Event-related potentials in the auditory oddball as a function of EEG alpha phase at stimulus onset
Introduction
Traditionally, event-related potentials (ERPs) have been extracted as an average response from large numbers of within-subject trials of a given stimulus. The ERP is generated in conjunction with the ongoing electroencephalogram (EEG), which is usually conceptualised by ERP researchers as background noise (i.e. neural activity unrelated to the event), and hence a problem to be eliminated in the averaging process. This approach overlooks the possibility that the ongoing EEG has a fundamental role to play in the genesis of the ERP. However, more than two decades ago, Başar (1980) stated that the self-oscillatory character of the brain's electrical activity should not be ignored by simply averaging out the spontaneous EEG activity, and argued that, just as it is important in physical systems to establish the conditions of the system at the time of excitation, this should be done with the dynamic analysis of ERPs.
Averaging is generally used to increase the signal:noise ratio of the small stimulus-related response hidden in the background EEG activity. The problem is that the resultant ERP is simply an average, which may not be truly representative of the actual response from any single event. There is increasing evidence to suggest that the average does not provide a good estimate of the trial-to-trial response, which appears to be highly variable (Anderson et al., 1991, Başar, 1980, Ford et al., 1994, Stampfer and Başar, 1985). Recognition of these limitations on conventional ERP extraction methods have led researchers to explore new methods of examining and interpreting the ERP.
Başar (1980) was able to show that the responses to individual stimuli depend largely on the nature of the EEG at stimulus onset. In this conceptualisation, the ‘background’ EEG activity is seen as exquisitely reflecting the brain's momentary state of activity, and subtly determining the response to the stimulus, reflected in both behavioural and ERP outcomes. Supporting evidence for this approach comes from studies of the effect of stimulus presentation in particular brain states. For example, there has been a substantial number of studies examining the effect of differences in the amplitude of particular EEG frequencies in the pre-stimulus period upon ERP components (Barry et al., 2000, Başar and Stampfer, 1985, Başar et al., 1989, Brandt, 1997, Brandt and Jansen, 1991, Brandt et al., 1991, Jasiukaitis and Hakerem, 1998, Price, 1997, Rahn and Başar, 1993).
Perhaps the most-widely-studied frequency range in these reports has been the alpha band. For example, Barry et al. (2000) concluded that amplitudes of both the N1P2 and N2P3 complexes, elicited by targets in an auditory oddball paradigm, were directly proportional to pre-stimulus RMS alpha amplitude—i.e. these components are larger when stimuli are presented during high amplitude alpha states. These data supported the previous findings of Brandt's group and Jasiukaitis and Hakerem (1998).
Early work (Callaway and Yeager, 1960, Dustman and Beck, 1965, Trimble and Potts, 1975) has shown differential effects of stimulation at negative and positive peaks of the alpha cycle. A common finding amongst these studies is that faster reaction times are more likely to coincide with stimuli presented at negative alpha peaks. Rémond and Lesèvre (1967) presented flash stimuli at the maximum, minimum and the two zero crossings of the alpha wave. Their data suggest enhancement of ERP components occurring around 80 ms (N1) when stimuli were presented at the maximum, and to a lesser degree, the positive zero crossing, of the alpha wave. A more-detailed examination by Jansen and Brandt (1991) investigated the relationship between pre-stimulus alpha phase and the N1 and P2 components of the ERP. They divided the pre-stimulus alpha cycle into 8 phases, allowing them to selectively average trials a posteriori on the basis of phase angle division. The N1 was found to be maximal when stimulus onset was near the positive-going zero crossing, with smaller effects being found for the P2. Unfortunately, this interesting study presented data from only 5 subjects, and it is unclear how these individual results can be generalised. Also, the study employed an eyes-closed condition, which, together with the absence of a task, would be expected to result in relatively high alpha amplitudes. Phase determination was then further restricted to selected 1 s pre-stimulus epochs with high alpha level (>100 μV). Later, Brandt (1997) was able to confirm that post-stimulus alpha in the N1 latency window was enhanced when stimuli are presented in the half-cycle associated with a positive zero crossing, but unfortunately this effect was not examined in terms of the broad-band ERP itself.
In addition to such pre-stimulus EEG effects on ERP amplitudes, a number of studies have shown that when stimuli appear at regular intervals, phase adjustments occur in the pre-stimulus EEG. Başar and Stampfer (1985) observed a phase reordering or ‘preferred phase angle’ in the delta (0.5–3.5 Hz) frequency range to produce maximal negativity at the time of stimulus onset. Phase alignment was also observable in the alpha range (the focus of this study). Further evidence for preferred (dynamic) EEG frequency phase alignment has been reported by Barry et al., 2003, Başar et al., 1984, Pleydell-Pearce, 1994, Rockstroh et al., 1989.
Taking a different approach, Haig and Gordon (1998b) introduced a new measure, alpha phase synchronicity, defined as the angular or circular variance of phase across electrode sites at a particular point in time, and used this approach to investigate ERP outcomes at parieto-occipital sites. They showed a correlation between alpha phase synchronicity (the degree to which alpha waves from different locations on the scalp are in phase) and the amplitude of the first notable ERP component (N1). Unfortunately, this measure appears to be confounded with alpha amplitude, in that highly synchronised alpha in the occipito-parietal region is likely to be of high amplitude. In a later study on the use of circular statistics in this field, Haig and Gordon (1998a) established, in a fixed inter-stimulus interval (ISI) auditory oddball paradigm, that single-trial target ERPs which had a detectable P3 component differed from those which did not—their mean alpha phase at stimulus onset was late in the period Brandt (1997) referred to as ‘positive zero crossing’.
While these studies have served to illustrate that EEG phase at stimulus onset is an important characteristic influencing the brain's response, differences in methodology make it difficult to compare results. Previous work from our group (Barry et al., 2003) noted that one of the impediments to research investigating phase effects is the difficulty of conceptualising, measuring, and analysing the phase of a frequency component as an independent variable in the experimental paradigm. We adopted a novel approach to the measurement of phase, in an effort to facilitate the relating of phase measures to a wider range of concepts, which appear useful in this arena, so that their effects on ERP measures could be better explored. Fig. 1 shows a simple sine wave, drawn with negative up in order to relate to common ERP conventions, with four phase divisions (A, B, C and D) at intervals of π/2. Barry et al. (2003) investigated two easily conceptualised physical variables. Cortical negativity can be examined in the comparison of the effects of (A+B) versus (C+D). This represents perhaps the earliest aspect of research into phase effects, investigated by presenting stimuli at the negative or positive peaks of alpha waves. A second physical variable, negative driving, can be examined in the comparison of (A+D) versus (B+C). This relates to the nature of the changing of the cortical negativity variable—is it increasing (phases A and D) or decreasing (phases B and C)? This negative driving covers Brandt's ‘negative zero crossing’ versus positive zero crossing effects. Note that both (A+D) and (B+C) cover negative and positive phases, and that effects of negative driving are independent of the effects of cortical negativity. A third mutually independent contrast introduced here examines the effect of whether the EEG waveform is waxing (A+C) or waning (B+D) at stimulus onset. Haig and Gordon's (1998a) results suggest that single-trial P3s may be more evident when alpha is waxing rather than waning.
Some studies have used Fast Fourier Transforms (FFTs) to obtain spectral information regarding pre-stimulus EEG (Brandt and Jansen, 1991), while others have employed bandpass filtering of the EEG on a trial-by-trial basis, using either traditional band definitions (e.g. 8–13 Hz alpha: Barry et al., 2000, Brandt, 1997, Haig and Gordon, 1998a, Haig et al., 1998b) or closely related bands derived from peaks in the individual's characteristic amplitude–frequency curve (Başar et al., 1984). Our previous study (Barry et al., 2003) pursued this issue with very narrow band (1 Hz bandwidth) digital filtering to investigate the occurrence of different phases of EEG activity at stimulus onset in a fixed-ISI auditory oddball task, at frequencies up to, and including, the alpha range. We also examined their effects on the amplitude of ERP components in the response to non-target stimuli. Across the frequency range explored, there were marked differences in the occurrence of different phases at stimulus onset. Cortical negativity occurred preferentially in both the delta (near significance at 1 and 3 Hz, significant at 2 Hz) and alpha (at 10–11 Hz) bands. In addition, cortical negative driving, defined as increasing negativity at stimulus onset, occurred preferentially for frequencies across the boundary between the traditional delta and theta bands at 3–4 Hz, with a smaller effect in the alpha band at 13 Hz. For the ERP components, it was shown that with frequencies up to 5 Hz, N1 amplitude was strongly enhanced when stimuli were presented in phases of negative driving and reduced when stimuli were presented during phases of cortical negativity. Weaker effects, but in the opposite direction, occurred with frequencies in the alpha range. The amplitude of P2 was found to reflect cortical negativity at stimulus onset strongly in the 1–3 Hz delta range, beyond which there was no discernable impact of this variable. In contrast, P2 amplitude was reduced by negative driving at stimulus onset strongly for delta activity from 1 to 2 Hz, and also (but less strongly) in the 5–6 Hz theta range.
That study showed that in a fixed-ISI paradigm, the component frequencies of the EEG are dynamically adjusted in order to provide particular brain states at stimulus occurrence to facilitate the brain's processing of the stimulus. Because ERPs were selectively averaged in terms of phase at stimulus onset, only the responses to the more-numerous background stimuli in that oddball task could be examined. This effectively limited analysis to the N1 and P2 components. Hence in this study we adopted a 50% target probability to obtain sufficient target responses for analysis.
We investigated the importance of the phase of alpha activity at stimulus onset in relation to the N1, P2, N2 and P3 components in the auditory ERP to target stimuli. These were examined in relation to the differential effects of cortical negativity, negative driving and waxing in the alpha activity at stimulus onset.
Section snippets
Subjects
Fourteen undergraduate subjects (8 females, 6 males), aged between 18 and 43 years, participated in this study as one means of satisfying a course requirement in Psychology. All claimed normal hearing, and gave written informed consent in accordance with a protocol approved by the joint University of Wollongong/Illawarra Area Health Service Human Research Ethics Committee. Subjects with a history of seizures, psychiatric illness or severe head injury were excluded, as were subjects currently
Results
The number of ERP responses accepted from the 300 target stimuli ranged across the 14 subjects from 164 to 284, with a mean of 235.4 (SD=38.9). The grand mean target ERPs obtained at Fz, Cz and Pz are illustrated in Fig. 2. There is evidence of a pre-stimulus contingent negative variation (CNV) at all sites. Post-stimulus there is a large frontal N1, with a peak amplitude of approximately 8.0 μV and mean peak latency 115–120 ms, and a centro-parietal P3 with a 2.5 μV amplitude near 285 ms.
Discussion
The existence of preferred brain states in this paradigm is apparent in Fig. 3—waxing and (to a lesser extent) negative driving were more common than would be expected if phase at stimulus onset occurred randomly. Whether or not these preferred brain states are functionally efficient in terms of ERP responding will be returned to later. Our previous phase study, Barry et al. (2003), found cortical negativity and (to a smaller extent) negative driving occurred preferentially in the alpha band.
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