Applied Acoustics 219 (2024) 109927

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Applied Acoustics
journal homepage: www.elsevier.com/locate/apacoustTechnical note

Neural activity and sound impression induced by virtual bass forindividuals who prefer bass-heavy audio
Kaori Asakawa a, *, Jin Hirano a, Takashi Yamazaki a, Masaru Kimura a, Yoichi Yamazaki b,
Kenji Katahira b, 1, Noriko Nagata babInformation Technology R&D Center, Mitsubishi Electric Corporation, Kamakura, Kanagawa 247-8501, Japan
School of Engineering, Kwansei Gakuin University, Sanda, Hyogo 669-1330, JapanA R T I C L E I N F O

A B S T R A C T

Keywords:
Virtual bass
Sound impression
Sound quality
Electroencephalogram (EEG)This study aimed to examine the impact of virtual bass technology on sound perception and its potential effects
on the electroencephalogram (EEG) of individuals who prefer bass-heavy audio. A group of eleven participants
was exposed to nine jazz music excerpts, with added harmonics generated from low-frequency components toenhance the bass sound virtually. The participants then evaluated the sound impression of these excerpts. The
findings revealed that the inclusion of bass harmonics in the sound sources led to a more pronounced “powerful”
impression compared to the absence of such enhancements. Furthermore, the study observed that the lower
alpha1 band power in the frontal and right centro-parietal regions increased when moderate harmonics wereadded, in contrast to the conditions without added harmonics or with excess harmonics. These results suggestthat virtual bass sound not only influences perceptual aspects but also induces physiological modulation.

1. Introduction
In recent years, advancements in technology have led to the devel­
opment of smaller digital media devices, including audio-visual moni­
tors and portable audio sets. However, these compact sizes often result
in compromised sound quality, particularly the bass frequency output ofsmall loudspeakers tends to be weak.
One approach to address this problem is the use of “virtual bass”[10,17,16] technology. Virtual bass technology amplifies the feeling ofbass by adding bass harmonics to sound using signal processing. The
technology is based on the phenomenon wherein humans perceive thepitch corresponding to the fundamental frequency upon hearing acomplex tone that lacks the fundamental frequency [15]. Previous
studies revealed that virtual bass technology can enhance the bass
feeling of a bass guitar solo having a stable frequency structure byadding a certain amount of bass harmonics, but its sound quality
decreased [17,16]. However, it is unclear whether virtual bass modu­
lates the acoustic perception of a musical track.
Recent studies on musical perception have focused on the correlation

between listener emotions and their physiological responses such as
brain activity. Sammler et al. [22] reported that there exists a correla­tion between listening to pleasant music and prefrontal midline theta
power. Some studies have demonstrated music-induced emotional
frontal asymmetry in the alpha (8–13 Hz), beta (18–22 Hz), and gamma(35–39 Hz) bands [23,6]. These studies have suggested that musicinduced negative or positive emotion is associated with the electroen­
cephalogram (EEG) power spectrum. While virtual bass sound, also
known as missing fundamental sound, has been used to investigate theneural basis for pitch perception [24,3,31] and the neural encoding ofsound frequency [5,32,11,33], it has been reported that virtual bass canalter the timber and sound quality [15,17,16]. Given that it has beensuggested that differences in sound quality affect brain activity [13],brain states caused by differences in the quality of virtual bass should beobserved. However, it is unclear whether virtual bass, which provides a
subtle but significant difference to the feeling of a sound, affects neural
responses.
This study aimed to examine the effect of virtual bass on soundimpression and brain activity. To this end, subjective rating experiments* Corresponding author.
E-mail addresses: Asakawa.Kaori@ab.MitsubishiElectric.co.jp, kaori.h.asakawa@gmail.com (K. Asakawa), Hirano.Jin@ah.mitsubishielectric.co.jp (J. Hirano),
Yamazaki.Takashi@ab.mitsubishielectric.co.jp (T. Yamazaki), Kimura.Masaru@ah.MitsubishiElectric.co.jp (M. Kimura), y-yamazaki@kwansei.ac.jp(Y. Yamazaki), katahira@waseda.jp (K. Katahira), nagata@kwansei.ac.jp (N. Nagata).
1
Current address: Waseda University, Shinjyuku, Tokyo 162-8644, Japan.
https://doi.org/10.1016/j.apacoust.2024.109927
Received 4 July 2023; Received in revised form 22 December 2023; Accepted 15 February 2024
Available online 26 February 2024
0003-682X/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).

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were conducted. The participants evaluated their emotion and impres­sion of music containing virtual bass. Moreover, we measured EEG
power while the participants were listening to music to determine theeffects of the added bass harmonics on brain activity.

based on the evaluation objective: physical quantity (e.g., “exist low/high frequency”), impressions (e.g., “gentle”), and representation (e.g.,“realistic-feeling”) on sound. Accordingly, 32, 32, and 31 words wereselected based on the appropriate score in each group, resulting inaverage appropriate scores of 3.49, 3.58, and 3.59, minimum scores
were 3.00, 2.67, and 3.33 respectively. (4) Six sound engineers evalu­
ated the similarity of all pairs of 32 words (i.e., 32 words × (32–1)
words ÷ 2 = 496 pairs) in the impression group using six scores from 1(“very similar”) to 6 (“very not similar”). (5) Hierarchical cluster anal­
ysis using Ward’s method was performed based on the similarity score,
which resulted in seven clusters. (6) One representative word and two
subsidiary words were extracted based on the closeness from the centerof the cluster with higher appropriate ratings by clusters. Finally, we
obtained the following adjectives2: “heavy feeling (punchy, weighty),”“have a spatial depth (fine, transparent),” “sharply defined (clear,chiseled),” “have a core (not get thin even though low-volume, care­free),” “harsh (hurtful, hard),” “thin (skinny, light),” and “cloudy(blurry, muffled).” In addition to these, we adopted “muddy” to evaluate
for sound with distortion [9], and “preference” as a word to indicate how
participants like the sound quality [19,28].
The participants were asked to rate the musical excerpts using thenine adjectives mentioned above on a scale ranging from 0 to 100. These
ratings were provided using the corresponding adjective words in Jap­
anese. Throughout the experiment, participants listened to nine music
excerpts under each of the three conditions, presented in a pseudorandom order. In the subjective evaluation experiment, each music
excerpt was played three times within a condition. The sequencing wasorganized to ensure that the same condition or piece of music was notplayed consecutively. After each music excerpt, participants wererequired to rate it on a scale of 0–100 and indicate their level of affective
response (valence and arousal) while listening to the piece of music
using the same scale. The stimuli were normalized using Adobe Audition
3.0 and presented at A-weighted sound pressure level of 76 dB through aheadphone amplifier (iFI nano iDSD) and a headphone (Sennheiser HD
650). The stimuli were adjusted to the average amplitude of all the files,
and a cosine window of 500 ms was applied to the beginning and end
portions. Fig. 2 illustrates an example of the final spectrum and original
spectrum for a sound stimulus, averaged over 60 s. According to the
product specifications, the headphone had a reproduction frequency
range of 10–41,000 Hz, which covered the required frequency range forthe experiment.
In the EEG experiment, the participants, stimuli, and apparatus usedwere the same as those in the subjective evaluation experiment. EEG
data were recorded using the BioSemi Active-Two system (BioSemi Inc.,
The Netherlands). An EEG array containing 64 active electrodes thatcover the entire scalp (according to the International 10/20 EEG sys­tem), along with an additional six channels of electrodes placed on theface, collected EEG data at a sampling rate of 1,024 Hz. Participants
wore the headphones over an EEG headcap. Prior to the experiment, a
trained experimenter confirmed that there was no interference to the
EEG waveform from the headphones. During each trial, the music
excerpt was played following a 1-minute period of silence. The silence
and music started 1 s after the participants pressed a button. Participants
were instructed to keep their eyes closed both during the silence andwhile listening to the music. After the music ended, participants wereasked to open their eyes and evaluate the excerpt to investigate thecorrelation between subjective evaluation and EEG power. Auditorycues, such as a beep, were used to indicate the start of the trial, when tolisten to the excerpt, and when to subjectively evaluate it. During theEEG experiment, each music excerpt within a condition was played only
once.
EEG data analysis was conducted in a similar manner to that of

2. Materials and methods
The virtual bass technology caters to individuals with a keen
perception of bass sound. To select these participants, a preliminary
experiment was conducted with 31 individuals (17 males and 14 fe­
males, average age 20.7 years) from Kwansei Gakuin University, whoprovided informed consent. All participants were native Japanese
speakers. In the preliminary experiment, three jazz music pieces (“Lul­laby for Rabbit” by Makoto Ozone, “Never Let Me Go” by Junko Onishi
Trio, and “So What” by Miles Davis) were presented three times under
three different conditions: bass frequencies below 100 Hz were filteredout using a fourth-order infinite impulse response (IIR) filter or aneighth-order IIR filter, or left non-filtered. The task was the same as theone in the subjective evaluation experiment explained in detail later.
The results revealed that 17 participants reported higher valence scoresfor the music excerpts that retained their original low frequencies
compared to those with the bass frequencies filtered out in the cases ofusing fourth- and eighth-order IIR. These participants were then selected
for the subsequent experiments.
For the subjective rating and EEG experiments, of the 17 partici­
pants, a total of eleven individuals (seven males and four females,
averaging 20.8 years of age) with normal hearing and right-handedness
were able to attend the subsequent experiments. The Research Ethics
Committee of Kwansei Gakuin University approved this study.
Stimuli for the experiments comprised nine jazz music pieces(“Beyond the Summer II” by Cro-magnon, “Blue Lights” by Art Farmer,“Cry Me A River” by Dexter Gordon, “Django” by Modern Jazz Quartet,“Home” by Makoto Ozone, “If…” by Hiromi Uehara, “Iqbal” by Yusef
Lateef, “Round Midnight” by Donald Byrd, and “Satin Doll” by Dave
Grusin). These differed from the ones used in the preliminary study.
These music sources were selected based on their rich low frequencies,
particularly below 100 Hz, such as pizzicato for double bass. Sixtysecond instrumental excerpts without vocals were extracted from un­compressed files (44, 100 Hz sampling, 16 bits, 2 channels) of theoriginal sound sources for signal processing.
To create the “virtual bass” sound, a signal processing algorithm
utilizing quarter cycle detection [30] was employed. The algorithm in­troduces harmonic distortion to the original sound by detecting quarterperiod intervals from the bass component and setting them to zero. Thisapproach enables the smooth generation of odd- and even-order har­monics regardless of the bass component’s amplitude, minimizing soundquality degradation [29]. Fig. 1 illustrates the framework for generating
virtual bass sound. In this study, harmonics generated from lowfrequency components in the range of 50–100 Hz were added to the
input signal, and components below 100 Hz were subsequently
removed. The harmonics were low-pass filtered at 150 Hz to preserve
the sound quality, and then high-pass filtered at 100 Hz to eliminate thefundamental frequency range. We used a finite impulse response (FIR)
filter designed using the Kaiser window, except for a 150 Hz low passfilter, which was a second-order IIR filter (Q = 0.707), designed to makethe attenuation slope 12 dB/oct. Each music piece was played under
three conditions: 0 %, 10 %, and 50 % harmonic distortion. The ratio of
the squares of the harmonics to the sum of squares of the input signal
from the original music was calculated.
Participants rated the musical excerpts on nine adjectives. These
adjectives were determined using following the steps. (1) The words
used for the description of sound representation quality by seven soundengineers were collected. (2) Six sound engineers evaluated the appro­
priateness of 245 words obtained in the first step using four scores–from
1 (“inappropriate”) to 4 (“appropriate”). (3) Two authors (one had
experience with sound quality evaluation) sorted them into three groups

2
The words in parentheses were subsidiary words. For simplicity, only
representative words were described in the following.

2

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Fig. 1. Framework of creating virtual bass sound stimuli. HPF: high-pass filter, LPF: low-pass filter. The numbers indicate cut-off frequency.

Fig. 2. Example of average sound spectrum for a sound file (“Cry Me A River”) for 60 s.

Katahira et al. [12]. The data was analyzed using EEGLAB (Version
13.4.4b) and FieldTrip (Build 20140522) working on MATLAB (Version
R2013b, MathWorks, Inc., Massachusetts, USA). The EEG data were
down-sampled to 256 Hz and filtered using 1 and 100 Hz bandpass. An
independent component analysis was performed on the EEG data toidentify independent components that correspond to noises that were to
be manually rejected using visual inspection. Additionally, the Lap­lacian filter was applied. Each trial was extracted from the preprocessed
data, and the trials, including eye blinks and noisy channels, where
potentials exceeded ±100 μV, were discarded.
The power spectrum analysis followed a setup similar to that of aprevious study conducted by Sammler et al. [22]. Welch’s method with awindow width of 2 s and a 50 % overlap was used to estimate the power
spectrum. The individual alpha frequency (IAF) was calculated byaveraging the peak frequency in the range of 8–13 Hz at three electrodes(CPz, Pz, and POz) from the individual power spectra during the silent
period in each trial. The mean IAF was 10.0 Hz (SD: 0.78 Hz; IAF range:
8.7–11.5 Hz). The relative frequency bands were defined individually bymultiplying the IAF with the following ranges: lower limit 0.4 to upper
limit 0.6 as theta, similarly, 0.6–0.8 as lower alpha1, 0.8–1.0 as lower
alpha2, 1.0–1.2 as upper alpha, and beta 1.2–30 Hz (only beta upper
limit was fixed frequency) [8]. These individual band power values werenormalized using Eq. (1) for each participant. For each condition (C),electrode (e), and frequency band (f), the individual band power was
divided by the average band power across N = 64 electrodes measured
in the 0 % condition, which served as a baseline (B) for the same
participant and frequency band. The EEG power spectra in each fre­quency band in each condition (0, 10, 50 %) were calculated using Eq.
, P(10%), and P(50%), respectively. In this study,(1) and denoted as P(0%)
C
CC
each EEG power value was normalized using PC (0%) as the baseline.
C (e, f )
̂ C (e, f ) = / P∑P
1 N * Ne=1 PB (e, f )(1)

For the statistical analysis, the electrodes were grouped into sevenregions of interest (ROI) as follows: fronto-central (AFz, Fz, FCz), left
frontal (AF3, F3, F7, FC3), right frontal (AF4, F4, F8, FC4), left centroparietal (C3, C5, CP3, CP5), right centro-parietal (C4, C6, CP4, CP6),
left parieto-occipital (P3, P5, PO3, PO7), and right parieto-occipital (P4,P6, PO4, PO8).
3. Results
Regarding data in the subjective evaluation experiment, rating scores3

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Applied Acoustics 219 (2024) 109927

As listed in Table 1, the first factor could be interpreted as a factor of“clarity” and included “cloudy,” “sharply defined,” “have a spatial
depth,” “muddy,” and “preference.” The second factor was a factor of“powerful,” and included “thin,” “heavy feeling,” “have a core,” and“harsh.”To examine whether these two factors were affected by the additionof bass harmonics, the factor score for each participant was calculated
for each condition and music excerpt. The regression method was used
to obtain the factor scores with IBM SPSS Statistics 24. Subsequently,
one-way ANOVAs with repeated measures on data averaged music ex­
cerpts were conducted separately for each factor.
The analysis revealed significant main effects and differences be­
tween conditions for the factor scores of “powerful” [F (2, 20) = 9.91, p< 0.01, 0 % < 10 %, 0 % < 50 %]. The “clarity” factor scores showed nosignificant differences between the conditions [F (2, 20) = 1.26, p =0.30] (Fig. 4).

were averaged across repetitions. The data for each participant in eachdistortion condition averaged for music excerpts were used for analysis
to examine the pure effect of the virtual bass, i.e., analyses of variance(ANOVAs) for subjective ratings and factor score, and correlation
analysis in the EEG experiment. In the analysis aimed at investigating
the psychological evaluation structure or naive EEG response to thevirtual bass, i.e., factor analysis for subjective ratings and ROI analysis
for EEG data, the data that were not averaged for music excerpts wereused.
3.1. Subjective evaluation
Fig. 3 shows the average subjective ratings for all music excerpts
across each condition of the subjective evaluation experiment. To
examine the effect of the conditions, separate one-way ANOVAs withrepeated measures on data averaged repetition and music excerpts were
performed for each adjective and affective response. Post hoc multiple
comparisons of means within each harmonic condition were conductedusing the Bonferroni method at a significance level of 0.05.
The analysis revealed significant main effects and differences be­
tween the conditions for “heavy feeling” [F (2, 20) = 9.84, p < 0.01, 0 %< 10 %, 0 % < 50 %], “have a core” [F (2, 20) = 7.84, p < 0.01, 0 % < 10%], and “thin” [F (2, 20) = 10.99, p < 0.01, 0 % > 10 %, 0 % > 50 %].
“Cloudy” showed a significant main effect [F (2, 20) = 3.74, p <0.05] but no significant differences between conditions. “Preference”
showed only a significant tendency of the main effect [F (2, 20) = 3.48,
p = 0.051].
The perception of “heavy feeling” and “have a core” was greater atthe 10 % condition compared to the 0 % condition. Although the p-value(p = 0.051) did not reach the significance level of 0.05, the “preference”
was higher only at the 10 % condition compared to the other conditions.
Additionally, a factor analysis on data averaged repetition was
conducted to explore the underlying common factors among the nine
adjectives. Using the maximum-likelihood method and promax rotation,a factor analysis revealed two factors based on parallel analysis, which
accounted for 59.49 % of the total variance.

3.2. EEG data
To examine the impact of adding harmonics on the EEG data, theKruskal–Wallis test was conducted for the data of each participant ineach distortion condition and music excerpts followed by a Steel–Dwass
Table 1Result of factor analysis of subjective ratings for adjectives.
CloudySharply defined
Have a spatial depth
Muddy
Preference
Thin
Heavy feeling
Have a core
HarshContribution ratio (%)

Factor 1

Factor 2− 0.890.86
0.81− 0.71
0.69
0.09
0.03
0.23
− 0.0332.99

0.01− 0.080.04− 0.090.08− 1.050.69
0.58− 0.1826.50

Fig. 3. Mean subjective ratings for 9 music excerpts in 0 %, 10 %, and 50 % added harmonics to high-pass filtered signal ratio. The marks (+p < 0.10, *p < 0.05, **p< 0.01) indicate the main effect of conditions for each word.
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multiple comparison (p < 0.05) for the five EEG rhythms, i.e., therelative frequency band of IAF, in each of the seven areas, individually.
The results showed significant main effects for lower alpha1 in thefronto-central [χ2 (2) = 7.26, 10 % > 50 %], left frontal [χ2 (2) = 10.25,
0 % < 10 %, 10 % > 50 %], right frontal [χ2 (2) = 7.10, 0 % < 10 %], andright centro-parietal [χ2 (2) = 6.62, 0 % < 10 %] (Fig. 5). Additionally,
upper alpha [χ2 (2) = 6.01] and beta [χ2 (2) = 6.06] in the left frontal
were significantly affected by the addition of bass harmonics; however,the differences between conditions were not significant.
A factor analysis was conducted on the subjective rating data ob­tained from the EEG experiment. Two factors, namely the clarity factor
and the powerful factor, were extracted using the same adjectives,
explaining a total variance of 57.35 % and 8.75 %, respectively. The factthat the same factors were obtained in both experiments suggests thatthe evaluation construction for the virtual bass sound is consistent
within participants. The contribution ratio of the clarity factor in the
EEG experiment was higher than that in the subjective evaluation
experiment. This could be because each excerpt was not repeated in the
EEG experiment.
Correlations between EEG power, which exhibited significant dif­ferences between conditions, and factor scores were examined. To
calculate the correlation coefficients, subjective evaluation data werepooled across individuals and the values at the 0 % condition weresubtracted from those at the 10 % or 50 % condition to account for in­dividual differences. Subjective evaluation data above 2SD were

Fig. 4. The “clarity’” and “powerful” factor scores in 0 %, 10 %, and 50 %added harmonics to signal ratio. The error bars show standard error. **p< 0.01.

Fig. 5. Lower alpha1 power in 0 %, 10 %, and 50 % added harmonics to signal ratio. *p < 0.05, **p < 0.01.
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excluded as outliers. EEG data in the 10 % and 50 % conditions weredirectly used as they were already normalized to the 0 % condition. The
subjective ratings of affect (valence and arousal) and factor scoresshowed no significant correlations with these EEG rhythms.

the consonant chords vs. the dissonant chords [20], the joyful consonant
dance music vs. the pitch-shifted dissonant versions of the same excerpts[22], the film soundtracks that many people rated as joy and pleasure vs.
angry and sad [14], the pleasurable chill-inducing music chosen by theparticipants themselves vs. the neutral music chosen by the experi­
menter [4]. The sound stimuli in this study comprised various jazz musicwith harmonics vs. low-pass filtered of the same excerpts, and there
were no significant differences in pleasantness ratings between theamount of harmonics. This could result in no correlation with emotion
ratings. On the other hand, when participants who prefer bass listen tomusic with enhanced or no bass, they could feel pleasure in a more
moderate level of bass that does not detract from the sound quality.
Although the correlation was not significant, as discussed above, theresults of this study suggested that the virtual bass sound improved
sound quality based on subjective evaluation. Furthermore, EEG activ­
ities in the low frequency band in the frontal region were higher when amedium amount of harmonics was added than in the other conditions.
Our results could be influenced by individual preferences for the musicor advanced sound impression, and it remains to be investigated. This is
the first study to address the relationship between factor scores of virtual
bass sound impressions and EEG, future studies could provide sugges­
tions in this perspective.
An increase of lower alpha1 in the right centro-parietal at the 10 %condition may be related to tension arousal [7]. Although the rightcentro-parietal alpha was not statistically correlated to the subjective
arousal score, the powerful impression of sound may lead to tension
arousal.
In this study, virtual bass technology was employed as a means tomodify sound quality and examine its effect on EEG. Future studies can
explore other aspects of sound, such as reality, powerful impressions,and the clarity factor of 3D sound, to gain further insights into theimpact of modifying sound quality and impressions on humanperception.
Moreover, as this study could be influenced by individual preferencein terms of sound quality and impression [2], future studies can explore
the degree of change in the EEG. Such investigations would be useful in
exploring the neural basis of the perception of sound impressions.

4. Discussion
In this study, we investigated the impact of virtual bass on subjective
impression of music excerpts and their brain activity involving partici­
pants who preferred bass-heavy audio.
The findings revealed that the inclusion of bass harmonics using
virtual bass resulted in a more powerful impression of the musiccompared to excerpts without the harmonics. Moreover, participantstended to prefer the music excerpt with an additional 10 % harmonics ofthe total power over the other conditions.
Furthermore, our study unveiled the influence of virtual bass on
brain activity. Specifically, the lower alpha1 band power in the frontal
and right centro-parietal regions increased when 10 % harmonics wereadded, in contrast to the conditions without added harmonics or with 50% harmonics.
The results that state that adding harmonics enhances powerfulimpression are consistent with those of a previous study by Mu et al.
[17]. They reported that adding harmonics impaired sound quality, thatis, noise and distortion, increased by the addition of virtual bass.
Meanwhile, our results indicate that the clarity of sound does notdecrease significantly when harmonics are added. This difference may
be attributed to some procedural factors such as the stimuli used or thealgorithms employed for creating the virtual bass sound.
EEG activities in the low frequency band (lower alpha1, 6–8 Hz) in
the frontal region were higher in the 10 % condition than in the others.
Virtual bass sound increased the powerful impression and did not reducethe impression of clarity in the subjective rating experiment. Interest­
ingly, it has been reported that the context of perception in concert halls,
the strength of bass (i.e., powerful impression) and clarity are consid­
ered the critical components for sound quality [26]. Accordingly, it is
implied that the sound with a high score in both powerful and clear
impression in virtual bass could be regarded as high-quality soundcompared to the sound with a low powerful impression in the 0 %condition. Alpha power has been understood to be relaxed andcomfortable, that is, a rest state in the brain [25,21]. Previous studies
showed that the frontal alpha power increases during meditation [1,27],
listening to preferred music [18] or consonant compared to dissonant
chords [20]. Hence, the participants in this study can be considered tohave experienced positive emotion while listening to virtual bass soundwith high quality compared to no virtual bass.
However, the effects of virtual bass were not identical in the amountof distortion, particularly in EEG data. The lower alpha1 power in the 50% condition significantly decreased compared to the 10 % condition in
the fronto-central and left frontal regions. Moreover, although it was
only a statistical tendency, participants preferred the sound in the 10 %condition over the other conditions. Collectively, it could be inferredthat sound with too much harmonic distortions undermine the benefit ofvirtual bass.
Our results did not show a simultaneous increase in pleasant ratings
and frontal midline (Fm) theta power while listening to virtual bass,
although Sammler et al. [22] reported an association between pleasantmusic and Fm theta rhythm. This can be attributed to the difference inthe extent of valence emotion induced by the stimuli (i.e., consonant anddissonant music) or whether the same excerpts add some harmonics.
The correlation analysis in this study between subjective ratings andlower alpha 1 power, which differed significantly between conditions,showed no significant effects. Previous studies have reported that EEG in
the frontal area in the lower frequency range, such as the lower alpha1
and theta bands, are closely associated with listening to music thatelicits pleasant emotions. The sound stimuli used in these studies pro­
duced strong and distinct positive or negative emotions. For example,

5. Conclusion
In this study, two experiments were conducted: a subjective rating
experiment, where listeners evaluated their emotions and sound im­pressions of music excerpts with virtual bass, and an EEG measurement
experiment while listening to the same excerpts.
The results demonstrated that the addition of bass harmonics using
virtual bass resulted in a heightened powerful impression retaining aclear impression compared to excerpts without bass harmonics. More­over, excerpts with a moderate (10 %) addition of harmonics tended to
be preferred over those with either too little (0 %) or too much (50 %)harmonics added. Additionally, the analysis revealed an increase inlower alpha1 band power in the frontal and right centro-parietal regions
under the added harmonics conditions.
The key findings of this study are as follows: First, the inclusion ofmoderate harmonics in virtual bass technology enhances not only theperception of bass, but also the impressions of power leaving clarity.
Second, virtual bass that induces significant differences in sound im­pressions without causing drastic emotional changes can influence brain
activity, particularly in the lower frequency band power. Accordingly,
our findings suggest that virtual bass technology has the potential toenhance sound quality and elicit positive experiences in listeners who
prefer bass sounds, from both psychological and physiological per­spectives. While this study focused on individuals with a preference forbass sounds, future research could explore the relationship between
sound quality preferences and brain activity by considering various
types of listener preferences to reach more generalized conclusions.

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Kaori Asakawa: Conceptualization, Data curation, Formal analysis,
Methodology, Resources, Software, Visualization, Writing – originaldraft, Writing – review & editing. Jin Hirano: Methodology, Resources.
Takashi Yamazaki: Methodology, Resources, Software. Masaru
Kimura: Conceptualization, Funding acquisition, Methodology, Project
administration, Resources, Software, Writing – review & editing. Yoichi
Yamazaki: Data curation, Formal analysis, Investigation, Methodology,
Resources, Software, Visualization, Writing – review & editing. Kenji
Katahira: Formal analysis, Investigation, Methodology, Resources.
Noriko Nagata: Funding acquisition, Methodology, Project adminis­
tration, Resources, Supervision, Writing – review & editing.
Declaration of competing interest
The authors declare the following financial interests/personal re­lationships which may be considered as potential competing interests:‘Noriko Nagata reports financial support was provided by Japan Scienceand Technology Agency (JST). Takashi Yamazaki, Masaru Kimura haspatent #Harmonic generator. Patent JP 2009-216797. licensed to Mit­
subishielectric corp.1’.
Data availability
Data will be made available on request.
Acknowledgments
This research was partially supported by the Center of Innovation
Program (Grant No. JPMJCE1314) from the Japan Science and Tech­
nology Agency (JST).
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