 η 2 = 0.64]. Subsequently performed post hoc tests (Bonferroni-Holm corrected) revealed that the ERS values were stronger during the 2nd and 3rd minutes of the music presentation than in the 1st minute. For most EEG measures, there were no differences in ERS during the 2nd and 3rd minutes of the music presentation, except for the beta band, where there was a linear increase from the 1st to the 3rd minutes (3 > 2, 2 > 1). For theta and all alpha bands, significant effects were seen for Scalp Location and Site. Bonferroni-Holm corrected post hoc tests revealed that the ERS values at the central and parietal sensor positions were the strongest, compared to the frontal positions. Site turned out to be significant for theta, low alpha-1, and low alpha-2, with the central sensor position yielding the largest ERS measures. Comparing ERS values obtained from the left and right sensors revealed practically identical measures for all frequency bands, except for low alpha-2, for which the right-sided sensors yielded the strongest ERS measures (p = 0.059, corrected for multiple comparisons).

Four-Way ANOVA for Testing between Different Frequency Bands (Step 9)
Since Trial turned out to be insensitive, we calculated mean values for ERS and the psychophysiological measures and subjected these measures to a four-way repeated measures ANOVA with Time Frame (1st, 2nd, and 3rd minutes of music listening), Site (left, central, and right), Scalp Location (frontal, central, and parietal), and Frequency (theta, low alpha-1, low alpha-2, upper alpha, and beta) as independent variables. The significant effects are shown in Table 2. The main effect of Frequency is qualified by larger ERS for low alpha-2 and upper alpha. The smallest ERS was found for beta and theta, with no differences between them. No differences were seen between low alpha-1 and upper alpha, low alpha-1 and theta, or low alpha-2 and upper alpha. Low alpha-2 did differ, however, from low alpha-1. The main effect of Scalp Location depended on larger ERS for central and parietal EOIs. The main effect of Frequency × Time Frame depended on differences in the power increase from the 1st to the 2nd and 3rd minutes of the music presentation. Descriptively, the strongest power increases were for low alpha-1 (mean = 0.16) and low alpha-2 (mean = 0.16), followed by upper-alpha (mean = 0.095). The smallest increases were found for beta (0.04) and theta (0.057). Bonferroni-Holm corrected post hoc tests revealed no significant differences between beta and theta, beta and low alpha-1, low alpha-1 and upper alpha, or low alpha-2 and upper alpha. However, low alpha-2 showed stronger increases from the 1st to the last 2 min, compared to theta, beta, and upper alpha. In addition, upper alpha revealed stronger increases than theta and beta. The Frequency × Scalp Location interaction was determined by the fact that there is no significant difference across frontal, central, and parietal EOIs for the beta band. For all other frequency bands, the central and parietal EOIs demonstrated stronger power increases than did the frontal EOI. Frequency × Site is qualified by larger power increases of the central EOI for low alpha-1 and low alpha-2. For the other frequency bands, we found no differences between the left, central, and right EOIs. The final significant interaction is qualified by larger increases from the 1st minute to the last 2 min over central EOIs. No differences were seen with respect to power increases of left or right EOIs (central: mean = 0.14, SD = 0.10; left: mean = 0.11, SD = 0.07; right: mean = 0.12, SD = 0.08). The mean relative EEG measures for each frequency band are shown as topoplots in Figure 10. In addition, the mean ERS are shown in Figure 11, together with the mean HR and EDA measures.

Discussion
This study was designed to examine five questions: (1) Does a consistent relationship exist between the time course of EEG oscillations and the time course of particular acoustic features of the presented musical piece? (2) Similarly, are the time courses of psychophysiological measures (HR and EDA) related to the time courses of the acoustic features of the musical piece? (3) How stable are the time courses of EEG oscillations and the time courses of psychophysiological measures across repeated music presentations? (4) Do the neurophysiological responses that occur during music listening change across repeated presentations of the same musical piece? (5) Similarly, do EDA and HR measured during music listening change across repeated exposures to the same musical piece? For the musical piece, we chose a popular and wellknown aria lasting more than 3 min. We chose this aria FIGURE 10 | Topoplots of the relative EEG power changes relative to the baseline for each frequency band and for the 3 min of the music presentation. Please note that we used the same scale for all frequency bands. All changes in power are positive, indicating ERS. (1m: 1st minute, 2m: 2nd minute, 3m: 3rd minute).
because its acoustic intensity varies considerably throughout the 3 min. The aria was presented 17 times to the subjects, while EEG oscillations, HR, and EDA were continuously measured. So far, examination of the time course of EEG oscillations during music listening has rarely been done. Two recent studies examined neurophysiological responses to realistic musical pieces (Cong et al., 2013;Sturm et al., 2014), but the results are difficult to relate to our project. The study by Cong et al. (2013) was published in the proceedings of a technical conference and clearly focused on the elaboration of specific techniques for analyzing the time courses of EEG data, without reference to emotional responses or repeated presentation musical stimuli. The paper by Sturm et al. (2014) reports on ten epileptic patients from whom the investigators measured electrocorticographic (ECoG) signals from 53 to 134 channels placed only over the left perisylvian brain while the patients listened to a rock song and a narrative. This was an invasive manipulation, thus definitively demolishing the pleasureful aspect of listening to music.
Our study does, however, have a strong relation with the two studies of Mikutta et al. (2012Mikutta et al. (, 2014, which specifically examined the time courses of EEG oscillations and emotional responses during music listening. In both studies, rather than studying the time courses of EEG oscillations in detail, the authors used an approach they named "covariance mapping." In this technique, the correlations (or covariances) between the time courses of subjective ratings (here, arousal) and the EEG oscillations are calculated and mapped onto topoplots. In this context, the particular time courses are not analyzed in detail. In addition, the authors of this pioneering work related the time courses of subjective ratings obtained in separate sessions to the EEG time courses. Thus, it is not clear whether the sequential measurement might have influenced their results. In our study, we examined the EEG oscillation time courses in more detail and related them to the time courses of acoustic features and psychophysiological variables. EDA and HR were used as psychophysiological measures representing general arousal, thus providing the opportunity to simultaneously obtain arousal measures with the EEG oscillations. Since no published studies have looked at the stability of EEG oscillations in the context of repeated presentation of musical pieces, we presented 17 repetitions of our chosen musical piece. This frequent repetition may mimic the repeated listening of musical pieces done in real-life situations. Furthermore, a wealth of studies have shown that our physiological system adapts, or habituates, to repeated exposure to the same stimulus. In the following section, we will discuss our results in a more comprehensive manner.

Behavioral and Psychophysiological Data
While the initial valence and arousal ratings indicate a general rating of the aria as positive and as arousing, different ratings were obtained after the 17th presentation of the aria. After the final presentation, the subjects rated the aria as less arousing and less positive than after the first presentation. Thus, a kind of habituation in terms of subjective rating took place. Habituation is normally accompanied by typical changes in psychophysiological measures, e.g., reduced HR and EDA reactions (Holzl et al., 1975;Elton et al., 1983). However, we did not find a habituation-like reduction in psychophysiological responses after repeated presentations. The HR and EDA responses to the musical stimuli were roughly similar across the 17 presentations. Thus, subjective ratings and psychophysiological responses do not correlate with one other and actually dissociate. Inspecting the mean HR time course reveals a pattern of initial acceleration up to 4 bpm (lasting ∼10 s) followed by HR deceleration within the 1st minute of music presentation. During the final 2 min of the music presentation, HR normalizes to the baseline level. A similar time course was found for EDA, which showed an initial increase followed by a decrease. However, at the end of the aria presentation, when the intensity of the acoustic stimulus increased, EDA responses increased again. Since EDA responses are known to correlate with subjective arousal (Ellis and Simons, 2005), it is likely that the louder music at the end of the presentation evoked greater psychophysiological arousal. A roughly similar HR time course in response to positively rated musical pieces was reported by Sammler et al. (2007), who saw an initial decrease in HR in the first 2 s of music presentation, followed by HR acceleration and normalization to baseline levels after 4-6 s. Our findings demonstrate that the chosen