The ERP Boot Camp Setting Up and Running an ERP Lab

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

Recording Chamber

• • • • • Do you really need one?

Probably not if: You’re looking at slow components and can low-pass filter with a 50% cutoff at 30 Hz And you’re not near any major source of electrical noise • Elevators, centrifuges, power transformers, ventilation fans You don’t care about gamma oscillations They’re good for keeping subjects focused They tend to get warm, so it may actually be better not to have one if skin potentials are a major source of noise You can build one from 2x4’s and copper screen

Courtesy of Lynne Reder

Seating

• • • • Key points: - Comfortable to avoid muscle noise Don’t want subjects to fall asleep Don’t want electrodes to rest on anything Recliners were once common - Not good if you have electrodes over the back of the head I recommend high-quality office chair - Glides rather than wheels - Mark the floor I haven’t had much luck with chin rests

Response Devices

• • Need to be held in a comfortable position Don’t want subject holding arms up - Standard computer keyboards are bad Game controllers work well - Mass-produced -> reliable • Constant and variable timing errors are possible - RT is so variable that a bit of timing variability will usually have virtually no impact (unless you are looking at response-locked averages) - EMG for best response timing - Can measure timing errors by putting a mic next to device and recording “click” along with event code

Hints for Running Subjects

• • • • • ~60 minutes of “run time” per session - More for interesting experiments - Whole session is about 3 hours Runs of 4-6 minutes with 2-3 20-second breaks - Less makes it inefficient to deal with electrodes, etc.

- More leads to fatigue - Some labs do all-day sessions with lot of breaks Dilution Rule: Don’t dilute good data with bad data Adding noisy trials doesn’t improve the S/N ratio Watch the EEG throughout the session - Look for artifacts, bad connections, etc.

Watch the subject with a video camera

Hints for Running Subjects

• • • Happiness Rule: A happy subject is a good subject - Compliance with task - Compliance with artifact control instructions - Less noise Talking to subjects - Treat subject like a person, not like a piece of meat - Chat while putting on electrodes (or video) • Tell them exactly what will happen -- this reduces stress - Chat during breaks Note: Some subjects don’t want to talk -- that’s OK Keeping subjects happy - Food and drink (is caffeine a confound?) - Eye drops (single-use) - Music

Looking at the Data

• • Do a fairly complete analysis of the first subject’s data before running anyone else - All the main comparisons among ERP waveforms - Accuracy (and RT if recorded) for each main condition - There may be a serious problem with event codes, etc. “Nothing focuses the mind quite as much as real data” Take a look at the individual subjects and the grand averages every 3-4 subjects - Grand averages will give you more power to see something funky in the data But don’t get too freaked out if the results look a little funny or aren’t conforming to your predications Be especially concerned about “impossible” results (e.g., effects that consistently begin before time zero) - Look at calibration data for each subject if you can

Ethical Issues

• • Everything that applies to behavioral experiments plus… - Risk of disease transmission • • High impedance helps Need thoughtful disinfection (even for high impedance) - Risk of electrical shock • Optical isolation and/or battery power - Headache from electrode cap - Gel in hair - Long duration of experiment - Claustrophobia - Concerns about privacy of EEG data Providing clear information in advance is the best way to prevent problems

Stimulus Presentation

• • Testing the timing of event codes - Digitize at ~1000 Hz (higher for auditory) - Present stimuli along with event codes - For auditory stimuli, connect auditory output (or a microphone) to the digitization system • You might want to use a square-wave tone or a 50-Hz sine wave - For visual stimuli, point some kind of light pen to the video monitor and connect to digitization system - See when the stimuli are actually presented relative to the event codes Auditory artifacts - Speaker in headphones may induce a current - Post-auricular muscle twitch

CRT Basics

When you draw something, nothing happens until the frame buffer is updated AND the raster beam reaches the right part of the monitor LCDs operate similarly, but often there is an additional delay of several milliseconds before the stimuli actually appear

Stimulus Timing Jitter

• • • • What does a constant delay between event code and stimulus do to the averaged ERP?

- Time shift - Can be fixed with a filter What does a variable delay between event code and stimulus do to the averaged ERP?

- Distribution of delays is convolved with jitter free “real” waveform - Modest low-pass filter For most cognitive paradigms, effects are minimal - But not always You need to understand exactly what the jitter is doing, and this usually requires measuring it

Stimulus Timing Jitter

1 0,8 0,6 0,4 0,2 0 -100 -0,2 0 -0,4 -0,6 1 0,8 0,6 0,4 0,2 0 Distribution of Stimulus Delays Example: Stimulus appears 0, 10, or 20 ms after event code with even distribution 0 ms 10 ms 20 ms Waveform appears at 0, 10, or 20 ms with equal likelihood Averaging these together is equal to replacing each point in the distribution of delays with a scaled and shifted version of the ERP waveform 100 200 300 400 500 600 0-ms delay 10-ms delay 20-ms delay Avg Across Delays 700 The result is slightly low-pass filtered and shifted to the right in time What is frequency response of filter produced by jitter?

Writing an ERP Paper

• • • • Rule #1: Write with a specific audience in mind - But keep in mind that the reviewers are the first and most important audience Rule #2: Intro must end with a set of competing hypotheses about a general issue and then a set of corresponding predictions - May want to explicitly address reason for using ERPs Rule #3: Results should be organized to lead reader to a conclusion (logical flow of ideas) - Descriptive statistics first, then inferential statistics Rule #4: Discussion should recap major results and conclusions that can be drawn - Often followed by possible objections that can be discarded (and perhaps some that cannot)

Ex a m ple Decision Let t e r

Dear Dr. XXXXX: Dear Dr. XXXXX: I gave your manuscript a quick reading so that I could choose appropriate reviewers. After this quick reading, it was clear to me that the manuscript cannot be accepted in its present form. Thus, to save everyone time and effort, I am rejecting this version of the manuscript, but I will allow you to submit a revised manuscript if you are certain you can overcome the problems with the current version.

Ex a m ple Decision Let t e r

In most ERP papers that make a significant contribution to broad questio ns in cognitive neuroscience, the Introduction ends with a set of specific predictions about the pattern of ERP results that will be obtained. That is, competing hypotheses about broad issues in cognitive neuroscience are raised in the first part of the Introduction, and then specific predictions about the ERP results are given that will distinguish between the competing hypotheses. This is both an indicator of the likely importance of the study I gave your manuscript a quick reading so that I could choose appropriate expected and how these results are related to the broad issues addressed by reviewers. After this quick reading, it was clear to me that the manuscript submit a revision, you should rewrite the Introduction in this manner. cannot be accepted in its present form. Thus, to save everyone time and effort, I am rejecting this version of the manuscript, but I will allow you to knowledge about the broad issues that were initially raised in the submit a revised manuscript if you are certain you can overcome the read the details of your methods and results, but instead wishes to quickly problems with the current version. you should rewrite the beginning portion of the Discussion in this manner. [Some details about this particular study] In most ERP papers that make a significant contribution to broad questio ns in cognitive neuroscience, the Introduction ends with a set of specific predictions about the pattern of ERP results that will be obtained. That is, manuscript draws solid and important conclusions about the cognitive and/or competing hypotheses about broad issues in cognitive neuroscience are raised in the first part of the Introduction, and then specific predictions about the ERP results are given that will distinguish between the competing hypotheses. This is both an indicator of the likely importance of the study and an important aid to readers, who can much better understand the methods and results if they know what general patterns of results can be expected and how these results are related to the broad issues addressed by the study. The present manuscript does not contain such predictions. If you submit a revision, you should rewrite the Introduction in this manner. It is also useful for the Discussion section to begin with a brief recap of the major findings and the conclusions that can be drawn from them. Again, this helps make it clear that the study represents a significant advance in knowledge about the broad issues that were initially raised in the Introduction. It is also helpful for the casual reader who does not want to read the details of your methods and results, but instead wishes to quickly see your “bottom line.” It was quite difficult for me to determine what conclusions could be drawn from this manuscript. If you submit a revision, you should rewrite the beginning portion of the Discussion in this manner. [Some details about this particular study] ACTION: I cannot accept the manuscript for publication in its current form. However, I invite you to submit a revision if you feel that you can fully address the concerns raised in this letter. Please note, however, that a substantial amount of time and effort is required to review a manuscript, so you should not submit a revision unless you are certain that the revised manuscript draws solid and important conclusions about the cognitive and/or neural processes involved in [topic of study]. That is, the paper should be of interest to [topic of study] researchers who are not interested in ERPs per se. If you believe that your revised manuscript makes this sort of strong and important contribution, I will be very happy to send the revision out for review.

Method Section Should Include…

• • • • • • Number of trials per condition (explicitly) Recording sites, electrode type, amplifier gain, filters, sampling rate and resolution, impedance, reference, and offline re-referencing - Include impulse response function details for offline filters Artifact rejection procedures - Include observed mean and range of % rejected trials - Include # of subjects rejected and standard for rejection - Rejection of trials with behavioral errors ERP measurement procedures - Measurement windows and perhaps justification Greenhouse-Geisser epsilon adjustment See Picton et al. (2000)