2021).

The Number of Channels and Channel Placement Typically, mEEG systems

have the case if one uses a wired mEEG solution . Perhaps of more importance is the location of the electrodes in mEEG systems. Typically, the key debate is whether or not the system attempts to get through hair. In other words, some mEEG systems only attempt to position electrodes directly on the skin; the Muse for instance only has electrodes on the forehead and directly above the ears. Other mEEG systems like the CGX Quick 20 attempt to make contact with the scalp through the hair. The systems that attempt full head/through the hair coverage typically have specially designed electrodes to penetrate through the hair. The position of electrodes is important as some EEG signals of interest might not be visible at the electrodes that a given mEEG has. For instance, in the work in our laboratory we have been able to measure the N200 and P300 components of the human event-related brain potential (ERP) but have had little success measuring the P100 and N100 ERP components which are typically measured over the occipital cortex. It is worth noting here that EEG and ERP signatures tend to look different when measured at non-traditional electrode sites. In our “ Choosing Muse” paper (Krigolson et al.,

2017), we did measure a P300 ERP component at electrodes TP9 and TP10, but the

research is whether to use a wet or dry system. Traditional systems rely on conduc­tive gel or a saline solution to reduce the impedance between an electrode and the scalp, thus yielding better overall signal quality. For example, the Brain Products X. on uses sponge electrodes that are soaked in saline prior to data collection. However, to promote ease of use and mobility, some mEEG systems utilize dry electrodes

wherein the electrode itself is made of a special material to promote conductivity. Typically, these systems operate with higher impedances which can have an impact on signal quality. Further, these systems typically rely on compression to maintain good contact between the electrodes and the scalp, and as such, the issue of participant comfort must be considered. It is beyond the scope of this chapter to dive into this issue further, but our recommendation when consider

2017; Sawangjai et al., 2020) or whether there are published research

papers using the potential mEEG system. For example, in our own work (Krigolson et al., 2017) we did demonstrate that the data quality of the Muse EEG system was not

, the issue of event-markers must be considered. In brief, in an ERP study, one typically wants to mark the data at the exact point in time associated with stimulus onset (visual, auditory, etc.) to be able to accurately generate ERP wave ­forms (see Luck, mark

stimulus generation computer and the amplifier is typically not present—but it could be if one uses a wired mEEG solution. As such, in the majority of mEEG studies without a wired connection, the researcher has to live with the issue of marker timing jitter. In systems such as the Muse EEG system, this could be as much as 20 ms (see Krigolson et al.,

10 for

has to be considered. In general, this is a non-issue with research mEEG systems as the expectation is that the researcher will want to access the raw EEG data for analysis purposes. However, with commercial mEEG systems, access to data is not

Software One of the challenges of working with mEEG systems is the software that will be used for experimental data collection, and potentially even to run the paradigms themselves. All research mEEG systems come with core software that typically allows for the recording of raw EEG data. Further, there are a limited number of commercially available solutions that also allow a researcher to easily run ERP paradigms (e.g., PEER: comm tems such as the Emotiv and Muse do come with research software to allow the collection of certain types of EEG data. More commonly however, the researcher needs to use MATLAB or Python and either program their own acquisition software or find existing software on the internet. Easily, the most popular choice here is the Lab Streaming Layer seri es of applications that allow the collection of data from not only mEEG systems, but also a wide range of other research devices.

people

with

increased beta power both predicts and follows the commission of motor skill errors (Kilavik et al.,

Fig. 29.8 Decreased EEG beta power predicts baseball batting performance. (From Pluta et al.,

2018)

which can be used to model movement and improve mEEG data quality (Grasso­Cladera et al.,

activity (Hammerstrom et al.,

mEEG provides a valid methodology for researchers who want to collect EEG data in the “real world”. Indeed, the myriad of research studies that utilize mEEG has grown exponentially in the past ten years. As more mEEG systems become avail­able, one can speculate that this trend will continue because at the end of the day, researchers ideally want to study cognitive phenomena where they occur as opposed to in a laboratory.