveform with the distribution of latencies The average will be equal to the convolution of the single-trial waveform with the distribution of latencies -The “Woody Filter” technique attempts to solve this problem -Response-locked averaging can sometimes solve this problem

17. Latency Jitter Note: For monophasic waveforms, area amplitude does not change when the degree of latency jitter changes

18. Latency Jitter & Convolution P3 when RT = 400 ms P3 when RT = 500 ms Time ERP Amplitude (Assumes P3 peaks at RT)

19. Latency Jitter & Convolution Time 25% of RTs at 400 ms Probability of Reaction Time 7% of RTs at 300 ms 17% of RTs at 350 ms If P3 is time-locked to the response, then we need to see the probability distribution of RT

20. Latency Jitter & Convolution Time Probability of Reaction Time 25% of P3s peak at 400 ms 7% of P3s peak at 300 ms 17% of P3s peak at 350 ms If X% of the trials have a particular P3 latency, then the P3 at that latency contributes X% to the averaged waveform

21. Latency Jitter & Convolution Time ERP Amplitude Averaged P3 waveform across trials = Sum of scaled and shifted P3s We are replacing each point in the RT distribution (function A) with a scaled and shifted P3 waveform (function B) This is called convolving function A and function B (“A * B”)

22. Example of Latency Variability Luck & Hillyard (1990)

23. Example of Latency Variability Luck & Hillyard (1990) Parallel Search Serial Search

24. The Overlap Problem

25. Steady-State ERPs Stimuli (clicks) EEG SOA is constant, so the overlap is not temporally smeared

26. Battista Azzena et al. (1995)Galambos et al. (1981) Transient ERP

27. Time-Frequency Analysis Single-Trial EEG Waveforms Conventional Average Average Power @ 10 Hz

29. Woody Filtering

30. Johnson, Pfefferbaum, & Kopell (1985)