On the relationship between EEG and P300: individual differences, aging, and ultradian rhythms

International Journal of Psychophysiology 26 Ž1997. 299]317

On the relationship between EEG and P300: individual differences, aging, and ultradian rhythms J. PolichU Department of Neuropharmacology TPC-10, The Scripps Research Institute, 10550 N. Torrey Pines Road, La Jolla, CA 92037, USA

Abstract The literature on the relationship between background EEG and event-related brain potentials ŽERPs. is reviewed, with the conclusion that variation in the former can contribute to individual variability in the latter. The effects of background EEG activity on the P300 component are then described using the results of three experiments. Study 1 assayed the association between EEG spectral powerrmean frequency and P300 amplituderlatency measures in young adults. For the slower delta, theta, and alpha bands generally strong correlations were obtained for both types of measures. Study 2 employed similar techniques to assess a large sample of adults who varied in age from 20]80 q years. EEG power in the slower bands was correlated positively with P300 amplitude across the age range, but few effects for mean frequencyrcomponent latency were observed. Study 3 measured a group of young adults ten times very 20 min to assess for temporal changes in the relationship between EEG and ERPs. The correlations between spectral power and P300 amplitude measures were found to vary in a manner that suggested the influence of ultradian rhythms on neuroelectric activity. Taken together, the findings from all three study indicate that background EEG variation contributes significantly to the individual variability of the P300 ERP. Theoretical and applied implications of the findings are discussed. Q 1997 Elsevier Science B.V. Keywords: EEG; ERP; ERD; P300; Spectral power analysis; Aging; Ultradian rhythms; Variability

1. Introduction The P3Ž00. event-related brain potential ŽERP. has demonstrated significant promise as an electrophysiological measure of human cognition ŽDonchin and Coles, 1988; Donchin et al., 1986.. Variation in its amplitude ŽPfefferbaum et al., U

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1989; Smith and Halgren, 1989; Fabiani et al., 1990; Noldy et al., 1990. and latency in normals ŽHoward and Polich, 1985; Emmerson et al., 1990; Johnson et al., 1985; O’Donnell et al., 1990; Polich et al., 1990b. as well as in clinical populations ŽPolich et al., 1986, 1990a; O’Donnell et al., 1987; Goodin et al., 1992. suggests that the P300 reflects individual differences in cognitive capability. However, despite the discovery of variables that contribute to between-subject variation in P300 measures Že.g. Geisler and Polich, 1990,

0167-8760r97r$17.00 Q 1997 Elsevier Science B.V. All rights reserved. PII S0167-8760Ž97.00772-1

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1992a,b; Polich and Geisler, 1991; Polich and Martin, 1992; see Polich and Kok, 1995 for a review., the utility of the P300 and ERPs in general has been limited by the variability observed for component measures ŽPolich, 1986; Polich, 1992; Alexander et al., 1994.. Even though the exact origins of ERP variability are not known, it is reasonable to suppose that background electroencephalographic ŽEEG. activity may contribute to variation in ERP values. This paper will review the literature context of this hypothesis and summarize the results of several initial investigations on the relationship between EEG and P300 variability. The focus of these studies was: Ž1. to determine if EEG and P300 measures are related in normal, young adult subjects; Ž2. to ascertain how such a relationship might change with normal aging; and Ž3. to explore the possible influence of ultradian rhythms on the EEGrP300 association. If variation in EEG is associated with individual differences in the P300 ERP, quantification of the connections between these neuroelectric phenomena should help account for some of the variability when ERPs are used to assess cognitive function. 1.1. EEG and e®ent-related potentials The effects of EEG activity on sensory evoked potentials ŽEPs. have been investigated in various studies. In general, comparisons between background EEG and EP values have been made either by: Ž1. recording EEG in groups of individuals } sometimes selected for special characteristics Že.g., low vs. high alpha power. } and assessing how EEG variability affects EP amplitude and latency values between subjects; or Ž2. recording pre- and post-stimulus EEG and assessing how pre-stimulus EEG variability affects the post-stimulus EP measures within subjects. Each of these approaches has produced a disparate literature, although some generalizations about the findings can be made. The first procedure was employed in many early studies and generally found that large magnitude EEG was associated with large EP amplitudes despite considerable differences in methodology ŽBarlow, 1960; Kooi and Bagchi, 1964; Rodin et al., 1965; Spilker et al., 1969.. These results were

supported by investigations demonstrating that experimentally induced changes in the EEG, especially in the alpha band, also affected EPs ŽCiganek, 1961; Garcıa-Austt, 1963; Remond and ´ ´ ´ Lesevre, 1967., perceptual phenomena ŽDonchin ` and Lindsley, 1965; Nunn and Osselton, 1974., and reaction time ŽCallaway and Yeager, 1960; Surwillo, 1963; Dustman and Beck, 1965; Surwillo, 1971.. Hence, variation of background EEG appears to influence sensory EP measures across individuals, although a clear consensus of the effects is not apparent primarily because of wide variation in recording techniques and experimental methodology. The second approach has been adopted by more recent reports, which was first introduced by Basar and his colleagues using animals ŽBasar et al., 1976a; Basar et al., 1979. and later applied to humans ŽBasar et al., 1976b, 1984; Basar, 1980; Basar and Stampfer, 1985; Stampfer and Basar, 1985.. These studies have found reasonably strong associations between pre-stimulus EEG and the post-stimulus EP measures primarily in the slower theta as well as alpha bands, with large-magnitude pre-stimulus EEG again related to large amplitude EPs. However, the results were affected by stimulus parameters and whether the subject was engaged in an explicit task ŽGrillon and Buchsbaum, 1986; Romani et al., 1988; Brandt et al., 1991; Jansen and Brandt, 1991.. Thus, variation in background EEG appears to be associated with characteristics of the averaged sensory EP within and between subjects across variegated studies ŽBoddy, 1971; Galin and Ellis, 1975; Rogers, 1980.. A positive relationship between pre-stimulus EEG spectral power, primarily in the theta and alpha bands, and amplitude of the P300 ERP has been reported ŽBasar et al., 1984; Pritchard et al., 1985; Jasiukaitis and Hakerem, 1988; Basar et al., 1989; Jasiukaitis et al., 1990.. Indeed, a recent investigation has found that EEG and the P300 component interactively reflect the mental events underlying information processing during complex memory tasks ŽMecklinger et al., 1992.. These studies suggest that within-subject EEG variability is associated with variation in P300 ERP measures. However, the relationship between background EEG and ERP variability needs to be

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characterized more precisely to articulate exactly how the EEG is related to ERP phenomena across individuals. 2. Overview and methodology Toward this end, the general method employed in the series of studies described below was to obtain both EEG and ERPs from the same subjects, quantify the electrophysiological data using standardized techniques, and correlate the measures from each procedure with one another across subjects. This approach was used in Study 1 to assess the individual variability in a homogeneous sample of young adults. Study 2 extended this method to characterize the effects of normal aging. Study 3 addressed the issue of how ultradian rhythms contribute to EEGrP300 data by obtaining repeated measures across subjects. After the results of these studies are described, conclusions will be drawn with respect to how EEG and the P300 may be related. Discussion of the possible relationships of the other ERP components are deferred to the primary reports. Both EEG and ERP data were recorded from the Fz, Cz, and Pz electrodes in all studies, with lateral electrode placements employed occasionally. EEG was recorded for 5 min while eyes were open and 5 min while eyes were closed. The EEG data were assessed visually by inspecting sequential 4-s displays for all channels. When a total of 120 s of artifact-free Žno excursion on any channel " 25 mV. EEG from each subject was obtained in each condition, the EEG was analyzed using spectral analysis to extract spectral power and mean frequency in six bands, with a Hanning smoothing algorithm employed: delta Ž1]4 Hz., theta Ž4]8 Hz., alpha-1 Ž7.5]9.5 Hz., alpha-2 Ž9.5]12.5 Hz., beta-1 Ž12]20 Hz., and beta-2 Ž20]70 Hz.. The theta, alpha-1, and alpha-2 bands often are defined to overlap by 0.5 Hz, and this approach has no consequences for the subsequent results. The spectral power data Ž mV 2 . were subjected to a log 10 transformation prior to statistical analysis to normalize the data distributions ŽPollock et al., 1990, 1992., with the EEG mean peak frequency ŽHz. data assessed directly since no transformation was required. The P300 ERP was elicited with binaural 1000

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Žstandard. and 2000 Žtarget. Hz tones presented at 60 dB SPL Ž9.9 rrf, 50 ms plateau.. The tones were presented in a random series once every 2 s, with a target stimulus probability of 0.20. ERPs were obtained in separate conditions from the EEG, and the subject indicated the occurrence of a target stimulus by pressing a button. The largest positive-going peak occurring between 250 and 400 ms was designated as the P300 component. P300 amplitude Ž mV. and latency Žms. were assessed by measuring component height relative to the pre-stimulus baseline and the time of peak amplitude from stimulus onset. 2.1. Study 1: Indi®idual ®ariation The first study was designed to explore how individual variability in the EEG is related to P300 component variation by assessing a relatively large group of normal young adult subjects, quantifying the background EEG and correlating these measures with the P300 ERP obtained independently under separate but similar conditions with an auditory oddball paradigm ŽIntriligator and Polich, 1994, 1995.. EEG and ERPs were both recorded under eyes open and closed regimes to assess the effects of changes in alpha on the relationship between the two neuroelectric measures. The major hypothesis was whether associations between EEG activity and P300 measures would be observed across a normative sample of individuals. If a relationship between EEG and P300 can be delineated, it should prove useful in helping to account and control ERP variation between individuals and therefore improve the applied utility of the P300 as a measure of cognition ŽPolich, 1991, 1993.. The grand average of EEG spectral power Žleft. and ERPs Žright. across subjects Ž N s 24. for the eyes openrclosed conditions and each recording site are presented in Fig. 1. Note that the P300 amplitude scalp distribution is very similar to that obtained for the alpha band of the EEG, although the eyes openrclosed manipulation did not produce a strong effect on the ERPs Žsee below.. To quantify the degree of association between the EEG and P300 measures, correlation coefficients were computed between the major dependent variables across 24 subjects: EEG

Fig. 1. Study 1: Grand averaged spectral frequency Žleft. and target stimuli event-related brain potentials Žright. from the eyes openrclosed conditions from each recording site Ž n s 24..

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power values Žtransformed. from each band were correlated with P300 amplitude measures, and EEG mean frequencies from each band were correlated with P300 latency measures. The correlations between EEG power and component peak latency, as well as those between EEG mean frequency and component amplitude also were examined. These analyses failed to demonstrate any consistent, interesting, or interpretable results and will not be considered further. 2.1.1. EEG and P300 correlations The relationship between the EEG spectral power and P300 amplitude and mean frequency and P300 latency measures are illustrated in Fig. 2 as a function of each band and recording condition. Each group of vertical lines reflects the size of the correlation between the variables as indicated by the scale in the middle of the figure. Hence, the length of the vertical line represents the degree of correlation between the specified EEG and P300 measures for each band, eyes openrclosed, targetrstandard stimulus condition, and electrode site Žsee figure caption for additional details.. Fig. 2 attempts to capture the overall association between the various EEG values and P300 measures by indicating the strength of the relationships and the scalp locations of the major effects. To illustrate these results more specifically and to provide a visual index of the between-subject variability observed for the major dependent variables, a subset of the data from Fig. 2 are presented as scattergrams from the Pz recording site for the target stimuli in Fig. 3. The statistical correlations from these scattergrams are summarized in Fig. 4. It is important to note that when performing such a large number of correlations, the pattern of correlational effects is often more informative than the values obtained for specific variables. Thus, the strength and reliability of the obtained effects should be considered by evaluating the relationships between the EEG and P300 measures within the framework of the overall pattern. The major findings from the correlational analyses are: Ž1. EEG spectral power and P300 amplitude Žtarget stimuli. are generally positively correlated. Ž2. Out of the large number of obtained correlations, the strongest overall relationships

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were observed for the delta, theta, and alpha-1 bands. Ž3. EEG frequency is associated with P300 latency in a variable fashion across bands and recording sites, with the alpha-1 and alpha-2 bands demonstrating significant but opposite-sign correlations. Ž4. The relationship between EEG and P300 was strongest when both measures were recorded with eyes open; eyes closed conditions tended to disrupt and mitigate all associations. Ž5. Few consistent EEGrP300 correlations were observed for the standard stimuli, most likely because the P300 to the standard stimuli was not robust. These findings are in agreement with investigations that found an association between the EEG and the P300 component ŽBasar et al., 1984, 1989; Pritchard et al., 1985; Jasiukaitis and Hakerem, 1988; Jasiukaitis et al., 1990.. However, in contrast to the comparatively limited nature of previous reports, the present study evaluated background EEG and the P300 component in terms of scalp distribution, eyes openrclosed conditions, and over a wide range of EEG bands for both spectral power and mean frequency in a comparatively large, homogeneous group of normal subjects. As a consequence of this approach, the relationship between EEG power and P300 amplitude was found to be strongest for the delta, theta, and slower alpha activity when eyes were open. When eyes were closed the association between EEG and P300 was disrupted, perhaps because the overall power produced greater EEG variability Žsee Fig. 3.. Since P300 amplitude and latency were not affected by the eyes openrclosed manipulation, some additional factor may be operating under these circumstances to obscure the association between alpha and P300 observed when these data are acquired with eyes open. An additional new finding was that EEG mean frequency within specific bands was found to correlate with P300 peak latency, although much less consistently than the EEGrP300 amplitude effects. 2.2. Study 2: Normal aging 2.2.1. Neuroelectric aging The effects of age on EEG and the P300 ERP are typically assessed separately. In general, EEG

Fig. 2. Study 1: Graphical representation of correlation coefficients between EEG spectral power and P300 amplitude Žleft. and EEG frequency and P300 latency Žright. for the eyes openrclosed conditions and targetrstandard stimulus conditions. Each group of vertical lines represents the size of the correlation between the variables as indicated by the scale in the middle of the figure. The relationship for each EEG band is indicated by its position on the horizontal line, such that proceeding from left to right is the correlation between the delta, theta, alpha-1, alpha-2, beta-1, and beta-2 bands with the corresponding P300 values. Electrode locations are reflected by the scalp positions of each vertical line group. Note that the EEG data derived from bipolar recordings Že.g., Fz]Cz, Cz]Pz, Pz]Oz, etc.. were correlated with the ERP derived from the analogous monopolar recording sites ŽFz]A1rA2, Cz]A1rA2, Pz]A1rA2, etc...

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Fig. 3. Study 1: Scattergrams illustrating the association between EEG spectral power and P300 amplitude Žleft. and EEG frequency and P300 latency Žright. from the central-parietal recording site for the eyes openrclosed conditions and each spectral band Žtarget stimuli only.. The Pearson’s r correlation coefficient and significance level is presented in the upper left hand corner of each scattergram.

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between changes in the EEG and its possible effects on P300 with age are not clear, in part because of the multiple methods used to quantify EEG parameters Žcf. Dustman et al., 1990b, 1993; Oken and Kaye, 1992; Wirsen ´ et al., 1992.. Hence, although it seems likely that there is a direct link between age effects on the EEG and those observed for the P300 and ERPs, this association has not been established directly. The present study was designed to quantify the effects of age on EEG and ERPs ŽPolich, 1997.. Young through elderly subjects were assessed using identical procedures, with a total of N s 120 Ž10M and 10Frdecade.. Background EEG was obtained from subjects under eyes openrclosed conditions to manipulate power of the EEG. In separate conditions, auditory and visual stimuli were elicited with a simple discrimination task to generate the P300 ERP component, with eyes open during both tasks. EEG power and mean frequency measures were computed by spectral analysis, and component amplitude and latency measures were obtained from the ERPs. Correlational analyses were then employed to discern which bands of the EEG were associated with the P300 component in terms of spectral powerramplitude and mean frequencyrpeak latency relationships, with only data from the Pz electrode presented here.

Fig. 4. Study 1: Summary of correlation coefficients illustrated in Fig. 3 for each eyes openrclosed condition and EEG band ŽPz electrode site..

power appears to decrease and background Žalpha. frequency slows with increased age ŽCelesia, 1986; Shearer et al., 1989; Dustman et al., 1990a.. P300 amplitude tends to decrease, while peak latency increases with age ŽPfefferbaum et al., 1984; Picton et al., 1984; Polich, 1991.. Only a few studies have examined the relationship between changes in the EEG with age and the concomitant effects on ERPs ŽOken and Kaye, 1992., with no reports providing a statistical association between EEG and ERP measures in normal aging. These findings indicate that the relationship

2.2.2. EEGr P300 correlations and aging The scattergrams and corresponding correlation coefficients Ž r . for the EEG data as a function of subject age are illustrated in Fig. 5. The corresponding scattergrams for P300 amplitude and latency as a function of age are presented in Fig. 6. Note that as age increases, power in the delta, theta, and alpha bands tends to decrease; a similar decrease in P300 amplitude also is observed as age increases. However, age has relatively little effect on mean frequency for the slower bands, although P300 latency increases systematically with increased age. A summary of the results for the correlations between Žlog 10 mV 2 . EEG power vs. P300 amplitude and EEG mean frequency vs. P300 latency are presented in Fig. 7 for both the auditory and visual

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Fig. 5. Study 2: Scattergrams of EEG power Žleft. and mean frequency Žright. and subject age for each band and eyes openrclosed condition. The correlation coefficient and significance level for each scattergram are noted in the upper left-hand corner of the figure.

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Fig. 6. Study 2: Scattergrams of P300 amplitude Žtop. and latency Žbottom. and subject age for each modality condition. The correlation coefficient and significance level for each scattergram are noted in the upper left-hand corner of the figure.

ERP stimulus conditions. In general, the strongest associations between EEG and P300 measures were obtained for the powerramplitude correlations from the slower EEG bands Ždelta, theta,

alpha.. A few relatively inconsistent and weak correlations between EEG mean frequency and P300 latency also were found. These results suggest that as adults age, EEG

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Fig. 7. Study 2: Summary of correlation coefficients between EEG power and P300 amplitude Žtop. and EEG mean frequency and P300 latency Žbottom. for the eyes openrclosed and auditoryrvisual modality conditions of N s 120 subjects who varied in age.

power in the slower bands decreases and that this decrease is associated with similar declines in P300 amplitude with advanced age } results that appear fairly consistent for ERPs from both auditory and visual stimuli. Some differences in

strength of association between EEG power and P300 amplitude Žalways obtained with eyes open. were observed with respect to whether the EEG was recorded with eyes open or closed. These effects were most consistent for the alpha bands,

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as might be expected since this band is most sensitive to eyes openrclosed manipulations. Somewhat surprisingly, no strong correlations between EEG mean frequency and P300 latency were obtained, perhaps because relatively little change with age was observed for most of the bands in terms of mean band frequency. Some association between the fast beta band and P300 component latency was found, but it is likely that this effect is an artifact of the increase in electromyographic activity of this frequency range that is common among elderly subjects ŽOken, 1986.. Thus, the major association between background EEG and ERP changes with aging appear to occur between the delta, theta, and alpha bands and P300 amplitude ŽCelesia, 1986; Shearer et al., 1989; Dustman et al., 1990a.. 2.3. Study 3: Ultradian rhythms 2.3.1. EEGr P300 rhythms A major facet of human physiology that is often ignored in EEG and ERP work is the influence of underlying biological rhythms. Rhythms are thought to be necessary for the coordination of internal processes such as hormonal fluctuations, cell metabolism, neural activity, and vigilance ŽOkawa et al., 1984; Lloyd and Stupfel, 1991.. The importance of these rhythms appears to stem from their theoretical purpose of anticipating external events and synchronizing them to internal processes ŽLloyd and Stupfel, 1991.. Thus, the impact of biological rhythmicity on psychological processes is not unexpected. Circadian or daily fluctuations occur for mental performance, but the effects depend on a mix of physiological and cognitive factors as well as individual differences ŽFolkard and Monk, 1983; Kerkhof, 1985.. The general influence of time-ofday on specific ERP components has been reviewed previously ŽWesensten and Badia, 1992., and the data suggest that weak if any daily or circadian influences affect the P300 component ŽGeisler and Polich, 1990.. However, for conditions in which an oddball task is repeatedly presented to assess habituation effects ŽLammers and Badia, 1989; Polich, 1989., cyclical patterns for P300 measures have been observed ŽHarsh et

al., 1988; Lew and Polich, 1993; Lin and Polich, 1997.. Given that EEG activity indexes ultradian fluctuations in arousal levels that occur in approximately 90 min cycles ŽOkawa et al., 1984; Tsuji and Kobayashi, 1988., it is not unreasonable that the P300 component also might be affected by these cyclical changes. Indeed, such periodic fluctuations in ERP measures might be reflective of the Basic Rest Activity Cycle ŽBRAC. that has been proposed to account for observations of oscillations in vigilance throughout the day that are similar to the periodicity of REMrNREM fluctuations during sleep ŽKleitman, 1963.. The present study was designed to assess subjects under repeated task conditions with both EEG and ERP measures ŽSchnaider and Polich, 1997.. A group of 20 young adult subjects Ž10M, 10F. were measured every 20 min by collecting 3 min of EEG data and participating in an auditory oddball paradigm, with both measures obtained when eyes were open. To minimize circadian effects, all subjects were run beginning at 9:00 h. Additional physiological Žheart rate, blood pressure, temperature. and self-report Žmental and physical activity levels. also were collected. EEG power and mean frequency measures were computed by spectral analysis, and component amplitude and latency measures were obtained from the ERPs. Correlational analyses helped to discern which bands of the EEG were associated with the P300 component for spectral powerramplitude and mean frequencyrpeak latency relationships, with only data from the Pz electrode presented here. 2.3.2. EEGr P300 ultradian ®ariation The relationships between the delta and alpha EEG bands and P300 are illustrated for three different subjects in Fig. 8. The alpha band reflects the joint activity of both the alpha-1 and alpha-2 bands. No ultradian effects for the beta bands were obtained and these will not be discussed further. The data from the different measures are expressed as z-scores computed from the distributions obtained for all N s 20 subjects at each time point. Thus, the association between EEG and P300 measures reflect the individuals plotted in reference to the group as a whole.

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Fig. 8. Study 3: Data from three individual subjects are illustrated: EEG delta and alpha power plotted with P300 amplitude as a function of measurement time, with each dependent variable expressed as its z-score equivalent for a specific time period Žleft.; EEG delta and alpha mean frequency plotted with P300 latency as a function of measurement time Žright.. See text for details.

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The three examples illustrate a lack of consistent association between background EEG and P300. For the EEG power and P300 relationships: Subject A demonstrated an association between delta band power and P300 amplitude across measurement times; Subject B demonstrated an association between alpha band power and P300 amplitude; Subject C demonstrated no consistent relationship between power from any EEG band and P300 amplitude. Similarly, for EEG mean band frequency and P300 latency: Subject A was

not well coupled for delta mean frequency and P300 latency; Subject B showed only a weak association between alpha mean frequency and P300 latency; Subject C evinced some association between delta frequency and P300 latency, but no relationship with alpha frequency. A similar inconsistency between background EEG and P300 measures was observed for the other subjects as well. Because of these effects, it was hypothesized that background EEG and P300 measures may

Fig. 9. Study 3: The correlation coefficients between EEG power and P300 amplitude Žleft. and EEG mean frequency and P300 latency Žright. for each measurement time period across N s 20 subjects. Note the cyclical variation for the EEG powerrP300 amplitude correlations. See text for details.

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not be consistently coupled across measurement times. To examine this possibility, the correlations between EEG power Žlog 10 mV 2 . and P300 amplitude Ž mV. measures across the subjects for each time period were obtained for each EEG band. Similarly, the correlations between EEG mean frequency ŽHz. and P300 latency Žms. also were calculated. These correlations are plotted as a function of measurement time in Fig. 9. For the EEG powerrP300 amplitude relationships, a cyclical pattern emerges for the slower bands: The correlational association between these variables varies systematically with measurement time with about a 90 min period. These effects are particularly pronounced for the alpha-1 and alpha-2 bands. The correlational associations between EEG mean frequency and P300 latency were not as systematic. However, some semblance of a cyclical pattern can be observed for the alpha-1 and alpha-2 that is reminiscent of the findings reported above for the individual difference study Žsee Figs. 2 and 3.. When viewed together, background EEG and P300 measures seem to be associated across repeated measurements, but the strength of this association appears to wax and wane in an ultradian fashion that appears related to the BRAC reviewed above ŽKleitman, 1963; Lavie, 1985; Ortega and Cabrera, 1990.. Thus, the relationship between EEG and P300 measures may vary in a cyclical manner related to ultradian oscillations. 2.3.3. EEG and P300 The relationship between background EEG activity and the P300 ERP component has been examined in three independent studies that had as their focus quite different topics: normal individual differences, aging effects, and ultradian rhythms. In each case, associations between EEG power, primarily in the delta, theta, and especially alpha bands, with P300 amplitude were observed. Some hints at an association between EEG mean frequency and P300 latency were observed, but these were not as consistently found. Differences between recording parameters in terms of eyes openrclosed and modality for ERP elicitation also were obtained, with the strongest relationships between EEG and P300 measures found

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when eyes were open and auditory stimuli were used to elicit the ERPs. In sum, EEG and P300 are related across individuals but the nature of this association is complex and may vary cyclically over repeated measurements. Despite the relative consistency of the relationship between EEG and ERP measures, however, the underlying reasons for these correlations are far from clear. Additional studies designed to isolate how attention to a stimulus affects the EEG indicate that variation in the EEG spectrum within an ERP epoch contributes to the resulting individual variability found for the P300 component ŽBasar et al., 1989; Spencer and Polich, 1992; Polich and Luckritz, 1995.. Hence, individual variability of EEG within an ERP epoch also can affect component measures. When taken together with the correlational relationships reported here, it seems likely that differences across individuals in the overall power of their EEG, especially for the slower bands Ždelta, theta, alpha., are a source of variability for the P300 and perhaps other ERP components. 3. Theoretical considerations Given this conclusion, one possible mechanism that could underlie the observed interaction between EEG and the P300 component may be ’event-related desynchronization’ ŽERD. of the EEG alpha band during information processing ŽPfurtscheller, 1977, 1992; Pfurtscheller and Aranibar, 1977; Klimesch et al., 1992.. ERDs are thought to originate from decreases in alpha band EEG power when attentional resources are allocated for cognitive operations Žvan Winsum et al., 1984; Sergeant et al., 1987; Boiten et al., 1992; Klimesch, 1996. and may reflect attentional processes in a manner similar to that of the P300 ŽWickens et al., 1983; Sommer et al., 1990.. It is possible that when specific stimuli receive attention, the EEG is ’desynchronized’ or restructured by the attention-driven discrimination process ŽBasar et al., 1984; Mecklinger et al., 1992; Spencer and Polich, 1992.. If this is the case, then how readily an individual can desynchronize the alpha portion of the EEG may be related to how well individual variation in the P300 reflects the

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allocation of attentional resources and therefore individual cognitive differences Že.g. Howard and Polich, 1985; Emmerson et al., 1990; O’Donnell et al., 1990; Polich et al., 1990a,b.. Despite these intriguing implications, it is important to note that the present results imply that an association exists between background EEG and P300 ERP values, since the power and timing of some EEG bands are correlated with the size and latency of the P300 } a relationship that suggests an inherent synchronization between the two measures. In contrast, ERD phenomena reflect decreases in EEG power, especially in the alpha band, so that the issue of exactly how EEG, ERPs, and ERD measures are related is still an open question. However, because attentional mechanisms can affect all three of these neuroelectric measures, ERDs may provide a possibly important link between the relatively gross changes in the EEG and the more specific cognitive events reflected by the P300 ERP. Of particular interest in this context are robust findings that link changes in early and late alpha frequency to the engagement of attentional and memory operations, respectively ŽKlimesch et al., 1990, 1993.. Hence, how readily an individual’s EEG desynchronizes may be related to how well that individual’s P300 indexes the amount and timing of attentional resource allocation, which together could foster differences in the speed with which cognitive function is engaged Že.g. Howard and Polich, 1985; Johnson et al., 1985; Polich et al., 1986, 1990a; Emmerson et al., 1990; O’Donnell et al., 1990; Goodin et al., 1992.. The findings of Study 1 indicate that P300 latency is correlated positively with early alpha and negatively with late alpha. Given that individual differences in performance of attentional neuropsychological tasks is reflected in shorter P300 latencies for good performers and longer latencies for poor performances Že.g. Howard and Polich, 1985; Emmerson et al., 1990; Polich et al., 1990b., differences in the underlying early alpha frequency could be contributing to these effects ŽKlimesch et al., 1990, 1993.. As another example, increases in P300 latency found with normal aging may be related to the changes in late alpha frequency found in the EEG ŽPfefferbaum et al., 1984;

Picton et al., 1984; Celesia, 1986; Dustman et al., 1990b; Polich, 1991, 1997; Oken and Kaye, 1992.. Taken together, these results suggest that the relationship between changes in the EEG and its possible effects on P300 latency changes with increased age may be related specifically to agerelated changes in late alpha activity. 4. Summary and caveat The present findings support the hypothesis that individual variation in background EEG activity is associated with P300 amplitude and latency variability. Given that P300 measures can reflect cognitive capability, investigation of how the EEG is related to ERP generation should help to delimit the individual variability observed in ERPs from both normal and clinical populations. It should be emphasized, however, that the results reported here are correlational and say little about possible causal mechanisms. Future studies are needed to limn more clearly the underlying reasons why EEG and ERPs are related across individuals. Acknowledgements This work was supported by NIA grant RO1A610604 and is publication number NP8856 from the The Scripps Research Institute. Portions of thispaper were reported at the meetings of the Society for Psychophysiological Research, Chicago, Illinois Ž1991. and the Evoked Potential International Congress X, Eger, Hungary Ž1992.. References Alexander, J.E., Polich, J., Bloom, F.E., Bauer, L.O., Kuperman, S., Rohrbaugh, J., Morzorati, S., O’Connor, S., Porjesz, B. and Begleiter, H. Ž1994. P300 from an auditory oddball task: inter-laboratory consistency. Int. J. Psychophysiol., 17: 35]46. Barlow, J.S. Ž1960. Rhythmic activity induced by photic stimulation in relation to intrinsic alpha activity of the brain in man. Electroenceph. Clin. Neurophysiol., 12: 317]326. Basar, E. Ž1980. EEG-brain Dynamics: Relation Between EEG and Brain Evoked Potentials. ElsevierrNorth Holland Biomedical Press, Amsterdam. Basar, E. and Stampfer, H.G. Ž1985. Important associations among EEG-dynamics, event-related potentials, short-term memory and learning. Int. J. Neurosci., 26: 161]180.

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