This paper is available in PDF format [here](https://www.biorxiv.org/content/10.1101/2021.04.02.438269v3.full.pdf).

## Abstract

The goal of this study was to investigate the effect of sounds on human
focus and to identify the properties that contribute most to increasing
and decreasing focus in people within their natural, everyday
environment. Participants (N=62, 18-65y) performed various tasks on a
tablet computer while listening to either no background sounds
(silence), popular music playlists designed to increase focus
(pre-recorded songs in a particular sequence), or engineered soundscapes
that were personalized to individual listeners (digital audio composed
in real-time based on input parameters such as heart rate, time of day,
location, etc.). Sounds were delivered to participants through
headphones while simultaneously their brain signals were recorded by a
portable electroencephalography headband. Participants completed four
one-hour long sessions at home during which different sound content
played continuously. Using brain decoding technology, we obtained
individual participant focus levels over time and used this data to
analyze the effects of various properties of sound. We found that while
participants were working, personalized soundscapes increased their
focus significantly above silence (p=0.008), while music playlists did
not have a significant effect. For the young adult demographic (18-36y),
all sound content tested was significantly better than silence at
producing focus (p=0.001-0.009). Personalized soundscapes increased
focus the most relative to silence, but playlists of pre-recorded songs
also increased focus significantly during specific time intervals.
Ultimately we found that it is possible to accurately predict human
focus levels that will be experienced in response to sounds _a priori_ based on the sound’s physical properties. We then applied this finding
to compare between music genres and revealed that classical music, engineered
soundscapes, and natural sounds were the best genres for increasing focus,
while pop and hip-hop were the worst. These insights can enable human and
artificial intelligence composers to produce increases or decreases in listener
focus with high temporal (millisecond) precision. Future research will include
real-time adaptation of sound libraries for other functional objectives beyond
affecting focus, such as affecting listener enjoyment, stress, and memory.

## Introduction

### The effect of sound on human experience

Sounds are all around us, from natural sounds like the wind, to
engineered sounds like music. It is well-established that sounds have a
major influence on the human brain and consequently, human experience ( **Levitin, 2006**; **Sacks, 2010**). Sounds can reduce stress ( **Davis & Thaut, 1989**), support learning and memory formation ( **Hallam et al., 2002**), improve mood ( **Chanda & Levitin, 2013**), and
increase motivation ( **Salimpoor et al., 2015**). Sounds
can also do the opposite and create aversive experiences ( **Kumar et al., 2012**; **Schreiber & Kahneman, 2000**; **Zald & Pardo, 2002**). One of the most significant effects of sounds is to impact focus.
Focus is commonly demanded by tasks of daily living and work, and in
these areas sounds offer a safe way to increase focus levels and
productivity. However, sounds can be beneficial or distracting, and
previous results have been inconclusive in determining the reason ( **de la Mora Velasco & Hirumi, 2020**).

For example, it has been found that listening to music with lyrics while
reading or working can decrease concentration or cognitive performance
(H. **Liu et al., 2021**; **Shih et al., 2012**), while several studies have shown oppositely that natural-occurring
sounds such as white noise, as well as classical music, can be
beneficial for increasing focus and can improve learning outcomes ( **Angwin et al., 2017**; **Chou, 2010**; **Davies, 2000**; **Gao et al., 2020**). Therefore, one interesting question emerges which is: what are the
specific properties of sounds that affect human focus levels the most?
Additionally, studies have shown that the effect of sounds is often
subjective, where whether one likes a given sound or not is a key factor
in its effect on their experience ( **Cassidy & Macdonald, 2009**; **Huang & Shih, 2011**; **Mori et al., 2014**). Although this finding about the subjectivity of sound reappears
across many studies, psychophysical thresholds are known to exist and
there are clearly natural laws governing much of the way humans hear and
experience sound ( **Levitin et al., 2012**; **Nia et al., 2015**; **Washburne, 2020**).

The potential of sounds to increase focus and demand for
non-pharmaceutical tools that enable individuals to enhance their
ability to focus has recently led several companies (including Endel,
Brain.fm, Mubert, Enophone, Focus@Will, Melodia, AIVA, and others) to
develop soundscapes that are dedicated to increasing focus on-demand.
These soundscapes include elements of white noise, music, and other
sonic properties that are functionally combined to increase a listener’s
focus and maintain high levels of focus over long durations of time. One
of the challenges in this field is to figure out the physical properties
of sound that contribute to human experience the most so that design
principles can be defined correctly to create soundscapes that achieve
the goal of increasing focus, opposed to the inverse of causing
distractions and hurting an individual’s ability to focus. Insights
about sound properties therefore have been sought by commercial groups
alongside academic groups in order to learn how to optimize experiences
through sound.

Many scientific studies have explored this question and looked for the
relationship between sound, music and human experience using objective
measures that empirically assess properties of audio and their emotional
correlates. For example, Cheung et al ( **Cheung et al., 2019**) found that pleasure from music depends on states of expectation, such
as a skipped rhythmic beat, which can either be pleasurable or
discomforting depending on the listener’s circumstance. _Sweet Anticipation_ ( **Huron, 2006**) similarly maps how music evokes
emotions within a theory of expectation and describes psychological
mechanisms that are responsible for many people’s mixed responses to
sounds. Other studies used machine learning methods to map from features
of audio signals to emotions ( **Brotzer et al., 2019**; **Cunningham et al., 2020**; **Hizlisoy et al., 2021**; **Vempala & Russo, 2012**; **Yang et al., 2008**). These machine learning studies
to date have, however, only aimed to predict emotions based on the
limited valence-arousal circumplex model, and as far as we know, no
attempts have been made to predict human focus levels exclusively based
on audio signal analysis.

One persistent obstacle to the field’s understanding has been studies
that rely on data with a low temporal resolution. Since sounds and
emotions evolve fast, on the order of tens of milliseconds, the current
lack of modeling tools capable of capturing the fast changes in human
experience that accompany changes in sound is a major hindrance to
progress ( **Cowen & Keltner, 2017**; **Larsen & Diener, 1992**). Commonly, for example, reports are based on data where there is a
single emotional label per song, while the song lasts ~2-3 minutes and
throughout it there are emotional dynamics that change dramatically.
This mismatch of data can lead to conclusions being drawn from
inadequately small amounts of samples, and worse than that, inaccurate
emotional labels.

### Attention and emotion decoding from brain signal

Brain decoding technology offers an exceptional opportunity to tackle
this issue, since it enables us to get an estimation for the experience
dynamics at the same time resolution as focus phenomena occur. Using
electroencephalogram (EEG) sensor data, which contains electrical brain
activity measured from the scalp (non-invasive) on the order of hundreds
of measurements per second, many studies have established that it is
possible to capture fast changes in human emotions and experience, such
as stress ( **Perez-Valero et al., 2021**), arousal ( **Faller et al., 2019**), fatigue ( **Hu, 2017**), and happiness ( **Lin et al., 2017**). Several studies have similarly shown the ability to capture focus
and attentional state changes, affirming that this information as well
is captured in EEG sensor data ( **Hamadicharef et al., 2009**; **Jung et al., 1997**; **Micoulaud-Franchi et al., 2014**; **Tuckute et al., 2021**). While brain decoding
technology has been applied widely to study the effects of different
types of stimuli (e.g visual, tactile, auditory) on human experience ( **Asif et al., 2019**; **Bhatti et al., 2016**; **Shahabi & Moghimi, 2016**), as far as we know, it has not been applied to study the joint
effects of sound and focus at the high temporal resolution needed to
explain both phenomena.

In recent years, progress in the development of non-clinical, wearable
EEG sensors (such as Muse, Neurosky, Emotiv, Bitbrain, etc.), which are
intended for consumer uses, has led to new research paradigms where
comfortable, affordable, wireless, and easy-to-use at-home measurement
devices collect neuroscientific data “in-the-wild” at a large scale and
make it possible for the first time to measure brain responses from
diverse audiences within their natural, real-world environment. Many of
the wearable devices offer decoding outputs beyond the raw sensor data,
and these “off-the-shelf” decoding outputs include attention,
relaxation, and other states ( **Abiri et al., 2019**; **Bird et al., 2019**; **González et al., 2015**; **Liu et al., 2013**; **Rebolledo-Mendez et al., 2009**). It is important to
note, however, that although decoder algorithms exist in the market for
consumer uses, verifying their reliability to accurately capture
attention, valence, arousal, stress and other attributes of human
experience at a high temporal resolution and research quality has
remained a challenge.

### Combining brain decoding with sound tests to increase focus

In the current study, we used Arctop’s brain decoding technology (neuOSTM) on data from portable EEG (Muse-S) headbands to measure human focus
levels in individuals performing tasks at home while listening to
different types of sounds. Since this is a relatively new decoding
technology, we first evaluate the validity of the focus outputs within
the experimental conditions. Then, once convinced of the output’s
veracity and reliability, we use the focus data to compare effects of
different sound stimuli on individuals while performing different tasks.

Next, we exploit the decoded data’s high time resolution to map between
raw audio signals and the focus dynamics. Based on this mapping, we
build a model that takes sound properties and predicts human focus
levels, enabling us to compare between new songs, sounds, and between
genres to gain additional insights about the nature of sounds which
drives human focus the most. These insights can help in the future to
generate optimal playlists to increase focus, engineer better
soundscapes, and even adapt sounds in real-time based on an individual’s
focus levels to enable them to precisely influence their own mental
state.

## Materials and Methods

### Participants

Sixty-two (62) participants (40 males, 22 females, 18-65 years),
completed four (4) sessions over a single (1) week at their own home.
All participants were recruited from an opt-in screening panel and were
distributed across the five (5) major regions of the continental United
States (Northeast, Southwest, West, Southeast, and Midwest). Only
participants who reported normal hearing, normal vision, or vision that
was corrected to normal with contact lenses, were included. We excluded
volunteers who reported using medication that might influence the
experiment and who reported neurological or psychiatric conditions that
could influence the results. Participants were native English speakers
and a written informed consent was obtained from each participant prior
to their participation. Participants received compensation for their
time.

### Paradigm

#### Tasks

Participants performed various tasks within a mobile app (neuOSTM by Arctop Inc.) while listening to one of three types of sound and wearing
a brain signal measuring headband (4-channel EEG Muse-S device by Interaxon
Inc.). Each participant received a kit at their home that included all the
equipment needed to participate, including over-ear (Sony) headphones, headband
and tablet computer with the mobile app installed. Participants recorded
four one hour long sessions, while listening to different types of sounds.
Sessions included 30 minutes of a “Preferred Task” — a task chosen by the
participant — followed by short tasks used to validate the brain decoding
outputs for each session. These validation tasks included video games (Tetris),
math problems (Arithmetics), and word problems (Creativity). Participants
were assigned to groups according to a pseudorandom schedule that controlled
for potential sequence effects of the tasks and different sound stimulus
types ( **Fig. 1**). The short tasks were used to calibrate
the Arctop decoding algorithms to a validated performance level, and
afterwards the validated model was used to measure each participant’s
focus level across the Preferred Task.

**Figure 1. Schematic illustration of the paradigm in each recording session.**

Each session started with 30 minutes of a task selected by the participant ("Preferred Task"), followed by 3 minutes of arithmetics exercises, 3 minutes of a creativity task, and two levels of Tetris the video game (each level lasted 1 minute regardless of performance). After each task, participants answered a survey where they reported on aspects of their experience (e.g. focus, enjoyment, stress) using linear scale sliders from "Not at all" (0) to "Very" (1).

Participants were instructed to choose a Preferred Task they could
perform in a seated position while listening to sounds through the
headphones, and which they would be happy to repeat in all four
sessions. For example, Preferred Tasks that were chosen included
knitting, working, reading, and solving Sudoku puzzles. At the end of
each task the participants self-reported their experience through a
survey in the app which used linearly-scaled slider buttons to quantify
experience along several dimensions (e.g. focus level, enjoyment,
stress, motivation, etc.). For the Preferred Task, the survey included
reporting on their focus level during the first and second half of the
task separately, resulting in six (6) self-reported quantitative focus
labels per session (Preferred Task: 2 labels, arithmetics: 1 label,
creativity: 1 label, tetris: 2 labels).

#### Sounds

Each participant experienced four sound conditions over the four days of
the study: two music playlists by leading digital service providers
Spotify and Apple (downloaded September 2020), one personalized
soundscape engineered by Endel, and silence (no audible sounds). We
selected Spotify’s ‘Focus Flow’ playlist and Apple Music’s ‘Pure Focus’
playlist to represent the category of pre-recorded sounds designed to
increase focus. For soundscapes we selected the mobile application Endel
to represent the category of real-time, engineered sounds that contain a
mixture of noise and musical properties. The Endel app ‘Focus’
soundscape was used by each participant on their own device. All sound
conditions were instrumental (i.e. did not include singing or any
audible lyrics). For the condition of silence, participants wore
headphones exactly as they did in the sound conditions, but no music or
audible sounds of any kind were played and no soundscape was generated -
participants simply completed the session in a quiet environment.

### Data processing

#### Data acquisition

While participants were listening to sounds and engaging in the
experimental tasks, their electrical brain activity was recorded using a
portable, noninvasive electroencephalograph (EEG) headband that weighed
41 grams (Muse-S device by Interaxon Inc). The headband included four
dry fabric EEG sensors (sampling rate: 256 Hz), photoplethysmography
(PPG) sensors (for heart rate) and motion sensors (gyroscope,
accelerometer). The brain-measuring EEG sensors are located on the scalp
at two frontal channels (AF7, AF8) and two temporal channels (TP9,
TP10), with the reference channel at Fpz. The headbands were put on by
participants themselves with the assistance of a quality control screen
that started each session by giving participants real-time feedback on
the signal quality and made it easy to adjust the headband appropriately
to acquire an optimal signal quality ( **Fig. 2**). No
technicians or other support staff assisted in the placement of the
headbands - the process was completely automated by the in-app prompts,
freeing the participants to complete sessions at any time of their
choosing.

**Figure 2. Schematic illustration of the data processing pipeline.**

Data acquisition included at-home recordings of four sessions, each with a different background sound type. Arctop's neuOS brain decoding technology was used to predict the focus dynamics at a rate of 5Hz. Obtaining the brain decoded focus dynamics synchronously with the sound content enables comparison of focus levels correlated with different physical properties of sound.

#### Brain data based models of focus

Arctop brain decoding technology (neuOS) was used to transform the
sensor data into predicted focus dynamics with a time resolution of 5Hz
( **Fig 2**). For each participant, short tasks (games, word
and math problems) were used to calibrate and validate a model of their
focus based on the brain data, and then once validated the model was
applied to the Preferred Task data. **Fig. 3** shows the resulting
brain decoded focus levels of two representative participants across all
four sessions during the Preferred Task. Model performance was evaluated
using Pearson correlation coefficient between the self-reported focus and
the brain decoded focus values, and after thresholding the values, with the
area under the ROC curve for binary classification of low/high focus ( **Fig. 5**). Eleven (11) participants were excluded from further analysis
following model validation due to excessive noise in their recorded
brain data and/or unreliable survey responses, leaving a total of 51
participants (mean age= 36, SD=8, 17 females and 34 males) in the
experimental analysis.

**Figure 3. Brain data based focus model dynamics of two representative participants during the Preferred Task performed at each of the four sessions.** Each row represents a session with a different sound stream playing in the background as participants perform their chosen task. Each session included 30 minutes (X axis = time in minutes) of a "Preferred Task" over which their focus level (Y axis = decoded focus) was measured. Participant 29 **(A)** was reading while Participant 45 **(B)** was working.

#### Statistical methods

For comparisons between average focus levels during the different sound
content presented, we calculated for each participant (N=51) the median
focus level while performing the preferred task and conducted a one-way
repeated measures ANOVA (Analysis of variance) test. Then, if p<0.05,
paired t-tests were applied post hoc to compare between pairs of sound
streams using Holm-Bonferroni correction. Time series statistical tests
were applied to compare focus level dynamics and discover specific time
periods of significant difference. A paired t-test was applied at each
second between focus levels of two sound streams. The p-values were then
corrected for multiple comparisons by setting a threshold for a minimum
significant sequential time-samples. The threshold was determined by
random permutations (1000 iterations) of participants’ conditions and
repeating the statistical test, resulting in a distribution of
significant sequential time samples. The threshold was set as the 95%
percentile of the resultant distribution ( **Broday-Dvir et al., 2018**).

#### Sound signal decomposition and feature extraction

The pre-recorded playlists conditions (Apple and Spotify) provided raw
sound data that we used to obtain sound property dynamics in the time
and frequency domain that could be correlated with the obtained focus
dynamics. Soundscapes were not used in this analysis because they were
produced in real-time personally for each participant, which limited the
ability to apply sound property analysis appropriately across the data
set. The sound features were calculated using Python’s library
pyAudioAnalysis ( **Giannakopoulos, 2015**), for example,
the sound signal energy, spectral entropy, and chroma coefficients. The
features were calculated in short-time windows of 50 ms with a sliding
window of 25 ms. Then, basic statistics were calculated over the sound
features in windows of 30 seconds (e.g. mean and std), resulting in 136
sound properties ( [link to full list](https://github.com/tyiannak/pyAudioAnalysis/wiki/3.-Feature-Extraction)). To enable mapping to the brain model, the brain decoded focus levels
were also averaged in the corresponding 30 seconds windows ( **Fig. 4**).

**Figure 4. Diagram demonstrating the framework for correlation of time-series focus values with sound properties. (A)** Example of a recorded brain data in microvolts (single channel of EEG) segment, which after applying the preprocessing and trained models on 30 minutes of recordings, transforms to the brain decoded focus dynamics ( **top (C)**). **(B)** Examples of a sound segment in decibels taken from one of the songs. **(C)** The sound features (y-axis) dynamics during 30 minutes of recordings (x-axis).

To obtain the threshold for significant correlations between sound
features and focus levels (p<0.05), a shuffle analysis was performed.
Random permutations (1000 iterations) of the brain decoded focus levels
were applied across songs to preserve the time dependency of focus
levels within a song and the focus levels distribution. The correlation
of each sound feature was calculated with the permuted focus level. The
threshold was set as the 95% percentile of the resulting correlation’s
distribution.

#### Obtaining the sound decoded focus model

To map the relationship between properties of the sounds heard and focus
levels measured from the brain, we first applied principal component
analysis (PCA) to reduce the dimensionality of the sound features. We
then trained regression models between the transformed sound features
and the brain decoded focus through a 5-fold cross validation procedure
that used 80% of the songs in each iteration to train and 20% to test.
The presented sound decoded focus model is a linear model based on the
first PCA component of the features (shifted and rescaled).

## Results

### Brain-measured focus levels accurately reflect self-reported focus levels

Before comparing focus levels elicited by the different sound types, we
validated the underlying brain decoding technology by comparing between
the brain-based focus predictions and the self-reported focus values. **Figure 5A** shows a histogram of the model performance per participant. The model is
evaluated based on the AUC score (of the ROC curve) for prediction of self-reported
focus during the Preferred Task (low-high focus) where the chance guessing
level is = 0.5 (black dashed line). The average result across participants
obtained was <AUC>=0.83 (N=51, SD=0.19), a strong validation of the
brain-measured focus model’s accuracy.

**Figure 5. Validation of focus measurements derived from brain data.**

**(A)** Histogram of focus models performance per participant (N=51), evaluated using the area under the ROC curve (AUC-ROC). Black dashed line marks chance level (0.5). **(B)** Average focus levels per event vs. self-reported focus resulted in Pearson correlation of 0.6. Inset shows ROC curves for different values of self-report threshold. **(C)** Confusion matrix after thresholding the focus score predictions and self-report. Classification scores for 2-classes (low focus vs. high focus) are AUC=0.87 (area under ROC curve), Accuracy=0.8. **(D)** Average brain decoded focus levels vs. average self-reported focus across the four sound types.

When aggregating the tasks from all participants, the Pearson
correlation between the brain decoded focus model and the self-reported
focus was Corr(414)=0.6, p<10−4 ( **Fig. 5B**). The inset in **Figure 5B** shows the ROC curves for different
values of self-reported threshold and the confusion matrix for one of these
thresholds (0.4) resulted in an accuracy score of 0.8 ( **Fig. 5C**). **Figure 5D** shows the average brain decoded focus level
per sound type vs. the average self-reported score.

### Soundscapes induce a higher focus level compared to silence

Using the validated focus models which output five measurements per
second (5Hz), we then compared between the average focus levels elicited
by the sounds during the Preferred Task. The background sound was found
to have a significant effect (top row in **Table 1**,
F(3,150)=4.28, p=0.006, statistical methods for details) on the elicited
focus level, and the post hoc tests (Holm-Bonferroni correction)
revealed that streaming soundscapes (with Endel app) was significantly
higher compared to silence ( **Fig. 6A1**, supp. Table 1,
M=0.090, SE=0.027, t(50)=-3.38, p=0.008), while streaming music using
Apple or Spotify did not have an effect (Apple: t(50)=-2.37, p=0.11,
Spotify: t(50)=-1.24, p=0.65). For 35.3% of the participants the Endel
session produced their highest focus level, while for 27.5% of
participants the Apple playlist produced the highest focus level. For
19.6% of participants Spotify was best for producing focus and for 17.6%
silence was ( **Fig. 6A2**, the details sorted focus levels
per participant are shown in Supp. Fig. 1).

**Table 1. Results of a one-way repeated measures ANOVA performed on each subgroup** comparing the average brain decoded focus levels of each sound stream during the Preferred Task. Sound most significantly affected those below 36 years old.**Figure 6. Comparison of the brain decoded focus during the Preferred Task while listening to different sounds.**

Top row - Average focus levels for each sound stream during the Preferred Task for each group of interest, including statistical results. Error bars are standard errors. Bottom row - Distribution of the best session (highest focus on average) for each participant per group. The groups of interest are: **(A)** All participants (51), **(B)** Participants who were working during the Preferred Task (26), **(C)** Participants who were not working (25 -reading, knitting, playing, etc). **(D)** Participants above 36 (26). **(E)** Participants below 36 (25).

To gain a better understanding of the conditions where sound affected
focus, we next split the participants into subgroups of interest and
repeated the statistical analysis. We first asked whether the focus
level difference is task dependent. During the Preferred Task, 51% of
the participants (26) chose to work, while the rest (49%) read a book
(29.4%), played games (9.8%) or did other various tasks (e.g. knitting,
9.8%). To assess the effect of sounds on focus levels during these
different tasks, we split the participants to the ones who worked and
those that did other tasks. We found that for the “working” group, the
focus level elicited by Endel’s soundscapes was higher compared to
silence (M=0.12, SE=0.04, t(25)=3.26, p=0.017), while for the
“not-working” group there was no difference ( **Fig. 6**,
supp. Table 1, M=0.06, SE=0.04, t(24)=1.552, p=0.447). These results
suggest that the focus level differences between Endel and Silence are
task-dependent, where the sound is beneficial for specific types of
tasks, namely, “working.”

We next split the participants into two age groups according to the
median age (36 years). We found that for the younger participants
(age<36, N=25), all sound types were superior to silence for
producing elevated focus levels ( **Fig. 6**, supp. Table 1,
M=0.14,0.13,0.12, SE=0.04,0.03,0.03, t(24)=3.79,4.49,3.67,
p=0.004,0.001,0.005 for Endel, Apple and Spotify respectively) while for
the older participants (age>36, N=26), there was no difference
between sound and silence. The focus level differences were therefore
found to also be age-dependent.

### Time series analysis of the focus dynamics reveal differences between all sound types and silence

Exploiting the high temporal resolution of the focus measurements, we
compared the focus dynamics to each sound stream that played during the
30 minutes of the Preferred Task ( **Fig. 7**, **table 2**). When comparing Endel’s soundscapes vs. Silence ( **Fig. 7A**), we found that the focus level elicited by Endel’s soundscape was
higher 87% of the time, a separation whose significance started after
2.5 minutes of listening. In addition, although on average there wasn’t
a significant difference, the focus level elicited by Apple’s playlist
was higher than Silence 60% of the time, starting at 12.5 minutes ( **Fig. 7C**), and the focus level elicited by Spotify’s playlist was higher than
Silence 27% of the time, starting at 17 minutes ( **Fig. 7B**). Focus elicited by Endel’s soundscape was higher than Spotify’s
playlist in 37% of the time, starting at 6 minutes ( **Fig. 7D**).

**Figure 7. Comparing brain decoded focus dynamics during the 30 minutes of the Preferred Task.**

Each subfigure shows a comparison between two sound streams, while the gray areas are the timings with a significant difference (p<0.05 corrected, see statistical methods for details).**Table 2.** Summary of focus time dynamics comparison, showing for each pair the percentage of time and time segments with significant difference (where 100% = 30 minutes).

### Focus levels in response to sounds can be predicted by the sound’s properties

Seeing that background sound had an effect on focus levels, we go
further and ask whether music and soundscapes can be composed according
to a formula to increase focus levels. Meaning, can we understand which
sound properties drive focus well enough to predict focus levels from
only an analysis of the sound properties themselves?

Leveraging the high temporal resolution of the noninvasive brain
measurements, we generated a prediction model which predicts the
brain-based focus level from sound features extracted from the audio
signal. Raw audio files containing the Apple and Spotify sessions were
used to extract different sound properties with a running sliding window
of 30 seconds. The personalized soundscape session (Endel) was not used
in this analysis since the real time streaming did not allow saving the
raw audio files that were consistent across participants. Each sound
property was treated as a unique feature and checked for its
contributory power to the measured average focus level. Supp. Fig. 2
shows the resulting correlations between each sound feature and the
brain based focus level. In total, only 20 features of the 136 features
evaluated were found to have significant correlations (p<0.05, see
Statistical methods).

We next combined multiple sound features to generate a sound data based
model that predicts focus levels (see Methods). **Figure 8** shows the dynamics of the focus predicted from the audio signal alone which
included only properties of the sounds, together with the brain decoded focus
that was derived from high resolution electrical brain measurements (Corr=0.7,
p<1e-4). **Figure 8D** shows that if we threshold our dynamics
to output a binary prediction (low/high focus), the audio model reaches 88%
accuracy in predicting the brain based focus (AUC=0.93).

**Figure 8. Results of predicting brain decoded focus from audio features.**

**(A+B)** Dynamics of brain decoded focus (dark blue) and audio decoded focus (light blue), during 30 minutes of the Preferred Task for Apple (A) and Spotify (B). **(C)** Brain decoded focus (y-axis) vs. Audio decoded focus (x-axis) for both playlists (Apple + Spotify). **(D)** Confusion matrix after thresholding the focus predictions to classify between low and high focus. Classification accuracy obtained: 88% (Area under ROC curve: 0.93).

Beyond composing soundscapes for focus, we can also use these prediction
models to rate the focus level of a song and assemble successful
playlists based on existing songs. To demonstrate this, we compared the
song average of the audio decoded output to the brain decoded output. As
shown in **Figure 9B**, there is a correlation of 0.74
between the focus models at the song level (df=16, p=0.0004). **Figure 9A** shows these averages sorted by the brain-based model.

**Figure 9. Averaging focus scores for each song.**

**(A)** Sorted focus scores per song obtained by the brain model (brain decoded - blue), next to the focus obtained by the audio model (audio decoded- light blue).

‍ **(B)** Focus scores per song - brain decoded (y axis) vs. audio decoded (x axis). Pearson correlation between them: Corr(16)=0.74, p=0.0004.

### Analysis of a sound’s properties can be used to predict its effect on focus

To gain additional insights about the effects of different sound types
on human focus, we used the trained audio model, and projected songs and
sounds which were not played during the brain recording experiment.
Meaning, we obtained their focus score and dynamics based solely on the
properties of the sounds they contained. We selected sounds that
challenged the validity of the audio model based on their categorical
exclusion from the brain recording experiment. A future approach can
include these different genres as controls for further brain measurement
validation studies. For example, Endel’s soundscapes which are not
personalized (taken from the playlist: “ [Focus: Calm Clear Morning](https://open.spotify.com/album/473AYNWq9Lxzs26qjnlVDy?si=iRcJWxMSTvGgba8Hd8TQuw&utm_source=copy-link&dl_branch=1&nd=1)”), natural sounds which are commonly used for increasing focus (such
as white noise, waves, rain, taken from: [https://mc2method.org/white-noise/](https://mc2method.org/white-noise/)), and popular songs from other music genres (classical music,
electronic, pop, rock, jazz and hip hop) were used.

**Figure 10A** shows the predicted focus score based on the
audio model which took into account only the properties of the sounds themselves.
Songs are sorted from the highest focus evoking song (Endel - Three No Paradoxes)
to the lowest (Dr. Dre - What’s The Difference). The top two songs are Endel
soundscapes which are not personalized, a finding which strengthens our main
result since it implies that the high focus scores elicited by Endel’s soundscape
was not a byproduct of personalization.
**Figure 10B** shows the sorted focus scores averaged across
genres, where notably sounds from classical music and natural sounds contained
properties that predicted high focus levels. In contrast, pop and hip-hop
songs predicted relatively low focus scores. Although we do not have ground
truth focus labels for these songs based on real human brain data, given
the relatively high scores of the sounds which are known to have generated
increased focus in the experimental data, we can conclude that there is a
consistent validity to the model. Future research will gather ground truth
labels for these songs and evaluate the model mathematically in this context.

**Figure 10. Projecting new songs into the trained audio model.**

**(A)** Sorted focus scores per song obtained by the audio model, colored by genre. **(B)** Average focus score per genre, sorted from the genre with the lowest score (Hip-hop) to the highest (Classical). **(C)** Average focus variance per genre, sorted from the genre with the lowest variance (Natural) to highest (Electronic). **(D-E)** Focus dynamics for the song with the lowest focus score (D) and the highest (E).

Analyzing the average within-song variance across different genres
reveals that the model predicts the largest variance on average for
electronic sounds ( **Fig. 10C**), while the lowest variance
was found for natural sounds. The variance can be interpreted as a range
of focus dynamics, and indeed the focus dynamics of the electronic
sounds might change dramatically during a song (supp. Fig3), confirming
the need for a tool which outputs dynamics with a high temporal
resolution when studying such sound content. **Figure 10D-E** shows the focus dynamics for the song with the lowest focus evoking score
and the highest. The dynamics for all songs can be seen in supp. Fig.3.

## Discussion

“The soundscape of the world is changing. Modern man is beginning to
inhabit a world with an acoustical environment radically different from
any he has hitherto known” said the composer R. Murray Schafer,
presaging the time we live in now when the sounds available to us
continue to multiply by the day.

As we have an increasing number of options to modulate our auditory
lives by, a handful of take-aways from this study standout:

### Brain-based measurement of focus is possible “in-the-wild”

Although the effects of sound and music on the human brain can be subtle
in measured brain signals when judging by the changes produced in raw
electromagnetic currents, they are robust and highly quantifiable with
effectively-trained algorithms, as shown here. Classifying emotional and
attentional responses is particularly useful when done at the sub-second
temporal resolution which allows one to track dynamics continuously over
time at the same timescale as the brain functions that impact perception
and behavior. In this study we demonstrated that noninvasive brain
decoding technology is able to deliver this needed resolution with a
high degree of accuracy (approximately 80% match to self-report, **Fig 5C**.). Since there are inherent biases in the subjective self-report for
experience ( **Kahneman et al., 1999**; **Mauss & Robinson, 2009**), when mapping physiological signals to self-reported experiences, as
done here, there is an upper boundary for accuracy beyond which the
model will over fit to the self-reported values and incorrectly
represent the information observed in physiological signals. According
to a recent review ( **Larradet et al., 2020**) which
summarizes multiple peer-reviewed studies that predict self-reported
emotions from physiological signals, the average accuracy reported was
~82%. Given this average and the experimental conditions here (small
amount of sensors, at home recordings, simple self-report scales), the
achieved accuracy was satisfactory for drawing deeper conclusions on
sound properties, and aligned with state-of-the-art emotion recognition
accuracies in the context of sound as a stimulus ( **Tripathi et al., 2017**, 81.41% and 73.35% for 2 classes of Valence and Arousal respectively).

A key benefit of the current approach is that this method of high
temporal resolution brain measurement can be performed reliably outside
of traditional laboratories. In this current study not a single
laboratory or facility was used for data acquisition. Instead, 18-65
year olds across the U.S. received a technology kit in the mail and
experienced music playlists and personalized soundscapes while they
recorded their own brain signals from the comfort of their own homes at
times of day of their choosing. In other words, in their natural
habitat, at their own pace, which lends the research a rare ecological
validity.

### Focus is increased most by personalized, engineered soundscapes

Within the at-home environment of this study, personalized, engineered
soundscapes were found to be the best at increasing participant’s focus
levels ( **Fig. 6A**). After 2.5 minutes, on average,
listeners of the personalized soundscapes evaluated experienced a
meaningful increase in their focus level, while for music playlists it
took approximately 15 minutes to gain a similarly appreciable increase ( **Fig. 7**). The audio effect on focus levels was found to be task dependent,
where soundscapes increased focus levels in participants who were
working ( **Fig 6B**). For participants who were not
working, no significant difference was found. This result suggests that
willful orientation of attention towards work tasks may have created a
brain context especially suited to modification by sound. While engaged
in work, participants may also have been more prone to distraction and
thus more impacted by the positive uplift of sound compared to reading
or playing a game which may have contained more intrinsic motivation to
stay focused on.

One limitation of this current study is that it did not allow us to
disentangle the effects of personalization of sounds on the listener,
since pre-recorded soundscapes were not tested. Equivalently, a
comparison of personalized soundscapes to personalized music playlists,
where audiences either made their own playlist for focus or were allowed
to skip songs whenever they wanted, will likely contribute to a fuller
understanding of how sound properties correlate with emotion and
attention changes. Follow-up research will incorporate these variables.
An additional limitation was the inability to reach conclusions
regarding gender-dependent effects which was at least partially due to
this study’s imbalanced data set. Despite efforts to recruit a balanced
group of participants, enrollment was done on a rolling basis and in the
end the female subgroup was statistically underpowered. In future
research, especially for closed loop, real-time testing, balanced
participant sets will be important for reaching more detailed
conclusions.

### Sound preferences and focus effects vary between people

It is important to emphasize that the results reported here are sound
effects on the average focus levels across a population, and that there
was a large variance in this effect between participants. Evidence for
this large variance can be seen in Supp Fig. 3 and in the age dependency
effect ( **Fig 6D-E**), where for the younger audience, all
sounds increased focus while for the older audience, the sounds did not
have any effect. These results are consistent with other studies showing
personal preferences are critical for the improvements possible by
sounds ( **Cassidy & Macdonald, 2009**; **Huang & Shih, 2011**; **Mori et al., 2014**). Due to this variety observed
together with the highest focus being elicited by the personalized
soundscapes, a next step will include closed-loop selections of sounds,
where iterative sound testing is used per person to identify the
significant parameters for maximizing focus for that person.

Personalized soundscapes specifically, and personalized audio in
general, should be investigated further for their capacity to increase
productivity, creativity and well-being as these attributes of human
experience are associated with one’s ability to focus. For clinical
populations as well, for example children with ADHD, the tailoring of
sounds for this purpose of increased focus can be particularly
impactful. It is possible that the seamlessness of the personalized
soundscapes tested here, which played continuously without gaps in the
sound like the music playlists had between songs, was a critical part of
the observed effect on focus. At every juncture of the experience there
is more to be learned, but at a high level, a main lesson of this study
is that there is a strong need for personalization of sound in order to
most effectively achieve functional goals like increasing focus.

### Brain decoded focus data enabled a new predictive model based on sound data alone

Leveraging the high temporal resolution of the brain decoded dynamics, a
focus prediction model based on the physical properties of sounds was
successfully trained, resulting in an accuracy score of 88% in
predicting the brain decoded focus score from an audio decomposition
that assessed 136 different properties of sounds as unique features ( **Fig. 8**). This model enabled a further examination of how sounds and different
genres effects focus and allowed testing additional conditions, such as
pre-recorded soundscapes and commonly used background sounds (e.g. white
noise), as well as other genres (pop, rock, jazz, etc). We found that
the model predicted the highest focus score to classical music, followed
by engineered soundscapes and natural sounds. These results complement
previous studies which showed natural sounds and classical music are
beneficial for learning and concentration ( **Angwin et al., 2017**; **Davies, 2000**; **DeLoach et al., 2015**;
H. **Liu et al., 2021**).

In contrast, the models predicted that genres such as pop and hip-hop
produce lower focus scores ( **Fig 10A-B**). It is possible
that these sounds contain more distractors that attract attention away
from other objects of attention, or that they contain types of sounds
that the brain requires more resources to process (depending on familiar
patterns, surprises and more), leading to less resources available to
perform other tasks. Sounds in these genres may also activate the reward
system more ( **Gold et al., 2019**; **Salimpoor et al., 2015**), which can increase motivation and improve learning of the songs
themselves rather than orientation towards other tasks. These sounds may
be optimal for driving focus on the songs in other words, rather than
focus on other things. Understanding the brain mechanisms that underlie
the modified focus is beyond the scope of this work, but the mapping
found here can potentially provide fruitful directions for future brain
imaging experiments that are equipped to answer these questions.

The analysis here demonstrates a process in which we utilize the
temporal resolution of the brain sensing technology to generate a
product where the neurotechnology is eventually out of the loop,
resulting in a stand alone sound model which gets as an input a raw
audio file and outputs a predicted focus score. This model can be used
independently to generate focus playlists or to compose optimal
soundscapes, and can further be improved by expanding to populations
outside the U.S. and different age groups.

Pythagoras, who first identified the mathematical connection between a
string’s length and it’s pitch, believed that the whole cosmos was a
form of musical composition ( **James, 1995**). We too see
the rich mathematical models obtained in this study, by mapping sound
properties to human experience, as a glimpse into the natural laws
governing how we feel and think. The better these laws can be
understood, the more empowered individuals will be to modulate their
sound environments to suit their goals and states of mind. There remains
much to figure out. While we as a species continue to cause a “shift in
the sensorium,” we simultaneously experience that shift all over daily
life and it is not clear where we as a species are headed. This study
showed that sounds have a distinct effect on our focus, and paves the
way for designing sounds to help us focus better in the future.

## Conclusions

We studied the effects of sound on human focus levels using noninvasive
brain decoding technology and to gain a better understanding of the
optimal sound properties for increasing focus levels in listeners. We
combined a custom app, portable EEG-measuring headbands, and brain
decoding technology that enabled us to obtain high temporal resolution
focus dynamics from participants at home. Using the brain decoded focus
dynamics, we then analyzed how various properties of sound affected
focus levels in different tasks.

We found that while performing a self-paced task for a long period of
time (such as working), personalized soundscapes increased focus the
most relative to silence. Curated playlists of pre-recorded songs by
Apple and Spotify also increased focus during specific time intervals,
especially for the youngest audience demographic. Large variance in
response profiles across participants, together with task and age
dependent effects, suggest that personalizing sounds in real-time may be
the best strategy overall for producing focus in a given listener.

Finally, we generated a sound property based focus model which
successfully predicts the brain decoded focus scores using only an audio
file as input. Using this model, we extracted predicted focus scores
from new songs based on audio decomposition and performed a genre
analysis to develop new intuitions about the findings and the source of
focus-producing sound content. We found that based on our model,
engineered soundscapes and classical music are the best for increasing
focus, while pop and hip-hop music are the worst.

The approach taken here can be adapted to include other emotions (e.g.
enjoyment, stress, happiness, etc.), attentional parameters (‘Flow
state,’ memory formation, etc.) and can be used to assess additional
content as well (e.g. visual, ambient, olfactory, etc.), including
interactive gaming and e-learning where personalization and high
temporal resolution experience measures may be especially beneficial.

## Data Availability Statement

The dataset for this study is available through an open Git repository ( [link](https://github.com/arctop/Measuring-And-Modeling-The-Effect-of-Audio-On-Human-Focus-In-Everyday-Environments-Using-BCI)). Data includes the brain decoded focus dynamics for each participant
together with scripts that run the statistical tests.

## Author Contributions

A.H., R.K, N.E, E.K, and D.F designed the experiment, A.H analyzed the
data, R.K, N.E, and D.F. advised on data analysis and statistics. S.K
and E.K developed the app and software platform for data collection. A.H
and D.F wrote the paper, R.K and E.K revised the paper. All authors
approved the work for publication.

## Conflict of Interest

This study received funding from Arctop Inc. and Endel Sound GmbH. The
funders had the following involvement with the study: Arctop Inc. was
involved in study design, collection, analysis, interpretation of data,
the writing of this article and the decision to submit it for
publication. Endel Sound GmbH was involved in study design and provided
audio stimuli used in the experiment. All authors declare no other
competing interests.

## Acknowledgments

We would like to thank Hillel Pratt, Kevin Liu, Alexander Kopanev,
Warner Music, Sony Music, Endel, and Universal Music for providing audio
content, data, and support in conducting this study and advancing
theoretical and applied aspects of the research.

## Funding

This study received funding from Arctop Inc. and Endel Sound GmbH. Endel
Sound GmbH was not involved in data collection, analysis, interpretation
of data, the writing of this article or the decision to submit it for
publication. All authors declare no other competing interests.

## References

01. Abiri, R., Borhani, S., Jiang, Y., & Zhao, X. (2019). Decoding Attentional State to Faces and Scenes Using EEG Brainwaves. _Complexity_, 2019, e6862031. [https://doi.org/10.1155/2019/6862031](https://doi.org/10.1155/2019/6862031)
02. Angwin, A. J., Wilson, W. J., Arnott, W. L., Signorini, A., Barry, R. J., & Copland, D. A. (2017). White noise enhances new-word learning in healthy adults. _Scientific Reports_, 7(1), 13045. [https://doi.org/10.1038/s41598-017-13383-3](https://doi.org/10.1038/s41598-017-13383-3)
03. Asif, A., Majid, M., & Anwar, S. M. (2019). Human stress classification using EEG signals in response to music tracks. _Computers in Biology and Medicine_, 107, 182–196.
04. Bhatti, A. M., Majid, M., Anwar, S. M., & Khan, B. (2016). Human emotion recognition and analysis in response to audio music using brain signals. _Computers in Human Behavior_, 65, 267–275. [https://doi.org/10.1016/j.chb.2016.08.029](https://doi.org/10.1016/j.chb.2016.08.029)
05. Bird, J. J., Ekart, A., Buckingham, C. D., & Faria, D. R. (2019). Mental emotional sentiment classification with an eeg-based brain-machine interface. _Proceedings of TheInternational Conference on Digital Image and Signal Processing (DISP'19)._
06. Broday-Dvir, R., Grossman, S., Furman-Haran, E., & Malach, R. (2018). Quenching of spontaneous fluctuations by attention in human visual cortex. _NeuroImage_, 171, 84–98. [https://doi.org/10.1016/j.neuroimage.2017.12.089](https://doi.org/10.1016/j.neuroimage.2017.12.089)
07. Brotzer, J. M., Mosqueda, E. R., & Gorro, K. (2019). Predicting emotion in music through audio pattern analysis. _IOP Conference Series: Materials Science and Engineering_, 482, 012021. [https://doi.org/10.1088/1757-899X/482/1/012021](https://doi.org/10.1088/1757-899X/482/1/012021)
08. Cassidy, G., & Macdonald, R. (2009). The effects of music choice on task performance: A study of the impact of self-selected and experimenter-selected music on driving game performance and experience. _Musicae Scientiae_, 13(2), 357–386. [https://doi.org/10.1177/102986490901300207](https://doi.org/10.1177/102986490901300207)
09. Chanda, M. L., & Levitin, D. J. (2013). The neurochemistry of music. _Trends in Cognitive Sciences_, 17(4), 179–193. [https://doi.org/10.1016/j.tics.2013.02.007](https://doi.org/10.1016/j.tics.2013.02.007)
10. Cheung, V. K. M., Harrison, P. M. C., Meyer, L., Pearce, M. T., Haynes, J.-D., & Koelsch, S. (2019). Uncertainty and Surprise Jointly Predict Musical Pleasure and Amygdala, Hippocampus, and Auditory Cortex Activity. _Current Biology_, 29(23), 4084–4092.e4. [https://doi.org/10.1016/j.cub.2019.09.067](https://doi.org/10.1016/j.cub.2019.09.067)
11. Chou, P. T.-M. (2010). Attention Drainage Effect: How Background Music Effects Concentration in Taiwanese College Students. _Journal of the Scholarship of Teaching and Learning_, 10(1), 36–46.
12. Cowen, A. S., & Keltner, D. (2017). Self-report captures 27 distinct categories of emotion bridged by continuous gradients. _Proceedings of the National Academy of Sciences_, 114(38), E7900–E7909.
13. Cunningham, S., Ridley, H., Weinel, J., & Picking, R. (2020). Supervised machine learning for audio emotion recognition. _Personal and Ubiquitous Computing_, 1–14.
14. Davies, M. A. (2000). Learning … the Beat Goes on. _Childhood Education_, 76(3), 148–153. [https://doi.org/10.1080/00094056.2000.10522096](https://doi.org/10.1080/00094056.2000.10522096)
15. Davis, W. B., & Thaut, M. H. (1989). The Influence of Preferred Relaxing Music on Measures of State Anxiety, Relaxation, and Physiological Responses 1. _Journal of Music Therapy_, 26(4), 168–187. [https://doi.org/10.1093/jmt/26.4.168](https://doi.org/10.1093/jmt/26.4.168)
16. de la Mora Velasco, E., & Hirumi, A. (2020). The effects of background music on learning: A systematic review of literature to guide future research and practice. _Educational Technology Research and Development_, 68(6), 2817–2837. [https://doi.org/10.1007/s11423-020-09783-4](https://doi.org/10.1007/s11423-020-09783-4)
17. DeLoach, A. G., Carter, J. P., & Braasch, J. (2015). Tuning the cognitive environment: Sound masking with “natural” sounds in open-plan offices. _The Journal of the Acoustical Society of America_, 137(4), 2291–2291. [https://doi.org/10.1121/1.4920363](https://doi.org/10.1121/1.4920363)
18. Faller, J., Cummings, J., Saproo, S., & Sajda, P. (2019). Regulation of arousal via online neurofeedback improves human performance in a demanding sensory-motor task. _Proceedings of the National Academy of Sciences_, 116(13), 6482–6490.
19. Gao, C., Fillmore, P., & Scullin, M. K. (2020). Classical music, educational learning, and slow wave sleep: A targeted memory reactivation experiment. _Neurobiology of Learning and Memory_, 171, 107206. [https://doi.org/10.1016/j.nlm.2020.107206](https://doi.org/10.1016/j.nlm.2020.107206)
20. Giannakopoulos, T. (2015). pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis. _PLOS ONE_, 10(12), e0144610. [https://doi.org/10.1371/journal.pone.0144610](https://doi.org/10.1371/journal.pone.0144610)
21. Gold, B. P., Mas-Herrero, E., Zeighami, Y., Benovoy, M., Dagher, A., & Zatorre, R. J. (2019). Musical reward prediction errors engage the nucleus accumbens and motivate learning. _Proceedings of the National Academy of Sciences_, 116(8), 3310–3315. [https://doi.org/10.1073/pnas.1809855116](https://doi.org/10.1073/pnas.1809855116)
22. D. D. Schmorrow & C. M. Fidopiastis González, V. M., Robbes, R., Góngora, G., & Medina, S. (2015). Measuring Concentration While Programming with Low-Cost BCI Devices: Differences Between Debugging and Creativity Tasks. In D. D. Schmorrow & C. M. Fidopiastis (Eds.), _Foundations of Augmented Cognition_ (pp. 605–615). Springer International Publishing. [https://doi.org/10.1007/978-3-319-20816-9\_58](https://doi.org/10.1007/978-3-319-20816-9_58) ‍
23. Hallam, S., Price, J., & Katsarou, G. (2002). The Effects of Background Music on Primary School Pupils’ Task Performance. _Educational Studies_, 28(2), 111–122. [https://doi.org/10.1080/03055690220124551](https://doi.org/10.1080/03055690220124551) ‍
24. Hamadicharef, B., Zhang, H., Guan, C., Wang, C., Phua, K. S., Tee, K. P., & Ang, K. K. (2009). Learning EEG-based spectral-spatial patterns for attention level measurement. _2009 IEEE International Symposium on Circuits and Systems_, 1465–1468.
25. Hizlisoy, S., Yildirim, S., & Tufekci, Z. (2021). Music emotion recognition using convolutional long short term memory deep neural networks. _Engineering Science and Technology, an International Journal_, 24(3), 760–767. [https://doi.org/10.1016/j.jestch.2020.10.009](https://doi.org/10.1016/j.jestch.2020.10.009) ‍
26. Hu, J. (2017). Automated Detection of Driver Fatigue Based on AdaBoost Classifier with EEG Signals. _Frontiers in Computational Neuroscience_, 0\. [https://doi.org/10.3389/fncom.2017.00072](https://doi.org/10.3389/fncom.2017.00072) ‍
27. Huang, R.-H., & Shih, Y.-N. (2011). Effects of background music on concentration of workers. _Work_, 38(4), 383–387. [https://doi.org/10.3233/WOR-2011-1141](https://doi.org/10.3233/WOR-2011-1141) ‍
28. Huron, D. B. (2006). _Sweet Anticipation: Music and the Psychology of Expectation_. MIT Press.
29. James, J. (1995). The Music of the Spheres: Music, Science, and the Natural Order of the Universe. _Copernicus._ [https://www.springer.com/gp/book/9780387944746](https://www.springer.com/gp/book/9780387944746) ‍
30. Jung, T.-P., Makeig, S., Stensmo, M., & Sejnowski, T. J. (1997). Estimating alertness from the EEG power spectrum. _IEEE Transactions on Biomedical Engineering_, 44(1), 60–69.
31. Kahneman, D., Diener, E., & Schwarz, N. (1999). _Well-being: Foundations of hedonic psychology._ Russell Sage Foundation.
32. Kumar, S., von Kriegstein, K., Friston, K., & Griffiths, T. D. (2012). Features versus feelings: Dissociable representations of the acoustic features and valence of aversive sounds. _Journal of Neuroscience_, 32(41), 14184–14192.
33. Larradet, F., Niewiadomski, R., Barresi, G., Caldwell, D. G., & Mattos, L. S. (2020). Toward Emotion Recognition From Physiological Signals in the Wild: Approaching the Methodological Issues in Real-Life Data Collection. _Frontiers in Psychology_, 11, 1111. [https://doi.org/10.3389/fpsyg.2020.01111](https://doi.org/10.3389/fpsyg.2020.01111) ‍
34. Larsen, R. J., & Diener, E. (1992). _Promises and problems with the circumplex model of emotion._ ‍
35. Levitin, D. J. (2006). _This is your brain on music: The science of a human obsession_. Penguin.
36. Levitin, D. J., Chordia, P., & Menon, V. (2012). Musical rhythm spectra from Bach to Joplin obey a 1/f power law. _Proceedings of the National Academy of Sciences_, 109(10), 3716–3720. [https://doi.org/10.1073/pnas.1113828109](https://doi.org/10.1073/pnas.1113828109) ‍
37. Lin, Y.-P., Jao, P.-K., & Yang, Y.-H. (2017). Improving Cross-Day EEG-Based Emotion Classification Using Robust Principal Component Analysis. _Frontiers in Computational Neuroscience_, 0\. [https://doi.org/10.3389/fncom.2017.00064](https://doi.org/10.3389/fncom.2017.00064) ‍
38. Liu, H., He, H., & Qin, J. (2021). Does background sounds distort concentration and verbal reasoning performance in open-plan office? _Applied Acoustics_, 172, 107577. [https://doi.org/10.1016/j.apacoust.2020.107577](https://doi.org/10.1016/j.apacoust.2020.107577) ‍
39. Liu, N.-H., Chiang, C.-Y., & Chu, H.-C. (2013). Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors. _Sensors (Basel, Switzerland)_, 13(8), 10273–10286. [https://doi.org/10.3390/s130810273](https://doi.org/10.3390/s130810273) ‍
40. Mauss, I. B., & Robinson, M. D. (2009). Measures of emotion: A review. _Cognition and Emotion_, 23(2), 209–237. [https://doi.org/10.1080/02699930802204677](https://doi.org/10.1080/02699930802204677) ‍
41. Micoulaud-Franchi, J.-A., Geoffroy, P. A., Fond, G., Lopez, R., Bioulac, S., & Philip, P. (2014). EEG neurofeedback treatments in children with ADHD: An updated meta-analysis of randomized controlled trials. _Frontiers in Human Neuroscience_, 0\. [https://doi.org/10.3389/fnhum.2014.00906](https://doi.org/10.3389/fnhum.2014.00906) ‍
42. Mori, F., Naghsh, F. A., & Tezuka, T. (2014). The Effect of Music on the Level of Mental Concentration and its Temporal Change. _Proceedings of the 6th International Conference on Computer Supported Education_ \- Volume 1, 34–42. [https://doi.org/10.5220/0004791100340042](https://doi.org/10.5220/0004791100340042) ‍
43. Nia, H. T., Jain, A. D., Liu, Y., Alam, M.-R., Barnas, R., & Makris, N. C. (2015). The evolution of air resonance power efficiency in the violin and its ancestors. _Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences_, 471(2175), 20140905. [https://doi.org/10.1098/rspa.2014.0905](https://doi.org/10.1098/rspa.2014.0905) ‍
44. Perez-Valero, E., Vaquero-Blasco, M. A., Lopez-Gordo, M. A., & Morillas, C. (2021). Quantitative Assessment of Stress Through EEG During a Virtual Reality Stress-Relax Session. _Frontiers in Computational Neuroscience_, 0\. [https://doi.org/10.3389/fncom.2021.684423](https://doi.org/10.3389/fncom.2021.684423) ‍
45. J. A. Jacko Rebolledo-Mendez, G., Dunwell, I., Martínez-Mirón, E. A., Vargas-Cerdán, M. D., de Freitas, S., Liarokapis, F., & García-Gaona, A. R. (2009). Assessing NeuroSky’s Usability to Detect Attention Levels in an Assessment Exercise. In J. A. Jacko (Ed.), _Human-Computer Interaction. New Trends_ (pp. 149–158). Springer. [https://doi.org/10.1007/978-3-642-02574-7\_17](https://doi.org/10.1007/978-3-642-02574-7_17) ‍
46. Sacks, O. (2010). _Musicophilia: Tales of music and the brain_. Vintage Canada.
47. Salimpoor, V. N., Zald, D. H., Zatorre, R. J., Dagher, A., & McIntosh, A. R. (2015). Predictions and the brain: How musical sounds become rewarding. _Trends in Cognitive Sciences_, 19(2), 86–91. [https://doi.org/10.1016/j.tics.2014.12.001](https://doi.org/10.1016/j.tics.2014.12.001) ‍
48. Schreiber, C. A., & Kahneman, D. (2000). Determinants of the remembered utility of aversive sounds. _Journal of Experimental Psychology: General_, 129(1), 27.
49. Shahabi, H., & Moghimi, S. (2016). Toward automatic detection of brain responses to emotional music through analysis of EEG effective connectivity. _Computers in Human Behavior_, 58, 231–239. [https://doi.org/10.1016/j.chb.2016.01.005](https://doi.org/10.1016/j.chb.2016.01.005) ‍
50. Shih, Y.-N., Huang, R.-H., & Chiang, H.-Y. (2012). Background music: Effects on attention performance. _Work_, 42(4), 573–578. [https://doi.org/10.3233/WOR-2012-1410](https://doi.org/10.3233/WOR-2012-1410) ‍
51. Tripathi, S., Acharya, S., Sharma, R. D., Mittal, S., & Bhattacharya, S. (2017). Using Deep and Convolutional Neural Networks for Accurate Emotion Classification on DEAP Dataset. _Twenty-Ninth IAAI Conference_.
52. Tuckute, G., Hansen, S. T., Kjaer, T. W., & Hansen, L. K. (2021). Real-Time Decoding of Attentional States Using Closed-Loop EEG Neurofeedback. _Neural Computation_, 33(4), 967–1004. [https://doi.org/10.1162/neco\_a\_01363](https://doi.org/10.1162/neco_a_01363) ‍
53. Vempala, N. N., & Russo, F. A. (2012). Predicting emotion from music audio features using neural networks. _Proceedings of the 9th International Symposium on Computer Music Modeling and Retrieval (CMMR)_, 336–343.
54. Washburne, C. (2020). “More Cowbell”: Latin Jazz in the Twenty-First Century. In _Latin Jazz_. Oxford University Press. [https://doi.org/10.1093/oso/9780195371628.003.0007](https://doi.org/10.1093/oso/9780195371628.003.0007) ‍
55. Yang, Y.-H., Lin, Y.-C., Su, Y.-F., & Chen, H. H. (2008). A Regression Approach to Music Emotion Recognition. _IEEE Transactions on Audio, Speech, and Language Processing_, 16(2), 448–457. [https://doi.org/10.1109/TASL.2007.911513](https://doi.org/10.1109/TASL.2007.911513) ‍
56. Zald, D. H., & Pardo, J. V. (2002). The neural correlates of aversive auditory stimulation. _Neuroimage_, 16(3), 746–753.

## About the authors

Aia Haruvi

[LinkedIn →](https://www.linkedin.com/in/aiaharuvi/)

Ronen Kopito

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Noa Brande-Eilat

[LinkedIn →](https://www.linkedin.com/in/noa-brande-eilat-604b1715b/)

Shai Kalev

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Eitan Kay

[LinkedIn →](https://www.linkedin.com/in/eitankay/)

Dan Furman

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