Ch 6: Practicalities of EEG Measurement and Experiment Design

This chapter goes over some more practical advice when designing an experiment that will use EEG headsets to collect data

6.1 Designing Experiments: Discuss, Pilot, Discuss, Pilot

It is much more important to have a well designed experiment over having perfectly formatted/low noise data. It is best to discuss experiment ideas with colleagues and do a pilot test run with 1-2 subjects, doing your intended complete set of analysis on the collected pilot data. By doing this, you can find issues with experiment design early, and correct them before you have spent the effort of going through many subjects and trials.

6.2 Event Markers

Event markets are square wave pulses that are sent to the EEG amplifiers on separate channel to denote events. properties of the pulse  (such as the amplitude) is used to denote the ID of the event. Event markers are critical to experiments as they time-lock specific event IDs to the EEG output data. It is trivial to ignore / remove excess event markers, but not trivial to "add" them back in after an experiment, so its better to err on having too many. However, many systems do not allow multiple markers to be made simultaneously, so be wary of this during the experiment design phase. Be wary of marker length (as they use square waves).

6.3 Intra- and Intertrial Timing

Due to analysis artifacts and also simply to allow the brain to return to its previous state, it is important that there is enough time between trial events (author recommends several hundred ms). 

The timing from one trial to the next trial (not events, called intertrial interval), consider the time needed for baseline normalization, including the time needed due to analysis artifacting. for ERPs, the baseline period ends at the t=0 event, but with the perdiodperiod of time that comes with time-frequency decomposition, you may need 200-500 ms before the event time for normalization, since temporal filtering can cause activity to leak into the time before the actual t-0 event. Generally, the author recommends an intertrial period of 1 second.

These intertrial intervals can be constant, or semi-random, with each having pros and cons. With constant intertrial intervals, the subject may start trying to "guess" the next event and mentally prepare themselves, which can impact the EEG data. While randomized intertrial periods can avoid this, users may try guessing anyways, and be 'surprised' which can again impact the data.

6.4 How Many Trials You Will Need

This depends on the signal to noise characteristics of the data collected, the size of the effect, and the type of analysis. The author recommends a minimum of 50 trials in general but it depends on various factors.

6.5 How Many Electrodes You Will Need

The number of EEG / MEG electrodes again depends on various factors, like the type of analysis. You will need more (over 100) particularly if you intend to do brain localization studies. In general, the author suggests having at least 64 electrodes if possible. There are challenges with incrementing electrode count. compute time increases with more data (but generally this isn't a massive concern). The big issues is that prep time increases too, especially with gel electrodes as each electrode needs to be prepped, thus a study with 256 electrodes vs 64 will be painfully slower to prep in between subjects for, so as a rule of practicality, go with what is needed for the experiment.

6.6 Which Sampling Rate to Use When Recording Data

 

6.7 Other Optional Equipment to Consider