Wavelet entropy analysis of event-related potentials indicates modality-independent theta dominance

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1 Journal of Neuroscience Methods 117 (2002) 99/109 Wavelet entropy analysis of event-related potentials indicates modality-independent theta dominance Juliana Yordanova a, *, Vasil Kolev a,osvaldo A. Rosso b, Martin Schürmann c, Oliver W. Sakowitz d, Murat Özgören e, Erol Basar e,f a Institute of Physiology, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 23, 1113 Sofia, Bulgaria b Instituto de Calculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Pabellon II, Ciudad Universitaria, 1428 Buenos Aires, Argentina c Institute of Physiology, Medical University Lübeck, Ratzeburger Allee 160, D Lübeck, Germany d Department of Neurosurgery, Charite-Humboldt University Berlin, D Berlin, Germany e Department of Biophysics, Medical School, Dokuz Eylül University, Balçova, Izmir, Turkey f Brain Dynamics Multidisciplinary Research Network, Ankara, Turkey Received 11 February 2002; received in revised form 5 April 2002; accepted 8 April 2002 Abstract Sensory/cognitive stimulation elicits multiple electroencephalogram (EEG)-oscillations that may be partly or fully overlapping over the time axis. To evaluate co-existent multi-frequency oscillations, EEG responses to unimodal (auditory or visual) and bimodal (combined auditory and visual) stimuli were analyzed by applying a new method called wavelet entropy (WE). The method is based on the wavelet transform (WT) and quantifies entropy of short segments of the event-related brain potentials (ERPs). For each modality, a significant transient decrease of WE emerged in the post-stimulus EEG epoch indicating a highly-ordered state in the ERP. WE minimum was always determined by a prominent dominance of theta (4/8 Hz) ERP components over other frequency bands. Event-related transition to order was most pronounced and stable at anterior electrodes, and after bimodal stimulation. Being consistently observed across different modalities, a transient theta-dominated state may reflect a processing stage that is obligatory for stimulus evaluation, during which interfering activations from other frequency networks are minimized. # 2002 Published by Elsevier Science B.V. Keywords: Event-related potentials (ERPs); Bimodal stimulation; Time/frequency analysis; Entropy; EEG 1. Introduction Several reports have pointed out that the electroencephalogram (EEG) reflects the activity of ensembles of generators producing spontaneous and event-related oscillations in several frequency ranges. Upon stimulation, functionally activated generators begin to act together in a coherent way. This transition from a disordered to an ordered state can be detected as frequency stabilization, synchronization, and enhancement of the ongoing EEG in the post-stimulus period (Basar et al., 1976; Basar, 1980). In this way, exogenous or endogenous inputs produce EEG responses from * Corresponding author. Tel./fax: / address: jyord@iph.bio.bas.bg (J. Yordanova). different frequency bands (delta 0.1 /4 Hz, theta 4/7 Hz, alpha 7/14 Hz, beta 14/30 Hz, and gamma 30/70 Hz) defined as event-related EEG oscillations or frequency components of the event-related potential (ERP) (Basar, 1998; Basar et al., 2000). These multiple frequencies are generated simultaneously in the eventrelated EEG and their superposition has important functional implications (Karakas et al., 2000a,b). However, although both global and local interactions among different frequencies may reveal neuroelectric functional involvement (Lisman and Idiart, 1995; Yordanova and Kolev, 1997, 1998a,b; Haenschel et al., 2000; Kolev and Yordanova, 2000; Bressler, 1995; Bressler and Kelso, 2001; Caplan et al., 2001), frequency-specific oscillations are typically analyzed independently of each other. Therefore, the aim of the present study was to evaluate co-existent multi-frequency oscillations by applying a /02/$ - see front matter # 2002 Published by Elsevier Science B.V. PII: S ( 0 2 ) X

2 100 J. Yordanova et al. / Journal of Neuroscience Methods 117 (2002) 99/109 new method quantifying the entropy of the event-related EEG (Rosso et al., 2001; Quian Quiroga et al., 2001). According to the information theory, entropy is a relevant measure of order and disorder in a dynamic system (Shannon, 1948). For analysis of EEG order/ disorder the spectral entropy has been introduced by Inouye et al. (1991, 1993). The Fourier spectral entropy measures how concentrated or widespread the Fourier power spectrum of a signal can be (Powell and Percival, 1979). Low entropy values correspond to a narrow-band (mono-frequency) activity characterizing highly ordered (regularized) bioelectrical states, and high entropy values reflect a wide-band (multi-frequency) activity (Inouye et al., 1991). However, because of the low time resolution of the conventional Fourier transform, the spectral entropy cannot reliably assess fast changes of EEG states. To overcome these limitations and quantify more precisely transitions of the EEG from short-lasting ordered to disordered states (or vice versa) a new method has been recently developed and applied to ERPs (Rosso et al., 2001; Quian Quiroga et al., 2001). The method is based on the time /frequency decomposition of the EEG by means of the wavelet transform (WT) and is called wavelet entropy (WE). The WT provides for optimal time resolution for each frequency (e.g. Schiff et al., 1994; Ademoglu et al., 1997; Blanco et al., 1998; Demiralp et al., 1999; Samar et al., 1999) and can accordingly extract in a reliable way superimposed event-related oscillations from different frequencies (Kolev et al., 1997; Demiralp et al., 1999; Yordanova et al., 2000; Demiralp and Ademoglu, 2001). Therefore, WE can quantify precisely time dynamics of order/ disorder states (defined here as microstates) in shortduration signals such as the ERPs (Rosso et al., 2001; Quian Quiroga et al., 2001). In the present study, the WE method is applied to study complex ERP behavior and frequency ERP components interaction in relation to sensory processing. Auditory and visual stimuli have been demonstrated to generate multiple event-related oscillations in delta, theta, alpha, gamma frequency ranges (rev. Basar, 1999; Basar et al., 2000). However, alpha and theta oscillations have been shown to differ between auditory and visual stimulus processing, with phase-locked alpha responses dominating over the primary sensory areas of each modality, and theta responses being more prominent over nonspecific (associative) cortical regions (Schürmann et al., 1997). Further, multisensory stimulation is known to involve not only mechanisms specific for each particular modality but also additional mechanisms underlying the integrative perception of the complex stimulus, both at subcortical and cortical levels (Stein, 1998). Such differences have been described in terms of specific frequency-domain characteristics of auditory, visual, and bimodal (audio /visual) evoked potentials (AEPs, VEPs, BEPs) recorded at the scalp. Amplitude-frequency characteristics (AFCs) of AEPs manifest a compound peak (resonance) in the alpha and theta ranges, VEPs display responsiveness in the upper (12/15 Hz) alpha band, and BEPs have highest peaks in the theta band (Sakowitz et al., 2000). Since differences in resonant frequencies have been established between uni- and bisensory modalities, it is important to study the interactions among coexistent frequency responses and their possible associations with modality-specific processing. With that aim, AEPs, VEPs, and BEPs recorded in passive (no-task) conditions were analyzed, and the minimal WE was evaluated as a marker of oscillatory response tuning in the ERP. The following questions were addressed: (1) do states of ERP ordering as reflected by WE minimum accompany simple stimulus processing? (2) Do the amount and temporal/spatial distribution of WE decrease depend on the modality and complexity of the stimulation? (3) Which oscillatory responses are affected in the process of EEG-ordering? 2. Methods 2.1. Subjects EEG was recorded from 15 right handed subjects (eight male, mean age of all subjects/26 years, S.D./ 3.2). Subjects were comfortably seated in a sound-proof and dimly illuminated room. Personal data (handedness, past medical history, medical family history, etc.) were acquired with a standardized interview before recordings. None of the subjects reported on any neurological disease in the past or had taken any drugs known to affect the EEG Data recording Raw data were recorded with Ag /AgCl disc electrodes placed at frontal, vertex, central, temporal, parietal and occipital sites (F3, F4, Cz, C3, C4, T3, T4, P3, P4, O1, O2) according to the international 10/20 system and referenced to linked earlobes. EEG signals were amplified with frequency limits of 0.1 and 70 Hz by means of a Schwarzer EEG machine. Additionally, a 50 Hz notch filter was used to avoid main interference. EEG epochs (1 s pre- and 1 s post-stimulus) were digitized at a rate of 250 Hz. During all sessions paper recordings and video monitoring were used to control for gross artifacts and subject s behavior. Bipolar electrooculogram (vertical /horizontal) and surface EMG of the frontal muscle were recorded for off-line artifact rejection.

3 J. Yordanova et al. / Journal of Neuroscience Methods 117 (2002) 99/ Experimental setup The following sessions were included in each experiment: 1) recording of AEPs in a passive condition. A total of 120 tone bursts (frequency 2 khz, intensity 80 db SPL, duration 1 s, r/f 5 ms, random inter-stimulus intervals 1 /3 s) were presented binaurally. 2) Recording of VEPs in a passive condition. A total of 120 rectangular light-stimuli centered in the visual field at a distance of 1.5 m were delivered for 1 s with inter-stimulus intervals varying between 1 and 3 s. Stimulus intensity exceeded surrounding illumination by approximately 5 lx. 3) Recording of BEPs: stimuli of (1) and (2) were applied simultaneously. Subjects were instructed to view and listen passively while maintaining focus on a marker placed in the middle of the visual stimulation field Data analysis The first ten trials of each experiment were always excluded to make sure that subjects were least affected by the novelty of the situation. After further visual inspection of the data, 64 artifact-free sweeps from each modality condition were selected for analysis, which included the following procedures: Time domain analysis Artifact-free single sweeps were averaged. In the averaged AEPs, VEPs, and BEPs, time-domain components were identified and their amplitudes and latencies were measured with a baseline of 200 ms before stimulus Wavelet transform The WT was used to represent the EEG signal in both time and frequency (Daubechies, 1992; Mallat, 1999). The wavelet representations provide precise measurements of when and to what degree transient events occur in a neuroelectric waveform and of when and how the frequency content of a neuroelectric wave-form changes over time (Samar et al., 1999). This is achieved by using families of functions (wavelets) that are generated from a single function (basic wavelet, which can be a smooth and quickly vanishing oscillation) by operations of scaling (stretching or shrinking the basic wavelet) and translation (moving the basic wavelet to different time positions at any scale without changing its shape). In this way, as shown in Fig. 1 and described in Samar et al. (1999), the WT performs a time /frequency decomposition of the signal, i.e. at each resolution level (corresponding roughly to a given frequency band) and each time position, the wavelet function is correlated with the shape of the neuroelectric waveform at that position. This correlation, known as a wavelet coefficient, measures how much of the wavelet at that resolution level and position is included in the neuroelectric waveform. This process produces a sequence of wavelet coefficients at each level. The sequences from different levels of decomposition can be arranged in a hierarchical scheme called multi-resolution decomposition (Mallat, 1999). Signals corresponding to different levels can be reconstructed by applying an inverse transform. More details of the multiresolution scheme and its implementation can be found in previous works (Schiff et al., 1994; Ademoglu et al., 1997; Mallat, 1999). In the present study, a multi-resolution decomposition (Mallat, 1999) was performed by applying a decimated discrete WT (Blanco et al., 1998; Rosso et al., 2001). In the discrete WT, the parameters of scaling and translation have discrete values, which can also be taken at logarithmic (dyadic) scales (Ademoglu et al., 1997; Demiralp and Ademoglu, 2001; Rosso et al., 2001). Orthogonal cubic spline functions were used here as mother wavelets and the time/frequency information was organized in a hierarchical scheme (Blanco et al., 1998; Rosso et al., 2001). Among several alternatives, cubic spline functions were used as symmetric, orthogonal, and combining in a suitable proportion smoothness with numerical advantages (for a complete discussion, see Unser, 1999; Thévenaz et al., 2000). Fig. 1 illustrates that after a five octave wavelet decomposition, the coefficients for the following frequency bands were obtained: 63 /125 Hz, 31 /63 Hz (gamma), 16/31 Hz (beta), 8/16 Hz (alpha) and 4/8Hz (theta), the residue was in the 0.1/4 Hz band (delta). The bottom of the figure also presents the number of coefficients and the time resolution (length of consecutive non-overlapping time windows) for each scale (frequency range). The number of coefficients and time windows used for computing the residue (0.1 /4 Hz) were the same as those used for the lowest resolution level (4/8 Hz). The highest frequency band (63/125 Hz) was not used further for analysis Wavelet energy In case of dyadic WT, the number of coefficients from all resolution levels is two times smaller than in the previous level. Here, the shortest time length including at least one coefficient from each resolution level was 128 ms. Hence, after the WT was performed, the analyzed signal was divided into non-overlapping time windows of 128 ms. Since the coefficients from each resolution level j correspond to different frequency bands, the energy E j for each frequency range in each time window of 128 ms can be computed as the corresponding squared coefficients (Fig. 1). For resolution levels with more than one

4 102 J. Yordanova et al. / Journal of Neuroscience Methods 117 (2002) 99/109 Fig. 1. Schematic illustration of the method: the ERP is transformed to the time/frequency domain by the WT. WT coefficients for each resolution level (gamma, beta, alpha, theta, and residual delta) are obtained and used for calculation of the wavelet energy, from which the relative energy is computed. Relative energies are further used for the calculation of WE. The minimum of WE in the post-stimulus epoch is identified (WEmin). Parameters of WT for each resolution level are given in the table. coefficient within 128 ms, E j was computed as the mean of squared coefficients within the respective 128 ms epoch. Total energy E tot of the signal in each time window was calculated as the sum of energies of all resolution levels. Thereafter, the relativewavelet energy P j was computed as the ratio between the energy of each level, E j, and total energy of the signal, E tot, in the respective time window: P j E j (1) E tot Relative energies were presented in percent to reflect the probability distribution of energies at different resolution levels Wavelet entropy The Shannon entropy (Shannon, 1948) gives a useful criterion for analyzing system s order/disorder by comparing probability distributions. When derived from the relative wavelet energies of EEG/ERPs, entropy measures reflect the degree of order/disorder of the EEG signal (Blanco et al., 1998; Quian Quiroga et al., 2001; Rosso et al., 2001): S WT X P j ln P j (2) j where S WT is the wavelet entropy designated as WE in the text. A very ordered EEG can be thought of as a periodic mono-frequency signal with a narrow band spectrum (Inouye et al., 1991, 1993). A wavelet representation of such a signal will be greatly resolved in one unique wavelet resolution level (scale). For this level, the relative wavelet energy will be almost 100% and the WE will be near zero or of a very low value. A signal generated by white noise can be taken as representing a very disordered behavior and will have a wavelet representation with significant contributions from all frequency bands. Moreover, these contributions can be expected to be of the same order and consequently, the relative wavelet energies will be almost equal for all resolutions levels, thus producing WE with maximal values. In the present study, WE of averaged potentials was evaluated to reflect the relationships among phaselocked multiple frequency ERP components. The temporal evolution of WE can be analyzed by computing WE for non-overlapping temporal windows of 128 ms. The obtained WE value was assigned to the central point of the respective time window. As illustrated in Fig. 1, the time window in the post-stimulus period, in which the WE was minimal (WEmin), was identified. The center of the window was used as a measure of the latency of WEmin, in which the stimulus induces the highest degree of frequency tuning in the brain electrical activity (the highest degree of order in the post-stimulus period). For appropriate evaluation of

5 J. Yordanova et al. / Journal of Neuroscience Methods 117 (2002) 99/ WEmin latency with the time resolution used here (128 ms), histograms were also constructed to reflect acrossindividuals frequency of occurrence of WEmin in different time windows. It is meaningful to validate whether the absolute value of the so identified WEmin differs significantly from a reference (pre-stimulus) epoch. Therefore, the WE change was used as a measurable parameter (Rosso et al., 2001). It reflects the ratio (in %) between WE of time windows in the post-stimulus period and a common reference time epoch from the pre-stimulus period. WE decrease (increase) implies that post-stimulus signal shows a higher degree of order (disorder) than the reference EEG signal Statistical analysis WE change, WEmin latency, and relative wavelet energies from the delta, theta, alpha, beta, and gamma ranges within the WEmin time window, were subjected to repeated-measures analysis of variance (ANOVA). In the ANOVA design, measures from F3, C3, P3 and T3 were used to form one level (left) of the laterality factor, and F4, C4, P4, and T4 were used for the second level (right) of the same factor. These electrodes were nested under four levels of the anterior-to-posterior region factor (frontal, central, parietal, and temporal). Thus, there were three within-subjects variables: modality (auditory vs. visual vs. bimodal) /laterality (right vs. left) /region (four levels). The Greenhouse /Geisser correction was applied to the repeated-measures factors with more than two levels. The original degrees of freedom df and the probability values from the reduced df are reported in the results. 3. Results 3.1. Time-domain evoked potentials Fig. 2 illustrates grand average AEPs, VEPs, and BEPs at 11 electrodes. For AEPs, an N1/P2 complex was clearly observed with central maximum. In addition, a P3-like wave was seen at frontal, parietal, and central sites at around 330 /350 ms (P330), perhaps due to the long and varying interstimulus intervals used here (Polich, 1998). For VEPs, a clear P1 wave occurred at occipital electrodes. A pronounced N1 /P2 complex with central maximum characterized further VEP morphology, with a P3-like wave being present at frontal locations. Although larger in amplitude, waveforms of BEPs at anterior sites were similar to those of AEPs. BEPs, like VEPs, manifested a well pronounced occipital P1, with the bimodal parieto /occipital P2 component being more prominent than after visual stimuli. Fig. 2. Grand average (N/15) auditory (AEP), visual (VEP), and bimodal (BEP) evoked potentials at 11 electrodes Characteristics of minimal WE Fig. 3 illustrates time courses of group mean WE for auditory, visual, and bimodal stimuli at 11 electrodes. The figure shows that (1) the WE was lower in the poststimulus than in the pre-stimulus time epochs, (2) stimulus-related decrease in WE was strong at frontal, central, and temporal locations and less evident at occipital sites for all stimulus conditions, (3) WE decrement was short-lasting and occurred with distinct time localization, (4) WEmin time localization was less specific, and WE decrease was less pronounced for visual than for auditory and bimodal stimuli Time localization of WEmin Fig. 4 presents the time localization of WEmin across individuals. The figure demonstrates that WEmin occurred within 128/256 ms in most of the cases (up to

6 104 J. Yordanova et al. / Journal of Neuroscience Methods 117 (2002) 99/109 Fig. 3. Group mean WE calculated from the averaged AEPs, VEPs and BEPs. 87%) for AEPs and BEPs. However, occipital ERPs did not manifest a stable time localization of WEmin. Also, a less stable time localization of WEmin was detected for VEPs, although frontal and central WEmin of VEPs occurred again most frequently in the 128/256 ms time epoch WE decrease Fig. 5 illustrates the WE decrease calculated in % for the time epoch of its absolute minimum in the poststimulus period. The effect of stimulus modality was significant (F(2,28) /18.06, P B/0.001), resulting from overall largest WE decrease for bimodal (mean /70.7%) as compared with auditory (/65.3%) and visual (/ 52.8%) stimuli. The difference between the two unimodal stimulus types (auditory vs. visual) was also significant. As also seen in the figure, the WE decrease was less expressed at occipital sites (region, F(4,56) / Fig. 4. Frequency histograms of individual WEmin occurrence at different time positions for AEPs, VEPs and BEPs. 7.52, P B/0.001). The significant modality /region interaction (F(8,112) /4.33, P B/0.01) resulted from a most pronounced WE decrease to auditory and bimodal stimuli at frontal, central, and parietal locations. Also, the difference between VEP and AEP/BEP was mostly expressed at the parietal electrodes Phase-locked frequency ERP components during WEmin Fig. 6 shows relative energies of different frequency ranges at the time of WEmin. It is remarkable that for

7 J. Yordanova et al. / Journal of Neuroscience Methods 117 (2002) 99/ Fig. 5. Group mean (9/1 S.E.) of WE decrease calculated in percents for the time epoch of its minimum (WEmin) in the post-stimulus period relative to a pre-stimulus reference. A, AEPs, V, VEPs, B, BEPs. each modality and lead, a strong predominance of relative theta power (up to 90%) was detected during WEmin. Subsequent analyses of relative band powers were performed to validate this observation and explore finer differences in frequency bands distribution during WEmin with respect to modality and topography Alpha components Alpha power contribution to WEmin was larger for VEPs (mean 20%) relative to AEPs (17%) and BEPs (10%), and at occipital /parietal (28 /30%) than at frontal /temporal (8 /9%) locations, but these effects did not reach a level of significance Delta components As seen in Fig. 6, delta power contribution to WEmin was very small (mean 7, 16, and 11% for AEPs, VEPs, and BEPs, respectively). It was more pronounced (12 / 20%) at occipital /parietal electrodes (region, F(4,56) / 3.04, P B/0.05), but this was valid only for VEPs and BEPs (modality/region, F(8,112) /3.28, P B/0.05) Theta components Although theta power largely predominated during WEmin for all stimulus conditions (Fig. 6), its contribution was significantly larger for bimodal (mean 76%) and auditory (mean 73%) than for visual (mean 56%) stimuli. Theta dominance was most pronounced at anterior (frontal /central/temporal) sites (region, F(4, 56) /13.74, P B/0.001), and less evident at parietal / occipital sites for visual, and at occipital sites for bimodal stimuli (modality /region, F(8,112) /4.69, P B/0.01) Beta components The relative power of beta WT components significantly differentiated bimodal from visual condition (modality, F(2, 28) /5.47, P B/0.05) and was larger for the visual stimuli, with this effect being most prominent at right-side electrodes (modality/laterality, F(2, 28) /3.67, P B/0.05). Since the contribution of relative gamma WT power was less than 0.5%, gamma components will not be considered Regression analysis These analyses demonstrated that WEmin was determined by dominance of theta frequency components of the evoked potentials. To determine to what extent the theta power may have also influenced WEmin latency (independently of modality and topography), a stepwise multiple regression analysis was performed. The dependent variable was the WEmin latency, and predictor variables were the relative powers of delta, theta, alpha,

8 106 J. Yordanova et al. / Journal of Neuroscience Methods 117 (2002) 99/109 Fig. 7. Time dynamics of relative energies of delta, theta, alpha and beta frequency ranges for six consecutive post-stimulus time windows. 4. Discussion 4.1. Wavelet entropy and frequency EEG responses Fig. 6. Relative energies of delta, theta, alpha, and beta frequency ranges during WEmin. Note that for each modality, a strong predominance of the relative theta power is clearly observed during WEmin. and beta activities, and coded vectors of region and modality. It was expected that variables that can affect WEmin latency independently of each other would be selected by the model. The model (R2 Total /0.249; F(3, 491) /54.47, P B/0.001) selected relative theta (B //0.32, P B/0.001), alpha (B //0.27, P B/0.001), and beta (B //0.55, P B/0.001) as independent predictors of WEmin latency. Fig. 7 illustrates that before 500 ms post-stimulus, and especially in the 128 /256 ms epoch, theta dominance determines WE decrement. After external stimulation oscillatory EEG responses from different frequency bands (gamma, beta, alpha, theta, delta) are generated simultaneously (Stampfer and Basar, 1985; Kolev et al., 1997; Demiralp et al., 1999; Basar, 1999; Karakas et al., 2000b). Oscillatory activity from each frequency band is basically characterized with its temporal dynamics (Basar et al., 2001). Typically, in the first 250/300 ms after external stimulus, oscillations from various frequencies are most enhanced and phaselocked (Basar, 1980, 1998), but in later post-stimulus epochs, they may be prolonged or suppressed depending on specific processing conditions (Basar-Eroglu et al., 1992; Krause et al., 1996; Yordanova et al., 2001; Kolev et al., 1999; Klimesch, 1996, 1999; Karakas et al., 2000a). Since the temporal dynamics of individual frequencies has been previously related with various aspects of stimulus evaluation and neural coding (Basar, 1999), it was important to establish if such dynamic changes reflected independent or interactive behavior of different frequencies. The present study demonstrates that during external stimulus processing, the simultaneous frequency ERP components interact in a specific way. The analysis of

9 J. Yordanova et al. / Journal of Neuroscience Methods 117 (2002) 99/ the ERP WE (Rosso et al., 2001; Rosso et al., in press) made it possible to confirm in an exact quantitative way that after auditory, visual, and bimodal stimulation there existed highly-ordered EEG microstates. For each modality, the decrease of entropy of multi-frequency EEG response occurred for short-lasting periods. This indicates that the ordering of event-related bioelectric activity is transient and localized in time. Furthermore, the highly-ordered microstates in each of the AEP, VEP, and BEP conditions were always determined by a regularity and dominance of synchronized theta responses (Fig. 6). Also, for each modality, ERP entropy decrease was most substantial and stable at anterior (central, frontal) locations and much less expressed and unstable at occipital sites (Fig. 5). The consistency of these observations across modalities implies that a thetadominated ERP microstate may reflect a modalityindependent fronto /central processing mechanism that is basically involved in sensory/cognitive processing (see also Kolev et al., 2001) Selectively distributed entropy changes in the framework of brain dynamics Standard measures of theta response amplitude have demonstrated that enhanced and phase-locked theta responses are generated at central/frontal locations upon visual and auditory stimuli (Sakowitz et al., 2000; Schürmann and Basar, 1994; Yordanova and Kolev, 1997, 1998a,b). Following bimodal (combined visual and auditory) stimulation, absolute amplitudes of frontal theta responses have been observed to be significantly larger than unimodal ones (Sakowitz et al., 2000). Notably, the theta dominance (largest relative theta energy) leading to ERP entropy minimum evaluated here also was maximal after bimodal stimuli and depended on the electrode such that entropy decreased mostly at anterior sites (Figs. 5 and 6). In view of these present and previous findings, it may seem that absolute rather than relative theta amplitudes have contributed substantially to entropy minimum. However, despite these signs of similarity, it has been previously shown that the most enhanced frequency ERP components vary with modality (Sakowitz et al., 2000). Only for BEPs, was the dominant peak of the AFCs in the theta range, indicating that theta ERP components were mostly amplified. In contrast, dominant AFC peaks for AEPs and VEPs occupied higherfrequency bands (slow and fast alpha, respectively). Yet, contrary to AFC results, the present study shows that for each modality (auditory, visual, bisensory), synchronized theta oscillations contribute to entropy decrease. Also, there is a remarkable difference between the spatial distribution of entropy and the spatial distribution of those oscillatory responses that are most enhanced in the modality-specific ERPs. For example, upon visual stimulation, alpha responses are most enhanced in comparison to other frequencies, and they are primarily distributed at central, parietal and occipital sites (Schürmann et al., 1997). However, the present results demonstrate that even with visual stimulation, entropy decrease is maximal at anterior locations and is determined by theta dominance. Furthermore, the spatial distribution of entropy decrease is similar across the three modalities. Possibly, a most important inference from these observations is that entropy decrements in distributed oscillatory responses are not necessarily linked to enhancements of oscillations and/or to the selectively distributed evoked coherences (Basar, 1980). Rather, the short-duration entropy decreases appear as complementary manifestations of oscillations and may act as additional operators for sensory and cognitive processes. It is not yet possible to make exact statements about the functional associations of ERP entropy and discussions have to be only limited to some theoretical implications in the framework of brain dynamics (Basar et al., 2001). In this regard, the present study provides evidence that the cooperative and integrative activity of multiple oscillations can specifically account for information processing mechanisms. The functional integration of multiple frequency oscillations acting in parallel can be conceptualized as reflecting basic properties of neural oscillatory systems: (1) collectivity, and (2) connectivity. Further, multiple neural oscillatory systems can be proposed to subserve brain information functions by the specific time localization and the specific spatial distribution of their interactions, which can be introduced as (1) time-coding and (2) space-coding operational principles of multiple oscillatory systems. Thus, time dynamics of individual frequency s power may be interpreted as resulting from interactive rather than independent behavior of oscillatory systems Theta dominance and cognitive processes: perspectives Synchronized event-related theta activity has been typically correlated with neural mechanisms related to higher brain functions including associative integration (Basar, 1998; Sakowitz et al., 2000), memory (Klimesch et al., 1997; Yordanova and Kolev, 1998a; Yordanova et al., 2000; Sarnthein et al., 1998; Burgess and Gruzelier, 2000), or focused attention (Basar-Eroglu et al., 1992; Karakas et al., 2000a). In the present study, task-related paradigms were not used. Accordingly, whether and how a prolongation of theta oscillations observed in task conditions (Stampfer and Basar, 1985; Basar- Eroglu et al., 1992; Kolev and Schürmann, 1992) would affect the spatio-temporal distribution of the ERP entropy decrease is a matter of interest. Further, Quian

10 108 J. Yordanova et al. / Journal of Neuroscience Methods 117 (2002) 99/109 Quiroga et al. (2001) have shown that the maximal entropy decrease occurs after target stimuli, possibly in relation with the so called P300-wave. Since P300 power comes from the delta frequency range, it is possible that a delta-dominated entropy may emerge during P300. Therefore, it is a matter of future investigation to establish if temporal-spatial distribution of entropy would show a specificity in relation to cognitive-specific processing and long distance space coherence. 5. Conclusion Entropy is a quantity describing the amount of order/ disorder in a system (Shannon, 1948). By summarizing a large amount of results on oscillatory brain dynamics, Basar (1980, 1999) made a statement on the basis of a semi-quantitative evaluation: Upon stimulation the brain oscillations (from delta to gamma frequency ranges) shift from disordered states to ordered states. By means of the WE method, this statement is verified in an exact quantitative way. Broad applications of this new metric of transient order/disorder EEG processes may gain new insights to the integrative sensorycognitive processing in the brain. In the present study, it is demonstrated that external stimulus processing produces a transient highly-ordered microstate in the ERPs reflected by WE minimum. The emergence of this ordered microstate (1) does not depend on stimulus modality, (2) is consistently determined by synchronized theta oscillations, (3) has a specific anterior distribution. This indicates that a transient dominance of stimuluslocked theta components may reflect a processing stage that is obligatory for stimulus evaluation, during which interfering activations from other frequency networks are minimized. Acknowledgements Work was supported by the James S. 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