Preprocessing: Referencing and Artifact Detection
The EEG signal from each electrode is usually recorded in reference to a scalp site (e.g., the vertex at Cz), with separate channels being recorded from other potential reference points, such as nosetip, mastoids, or earlobes. The
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preferred reference configuration can then be derived offline. Issues related to the choice of reference configuration have been debated elsewhere (e.g., Hagemann, Naumann, & Thayer,2001), although developmental aspects of this choice have not generally been considered.
Before the EEG signal is analyzed, epochs containing artifact must be identified. This includes the detection of artifact related to eye movement (the contamination of the EEG by ocular activity) and artifact from muscle activity and gross motor movements. In terms of artifact detection, there have been a variety of approaches in the adult EEG literature including manual editing of artifact (Lawson et al.,2003), fully automated procedures (Moretti et al., 2003), or a combination of the two. The use of large numbers of electrodes increases the potential number of channels that are contaminated by artifact, and data from dense arrays may need specific techniques for pre- processing. For example, statistical methods have been proposed for dealing with the potentially larger number of channels that are contaminated by artifact in dense arrays (Junghofer, Elbert, Tucker, & Rockstroh,2000), and for the identification of electrolyte bridges between channels (Tenke & Kayser, 2001).
The electrical activity associated with eyeblinks propagates across the scalp and may therefore become part of the scalp potentials picked up by EEG electrodes, particularly at frontal sites. Ocular activity is routinely recorded during EEG acquisition by means of the electrooculogram (EOG). Various approaches exist for dealing with the transmission of EOG activity to frontal electrode sites. Epochs containing EOG activity can be excluded from further analysis, or correction algorithms may be employed in an attempt to remove EOG contamination from the EEG channels (Berg & Scherg, 1994; Grat- ton, Coles, & Donchin,1983). Developmental considerations relating to the application of correction techniques have rarely been discussed. However, in EEG data from children aged between 5 and 12 years, Somsen and van Beek (1998) concluded that for EEG analysis in children aged 5–12 years, rejection from further analysis of epochs containing eyeblinks is preferable to the use of correction algorithms. This conclusion was based on the observation that in addition to removing EOG contamination, EOG correction also appeared to remove non-artifactual EEG at low frequencies, particularly from frontal electrode sites. In adults, it has been suggested that control of eye movements may not be necessary for correlational analyses involving frontal EEG asym- metry (Hagemann & Naumann,2001), although this more liberal approach has not yet been utilized in developmental studies.
In ERP analyses, it may be necessary to use correction algorithms rather than rejection of eyeblink artifact because of the need to maximize the num- ber of trials collected. Developmental ERP protocols are frequently shorter
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than similar protocols in adults, because infants and children will generally not tolerate the same experimental durations and numbers of trials as adults. In addition, more data tend to be lost due to motor artifact in developmen- tal studies, hence the need to maximize the number of trials through blink correction rather than rejection. However, the ability to effectively regress blinks out of the EEG depends on the collection of a clean vertical EOG sig- nal, which can be difficult with older infants and toddlers since they tend to pull at any electrodes that are placed on their face.
Gross motor movement is a significant source of artifact in the EEG from young children. Detection of such artifact may be done manually, or it may be done in software via the imposition of a fixed threshold. If the EEG at a specific time point goes out of this range, this time point is excluded from further analysis. Since the absolute amplitude of the EEG declines over infancy and childhood, the magnitude of this threshold is partly dependent on the age of the participants.
EEG Analysis of Band Power
The main approach to analyzing EEG is through the decomposition of the signal into component frequency bands, through the use of spectral analysis techniques. Approximate frequency ranges for EEG bands in adults are delta (1–3 Hz), theta (4–7 Hz), alpha (8–12 Hz), beta (13–30 Hz), and gamma (30–50 Hz), with the boundaries of corresponding bands being generally lower in infants and children (Marshall, Bar-Haim, & Fox,2002). In work on social and emotional development in infants and children, most research has focused on the alpha band, since activity in this band is prominent from early infancy onwards, and its magnitude has been assumed to be inversely proportional to cortical activity. This pattern is based on the principle that high amplitude alpha reflects “idling” of sensory cortex. For this reason, alpha wave activity has been commonly used as a measure of regional brain activity, with decreased alpha power corresponding to increased neuronal activity (Davidson et al.,2000). Alpha rhythm is most pronounced at occip- ital and parietal recording sites, but can be recorded in a weaker form at other locations on the scalp, and is stronger when the eyes are closed and is desynchronized (blocked) when the eyes are opened.
There is some debate over the precise boundaries of the alpha band and how it changes over infancy and childhood. Some studies examining the developmental course of the EEG have extrapolated the commonly accepted adult frequency bands back to infancy and childhood in order to calcu- late the developmental trajectories of power in these conventional bands
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(e.g., John et al.,1980; Matousek & Petersen,1973). However, given clear developmental changes in alpha peak frequency, it is likely that alternative frequency bands are needed for developmental work (Marshall et al.,2002). To aid in the selection of such frequency bands, there is a rich history of exam- ining the development of the EEG of infants and young children (for reviews see Bell,1998; Schmidt & Fox,1998). In general, early studies such as those of Smith (1938), Lindsley (1938), and Henry (1944) focused on the develop- ment of the occipital alpha rhythm over infancy and childhood. Using visual quantification techniques, these authors noted the emergence of a 3–5 Hz occipital rhythm at around 3 months of age. The frequency range in which this oscillation occurred was seen to increase to around 6–7 Hz by the end of the first year. The oscillations in early infancy were labeled as “alpha” by these original authors because of a visual resemblance to the classical adult alpha rhythm. Lindsley (1938) observed that the infant alpha rhythm was blocked by visual stimulation even in infants a few months of age, suggesting a func- tional as well as a visual similarity between infant alpha at posterior sites and the adult alpha rhythm. Drawing in part on this early work, we showed that the 6–9 Hz band appears to be a suitable alpha band for use in developmental EEG research from the end of the first postnatal year into early childhood (Marshall et al.,2002). The functional meaning and interpretation of this band depends on age and the scalp region of interest, but the 6–9 Hz band in infants and young children appears to correspond to sensory rhythms in the alpha frequency range in adults. Orekhova and colleagues used a fre- quency band of 6.4–10.0 Hz as the “alpha range” in a sample of infants aged 7–12 months. They conceptualized this band as encompassing both poste- rior alpha rhythms (the classical alpha rhythm) and sensorimotor rhythms such as the “mu” rhythm, which is an alpha-range rhythm prominent at central electrode sites in infants (Orekhova, Stroganova, & Posikera,2001). In adults, the classical mu rhythm occurs in the 7–13 Hz range, is maximal over central sensorimotor areas and is attenuated by voluntary movement and somatosensory stimulation (Gastaut, Dongier, & Courtois,1954). It is also minimally affected by changes in visual stimulation and is considered to be a somatosensory alpha rhythm that is sensitive to somatic afferent input (Kuhlman,1978). Although little research has addressed the develop- ment of the mu rhythm, there appears to be a functional relation between the 6–9 Hz oscillation at central sites in infancy and early childhood with the adult mu rhythm (Galkina & Boravova,1996; Stroganova, Orekhova, & Posikera,1999). In a longitudinal sample, Marshall et al. (2002) found that the relative contribution of 6–9 Hz power at central sites peaked at around 14–24 months of age, before declining into early childhood. This pattern
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suggests a decrease in the saliency of the central sensorimotor rhythm from infancy into early childhood, which may not be coincidental given that the second year after birth is a time of such intense development of locomotor ability.
Although researchers of social and emotional development have primarily considered alpha activity in their analyses of the EEG, other rhythms been examined in various contexts in the domains of social, emotional, and cogni- tive development. Theta rhythms have usually been considered in the context of cognitive development: for example, in learning disorders in older chil- dren (Chabot, di Michele, Prichep, & John,2001), joint attention (Mundy, Card, & Fox,2000), voluntary attention (Orekhova, Stroganova, & Posikera, 1999), and working memory processes in infants (Bell,2002). Although pha- sic increases in theta activity during visual attention have been found to be positively correlated with positive affect (Orekhova, Stroganova, Posikera, & Malykh,2003), high levels of tonic theta activity have been associated with developmental disorders of learning and attention (Barry, Clarke, & John- stone,2003). Tonically high levels of theta activity (especially the relative amount of theta in the power spectrum) have also been associated with envi- ronmental adversity such as early institutionalization (Marshall, Fox, & the BEIP Core Group,2004) or sociocultural risk factors (Harmony et al.,1988). The finding of high relative theta is often accompanied by (and may be a result of) reductions in the amount of power in higher frequency bands (e.g., alpha and beta). Since the amount of low-frequency power (e.g., delta and theta power) in the EEG decreases with age, and the amount of higher-frequency power (e.g., alpha and beta) increases with age (Matousek & Petersen,1973), this EEG pattern has been suggested to indicate a maturational lag in the development of the EEG (Matsuura et al.,1993). Alternatively, an excess of low-frequency power and a deficit in higher-frequency power has been pro- posed as an indicator of a state of chronic underarousal (Satterfield, Cantwell, & Satterfield,1974). Both models have been subject to criticism, and distin- guishing between these two possibilities is challenging, although recent work has shown promise in this respect (for review see Barry et al.,2003).
The beta frequency band has not generally been considered in work on social and emotional development, with a few exceptions (Marshall et al., 2004; McManis, Kagan, Snidman, & Woodward, 2002). Likewise, gamma activity has received little consideration, although infant gamma has been studied in relation to visual processing in both typically developing and developmentally disordered populations (Csibra, Davis, Spratling, & John- son,2000; Grice et al.,2001).
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