Could automatic algorithms be the solution to standardize

dardize infant ERP editing practices?

There are several limitations to the manual editing of infant ERP data where auto- matic algorithms may well improve current practice. As explained in section 5.4.1, the main limitation of manual editing practices is the variability that human sub- jective criteria introduce in trial selection process. Also, manual editing is time consuming for the researcher as it requires a trial-by-trial visual inspection. Fi- nally, the detection of EEG artifacts by visual inspection is limited by temporal characteristics of the EEG signal, which are mainly based on amplitude changes of the signal. Automatic algorithms would be a way to improve the three main disadvantages of manual editing practices. First, they ensure a reliable and repro- ducible way of detecting artifacts in the EEG data as the mathematical rules applied would be the same across participants and ERP studies. Second, it is likely that the editing time would substantially decrease. This would depend on the level of automatization of the algorithm and the need for a visual inspection after applying the algorithm to confirm the results. For example, when an automatic algorithm is applied currently to edit infant ERP data, it is common that it is followed by a manual visual inspection to validate the algorithm results (Hoehl, Wiese, & Striano, 2008; Kaduk et al., 2016). Third, automatic algorithms can apply complex mathe- matical transformations of the EEG signal that may be able to help the detection

of EEG characteristics. An example is Independent Component Analysis (ICA) al- gorithms that separate independent sources that are linearly mixed in several EEG channels (e.g., oculomotor artifacts; see Delorme et al., 2007) or wavelet analysis that uses joint time-frequency features to extract information from a signal (see Unser & Aldroubi, 1996).

Automatic algorithms based on advanced mathematical transformations are widely applied to many biomedical signals, both in clinical and research applications. In almost all cases, these algorithms have been designed for adult EEG signals. The type, amount and magnitude of the artifacts that most of the algorithms are de- signed for are very different from the artifacts present in an infant EEG signal. There have been some attempts to apply some of these techniques to infant EEG signals, such as ICA. Some infant EEG studies where ICA techniques were used to detect artifacts—predominantly, oculomotor artifacts—can be found in the lit- erature (e.g., Marshall, Young, & Meltzoff, 2011; Orekhova, Stroganova, Posikera, & Elam, 2006; Saby, Marshall, & Meltzoff, 2012). However, ICA techniques are not appropriate when non-stereotyped artifacts (e.g., head movement and electrode movements) are present in the EEG data. ICA decompositions may even be com- promised when such artifacts are contained in the data (Delorme et al., 2007). In their study, Delorme et al. (2007) highlights the importance of visually identifying and discarding periods with such artifacts from the data before running ICA . In the case of infant EEG data, abrupt moves that induce electrode artifacts in the data are very common, which may in turn make the application of ICA techniques to infant data ineffective.

ferent artifact detection methods for infant EEG data applied to the extraction of ERP signals (Fujioka, Mourad, He, & Trainor, 2011). The authors synthetized an ERP signal into a real 4-month-old infant EEG dataset and compared three artifact removal techniques: conventional trial rejection based on visual inspection, the Ar- tifact Blocking technique (AB; Mourad, Reilly, De Bruin, Hasey, & MacCrimmon, 2007) and the Independent Channel Rejection technique (ICR; C. He, Hotson, & Trainor, 2007). Both AB and ICR techniques were found to be significantly better in identifying the noise and isolating the ERP signal than conventional trial rejec- tion. A thing to note is that the study simulated an auditory ERP signal, which means that the synthetized ERP signal contained more than 500 trials. This is a common number of trials in an auditory ERP experiment. Also, the infants watched a silent movie with a puppet show during the EEG recording. This is probably a more engaging stimulus than repeated images, which it is typically the case during visual ERP stimuli. It is unknown whether the AB and ICR techniques are equally valid with EEG recordings containing more noise as it is likely to be the case of a visual ERP experiment and with participants older than 4 months.

Infant EEG data are, in general terms, significantly more complex than EEG data recorded from participants that can follow instructions. Some of the variables that contribute to the complexity in the EEG signal are likely to be due to the specific infant’s behaviour and they are likely to vary between different participants (Thierry, 2005). Conventional automatic artifact rejection methods based on simple characteristics—like the amplitude level algorithm applied in Chapter 4—might not be able to reflect this complexity. An automatic algorithm suited for infant EEG data would need to take into account this complexity and be capable of adapting to the existing signal variability. An example of an algorithm that tries to capture this

variability is described in Kulke et al. (2016b). In this study, the EEG noisy data was defined based on individual infant’s EEG data, following the median absolute deviation procedure (Hampel, 1974) . To create and validate an automatic algo- rithm that aimed to capture the infant’s EEG complexity, it would be necessary to first better understand the variables that generate the complexity in the infant EEG signal. In this sense, the developmental field would benefit from collaborating with researchers from more technical fields, like mathematicians or statisticians. They could investigate and apply more advanced methods to help understand the infant EEG signal and find a suitable automatic algorithm for noise rejection or correc- tion. Due to the magnitude of this task, currently a fully automatic noise detection algorithm seems to be a long-term solution. Semi-automatic methods that provide researchers objective measures about the level of noise of the EEG signal and help them make a more informed decision about valid trials could be a medium-term solution as a first step towards the standardization of the editing methods.