Time-Domain and Time-Frequency Properties of ERPs
To study developmental EEG responses, we use an advanced conceptual and methodological framework (Yordanova et al., 2004; Yordanova & Kolev, 2004) based on the following. An EEG signal can be described in three dimensions: (1) amplitude, (2) time, and (3) frequency, although phase- relations should be also quantified for a complete signal description. Typi- cally, developmental ERPs are analyzed in the time domain (Figure2.1a). This analysis has shown that both the early (P1, N1) and late ERP components (P300, N400) change as a function of development (Courchesne,1983; Rid- derinkhof & van der Stelt,2000; Rothenberger,1982; Taylor,1989). A classical time-domain representation of ERPs reveals the timing of underlying neural events. As illustrated in Figure2.1a, the peak latencies of P1, N1, and P3 com- ponents can be precisely determined. However, the frequency characteristics
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(a)
(b)
(c)
Figure 2.1. ERPs can be described in three dimensions: amplitude, time, and frequency. (a) Time domain presentation of averaged ERP. Amplitude vs. time information is present, whereas no information exists about frequency events. (b) Frequency domain presentation of the same ERP. Amplitude (power) vs. frequency information is present, whereas no information exists about time events. (c) Time-frequency representation of the same ERP by means of Wavelet transform. Events can be localized in both time and frequency domain.
of those time-domain events remain obscure, and no information can be obtained about rhythmic or oscillatory events from various frequency bands present in the signal. Figure2.1b shows that the same ERP is characterized by peaks from the sub-delta (below 2 Hz), delta (2–4 Hz), theta (5–8 Hz), and alpha (around 10 Hz) frequency ranges. The inability of time-domain ERPs to present frequency characteristics of the signal seems to be a disad- vantage because EEG activities from several frequency ranges (theta, alpha, beta, gamma) have been associated consistently with sensory, cognitive, and motor performance in both children and adults (e.g., Basar,1998; Gevins, 1987; Klimesch et al., 1994; Klimesch, 1999; Krause et al., 1996, 2000;
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Pfurtscheller et al.,2000; Yordanova & Kolev,1998a; Yordanova et al.,2004). Furthermore, oscillatory responses from different frequency ranges can be generated simultaneously, with each frequency-specific response manifesting specific reactivity to task variables (Basar et al.,2000). On the other hand, analysis only in the frequency domain does not tell us how frequency com- ponents vary over time and whether they are temporally linked to event pro- cessing (Figure2.1b). Therefore, one goal of developmental research should be to characterize ERPs in the time, frequency, and time-frequency domains. This task can be achieved by time-frequency decomposition of ERPs, which provides information about time, frequency, and magnitude of the signal (Figure2.1c).
Single-Sweep Analysis of ERPs
Depending on the internal oscillatory properties of the responding struc- tures, oscillatory EEG responses may be tightly or loosely phase-coupled to the stimulus. Tightly phase-locked responses (illustrated in Figure 2.2, time window I) are called evoked oscillations, whereas stimulus-related but loosely phase-locked responses (Figure2.2, time windows II and III) are called induced oscillations (Galambos,1992). For analysis of non-phase-locked or both phase-locked and non-locked EEG responses, different approaches have been used (Kalcher & Pfurtscheller, 1995; Pfurtscheller & Aranibar, 1977; Sinkkonen et al.,1995). These methods are based primarily on power (or amplitude) measurements of the EEG in the post-stimulus period. For quantification of the phase-locked activity in ERPs, the averaging procedure is usually applied because this way the phase-locked responses are enhanced and the non-phase-locked ones are attenuated, as illustrated in the bottom of Figure2.2(Gevins,1987; Ruchkin,1988). Although the phase-locked EEG activity can be extracted by means of averaging (Figure2.2, bottom), the shapes of complex waves in the averaged ERP depend strongly not only on the time relations or phases but also on the amplitudes of single EEG tri- als. However, both single-sweep amplitudes and phase-relations may vary substantially and thus contribute differentially to the averaged ERP (Kolev & Yordanova,1997; Yordanova & Kolev,1998c). Because the phase-locking and power (amplitude) contributions cannot be separated in the averaged waveform, the averaged ERP is regarded only as a rough estimation and a first approximation of the brain response (Basar,1980).
More important from a functional point of view are the observations that amplitude and phase-locking may reflect specific aspects of information
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Figure 2.2. Effect of single-sweep phase-locking and amplitude on the averaged waveform illustrated in three time windows: ten single-sweeps (from the top) filtered in the frequency band 8–15 Hz and their averaged wave- form (bottom line).
(Time window I) All single-sweeps are in congruence (strongly phase-locked). As a result, an enhanced aver- aged ERP is obtained.
(Time window II) Some of the single-sweeps are phase- locked, and some of them are not: the averaged amplitude is smaller in comparison to that found in time window I. (Time window III) No phase-locking between all single- sweeps: the averaged amplitude is significantly smaller than that found in time windows I and II (with modifi- cations from Yordanova & Kolev,1997b).
processing; however, only a few investigations focus on the measurement of phase characteristics at the moment of, or shortly after, stimulus delivery (Brandt et al.,1991; Jervis et al.,1983; Kolev & Yordanova,1997; Sayers et al., 1974; Tallon-Baudry & Bertrand,1999; Yordanova & Kolev, 1996,1998a, 1998b,1998c). Jervis et al. (1983) have shown that the slow ERP components (theta, delta) which typically manifest an additive power effect also have a strong phase-locking effect. However, other frequency components may react with only phase-locking, without being enhanced in amplitude after stimulation (Yordanova & Kolev,1998b). If such components are very small in amplitude relative to other frequency ERP responses, as is the case with the evoked gamma-band response, they cannot be reliably identified in the averaged potentials because they are masked by the high power components. Hence, relevant phase-locked components may be confounded by power factors. In addition, estimation of component stability at the level of single- sweep analysis has demonstrated significant differences between the variabil- ity (or stability) of exogenous and endogenous components (Michalewski
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et al.,1986). Specific contributions of factors like aging (Fein & Turetsky, 1989; Pfefferbaum & Ford,1988; Smulders et al.,1994) or pathology (Ford et al.,1994; Unsal & Segalowitz,1995) to either phase-locking or power of single responses have also been suggested (Kolev et al.,2002; Yordanova et al., 1998). Taken together, these findings imply that quantification of both aspects of single-sweep behavior might be independently informative for revealing significant information about stimulus processing (Kolev & Yordanova,1997; Yordanova & Kolev,1998c).