View More View Less
  • 1 University of Illinois College of Medicine at Peoria, USA
  • | 2 University of Illinois College of Medicine at Peoria, USA
  • | 3 University of Illinois College of Medicine at Peoria, USA
  • | 4 Bradley University, USA
  • | 5 Saint Louis University School of Medicine, USA
  • | 6 University of Missouri School of Medicine, USA
Open access

Background

Music has been associated with therapeutic properties for thousands of years across a vast number of diverse regions and cultures. This study expands upon our current understanding of music’s influence on human neurophysiology by investigating the effects of various music genres on cerebral cortex activity using electroencephalography (EEG).

Methods

A randomized, controlled study design was used. EEG data were recorded from 23 healthy adults, aging 18–29 years, while listening to a music sequence consisting of five randomized songs and two controls. The five studied music genres include: Classical, Tribal Downtempo, Psychedelic Trance (Psytrance), Goa Trance, and Subject Choice.

Results

Controls were most strongly associated with relative decreases in beta frequencies and increases in alpha frequencies. Psytrance was most strongly associated with relative increases in theta and delta frequencies. The lowest relative percentages of beta frequencies and highest relative percentages of alpha frequencies occurred in the occipital and parietal regions. The highest relative percentages of theta and delta frequencies occurred in the frontal and temporal regions. Subjects with prior music training exhibited relative increases in delta frequencies in the frontal region. Subject gender and music preferences did not have a significant influence on EEG activity.

Conclusions

Findings from this study support those of previous music therapy studies and provide novel insights regarding music’s influence on human neurophysiology. Our findings also support the hypothesis that music may promote changes in cerebral cortex activity that has similarities to non-rapid eye movement sleep, while the listener remains awake.

Abstract

Background

Music has been associated with therapeutic properties for thousands of years across a vast number of diverse regions and cultures. This study expands upon our current understanding of music’s influence on human neurophysiology by investigating the effects of various music genres on cerebral cortex activity using electroencephalography (EEG).

Methods

A randomized, controlled study design was used. EEG data were recorded from 23 healthy adults, aging 18–29 years, while listening to a music sequence consisting of five randomized songs and two controls. The five studied music genres include: Classical, Tribal Downtempo, Psychedelic Trance (Psytrance), Goa Trance, and Subject Choice.

Results

Controls were most strongly associated with relative decreases in beta frequencies and increases in alpha frequencies. Psytrance was most strongly associated with relative increases in theta and delta frequencies. The lowest relative percentages of beta frequencies and highest relative percentages of alpha frequencies occurred in the occipital and parietal regions. The highest relative percentages of theta and delta frequencies occurred in the frontal and temporal regions. Subjects with prior music training exhibited relative increases in delta frequencies in the frontal region. Subject gender and music preferences did not have a significant influence on EEG activity.

Conclusions

Findings from this study support those of previous music therapy studies and provide novel insights regarding music’s influence on human neurophysiology. Our findings also support the hypothesis that music may promote changes in cerebral cortex activity that has similarities to non-rapid eye movement sleep, while the listener remains awake.

Introduction

Music has been associated with therapeutic properties for thousands of years across a vast number of diverse regions and cultures. The earliest documented reports of music therapy are found in historic writings from many ancient civilizations including Egypt, China, India, Greece, and Rome (University Hospitals of Cleveland, 2011). The first recorded use of music therapy in a medical setting dates back to World Wars I and II, when music was used to relieve pain and anxiety in soldiers with traumatic war injuries (University Hospitals of Cleveland, 2011). Music continues to be used today by countless individuals across the world for a wide variety of reasons. From ceremonies and celebrations, to routine listening during work, exercise, and travel, countless people continue to rely on music for its many reported therapeutic properties.

Over the past 30 years, a large number of scientific studies have generated a robust body of evidence, which suggests that music can provide significant benefits as an adjuvant treatment modality in a variety of clinical settings (Aragon, Farris, & Byers, 2002; Binek et al., 2003; Bradt & Dileo, 2009; Bradt, Dileo, Grocke, & Magill, 2011; Bringman, Giesecke, Thörne, & Bringman, 2009; Buffum et al., 2006; Chan, Chan, Mok, & Kwan Tse, 2009; Chan, Lee, Ng, Ngan, & Wong, 2003; Chlan, Evans, Greenleaf, & Walker, 2000; Conrad et al., 2007; Cooke, Chaboyer, Schluter, & Hiratos, 2005; Ebneshahidi & Mohseni, 2008; Evans, 2002; Galaal, Deane, Sangal, & Lopes, 2007; Han et al., 2010; Hatem, Lira, & Mattos, 2006; Kim, Kim, & Myoung, 2011; Klassen, Liang, Tjosvold, Klassen, & Hartling, 2008; Kliempt, Ruta, Ogston, Landeck, & Martay, 1999; Korhan, Khorshid, & Uyar, 2011; Kotwal, Rinchhen, & Ringe, 1998; Kwon, Kim, & Park, 2006; Lai & Li, 2011; Lee, Chao, Yiin, Chiang, & Chao, 2011; Lee et al., 2012; Lee, Chung, Chan, & Chan, 2005; Lepage, Drolet, Girard, Grenier, & DeGagné, 2001; Lin, Lin, Huang, Hsu, & Lin, 2011; Loomba, Arora, Shah, Chandrasekar, & Molnar, 2012; Madson & Silverman, 2010; Maeyama, Kodaka, & Miyao, 2009; Mattei, Rodriguez, & Bassuner, 2013; Ni, Tsai, Lee, Kao, & Chen, 2012; Nilsson, 2008, 2009; Nilsson, Unosson, & Rawal, 2005; Rudin, Kiss, Wetz, & Sottile, 2007; Salimpoor, Benovoy, Larcher, Dagher, & Zatorre, 2011; Sendelbach, Halm, Doran, Miller, & Gaillard, 2006; Shabanloei, Golchin, Esfahani, Dolatkhah, & Rasoulian, 2010; Slevc & Okada, 2015; Smolen, Topp, & Singer, 2002; Tam, Wong, & Twinn, 2008; Triller, Erzen, Duh, Petrinec Primozic, & Kosnik, 2006; Tse, Chan, & Benzie, 2005; Vaajoki, Kankkunen, Pietilä, & Vehviläinen-Julkunen, 2011; Voss et al., 2004; Wang, Kulkarni, Dolev, & Kain, 2002; Yilmaz et al., 2003; Zalewsky, Vinker, Fiada, Livon, & Kitai, 1998; Zare, Ebrahimi, & Birashk, 2010). Some of the many therapeutic effects of music therapy that have been recently investigated include decreased pain and anxiety; decreased analgesic and anxiolytic medication requirements; improvements in mood, emotion, and quality of life; improvements in symptoms associated with chronic cognitive illnesses, such as Alzheimer’s dementia and Parkinson’s disease; and improvements in many physiological variables including heart rate, blood pressure, and more, in a large number of clinical scenarios (Aragon et al., 2002; Binek et al., 2003; Bradt & Dileo, 2009; Bradt et al., 2011; Bringman et al., 2009; Buffum et al., 2006; Chan et al., 2003, 2009; Chlan et al., 2000; Conrad et al., 2007; Cooke et al., 2005; Ebneshahidi & Mohseni, 2008; Evans, 2002; Galaal et al., 2007; Han et al., 2010; Hatem et al., 2006; Kim et al., 2011; Klassen et al., 2008; Kliempt et al., 1999; Korhan et al., 2011;Kotwal et al., 1998; Kwon et al., 2006; Lai & Li, 2011; Lee et al., 2005, 2011, 2012; Lepage et al., 2001; Lin et al., 2011; Loomba et al., 2012; Madson & Silverman, 2010; Maeyama et al., 2009; Mattei et al., 2013; Ni et al., 2012; Nilsson, 2008, 2009; Nilsson et al., 2005; Rudin et al., 2007; Salimpoor et al., 2011; Sendelbach et al., 2006; Shabanloei et al., 2010; Slevc & Okada, 2015; Smolen et al., 2002; Tam et al., 2008; Triller et al., 2006; Tse et al., 2005; Vaajoki et al., 2011; Voss et al., 2004; Wang et al., 2002; Yilmaz et al., 2003; Zalewsky et al., 1998; Zare et al., 2010).

Our current understanding of the neurophysiological mechanisms underlying the therapeutic effects of music suggests that the cerebral cortex, basal ganglia, and hypothalamic–pituitary–adrenal axis are involved in various degrees (Baumgartner, Esslen, & Jäncke, 2006; Conrad et al., 2007; Jacobs & Friedman, 2004; Kabuto, Kageyama, & Nitta, 1993; Koelsch, Maess, Grossmann, & Friederici, 2003; Lin, Duann, Chen, & Jung, 2010; Mattei et al., 2013; Salimpoor et al., 2011). Previous studies utilizing electroencephalography (EEG) to examine cerebral cortex activity in subjects undergoing music therapy suggest that increased alpha, theta, and delta frequency activities may be part of the central nervous system response (Baumgartner et al., 2006; Jacobs & Friedman, 2004; Kabuto et al., 1993; Lin et al., 2010). Taken together, these findings suggest that various types of music may promote changes in central nervous system activity and hypothalamic–pituitary–adrenal function that have similarities in different stages of sleep, while the listener remains awake (Baumgartner et al., 2006; Britton et al., 2016; Conrad et al., 2007; Jacobs & Friedman, 2004; Kabuto et al., 1993; Lin et al., 2010; Mattei et al., 2013; Salimpoor et al., 2011; White & Richard, 2009).

In addition, despite the recent expansion of scientific literature exploring outcomes-based research aimed at investigating how common, popular music genres (e.g., classical) may be beneficial for various applications related to mood, anxiety, pain, and other variables, there has been significantly less attention devoted to comparing different music genres for potential variabilities in their efficacy and potency to impact the human brain and body. Due to recent advances in computer technology and music composition software, many forms of music are currently being composed digitally, including classical symphonies, theatrical soundtracks, cultural music, etc. Digital music composition allows for an expanded range of instruments, pitches, resonances, melodies, harmonies, and other musical variables, all assembled with precise timing and flawless uniformity, without the irregularities or errors that often occur during organic music composition with manual instruments.

This study aims to investigate the neurophysiological activity associated with the therapeutic effects of music using EEG in healthy young adults, by comparing relative percentages of beta, alpha, theta, and delta frequencies in each major region of the cerebral cortex while subjects listen to randomized sequences of five unique music genres and controls. Interregion cortical comparisons will also be investigated. The potential influences of subject gender, music preferences, and prior music training will also be examined. By utilizing a randomized, controlled design and a variety of carefully selected music genres, this study aims to expand on our current understanding of how various types of music may give rise to therapeutic effects in the human brain and body. By improving our understanding of the neurophysiological effects of various music genres on different regions of the cerebral cortex, we can more effectively apply music as an adjuvant therapeutic tool in modern medical practice to benefit humankind.

Methods

Subject details

After institutional review board (IRB) approval (IRB #267944-1), 25 healthy adults, aging 18–28 years, with normal hearing function, were recruited from the University of Illinois College of Medicine at Peoria, Bradley University and Illinois Central College campuses. Subjects were asked to avoid caffeine, tobacco, and all other psychoactive chemicals for at least 6 hr prior to their study participation. Subjects were also asked to be at adequate rest prior to study participation. Informed consent was obtained from all subjects prior to data collection.

Study design

To investigate the suspected ability of various music genres to promote changes in cerebral cortex activity that have similarities to non-rapid eye movement (NREM) sleep, this study aimed to measure relative percentages of beta, alpha, theta, and delta frequencies during each segment of a subject’s randomized music sequence. Wakefulness characterized by alertness and active cognition is predominately accompanied by beta frequencies (>13 Hz), whereas wakefulness characterized by relaxation and drowsiness (e.g., meditation) is predominately accompanied by alpha frequencies (8–12 Hz; Britton et al., 2016; White & Richard, 2009). NREM sleep is generally categorized into three stages (N1–3): N1 (Stage 1) is characterized by light NREM sleep and is predominately accompanied by theta frequencies (4–7 Hz), with a lesser mixture of alpha frequencies (8–12 Hz). N2 (Stage 2) is characterized by deeper NREM sleep and is predominately accompanied by theta frequencies (4–7 Hz) interspersed with sleep spindles and K-complexes, with a lesser prevalence of delta frequencies (1–3 Hz). N3 (Stage 3) is characterized by the deepest NREM sleep and is predominately accompanied by delta frequencies (1–3 Hz; Britton et al., 2016; White & Richard, 2009). Rapid eye movement (REM) sleep is characterized by the deepest stage of sleep, and is predominately accompanied by beta frequencies (>13 Hz) coupled with REMs, dreaming, and muscle paralysis (Britton et al., 2016; White & Richard, 2009).

Data were collected at the Illinois Neurological Institute (INI) Sleep Center at OSF Saint Francis Medical Center in Peoria, Illinois, using a randomized, controlled design. Each subject participated in a single data gathering session approximately 1 hr in length, during which they completed a pre-music survey (Appendix), then listened to a randomized music sequence, and completed a post-music survey (Appendix). EEG leads were placed by a certified INI EEG technician. After completing the pre-music survey, subjects were fitted with professional, closed-ear, studio quality headphones and a special blindfold that allowed subjects to keep their eyes open in complete darkness throughout the duration of the music sequence. This blindfold was chosen, so that subjects could keep their eyes open throughout the duration of the music sequence, since closure of the eyes often leads to states of relaxed wakefulness (predominately accompanied by alpha frequencies) and Stage 1 (N1) NREM sleep (predominately accompanied by theta and alpha frequencies; Britton et al., 2016; White & Richard, 2009).

Blindfold position was adjusted to achieve complete darkness for each subject before the music sequence began. Subjects were asked to stay awake and to keep their eyes open for the entire duration of the music sequence, and to report on the post-music survey, if they had fallen asleep or had any other concerns that arose during the music sequence. Subjects were asked not to take breaks during the music sequence. Timestamps were documented during each subject’s music sequence, so that start and end points of each song and control could be clearly identified in the EEG data. Subjects briefly rated each song immediately after hearing it without removing their blindfold or headphones using a verbal equivalent of a Visual Analog Scale (Appendix). Subjects were not informed of which music genre they had just heard when providing this rating. After the music sequence had been complete, subjects ranked the songs relative to each other as part of the post-music survey (Appendix).

Each subject’s music sequence consisted of five songs in randomized order, flanked by a control (Victoria Falls waterfall recording). Except for the subject-chosen song in each music sequence, all subjects listened to the same pre-selected music genres and controls that had been chosen in advance by the study team. One song was chosen to represent each music genre, and the study team was very careful to select a song that accurately represented each genre. Song order for each subject’s music sequence was randomized using a standard permutation. The song lengths in each subject’s music sequence were standardized to the shortest song of the sequence by removing an appropriate terminal segment from the end of longer songs and controls. Each subject’s music sequence was flanked with a control of identical length at the beginning (Control 1) and end (Control 2). Brief 2.5-min control periods were placed between songs in each music sequence for a neutral, standardized transition between genres. All music used in this study was high-quality 320 kbps digital MP3 format or better, purchased from various accredited online music vendors. The music genres selected for this study were chosen because they are widely reported as therapeutic, and for additional analytical reasons:

Classical music has been frequently used in previous music therapy studies and is recognized throughout the world. One study investigated the impact of classical music on various physiological parameters (e.g., blood pressure, heart rate, etc.) and hormones of the hypothalamic–pituitary–adrenal axis (e.g., cortisol, growth hormone, etc.) in a group of critically ill patients (Conrad et al., 2007). Compositions from world-renowned composers such as Ludwig van Beethoven, Wolfgang Amadeus Mozart, and Johann Sebastian Bach are widely recognized and are still commonly played in modern times by a variety of both public (e.g., radio and television) and private listeners. It is frequently reported to be therapeutic by listeners around the world. Song: Ludwig van Beethoven – Symphony No. 5 in C-Minor.

Tribal Downtempo is a broad category of electronic music that features a blend of vocal chants, hand drumming, and organic instruments, which have cultural and historical relevance to human ancestral past. Some of the common instruments featured in this music genre include the djembe, doumbek, various wooden flutes, and more, which contribute to the “tribal” atmospheres present in this genre. Tribal Downtempo varies widely in tempo, but is typically much slower (<100 bpm) than more fast-paced genres of music, such as most types of electronic dance music. It is widely reported to be therapeutic by listeners around the world. Song: Koan – When We Left Arkaim.

Psychedelic Trance (Psytrance) is a unique genre of electronic dance music that features a wide variety of melodies, harmonies, and atmospheres centered around a unique rhythm composition that is typically produced in a widely recognized 4/4 time signature, but using 16th bass notes (i.e., four bass notes per beat) instead of simple quarter notes. The typical tempo of Psytrance is around 145–155 bpm, which results in a frequency range similar to the alpha frequency band (8–12 Hz), since there are four bass notes for every beat (4 bass notes × 150 bpm = 600 bpm = ∼10 Hz). Psytrance is widely reported to be both therapeutic and “trance-inducing.” Psytrance originally developed from another type of electronic dance music known as Goa Trance in the 1970s and 1980s, and has since spread throughout the world (St. John, 2010). Song: M-Theory – L6 Echo.

Goa Trance (Goa) is very similar in composition to Psytrance, except with a standard quarter note beat structure (i.e., no 16th bass notes), making it an excellent music genre for comparison to Psytrance. Although Goa Trance typically utilizes a standard quarter-note beat structure, it is still typically produced at a tempo very similar to Psytrance (145–155 bpm), and with similar melodies, harmonies, and atmospheres. Like Psytrance, Goa Trance is also widely reported to be both therapeutic and “trance-inducing.” Goa Trance was originally developed on the beautiful beaches of Goa, India through various collaborative organic and digital music projects in the 1970s and 1980s, and eventually spread throughout the world (St. John, 2010). Song: Goalien – Do It Now.

Subject Song is a subject-chosen song that was included in each subject’s personalized music sequence to evaluate the importance of subjective music preferences. Subjects were asked to provide their three most favorite, pleasurable songs after qualifying for the study. The study team then chose one of these songs based on its availability in a high-quality digital format (e.g., MP3), and its similarity to the other four music genres. If more than one song was available in an appropriate digital format, the principal investigator chose the song that was least similar to the other four music genres. Typical songs selected by subjects included common music genres one might hear on popular radio and television stations today, including Pop, R&B, Hip-Hop, Rock, etc.

Control (white noise) was selected to evaluate subject’s cerebral cortical activity in response to non-musical sound, for comparison to the cortical activity observed during the five music genres described above. Consideration was given to artificial types of control noise (e.g., television static, radio static, and traffic sounds), including the background noise encountered at OSF Saint Francis Medical Center where data collection took place, but this study team opted to use a natural form of white noise (i.e., waterfall recording) to increase the likelihood that subjects would complete the entire 1-hr music sequence with minimal discomfort and anxiety. Song: Victoria Falls (audio recording).

Data measurement

Subjective data were obtained using pre-music and post-music printed surveys (Appendix). EEG data were obtained using Nihon Kohden Neurofax EEG-1200A hardware and Neurofax QP 112AK v06-80 software (Appendix). Nineteen EEG electrodes were placed according to the standard international 10–20 system by a certified technician. Measurement of recorded EEG data was then performed by exporting the raw, unedited data from the Neurofax software, converting to European Data Format Plus (EDF+) format and then importing to Novatech WinEEG v2.7+ software for quantitative spectral analysis. Each subject’s EEG data were analyzed using a speed of 30 mm/s, gain of 100 μV, baseline of 0.00 μV, low cut of 0.1 s (1.6 Hz), high cut of 50 Hz, Notch of 50–70 Hz, and “monopolar average 1” montage. No additional processing, “cleaning”, or modification of the EEG data was performed.

The relative percentage of beta, alpha, theta, and delta frequencies from each song and control of a subject’s music sequence were determined using the documented timestamps to identify the corresponding EEG data for a given song or control, then running the WinEEG artifact correction tool followed by the spectral analysis tool to generate a table that displayed the relative percentages of each frequency band that occurred during an individual song or control. This was performed by the principle investigator under the guidance of an attending neurologist from the INI at OSF Saint Francis Medical Center. The settings used by this study team when applying the WinEEG spectral analysis tool included: epoch length of 4 s, overlap of 50%, and Hanning time windows. The EEG data were also grouped into cortical regions by averaging the resulting percentages from individual leads corresponding to each cortical lobe (e.g., data from Fp1, Fp2, and Fz leads were averaged to generate a “frontal region” value for each song). Individual hemispheres were not studied in isolation, so this study team did not perform left versus right comparisons for each cortical region. Somatotopic cortical maps were generated from the relative percentage tables in WinEEG to visually represent the cortical regions where the greatest relative percentages of each frequency band occurred, using a brightness scale of 0%–60% (0% = no brightness, 60% = maximum brightness) to provide optimal visualization of data, since no individual frequency band showed relative percentages greater than ∼60% in any cortical region.

Statistical analyses

Processed EEG data tables were exported from WinEEG and then imported into a Microsoft Excel spreadsheet for analysis using IBM SPSS software (version 21.0.0, Armonk, NY, USA). Standard descriptive statistics were calculated including mean, standard deviation, range, and correlation coefficients. Wilcoxon signed-rank tests were used to compare EEG results between pairs of music genres and controls and between pairs of individual cortical regions. Spearman’s rank-order correlation tests were used to investigate possible influences of subjective variables including subject music preferences (ranks and ratings), subject gender, and previous subject music training. A strict statistical significance threshold of p ≤ .01 was used to help offset, in part, the relatively small sample size.

Ethics

This study was conducted with strict adherence to all applicable local and international laws and standards. All study authors conformed to the highest standards of ethical conduct throughout all portions of this study, including accurate data submission, acknowledging the work of others, and divulging potential conflicts of interests.

Results

Twenty-three out of the 25 recruited subjects successfully participated in data collection. Two subjects dropped out of the study prior to data collection; one subject cancelled, and a second subject was outside of the required age range. Of the 23 participating study subjects, 9 were male and 14 were female. The subjects were distributed among the following ages: 19 (2), 20 (1), 23 (3), 24 (4), 25 (3), 26 (4), 27 (1), and 28 (5), with a mean age of 24.7 years. The ethnicity of these subjects included Chinese, African, Caucasian, Hispanic, Arab, Indian, Ashkenazi Jew, and Asian/Pacific Islander. Seventeen subjects reported previous music training (e.g., playing an instrument, singing in choir, etc.). Only one subject reported briefly falling asleep during their music sequence.

Music genre comparisons

Mean relative percentages of beta, alpha, theta, and delta frequencies for all randomized music genres and controls within individual cortical regions are displayed in Table 1 and Figures 14.

Table 1.

Mean relative beta, alpha, theta, and delta frequency percentages for randomized music genres and controls

Control 1ClassicalTribalPsytranceGoaSubjectControl 2SD
Beta
Frontal10.1710.379.579.6010.5210.139.270.47
Parietal8.228.187.887.858.458.567.680.33
Occipital6.787.547.086.967.167.776.760.38
Temporal9.598.898.558.088.739.788.240.64
Alpha
Frontal31.9730.5829.6929.3428.3929.5934.121.94
Parietal53.6552.9752.0749.8250.9751.2155.872.00
Occipital63.0757.4456.1955.5657.9057.8261.362.74
Temporal32.9133.4532.5932.0731.8831.1136.751.83
Theta
Frontal12.9412.7012.7913.6212.8811.4012.530.67
Parietal10.6011.5512.1813.1311.2610.6211.340.89
Occipital8.5510.1511.6112.1110.359.0510.101.27
Temporal11.3712.2112.4113.3611.7010.0412.071.02
Delta
Frontal13.8414.3215.1916.1215.0314.0014.000.84
Parietal9.2710.0510.4112.1010.759.619.610.96
Occipital7.629.0910.1710.839.688.338.691.11
Temporal13.7514.5114.2815.7814.3812.5313.810.98

Note. SD: standard deviation.

Figure 1.
Figure 1.

Mean relative beta frequency percentages for randomized music genres and controls

Citation: Journal of Psychedelic Studies JPS 2021; 10.1556/2054.2019.027

Figure 2.
Figure 2.

Mean relative alpha frequency percentages for randomized music genres and controls

Citation: Journal of Psychedelic Studies JPS 2021; 10.1556/2054.2019.027

Figure 3.
Figure 3.

Mean relative theta frequency percentages for randomized music genres and controls

Citation: Journal of Psychedelic Studies JPS 2021; 10.1556/2054.2019.027

Figure 4.
Figure 4.

Mean relative delta frequency percentages for randomized music genres and controls

Citation: Journal of Psychedelic Studies JPS 2021; 10.1556/2054.2019.027

Wilcoxon signed-rank analyses revealed significant differences in relative percentages of beta frequencies for paired comparisons of music genres and/or controls within individual cortical regions, which are displayed in Table 2. In the occipital region, Control 1 was associated with decreased relative percentages of beta frequencies compared to both Classical (p = .002) and Subject Song (p = .008), whereas Control 2 was associated with decreased relative percentages of beta frequencies compared to Subject Song (p = .008) only. In the temporal region, Tribal Downtempo, Psytrance, and Control 2 were associated with decreased relative percentages of beta frequencies compared to Control 1 (p = .006, p = .008, p = .010), whereas both Psytrance and Goa Trance were associated with decreased relative percentages of beta frequencies compared to Subject Song (p = .004, p = .010).

Table 2.

Wilcoxon signed-rank analyses of paired music genres and control comparisons for the beta frequency band

GenresFrontal (p)Parietal (p)Occipital (p)Temporal (p)
B vs. A.429.224.191.738
C vs. A.248.715.330.136
D vs. A.976.543.673.951
E vs. A.784.301.484.026
C1 vs. A.951.362.002*.039
C2 vs. A.043.068.026.101
C vs. B.879.627.951.191
D vs. B.121.153.595.761
E vs. B.855.083.144.024
C1 vs. B.784.605.236.006*
C2 vs. B.831.236.171.301
D vs. C.465.484.927.212
E vs. C.394.089.019.004*
C1 vs. C.627.605.248.008*
C2 vs. C.523.114.055.784
E vs. D.693.670.171.010*
C1 vs. D.627.784.110.033
C2 vs. D.191.045.447.543
C1 vs. E.976.114.008*.584
C2 vs. E.260.021.008*.019
C2 vs. C1.089.191.903.010*

Note. A: Classical; B: Tribal Downtempo; C: Psytrance; D: Goa Trance; E: Subject Song; C1: Control 1; C2: Control 2.

*Data that meet this study’s statistical significance threshold of p = .01 or less in comparison to non-significant results.

Wilcoxon signed-rank analyses revealed significant differences in relative percentages of alpha frequencies for paired comparisons of music genres and/or controls within individual cortical regions, which are displayed in Table 3. In the frontal region, Control 2 was associated with increased relative percentages of alpha frequencies compared to Classical (p = .006), Goa Trance (p = .003), and Subject Song (p = .010). In the parietal region, Control 2 was associated with increased relative percentages of alpha frequencies compared to Psytrance (p = .007). In the occipital region, Control 1 was associated with increased relative percentages of alpha frequencies compared to Classical (p = .010), Tribal Downtempo (p = .004), Psytrance (p = .010), and Goa Trance (p = .005). In the temporal region, Control 2 was associated with increased relative percentages of alpha frequencies compared to Tribal Downtempo (p = .001), Psytrance (p = .004), Goa Trance (p = .007), and Subject Song (p = .002).

Table 3.

Wilcoxon signed-rank analyses of paired music genres and control comparisons for the alpha frequency band

GenresFrontal (p)Parietal (p)Occipital (p)Temporal (p)
B vs. A.563.114.068.136
C vs. A.715.031.260.316
D vs. A.107.114.738.330
E vs. A.627.171.563.059
C1 vs. A.316.927.010*.761
C2 vs. A.006*.236.201.023
C vs. B.726.224.465.951
D vs. B.301.394.563.429
E vs. B.784.429.879.212
C1 vs. B.021.503.004*.715
C2 vs. B.039.039.026.001*
D vs. C.523.412.648.951
E vs. C.784.951.761.412
C1 vs. C.346.064.010*.523
C2 vs. C.029.007*.052.004*
E vs. D.627.903.584.563
C1 vs. D.063.260.005*.316
C2 vs. D.003*.068.394.007*
C1 vs. E.114.078.015.248
C2 vs. E.010*.026.094.002*
C2 vs. C1.378.260.503.029

Note. A: Classical; B: Tribal Downtempo; C: Psytrance; D: Goa Trance; E: Subject Song; C1: Control 1; C2: Control 2.

*Data that meet this study’s statistical significance threshold of p = .01 or less in comparison to non-significant results.

Wilcoxon signed-rank analyses revealed significant differences in relative percentages of theta frequencies for paired comparisons of music genres and/or controls within individual cortical regions, which are displayed in Table 4. In the frontal region, Psytrance was associated with increased relative percentages of theta frequencies compared to Subject Song (p = .002). In the parietal region, Psytrance was associated with increased relative percentages of theta frequencies compared to both Subject Song (p = .002) and Control 1 (p = .009). In the occipital region, Tribal Downtempo and Psytrance were associated with increased relative percentages of theta frequencies compared to Control 1 (p = .003, p = .001). In the temporal region, Classical, Tribal Downtempo, and Psytrance were associated with increased relative percentages of theta frequencies compared to Subject Song (p = .006, p = .004, p < .001).

Table 4.

Wilcoxon signed-rank analyses of paired music genres and control comparisons for the theta frequency band

GenresFrontal (p)Parietal (p)Occipital (p)Temporal (p)
B vs. A.784.316.019.543
C vs. A.073.031.018.048
D vs. A.976.693.287.648
E vs. A.083.761.648.006*
C1 vs. A.693.976.301.394
C2 vs. A.761.831.784.784
C vs. B.191.132.808.315
D vs. B.738.412.346.248
E vs. B.144.236.089.004*
C1 vs. B.831.128.003*.101
C2 vs. B.855.218.114.563
D vs. C.447.061.412.015
E vs. C.002*.002*.052<.001*
C1 vs. C.429.009*.001*.029
C2 vs. C.287.042.224.059
E vs. D.248.951.176.059
C1 vs. D.903.738.033.267
C2 vs. D.808.808.693.715
C1 vs. E.104.595.248.078
C2 vs. E.191.584.808.014
C2 vs. C1.543.976.224.523

Note. A: Classical; B: Tribal Downtempo; C: Psytrance; D: Goa Trance; E: Subject Song; C1: Control 1; C2: Control 2.

*Data that meet this study’s statistical significance threshold of p = .01 or less in comparison to non-significant results.

Wilcoxon signed-rank analyses revealed significant differences in relative percentages of delta frequencies for paired comparisons of music genres and/or controls within individual cortical regions, which are displayed in Table 5. In the parietal region, Psytrance was associated with increased relative percentages of delta frequencies compared to Classical (p = .008), Control 1 (p = .008), and Control 2 (p = .010). In the occipital region, both Psytrance and Goa Trance were associated with increased relative percentages of delta frequencies compared to Control 1 (p = .009, p = .010). In the temporal region, Psytrance was associated with increased relative percentages of delta frequencies compared to Subject Song (p = .005).

Table 5.

Wilcoxon signed-rank analyses of paired music genres and control comparisons for the delta frequency band

GenresFrontal (p)Parietal (p)Occipital (p)Temporal (p)
B vs. A.394.543.068.701
C vs. A.212.008*.101.301
D vs. A.447.176.394.465
E vs. A.584.783.808.016
C1 vs. A.761.412.162.465
C2 vs. A.503.308.648.563
C vs. B.855.013.915.114
D vs. B.563.316.879.951
E vs. B.523.903.354.136
C1 vs. B.267.171.011.465
C2 vs. B.191.236.070.648
D vs. C.903.301.627.107
E vs. C.191.031.114.005*
C1 vs. C.543.008*.009*.048
C2 vs. C.089.010*.128.107
E vs. D.976.330.715.114
C1 vs. D.784.094.010*.648
C2 vs. D.403.128.287.543
C1 vs. E.761.236.114.162
C2 vs. E.831.378.445.563
C2 vs. C1.584.484.503.808

Note. A: Classical; B: Tribal Downtempo; C: Psytrance; D: Goa Trance; E: Subject Song; C1: Control 1; C2: Control 2.

*Data that meet this study’s statistical significance threshold of p = .01 or less in comparison to non-significant results.

Cortical region comparisons

A somatotopic cortical activity map for EEG frequency band activity summarizing our EEG findings for interregion cortical comparisons across all music genres and controls is displayed in Figure 5. Statistical data analyses leading to this visual somatotopic representation of our interregion cortical comparison findings can be found in Tables 612 and the subsequent paragraphs below.

Table 6.

Wilcoxon signed-rank analyses of paired cortical region comparisons for Classical

RegionsBeta (p)Alpha (p)Theta (p)Delta (p)
P vs. F.004*<.001*.036<.001*
O vs. F.006*<.001*.007*<.001*
T vs. F.045.107.260.927
O vs. P.045.026.014.036
T vs. P.171<.001*.287<.001*
T vs. O.036<.001*.015<.001*

Note. P: parietal; F: frontal; O: occipital; T: temporal.

*Data that meet this study’s statistical significance threshold of p = .01 or less in comparison to non-significant results.

Table 7.

Wilcoxon signed-rank analyses of paired cortical region comparisons for Tribal Downtempo

RegionsBeta (p)Alpha (p)Theta (p)Delta (p)
P vs. F.015<.001*.330<.001*
O vs. F.013<.001*.144.002*
T vs. F.029.019.595.523
O vs. P.029.015.083.236
T vs. P.128<.001*.761<.001*
T vs. O.012<.001*.181.001*

Note. P: parietal; F: frontal; O: occipital; T: temporal.

*Data that meet this study’s statistical significance threshold of p = .01 or less in comparison to non-significant results.

Table 8.

Wilcoxon signed-rank analyses of paired cortical region comparisons for Psytrance

RegionsBeta (p)Alpha (p)Theta (p)Delta (p)
P vs. F.073<.001*.136.001*
O vs. F.018<.001*.212.001*
T vs. F.031.128.412.808
O vs. P.018.005*.073.021
T vs. P.927<.001*.543<.001*
T vs. O.078<.001*.274<.001*

Note. P: parietal; F: frontal; O: occipital; T: temporal.

*Data that meet this study’s statistical significance threshold of p = .01 or less in comparison to non-significant results.

Table 9.

Wilcoxon signed-rank analyses of paired cortical region comparisons for Goa Trance

RegionsBeta (p)Alpha (p)Theta (p)Delta (p)
P vs. F.005*<.001*.036<.001*
O vs. F.001*<.001*.012<.001*
T vs. F.007*.045.055.523
O vs. P.002*.002*.078.029
T vs. P.503<.001*.447.001*
T vs. O.007*<.001*.078<.001*

Note. P: parietal; F: frontal; O: occipital; T: temporal.

*Data that meet this study’s statistical significance threshold of p = .01 or less in comparison to non-significant results.

Table 10.

Wilcoxon signed-rank analyses of paired cortical region comparisons for Subject Song

RegionsBeta (p)Alpha (p)Theta (p)Delta (p)
P vs. F.064<.001*.212.001*
O vs. F.033<.001*.019.001*
T vs. F.648.236.068.094
O vs. P.018<.001*.001*.015
T vs. P.005*<.001*.465.002*
T vs. O.007*<.001*.083.001*

Note. P: parietal; F: frontal; O: occipital; T: temporal.

*Data that meet this study’s statistical significance threshold of p = .01 or less in comparison to non-significant results.

Table 11.

Wilcoxon signed-rank analyses of paired cortical region comparisons for Control 1

RegionsBeta (p)Alpha (p)Theta (p)Delta (p)
P vs. F.003*<.001*.001*<.001*
O vs. F<.001*<.001*.001*<.001*
T vs. F.484.523.023.523
O vs. P<.001*<.001*.002*.004*
T vs. P.004*<.001*.121<.001*
T vs. O<.001*<.001*.001*<.001*

Note. P: parietal; F: frontal; O: occipital; T: temporal.

*Data that meet this study’s statistical significance threshold of p = .01 or less in comparison to non-significant results.

Table 12.

Wilcoxon signed-rank analysis of paired cortical region comparisons for Control 2

RegionsBeta (p)Alpha (p)Theta (p)Delta (p)
P vs. F.073<.001*.083<.001*
O vs. F.010*<.001*.014.001*
T vs. F.121.107.248.927
O vs. P.004*.001*.018.018
T vs. P.224<.001*.162<.001*
T vs. O.031<.001*.006*<.001*

Note. P: parietal; F: frontal; O: occipital; T: temporal.

*Data that meet this study’s statistical significance threshold of p = .01 or less in comparison to non-significant results.

Figure 5.
Figure 5.

Somatotopic cortical activity map summarizing relative percentages of each frequency band for different cortical regions, across all music genres and controls (0% = no brightness, 60% = maximum brightness)

Citation: Journal of Psychedelic Studies JPS 2021; 10.1556/2054.2019.027

Wilcoxon signed-rank analyses revealed significant differences when comparing the effect of Classical music on relative percentages of beta, alpha, theta, and delta frequencies between different cortical regions, which are displayed in Table 6. Both the parietal and occipital regions were associated with decreased relative percentages of beta frequencies compared to the frontal region (p = .004, p = .006). Both the parietal and occipital regions were associated with increased relative percentages of alpha frequencies compared to the frontal (p < .001, p < .001) and temporal (p < .001, p < .001) regions. The frontal region was associated with increased relative percentages of theta frequencies compared to the occipital region (p = 0.007). Both the frontal and temporal regions were associated with increased relative percentages of delta frequencies compared to the parietal (p < .001, p < .001) and occipital (p < .001, p < .001) regions.

Wilcoxon signed-rank analyses revealed significant differences when comparing the effect of Tribal Downtempo on relative percentages of alpha and delta frequencies between different cortical regions, which are displayed in Table 7. Both the parietal and occipital regions were associated with increased relative percentages of alpha frequencies compared to the frontal (p < .001, p < .001) and temporal (p < .001, p < .001) regions. Both the frontal and temporal regions were associated with increased relative percentages of delta frequencies compared to the parietal (p < .001, p < .001) and occipital (p = .002, p = .001) regions.

Wilcoxon signed-rank analyses revealed significant differences when comparing the effect of Psytrance on relative percentages of alpha and delta frequencies between different cortical regions, which are displayed in Table 8. Both the parietal and occipital regions were associated with increased relative percentages of alpha frequencies compared to the frontal (p < .001, p < .001) and temporal (p < .001, p < .001) regions, whereas the occipital region was also associated with increased relative percentages of alpha frequencies compared to the parietal region (p = .005). Both the frontal and temporal regions were associated with increased relative percentages of delta frequencies compared to the parietal (p = .001, p < .001) and occipital (p = .001, p < .001) regions.

Wilcoxon signed-rank analyses revealed significant differences when comparing the effect of Goa Trance on relative percentages of beta, alpha, and delta frequencies between different cortical regions, which are displayed in Table 9. The parietal, occipital, and temporal regions were all associated with decreased relative percentages of beta frequencies compared to the frontal region (p = .005, p = .001, p = .007), whereas the occipital region was also associated with decreased relative percentages of beta frequencies compared to the parietal (p = .002) and temporal (p = .007) regions. Both the parietal and occipital regions were associated with increased relative percentages of alpha frequencies compared to the frontal (p < .001, p < .001) and temporal (p < .001, p < .001) regions, whereas the occipital region was also associated with increased relative percentages of alpha frequencies compared to the parietal region (p = .002). Both the frontal and temporal regions were associated with increased relative percentages of delta frequencies compared to the parietal (p < .001, p = .001) and occipital (p < .001, p < .001) regions.

Wilcoxon signed-rank analyses revealed significant differences when comparing the effect of Subject Song on relative percentages of beta, alpha, theta, and delta frequencies between different cortical regions, which are displayed in Table 10. Both the parietal and occipital regions were associated with decreased relative percentages of beta frequencies compared to the temporal region (p = .005, p = .007). Both the parietal and occipital regions were associated with increased relative percentages of alpha frequencies compared to the frontal (p < .001, p < .001) and temporal (p < .001, p < .001) regions, whereas the occipital region was also associated with increased relative percentages of alpha frequencies compared to the parietal region (p < .001). The occipital region was associated with decreased relative percentages of theta frequencies compared to the parietal region (p = .001). Both the frontal and temporal regions were associated with increased relative percentages of delta frequencies compared to the parietal (p = .001, p = .002) and occipital (p = .001, p = .001) regions.

Wilcoxon signed-rank analyses revealed significant differences when comparing the effect of Control 1 on relative percentages of beta, alpha, theta, and delta frequencies between different cortical regions, which are displayed in Table 11. Both the parietal and occipital regions were associated with decreased relative percentages of beta frequencies compared to the frontal (p = .003, p < .001) and temporal (p = .004, p < .001) regions, whereas the occipital region was also associated with decreased relative percentages of beta frequencies compared to the parietal region (p < .001). Both the parietal and occipital regions were associated with increased relative percentages of alpha frequencies compared to the frontal (p < .001, p < .001) and temporal (p < .001, p < .001) regions, whereas the occipital region was also associated with increased relative percentages of alpha frequencies compared to the parietal region (p < .001). The frontal, parietal, and temporal regions were all associated with increased relative percentages of theta frequencies compared to the occipital region (p = .001, p = .002, p = .001), whereas the frontal region was also associated with increased relative percentages of theta frequencies compared to the parietal region (p = .001). Both the frontal and temporal regions were associated with increased relative percentages of delta frequencies compared to the parietal (p < .001, p < .001) and occipital (p < .001, p > .001) regions, whereas the occipital region was also associated with decreased relative percentages of delta frequencies compared to the parietal region (p = .004).

Wilcoxon signed-rank analyses revealed significant differences when comparing the effect of Control 2 on relative percentages of beta, alpha, theta, and delta frequencies between different cortical regions, which are displayed in Table 12. The occipital region was associated with decreased relative percentages of beta frequencies compared to both the frontal (p = .010) and parietal (p = .004) regions. Both the parietal and occipital regions were associated with increased relative percentages of alpha frequencies compared to the frontal (p < .001, p < .001) and temporal (p < .001, p < .001) regions, whereas the occipital region was also associated with increased relative percentages of alpha frequencies compared to the parietal region (p = .001). The temporal region was associated with increased relative percentages of theta frequencies compared to the occipital region (p = .006). Both the frontal and temporal regions were associated with increased relative percentages of delta frequencies compared to the parietal (p < .001, p < .001) and occipital (p = .001, p < .001) regions.

Subject music preferences

Mean subject ratings and rankings for music genres and controls are displayed in Table 13. A positive correlation (R = .579) was found between subject ratings for Goa Trance and relative theta frequency percentages in the temporal region (p = .004). There were no other significant correlations noted between subject ratings and relative frequency band percentages for any other music genre or control in any other cortical region. There were no significant correlations between subject rankings and relative frequency band percentages for any music genre or control in any cortical region.

Table 13.

Mean subject ratings and rankings for music genres and controls

Control 1ClassicalTribalPsytranceGoaSubjectControl 2SD
Ratings3.177.806.435.945.919.63N/A2.16
RankingsN/A2.703.433.743.911.22N/A1.10

Note. Ratings (1 = worst, 10 = best) and rankings (1 = best, 5 = worst). SD: standard deviation.

Subject gender and music training

Gender was not found to have a significant influence on relative beta, alpha, theta, or delta frequency percentages in any cortical region. Music training was associated with increased relative delta percentages in the frontal region (p = .002), as displayed in Figure 6.

Figure 6.
Figure 6.

Impact of music training on relative percentages of delta frequencies in the frontal region

Citation: Journal of Psychedelic Studies JPS 2021; 10.1556/2054.2019.027

Discussion

Several findings from this study support those of previous music therapy studies, whereas other findings provide novel insights regarding how different music genres and pertinent variables affect neurophysiological activity in the human cerebral cortex. In particular, this study provides further supportive evidence that various genres of music may impact the central nervous system by promoting changes in cerebral cortex activity that have similarities to NREM sleep, while the listener remains awake (Baumgartner et al., 2006; Britton et al., 2016; Jacobs & Friedman, 2004; Kabuto et al., 1993; Lin et al., 2010; White & Richard, 2009).

Music genre comparisons

Decreased relative percentages of beta frequencies were found to be strongly associated with several music genres and controls within individual cortical regions, including Controls 1 and 2 in the occipital region, and Tribal Downtempo, Psytrance, Goa Trance, and Control 2 in the temporal region. Given that a decrease in beta frequencies is consistent with cortical activity observed during stages of NREM sleep, these findings support the findings of other music–EEG studies, which suggest that various music genres may impact the central nervous system by promoting changes in cerebral cortex activity that have similarities to NREM sleep, while the listener remains awake (Baumgartner et al., 2006; Britton et al., 2016; Jacobs & Friedman, 2004; Kabuto et al., 1993; Lin et al., 2010; White & Richard, 2009). However, given that our chosen non-musical control (Victoria Falls waterfall audio recording) was quite pleasant and was associated with a similar reduction in relative percentages of beta frequencies in comparison to several other music genres, it is difficult to draw strong conclusions from these findings. Future studies should better investigate music genres and non-musical controls by including a randomized control within each subject’s music sequence, and by selecting controls that are less pleasant (e.g., television static, radio static, traffic noise, etc.).

Increased relative percentages of alpha frequencies were found to be most strongly associated with controls within individual cortical regions. Most of these significant differences were associated with Control 2 (frontal, parietal, and temporal regions), whereas only one was associated with Control 1 (occipital region). These results may be related to the pleasant nature of our chosen control (Victoria Falls waterfall audio recording), as suggested by previous music–EEG studies, which observed increased alpha frequency activity in response to pleasant music (Kabuto et al., 1993; Lin et al., 2010). The only difference between Controls 1 and 2 is that Control 2 was played at the end of each subject’s music sequence, whereas Control 1 was played at the beginning of the sequence. This suggests that subjects may have been more relaxed toward the end of their music sequence. This possibility was anticipated during planning of this study, and was controlled for by randomizing the sequence of music genres for each subject and thus the generally relaxing nature of each subject’s music sequence should not have significantly impacted the results we observed across the music genres investigated in this study. In future studies, it would be helpful to incorporate a randomized control in the music sequence in addition to the flanking controls, to better assess cortical responses for a control that does not always occur at the same points in a subject’s music sequence. It would also be helpful to select a less-relaxing control, such as television static or radio static, or perhaps an audio-recording representative of typical non-musical noise encountered during one’s routine day, such as traffic noise.

Increased relative percentages of theta frequencies were found to be most strongly associated with Psytrance within individual cortical regions. Tribal Downtempo and Classical also exhibited significant but weaker associations. The superior association observed between Psytrance and increased relative percentages of theta frequencies may be attributed to its unique 16th note rhythmic beat structure, as that is the primary difference between Psytrance and the other music genres investigated in this study, including Goa Trance. Goa Trance lacks the unique 16th note rhythmic beat structure that is found in Psytrance, but is otherwise very similar. Further investigation is warranted to better clarify the reasons why Psytrance exhibited a superior association with increased relative percentages of theta frequencies compared to the other studied music genres. Given that increased theta frequency activity is associated with stages of NREM sleep, these findings support those of previous music therapy studies, which suggest that certain types of music may impact the central nervous system by promoting changes in cerebral cortex activity that have similarities to NREM sleep, while the listener remains awake (Baumgartner et al., 2006; Jacobs & Friedman, 2004; Kabuto et al., 1993; Lin et al., 2010).

Increased relative percentages of delta frequencies were found to be most strongly associated with Psytrance within individual cortical regions. Goa Trance also exhibited a significant but weaker association. The superior association observed between Psytrance and increased relative percentages of delta frequencies may again be attributed to its unique 16th note rhythmic beat structure. However, the finding that Goa Trance also had a significant association, although far less robust than what was observed for Psytrance, suggests that perhaps the compositional structure of Goa Trance is similar enough to that of Psytrance that there is some overlap or sharing in their ability to increase delta frequency activity in the cerebral cortex of listeners. Goa Trance has a very similar compositional structure to that of Psytrance, and although it lacks the 16th note beat structure found in Psytrance, it still is often composed of melodies and harmonies made from sixteenth notes layered over a standard quarter note beat structure. Further investigation is warranted to better clarify the reasons why Psytrance exhibited a superior association with increased relative percentages of delta frequencies compared to the other studied music genres. Given that increased delta frequency activity is associated with stages of NREM sleep, these findings support those of previous music therapy studies, which suggest that certain types of music may impact the central nervous system by promoting changes in cerebral cortex activity that have similarities to NREM sleep, while the listener remains awake (Baumgartner et al., 2006; Jacobs & Friedman, 2004; Kabuto et al., 1993; Lin et al., 2010).

Overall, we observed several findings consistent with previous music therapy studies, namely the finding that various types of music influence the central nervous system by promoting changes in cerebral cortex activity that have similarities to NREM sleep, while the listener remains awake (Baumgartner et al., 2006; Jacobs & Friedman, 2004; Kabuto et al., 1993; Lin et al., 2010). Furthermore, by comparing a variety of carefully selected music genres and controls, we observed potentially novel insights regarding how some music genres may have a more robust association with these changes in cerebral cortex activity, such as the observed findings regarding Psytrance and increased relative percentages of theta and delta frequencies.

Cortical region comparisons

Decreased relative percentages of beta frequencies and increased relative percentages of alpha frequencies were found to be most strongly associated with the parietal and occipital regions in comparison with other cortical regions, with the most robust association being observed in the occipital region. These findings were rather consistent across all music genres and controls. The observed association between decreased relative percentages of beta frequencies in both the parietal and occipital regions appears to be closely correlated with the association we observed between increased relative percentages of alpha frequencies in both the parietal and occipital regions. Both the frontal and temporal regions showed significantly increased relative percentages of theta and delta frequencies when compared to other cortical regions. In contrast, the lowest relative percentages of theta frequencies were observed in the parietal and occipital regions. These findings suggest that individual cortical regions have unique, region-specific responses to music that remain somewhat consistent across a variety of musical (i.e., music genres) and non-musical (i.e., controls) auditory input.

Given that the unique responses of each individual cortical region were observed to be rather consistent across all music genres and controls, these findings suggest that although individual cortical regions exhibit different frequency band responses in comparison to each other when subjected to one particular music genre, individual cortical regions still respond rather consistently across a wide variety of musical (i.e., music genres) and non-musical (i.e., controls) auditory input. These findings are similar to that was observed in other music–EEG studies, including one that reported increased alpha frequency activity in the occipital region in response to Classical music and other “pleasant” types of music (Kabuto et al., 1993; Lin et al., 2010).

Subject music preferences

The observed correlation between subject music genre ratings and the association of Goa Trance with increased relative percentages of theta frequencies in the temporal region suggests that subject music preferences may possibly influence the amount of theta activity in the temporal region when listening to music, but this finding is likely spurious since no other relationship was found between subject ratings or rankings for any music genre or control in any other cortical region. This finding, although statistically significant, is rather inconclusive, and further investigation is both warranted and encouraged. Given that no other significant correlations were observed between subject music preferences and relative percentages of beta, alpha, theta, or delta frequencies, these findings suggest overall that subject music preferences do not play a significant role in determining the relative percentages of each frequency band that are generated within a given cortical region when listening to various music genres. Overall, our findings do not suggest a significant impact of subjective music preferences on relative frequency band percentages in any cortical region.

The observed paucity of significant correlations between subject music preferences and relative frequency band percentages in all cortical regions is supported by other findings from this study that have been discussed in previous sections, such as the finding that the music genres most effective at significantly modifying relative percentages of beta, alpha, theta, or delta frequencies were not rated or ranked very favorably by most subjects (e.g., Controls, Psytrance, etc.). However, it is important to note that these findings do not address the likely possibility that personal music preferences may still play a very important role in the subjective sensations (e.g., relaxation, pleasure, happiness, etc.) that are experienced while listening to various music genres.

Subject gender and music training

The lack of significant findings observed when examining the effect of gender on relative percentages of beta, alpha, theta, and delta frequencies in the cerebral cortex suggests that music has a similar impact on EEG activity in males and females within individual frequency bands and cortical regions. However, this study did not compare individual EEG leads, individual cerebral hemispheres, or other related variables that could still potentially be influenced by gender. Although this study did not find any gender-related differences when comparing individual frequency bands and cortical regions, a previous music–EEG study observed gender-related differences in music processing when comparing individual cerebral hemispheres (Koelsch et al., 2003). Given this finding, future studies should examine both interregional and interhemispheric cortical comparisons to further clarify the current understanding of gender-related differences in cortical music processing.

The finding that subjects with previous music training had increased relative percentages of delta frequencies in the frontal region suggests that music training may enable subjects to have stronger neurophysiological responses to music in the frontal cortex, at least in regard to delta activity. This finding may possibly be related to the association of the frontal lobe with executive function, judgment, cognition, and abstract thought. This possibility is supported by other music therapy studies, which have described multiple cognitive processes, including some with contribution from the frontal lobe, that have been associated with music perception and processing (Maeyama et al., 2009; Shabanloei et al., 2010). Given that music training did not have a significant influence on relative percentages of beta, alpha, theta, or delta frequencies in any other area of the cerebral cortex, the potential influence of music training on music perception and processing may be limited to the frontal lobe.

Study limitations and future research

This study has a number of limitations that could be improved during future research. A larger sample size would be beneficial and would allow for stronger conclusions to be drawn. Subjects could be asked to abstain from routinely encountered psychoactive chemicals for a longer period of time (e.g., >12 hr), since many medications and chemicals continue having psychoactive effects beyond the 6-hr abstinent period required by this study. Subjects also could be screened to rule out recent consumption of long-acting psychoactive chemicals, such as methamphetamines, lysergic acid diethylamide, etc. Future studies may be improved by utilizing different subject groups based on certain variables (e.g., age, music training, etc.), allowing for intergroup comparisons and a deeper understanding of the resulting EEG data.

Future studies may be improved by focusing on a smaller number of variables during the investigation. Because the study attempted to investigate a large number of different variables, there were many pieces of data we collected from subjects that we were unable to include in our analysis due to limitations in time, personnel, and length of this manuscript. For example, we assessed the ethnicity of each subject using our pre-music questionnaire, as well as the familiarity of each subject with the music genres included in this study, but were unable to thoroughly investigate these data and variables. Although we investigated the potential influence of subject music preferences in this study, we did not investigate other closely related variables using other subjective information that we gathered in our pre-music questionnaire, such as subject ethnicity or familiarity with each music genre.

Future studies may be improved using a non-musical control that more accurately represents commonly encountered non-musical noise (e.g., television static, radio static, traffic noise, construction sounds, footsteps, etc.). The soft, monotonous waterfall recording used as the sole control in this study was suboptimal, as it was quite relaxing for many subjects, which may have influenced some of our findings and confounded various comparative analyses. Selecting a more common, less pleasant type of non-musical control would provide a stronger comparison between music genres and non-musical sound in future studies. The randomized music sequences could potentially be improved if the non-musical control was randomized like the other music genres, rather than only flanking each music sequence with controls at the beginning and end. Choosing not to randomize the non-musical controls somewhat limited this study’s ability to assess how subjects truly responded to the controls. Music sequences used in future studies that standardize the length of each song may wish to gradually fade in the beginning and fade out the ending of each song to avoid any unintended abruptness that is created during the process (e.g., cropping) of standardizing song lengths, and to avoid atypical introductions or endings of songs that are not truly representative of the music genre (e.g., words at the beginning or end of a song that does not typically have lyrics, such as classical music, etc.).

Future studies should consider investigating additional music genres, as there are many common music genres that we did not investigate with this study. In addition to studying various music genres, future studies could also investigate sound recordings from individual musical instruments, such as the piano, flute, clarinet, drums, violin, etc., as well as vocal music (e.g., choir). Future studies should also be performed to investigate the influence of lyrics within music by comparing lyrical to non-lyrical music. Future studies should also consider investigating other important variables that distinguish different music genres from each other, such as tempo, time signature, harmonics, melodies, pitch, tone, etc. For example, it is possible that faster music genres may be associated with significantly different EEG activity compared to slower music genres. Future studies could also be improved by utilizing EEG power analysis of each frequency band, rather than examining the relative percentages of each frequency band, as frequency band power analysis is more commonly used by studies analyzing EEG data.

Future studies could also be improved by taking into account the unilateral nature of certain EEG leads to allow for interhemispheric comparisons, as has been done in some previous music therapy studies (Koelsch et al., 2003). Rather than simply grouping individual leads according to cortical region, individual leads could easily be grouped based on both cortical region and hemisphere (e.g. left temporal, right frontal, etc.), allowing for additional comparisons and variables to be analyzed with little additional effort. Future studies could also combine monitoring of various therapeutic outcomes along with monitoring of EEG data to draw stronger correlations between therapeutic outcomes and specific EEG findings. For example, it would not require significant additional effort to monitor basic physiological variables such as blood pressure, heart rate, etc., and subjective variables such as mood, pleasure, anxiety, etc., while a subject completes their music sequence using a data collection protocol similar to that of this study.

Potential bias in the study could be prevented by blinding the individuals who measured the EEG data from the order of the songs in each music sequence. Potential bias could also be prevented using multiple representative songs for each music genre, and by having multiple individuals select the songs used to represent each music genre, given that different individuals may disagree as to whether or not an individual song is an appropriate representation of a broad music genre. This study used only one song to represent each music genre, and only one individual selected each song (other than Subject Song). This study was also biased by the age of the subjects, as all were young adults, and by their occupations, as most were undergraduate or graduate students.

Conclusions

Controls were most strongly associated with decreased relative percentages of beta frequencies, while Psytrance, Goa Trance, and Tribal Downtempo also had significant but weaker associations. Controls were most strongly associated with increased relative percentages of alpha frequencies. Psytrance was most strongly associated with increased relative percentages of theta frequencies, while Tribal Downtempo and Classical also had significant but weaker associations. Psytrance was most strongly associated with increased relative percentages of delta frequencies, while Goa Trance also had a significant but weaker association.

The lowest relative percentages of beta frequencies and the highest relative percentages of alpha frequencies across all music genres and controls occurred in the occipital and parietal regions. The greatest relative percentages of theta and delta frequencies across all music genres and controls occurred in the frontal and temporal regions. Subjects with previous music training exhibited greater relative percentages of delta frequencies in the frontal region. Subject gender and music preference did not have a significant influence on relative frequency band percentages in any cortical region.

Findings from this study support those of previous music therapy studies and provide novel insights regarding music’s influence on human neurophysiology. Our findings also support the hypothesis that music may promote changes in cerebral cortex activity that have similarities to NREM sleep, while the listener remains awake. In addition, findings from this study suggest that certain music genres may have a more robust association with these changes in cerebral cortex activity than other music genres.

This study expands upon our current understanding of music’s influence on the human brain and body by providing evidence that further elucidates the neurophysiological activity that arises in the cerebral cortex when listening to various music genres. Future studies are required to better investigate and clarify these findings. The authors of this study encourage the scientific and medical communities to further investigate music’s therapeutic properties and its ability to influence human physiology. By improving our understanding of the physiological effects of music, we can more effectively apply music as an adjuvant therapeutic modality to benefit humankind.

Acknowledgements

Financial support for this study was provided by the Illinois Neurological Institute (INI). AHR is the principal investigator, primary manuscript author, and corresponding author, who was involved in study design. SNZ is the secondary manuscript author and involved in study design. MX is also the secondary manuscript author and involved in study design and EEG data analysis. JA is the tertiary manuscript author and involved in study design and statistical data analysis. LR-C, TAM, and NSL are the tertiary manuscript authors of this manuscript.

Conflict of interest

No conflicts of interest were identified for any study authors.

References

  • Aragon, D., Farris, C., & Byers, J. F. (2002). The effects of harp music in vascular and thoracic surgical patients. Alternative Therapies in Health and Medicine, 8(5), 5254, 56–60. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/12233803#

    • Search Google Scholar
    • Export Citation
  • Baumgartner, T., Esslen, M., & Jäncke, L. (2006). From emotion perception to emotion experience: Emotions evoked by pictures and classical music. International Journal of Psychophysiology, 60(1), 3443. doi:10.1016/j.ijpsycho.2005.04.007

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Binek, J., Sagmeister, M., Borovicka, J., Knierim, M., Magdeburg, B., & Meyenberger, C. (2003). Perception of gastrointestinal endoscopy by patients and examiners with and without background music. Digestion, 68(1), 58. doi:10.1159/000073219

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bradt, J., & Dileo, C. (2009). Music for stress and anxiety reduction in coronary heart disease patients. Cochrane Database of Systematic Reviews, 2, CD006577. doi:10.1002/14651858.CD006577.pub3

    • Search Google Scholar
    • Export Citation
  • Bradt, J., Dileo, C., Grocke, D., & Magill, L. (2011). Music interventions for improving psychological and physical outcomes in cancer patients. Cochrane Database of Systematic Reviews, 8, CD006911. doi:10.1002/14651858.CD006911.pub3

    • Search Google Scholar
    • Export Citation
  • Bringman, H., Giesecke, K., Thörne, A., & Bringman, S. (2009). Relaxing music as pre-medication before surgery: A randomised controlled trial. Acta Anaesthesiologica Scandinavica, 53(6), 759764. doi:10.1111/j.1399-6576.2009.01969.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Britton, J. W., Frey, L. C., Hopp, J. L., Korb, P., Koubeissi, M. Z., Lievens, W. E., Pestana-Knight, E. M. (Authors), St. Louis, E. K., & Frey, L. C. (Eds.). (2016). Electroencephalography (EEG): An introductory text and atlas of normal and abnormal findings in adults, children, and infants [Internet]. Chicago, IL: American Epilepsy Society.

    • Search Google Scholar
    • Export Citation
  • Buffum, M. D., Sasso, C., Sands, L. P., Lanier, E., Yellen, M., & Hayes, A. (2006). A music intervention to reduce anxiety before vascular angiography procedures. Journal of Vascular Nursing, 24(3), 6873. doi:10.1016/j.jvn.2006.04.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, M. F., Chan, E. A., Mok, E., & Kwan Tse, F. Y. (2009). Effect of music on depression levels and physiological responses in community-based older adults. International Journal of Mental Health Nursing, 18(4), 2852944. doi:10.1111/j.1447-0349.2009.00614.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, Y. M., Lee, P. W., Ng, T. Y., Ngan, H. Y., & Wong, L. C. (2003). The use of music to reduce anxiety for patients undergoing colposcopy: A randomized trial. Gynecologic Oncology, 91(1), 213217. doi:10.1016/S0090-8258(03)00412-8

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chlan, L., Evans, D., Greenleaf, M., & Walker, J. (2000). Effects of a single music therapy intervention on anxiety, discomfort, satisfaction, and compliance with screening guidelines in outpatients undergoing flexible sigmoidoscopy. Gastroenterology Nursing, 23(4), 148156. doi:10.1097/00001610-200007000-00003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Conrad, C., Niess, H., Jauch, K. W., Bruns, C. J., Hartl, W., & Welker, L. (2007). Overture for growth hormone: Requiem for interleukin-6? Critical Care Medicine 35(12), 27092713. doi:10.1097/00003246-200712000-00005

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cooke, M., Chaboyer, W., Schluter, P., & Hiratos, M. (2005). The effect of music on preoperative anxiety in day surgery. Journal of Advanced Nursing, 52(1), 4755. doi:10.1111/j.1365-2648.2005.03563.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ebneshahidi, A., & Mohseni, M. (2008). The effect of patient-selected music on early postoperative pain, anxiety, and hemodynamic profile in cesarean section surgery. Journal of Alternative and Complementary Medicine, 14(7), 827831. doi:10.1089/acm.2007.0752

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evans, D. (2002). The effectiveness of music as an intervention for hospital patients: A systematic review. Journal of Advanced Nursing, 37(1), 818. doi:10.1046/j.1365-2648.2002.02052.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Galaal, K. A., Deane, K., Sangal, S., & Lopes, A. D. (2007). Interventions for reducing anxiety in women undergoing colposcopy. Cochrane Database of Systematic Reviews, 3, CD006013. doi:10.1002/14651858.CD006013.pub3

    • Search Google Scholar
    • Export Citation
  • Han, L., Li, J. P., Sit, J. W., Chung, L., Jiao, Z. Y., & Ma, W. G. (2010). Effects of music intervention on physiological stress response and anxiety level of mechanically ventilated patients in China: A randomised controlled trial. Journal of Clinical Nursing, 19(7–8), 978987. doi:10.1111/j.1365-2702.2009.02845.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hatem, T. P., Lira, P. I., & Mattos, S. S. (2006). The therapeutic effects of music in children following cardiac surgery. Jornal de Pediatria, 82(3), 186192. doi:10.2223/JPED.1473

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jacobs, G. D., & Friedman, R. (2004). EEG spectral analysis of relaxation techniques. Applied Psychophysiology and Biofeedback, 29(4), 245254. doi:10.1007/s10484-004-0385-2

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kabuto, M., Kageyama, T., & Nitta, H. (1993). EEG power spectrum changes due to listening to pleasant music and their relation to relaxation effects. Nihon Eiseigaku Zasshi, 48(4), 807818. doi:10.1265/jjh.48.807

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, Y. K., Kim, S. M., & Myoung, H. (2011). Musical intervention reduces patients’ anxiety in surgical extraction of an impacted mandibular third molar. Journal of Oral and Maxillofacial Surgery, 69(4), 10361045. doi:10.1016/j.joms.2010.02.045

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klassen, J. A., Liang, Y., Tjosvold, L., Klassen, T. P., & Hartling, L. (2008). Music for pain and anxiety in children undergoing medical procedures: A systematic review of randomized controlled trials. Ambulatory Pediatrics, 8(2), 117128. doi:10.1016/j.ambp.2007.12.005

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kliempt, P., Ruta, D., Ogston, S., Landeck, A., & Martay, K. (1999). Hemispheric-synchronisation during anesthesia: A double-blind randomised trial using audiotapes for intra-operative nociception control. Anaesthesia, 54(8), 769773. doi:10.1046/j.1365-2044.1999.00958.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koelsch, S., Maess, B., Grossmann, T., & Friederici, A. D. (2003). Electric brain responses reveal gender differences in music processing. Neuroreport, 14(5), 709713. doi:10.1097/00001756-200304150-00010

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Korhan, E. A., Khorshid, L., & Uyar, M. (2011). The effect of music therapy on physiological signs of anxiety in patients receiving mechanical ventilatory support. Journal of Clinical Nursing, 20(7–8), 10261034. doi:10.1111/j.1365-2702.2010.03434.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kotwal, M. R., Rinchhen, C. Z., & Ringe, V. V. (1998). Stress reduction through listening to Indian classical music during gastroscopy. Diagnostic and Therapeutic Endoscopy, 4(4), 191197. doi:10.1155/DTE.4.191

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kwon, I. S., Kim, J., & Park, K. M. (2006). Effects of music therapy on pain, discomfort, and depression for patients with leg fractures. TaehanKanhoHakhoe Chi, 36(4), 630666. doi:10.4040/jkan.2006.36.4.630

    • Search Google Scholar
    • Export Citation
  • Lai, H. L., & Li, Y. M. (2011). The effect of music on biochemical markers and self-perceived stress among first-line nurses: A randomized controlled crossover trial. Journal of Advanced Nursing, 67(11), 24142424. doi:10.1111/j.1365-2648.2011.05670.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, K. C., Chao, Y. H., Yiin, J. J., Chiang, P. Y., & Chao, Y. F. (2011). Effectiveness of different music-playing devices for reducing preoperative anxiety: A clinical control study. International Journal of Nursing Studies, 48(10), 11801187. doi:10.1016/j.ijnurstu.2011.04.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, K. C., Chao, Y. H., Yiin, J. J., Hsieh, H. Y., Dai, W. J., & Chao, Y. F. (2012). Evidence that music listening reduces preoperative patients’ anxiety. Biological Research for Nursing, 14(1), 7884. doi:10.1177/1099800410396704

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, O. K., Chung, Y. F., Chan, M. F., & Chan, W. M. (2005). Music and its effect on the physiological responses and anxiety levels of patients receiving mechanical ventilation: A pilot study. Journal of Clinical Nursing, 14(5), 609620. doi:10.1111/j.1365-2702.2004.01103.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lepage, C., Drolet, P., Girard, M., Grenier, Y., & DeGagné, R. (2001). Music decreases sedative requirements during spinal anesthesia. Anesthesia and Analgesia, 93(4), 912916. doi:10.1097/00000539-200110000-00022

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, P. C., Lin, M. L., Huang, L. C., Hsu, H. C., & Lin, C. C. (2011). Music therapy for patients receiving spine surgery. Journal of Clinical Nursing, 20(7–8), 960968. doi:10.1111/j.1365-2702.2010.03452.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y. P., Duann, J. R., Chen, J. H., & Jung, T. P. (2010). Electroencephalographic dynamics of musical emotion perception revealed by independent spectral components. Neuroreport, 21(6), 410415. doi:10.1097/WNR.0b013e32833774de

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loomba, R. S., Arora, R., Shah, P. H., Chandrasekar, S., & Molnar, J. (2012). Effects of music on systolic blood pressure, diastolic blood pressure, and heart rate: A meta-analysis. Indian Heart Journal, 64(3), 309313. doi:10.1016/S0019-4832(12)60094-7

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madson, A. T., & Silverman, M. J. (2010). The effect of music therapy on relaxation, anxiety, pain perception, and nausea in adult solid organ transplant patients. Journal of Music Therapy, 47(3), 220232. doi:10.1093/jmt/47.3.220

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maeyama, A., Kodaka, M., & Miyao, H. (2009). Effect of the music-therapy under spinal anesthesia. Masui 58(6), 684691. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/19522258

    • Search Google Scholar
    • Export Citation
  • Mattei, T. A., Rodriguez, A. H., & Bassuner, J. (2013). Selective impairment of emotion recognition through music in Parkinson’s disease: Does it suggest the existence of different networks for music and speech prosody processing? Frontiers in Neuroscience, 7, 161. doi:10.3389/fnins.2013.00161

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ni, C. H., Tsai, W. H., Lee, L. M., Kao, C. C., & Chen, Y. C. (2012). Minimising preoperative anxiety with music for day surgery patients – A randomised clinical trial. Journal of Clinical Nursing, 21(5–6), 620625. doi:10.1111/j.1365-2702.2010.03466.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nilsson, U. (2008). The anxiety- and pain-reducing effects of music interventions: A systematic review. AORN Journal, 87(4), 780807. doi:10.1016/j.aorn.2007.09.013

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nilsson, U. (2009). The effect of music intervention in stress response to cardiac surgery in a randomized clinical trial. Heart Lung, 38(3), 201207. doi:10.1016/j.hrtlng.2008.07.008

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nilsson, U., Unosson, M., & Rawal, N. (2005). Stress reduction and analgesia in patients exposed to calming music postoperatively: A randomized controlled trial. European Journal of Anaesthesiology, 22(2), 96102. doi:10.1017/S0265021505000189

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rudin, D., Kiss, A., Wetz, R. V., & Sottile, V. M. (2007). Music in the endoscopy suite: A meta-analysis of randomized controlled studies. Endoscopy, 39(6), 507510. doi:10.1055/s-2007-966362

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Salimpoor, V. N., Benovoy, M., Larcher, K., Dagher, A., & Zatorre, R. J. (2011). Anatomically distinct dopamine release during anticipation and experience of peak emotion to music. Nature Neuroscience, 14(2), 257262. doi:10.1038/nn.2726

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sendelbach, S. E., Halm, M. A., Doran, K. A., Miller, E. H., & Gaillard, P. (2006). Effects of music therapy on physiological and psychological outcomes for patients undergoing cardiac surgery. The Journal of Cardiovascular Nursing, 21(3), 194200. doi:10.1097/00005082-200605000-00007

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shabanloei, R., Golchin, M., Esfahani, A., Dolatkhah, R., & Rasoulian, M. (2010). Effects of music therapy on pain and anxiety in patients undergoing bone marrow biopsy and aspiration. AORN Journal, 91(6), 746751. doi:10.1016/j.aorn.2010.04.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slevc, L. R., & Okada, B. M. (2015). Processing structure in language and music: A case for shared reliance on cognitive control. Psychonomic Bulletin & Review, 22(3), 637652. doi:10.3758/s13423-014-0712-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smolen, D., Topp, R., & Singer, L. (2002). The effect of self-selected music during colonoscopy on anxiety, heart rate, and blood pressure. Applied Nursing Research, 15(3), 126136. doi:10.1053/apnr.2002.34140

    • Crossref
    • Search Google Scholar
    • Export Citation
  • St. John, G. (2010). Psytrance: An introduction. In G. St. John (Ed.), The local scenes and global culture of Psytrance. London, UK: Routledge.

  • Tam, W. W., Wong, E. L., & Twinn, S. F. (2008). Effect of music on procedure time and sedation during colonoscopy: A meta-analysis. World Journal of Gastroenterology, 14(34), 53365343. doi:10.3748/wjg.14.5336

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Triller, N., Erzen, D., Duh, S., Petrinec Primozic, M., & Kosnik, M. (2006). Music during bronchoscopic examination: The physiological effects. A randomized trial. Respiration, 73(1), 9599. doi:10.1159/000089818

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tse, M. M., Chan, M. F., & Benzie, I. F. (2005). The effect of music therapy on postoperative pain, heart rate, systolic blood pressures and analgesic use following nasal surgery. Journal of Pain & Palliative Care Pharmacotherapy, 19(3), 2129. doi:10.1080/J354v19n03_05

    • Crossref
    • Search Google Scholar
    • Export Citation
  • University Hospitals of Cleveland. (2011). History of music therapy. University Hospitals Music as Medicine. Retrieved January 9, 2011, from http://www.musicasmedicine.com/about/history.cfm

    • Search Google Scholar
    • Export Citation
  • Vaajoki, A., Kankkunen, P., Pietilä, A. M., & Vehviläinen-Julkunen, K. (2011). Music as a nursing intervention: Effects of music listening on blood pressure, heart rate, and respiratory rate in abdominal surgery patients. Nursing & Health Sciences, 13(4), 412418. doi:10.1111/j.1442-2018.2011.00633.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Voss, J. A., Good, M., Yates, B., Baun, M. M., Thompson, A., & Hertzog, M. (2004). Sedative music reduces anxiety and pain during chair rest after open-heart surgery. Pain, 112(1–2), 197203. doi:10.1016/j.pain.2004.08.020

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, S. M., Kulkarni, L., Dolev, J., & Kain, Z. N. (2002). Music and preoperative anxiety: A randomized, controlled study. Anesthesia and Analgesia, 94(6), 14891494. doi:10.1097/00000539-200206000-00021

    • Search Google Scholar
    • Export Citation
  • White, N. E., & Richard, L. M. (2009). Chapter 2 – History of the scientific standards of QEEG normative databases. In T. Budzinsky, H. Budzinski, J. Evans, & A. Abarbanel (Eds.), Introduction to quantitative EEG and neurofeedback (2nd ed., pp. 2960). San Diego, CA: Academic Press.

    • Search Google Scholar
    • Export Citation
  • Yilmaz, E., Ozcan, S., Basar, M., Basar, H., Batislam, E., & Ferhat, M. (2003). Music decreases anxiety and provides sedation in extracorporeal shock wave lithotripsy. Urology, 61(2), 282286. doi:10.1016/S0090-4295(02)02375-0

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zalewsky, S., Vinker, S., Fiada, I., Livon, D., & Kitai, E. (1998). Background music in the family physician’s surgery: Patient reactions. Harefuah, 135(3–4), 9697, 168, 167. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/9885649

    • Search Google Scholar
    • Export Citation
  • Zare, M., Ebrahimi, A. A., & Birashk, B. (2010). The effects of music therapy on reducing agitation in patients with Alzheimer’s disease, a pre-post study. International Journal of Geriatric Psychiatry, 25(12), 13091310. doi:10.1002/gps.2450

    • Crossref
    • Search Google Scholar
    • Export Citation

APPENDIX

Neurofax EEG-1200A Electroencephalograph

  1. Neurofax EEG-1200 PC-based EEG and polygraph system enables registration, evaluation, and analysis of EEG and polygraph data. This technical high-end solution includes amplifiers for recording 38, 44, 64, 128, or 192 channels at sampling frequencies of up to 10,000 Hz. Sixteen additional DC channels including eight external triggers are available optional. Up to 250 EEG channels can be traced at the same time in real time. The Neurofax EEG-1200 system includes a highly functional and intuitive software package for data recording, playback, and quantitative analysis.
  2. Product URL: http://www.nihonkohden.de/products/neurology/eeg/research/eeg-1200.html?L=1

Pre-Music Questionnaire

  1. What is your name? What is your age? What is your gender? What is your ethnicity?
  2. Have you ever had any type of musical training, e.g., have you ever composed or produced music, played a musical instrument, or had singing lessons?
    1. 1.If so, what music training do you have and how much?
  3. What are your three most favorite genres of music (any)?
  4. What are your three least favorite genres of music (any)?
  5. Have you heard songs from any of this study’s music genres before?
    1. 1.If so, how many times have you heard a song from each genre?
    2. 2.If so, how do you feel about the genres (i.e., like, dislike, and neutral)?
  6. How much sleep did you get last night?
  7. How tired do you feel on a scale of 1–10 (1 = very alert, 10 = extremely tired)?

Visual Analog Scale used for subject rating of each music genre

Citation: Journal of Psychedelic Studies JPS 2021; 10.1556/2054.2019.027

Post-Music Questionnaire

  1. Now that you have heard all five music genres, will you please rank the songs you heard in order of how much you liked them?
    1. 1.List the songs in order of how much you liked them, starting with the song you liked most in the #1 position at the top, and finishing with the song you liked least in the #5 position at the bottom.
  2. Did you fall asleep at all during the music therapy session?
  3. Do you have any questions or concerns?

Study images

Citation: Journal of Psychedelic Studies JPS 2021; 10.1556/2054.2019.027

Citation: Journal of Psychedelic Studies JPS 2021; 10.1556/2054.2019.027

Citation: Journal of Psychedelic Studies JPS 2021; 10.1556/2054.2019.027

Citation: Journal of Psychedelic Studies JPS 2021; 10.1556/2054.2019.027

Citation: Journal of Psychedelic Studies JPS 2021; 10.1556/2054.2019.027

Citation: Journal of Psychedelic Studies JPS 2021; 10.1556/2054.2019.027

Citation: Journal of Psychedelic Studies JPS 2021; 10.1556/2054.2019.027

Citation: Journal of Psychedelic Studies JPS 2021; 10.1556/2054.2019.027

  • Aragon, D., Farris, C., & Byers, J. F. (2002). The effects of harp music in vascular and thoracic surgical patients. Alternative Therapies in Health and Medicine, 8(5), 5254, 56–60. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/12233803#

    • Search Google Scholar
    • Export Citation
  • Baumgartner, T., Esslen, M., & Jäncke, L. (2006). From emotion perception to emotion experience: Emotions evoked by pictures and classical music. International Journal of Psychophysiology, 60(1), 3443. doi:10.1016/j.ijpsycho.2005.04.007

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Binek, J., Sagmeister, M., Borovicka, J., Knierim, M., Magdeburg, B., & Meyenberger, C. (2003). Perception of gastrointestinal endoscopy by patients and examiners with and without background music. Digestion, 68(1), 58. doi:10.1159/000073219

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bradt, J., & Dileo, C. (2009). Music for stress and anxiety reduction in coronary heart disease patients. Cochrane Database of Systematic Reviews, 2, CD006577. doi:10.1002/14651858.CD006577.pub3

    • Search Google Scholar
    • Export Citation
  • Bradt, J., Dileo, C., Grocke, D., & Magill, L. (2011). Music interventions for improving psychological and physical outcomes in cancer patients. Cochrane Database of Systematic Reviews, 8, CD006911. doi:10.1002/14651858.CD006911.pub3

    • Search Google Scholar
    • Export Citation
  • Bringman, H., Giesecke, K., Thörne, A., & Bringman, S. (2009). Relaxing music as pre-medication before surgery: A randomised controlled trial. Acta Anaesthesiologica Scandinavica, 53(6), 759764. doi:10.1111/j.1399-6576.2009.01969.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Britton, J. W., Frey, L. C., Hopp, J. L., Korb, P., Koubeissi, M. Z., Lievens, W. E., Pestana-Knight, E. M. (Authors), St. Louis, E. K., & Frey, L. C. (Eds.). (2016). Electroencephalography (EEG): An introductory text and atlas of normal and abnormal findings in adults, children, and infants [Internet]. Chicago, IL: American Epilepsy Society.

    • Search Google Scholar
    • Export Citation
  • Buffum, M. D., Sasso, C., Sands, L. P., Lanier, E., Yellen, M., & Hayes, A. (2006). A music intervention to reduce anxiety before vascular angiography procedures. Journal of Vascular Nursing, 24(3), 6873. doi:10.1016/j.jvn.2006.04.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, M. F., Chan, E. A., Mok, E., & Kwan Tse, F. Y. (2009). Effect of music on depression levels and physiological responses in community-based older adults. International Journal of Mental Health Nursing, 18(4), 2852944. doi:10.1111/j.1447-0349.2009.00614.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, Y. M., Lee, P. W., Ng, T. Y., Ngan, H. Y., & Wong, L. C. (2003). The use of music to reduce anxiety for patients undergoing colposcopy: A randomized trial. Gynecologic Oncology, 91(1), 213217. doi:10.1016/S0090-8258(03)00412-8

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chlan, L., Evans, D., Greenleaf, M., & Walker, J. (2000). Effects of a single music therapy intervention on anxiety, discomfort, satisfaction, and compliance with screening guidelines in outpatients undergoing flexible sigmoidoscopy. Gastroenterology Nursing, 23(4), 148156. doi:10.1097/00001610-200007000-00003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Conrad, C., Niess, H., Jauch, K. W., Bruns, C. J., Hartl, W., & Welker, L. (2007). Overture for growth hormone: Requiem for interleukin-6? Critical Care Medicine 35(12), 27092713. doi:10.1097/00003246-200712000-00005

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cooke, M., Chaboyer, W., Schluter, P., & Hiratos, M. (2005). The effect of music on preoperative anxiety in day surgery. Journal of Advanced Nursing, 52(1), 4755. doi:10.1111/j.1365-2648.2005.03563.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ebneshahidi, A., & Mohseni, M. (2008). The effect of patient-selected music on early postoperative pain, anxiety, and hemodynamic profile in cesarean section surgery. Journal of Alternative and Complementary Medicine, 14(7), 827831. doi:10.1089/acm.2007.0752

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evans, D. (2002). The effectiveness of music as an intervention for hospital patients: A systematic review. Journal of Advanced Nursing, 37(1), 818. doi:10.1046/j.1365-2648.2002.02052.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Galaal, K. A., Deane, K., Sangal, S., & Lopes, A. D. (2007). Interventions for reducing anxiety in women undergoing colposcopy. Cochrane Database of Systematic Reviews, 3, CD006013. doi:10.1002/14651858.CD006013.pub3

    • Search Google Scholar
    • Export Citation
  • Han, L., Li, J. P., Sit, J. W., Chung, L., Jiao, Z. Y., & Ma, W. G. (2010). Effects of music intervention on physiological stress response and anxiety level of mechanically ventilated patients in China: A randomised controlled trial. Journal of Clinical Nursing, 19(7–8), 978987. doi:10.1111/j.1365-2702.2009.02845.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hatem, T. P., Lira, P. I., & Mattos, S. S. (2006). The therapeutic effects of music in children following cardiac surgery. Jornal de Pediatria, 82(3), 186192. doi:10.2223/JPED.1473

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jacobs, G. D., & Friedman, R. (2004). EEG spectral analysis of relaxation techniques. Applied Psychophysiology and Biofeedback, 29(4), 245254. doi:10.1007/s10484-004-0385-2

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kabuto, M., Kageyama, T., & Nitta, H. (1993). EEG power spectrum changes due to listening to pleasant music and their relation to relaxation effects. Nihon Eiseigaku Zasshi, 48(4), 807818. doi:10.1265/jjh.48.807

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, Y. K., Kim, S. M., & Myoung, H. (2011). Musical intervention reduces patients’ anxiety in surgical extraction of an impacted mandibular third molar. Journal of Oral and Maxillofacial Surgery, 69(4), 10361045. doi:10.1016/j.joms.2010.02.045

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klassen, J. A., Liang, Y., Tjosvold, L., Klassen, T. P., & Hartling, L. (2008). Music for pain and anxiety in children undergoing medical procedures: A systematic review of randomized controlled trials. Ambulatory Pediatrics, 8(2), 117128. doi:10.1016/j.ambp.2007.12.005

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kliempt, P., Ruta, D., Ogston, S., Landeck, A., & Martay, K. (1999). Hemispheric-synchronisation during anesthesia: A double-blind randomised trial using audiotapes for intra-operative nociception control. Anaesthesia, 54(8), 769773. doi:10.1046/j.1365-2044.1999.00958.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koelsch, S., Maess, B., Grossmann, T., & Friederici, A. D. (2003). Electric brain responses reveal gender differences in music processing. Neuroreport, 14(5), 709713. doi:10.1097/00001756-200304150-00010

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Korhan, E. A., Khorshid, L., & Uyar, M. (2011). The effect of music therapy on physiological signs of anxiety in patients receiving mechanical ventilatory support. Journal of Clinical Nursing, 20(7–8), 10261034. doi:10.1111/j.1365-2702.2010.03434.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kotwal, M. R., Rinchhen, C. Z., & Ringe, V. V. (1998). Stress reduction through listening to Indian classical music during gastroscopy. Diagnostic and Therapeutic Endoscopy, 4(4), 191197. doi:10.1155/DTE.4.191

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kwon, I. S., Kim, J., & Park, K. M. (2006). Effects of music therapy on pain, discomfort, and depression for patients with leg fractures. TaehanKanhoHakhoe Chi, 36(4), 630666. doi:10.4040/jkan.2006.36.4.630

    • Search Google Scholar
    • Export Citation
  • Lai, H. L., & Li, Y. M. (2011). The effect of music on biochemical markers and self-perceived stress among first-line nurses: A randomized controlled crossover trial. Journal of Advanced Nursing, 67(11), 24142424. doi:10.1111/j.1365-2648.2011.05670.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, K. C., Chao, Y. H., Yiin, J. J., Chiang, P. Y., & Chao, Y. F. (2011). Effectiveness of different music-playing devices for reducing preoperative anxiety: A clinical control study. International Journal of Nursing Studies, 48(10), 11801187. doi:10.1016/j.ijnurstu.2011.04.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, K. C., Chao, Y. H., Yiin, J. J., Hsieh, H. Y., Dai, W. J., & Chao, Y. F. (2012). Evidence that music listening reduces preoperative patients’ anxiety. Biological Research for Nursing, 14(1), 7884. doi:10.1177/1099800410396704

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, O. K., Chung, Y. F., Chan, M. F., & Chan, W. M. (2005). Music and its effect on the physiological responses and anxiety levels of patients receiving mechanical ventilation: A pilot study. Journal of Clinical Nursing, 14(5), 609620. doi:10.1111/j.1365-2702.2004.01103.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lepage, C., Drolet, P., Girard, M., Grenier, Y., & DeGagné, R. (2001). Music decreases sedative requirements during spinal anesthesia. Anesthesia and Analgesia, 93(4), 912916. doi:10.1097/00000539-200110000-00022

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, P. C., Lin, M. L., Huang, L. C., Hsu, H. C., & Lin, C. C. (2011). Music therapy for patients receiving spine surgery. Journal of Clinical Nursing, 20(7–8), 960968. doi:10.1111/j.1365-2702.2010.03452.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y. P., Duann, J. R., Chen, J. H., & Jung, T. P. (2010). Electroencephalographic dynamics of musical emotion perception revealed by independent spectral components. Neuroreport, 21(6), 410415. doi:10.1097/WNR.0b013e32833774de

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loomba, R. S., Arora, R., Shah, P. H., Chandrasekar, S., & Molnar, J. (2012). Effects of music on systolic blood pressure, diastolic blood pressure, and heart rate: A meta-analysis. Indian Heart Journal, 64(3), 309313. doi:10.1016/S0019-4832(12)60094-7

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madson, A. T., & Silverman, M. J. (2010). The effect of music therapy on relaxation, anxiety, pain perception, and nausea in adult solid organ transplant patients. Journal of Music Therapy, 47(3), 220232. doi:10.1093/jmt/47.3.220

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maeyama, A., Kodaka, M., & Miyao, H. (2009). Effect of the music-therapy under spinal anesthesia. Masui 58(6), 684691. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/19522258

    • Search Google Scholar
    • Export Citation
  • Mattei, T. A., Rodriguez, A. H., & Bassuner, J. (2013). Selective impairment of emotion recognition through music in Parkinson’s disease: Does it suggest the existence of different networks for music and speech prosody processing? Frontiers in Neuroscience, 7, 161. doi:10.3389/fnins.2013.00161

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ni, C. H., Tsai, W. H., Lee, L. M., Kao, C. C., & Chen, Y. C. (2012). Minimising preoperative anxiety with music for day surgery patients – A randomised clinical trial. Journal of Clinical Nursing, 21(5–6), 620625. doi:10.1111/j.1365-2702.2010.03466.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nilsson, U. (2008). The anxiety- and pain-reducing effects of music interventions: A systematic review. AORN Journal, 87(4), 780807. doi:10.1016/j.aorn.2007.09.013

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nilsson, U. (2009). The effect of music intervention in stress response to cardiac surgery in a randomized clinical trial. Heart Lung, 38(3), 201207. doi:10.1016/j.hrtlng.2008.07.008

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nilsson, U., Unosson, M., & Rawal, N. (2005). Stress reduction and analgesia in patients exposed to calming music postoperatively: A randomized controlled trial. European Journal of Anaesthesiology, 22(2), 96102. doi:10.1017/S0265021505000189

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rudin, D., Kiss, A., Wetz, R. V., & Sottile, V. M. (2007). Music in the endoscopy suite: A meta-analysis of randomized controlled studies. Endoscopy, 39(6), 507510. doi:10.1055/s-2007-966362

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Salimpoor, V. N., Benovoy, M., Larcher, K., Dagher, A., & Zatorre, R. J. (2011). Anatomically distinct dopamine release during anticipation and experience of peak emotion to music. Nature Neuroscience, 14(2), 257262. doi:10.1038/nn.2726

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sendelbach, S. E., Halm, M. A., Doran, K. A., Miller, E. H., & Gaillard, P. (2006). Effects of music therapy on physiological and psychological outcomes for patients undergoing cardiac surgery. The Journal of Cardiovascular Nursing, 21(3), 194200. doi:10.1097/00005082-200605000-00007

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shabanloei, R., Golchin, M., Esfahani, A., Dolatkhah, R., & Rasoulian, M. (2010). Effects of music therapy on pain and anxiety in patients undergoing bone marrow biopsy and aspiration. AORN Journal, 91(6), 746751. doi:10.1016/j.aorn.2010.04.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slevc, L. R., & Okada, B. M. (2015). Processing structure in language and music: A case for shared reliance on cognitive control. Psychonomic Bulletin & Review, 22(3), 637652. doi:10.3758/s13423-014-0712-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smolen, D., Topp, R., & Singer, L. (2002). The effect of self-selected music during colonoscopy on anxiety, heart rate, and blood pressure. Applied Nursing Research, 15(3), 126136. doi:10.1053/apnr.2002.34140

    • Crossref
    • Search Google Scholar
    • Export Citation
  • St. John, G. (2010). Psytrance: An introduction. In G. St. John (Ed.), The local scenes and global culture of Psytrance. London, UK: Routledge.

  • Tam, W. W., Wong, E. L., & Twinn, S. F. (2008). Effect of music on procedure time and sedation during colonoscopy: A meta-analysis. World Journal of Gastroenterology, 14(34), 53365343. doi:10.3748/wjg.14.5336

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Triller, N., Erzen, D., Duh, S., Petrinec Primozic, M., & Kosnik, M. (2006). Music during bronchoscopic examination: The physiological effects. A randomized trial. Respiration, 73(1), 9599. doi:10.1159/000089818

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tse, M. M., Chan, M. F., & Benzie, I. F. (2005). The effect of music therapy on postoperative pain, heart rate, systolic blood pressures and analgesic use following nasal surgery. Journal of Pain & Palliative Care Pharmacotherapy, 19(3), 2129. doi:10.1080/J354v19n03_05

    • Crossref
    • Search Google Scholar
    • Export Citation
  • University Hospitals of Cleveland. (2011). History of music therapy. University Hospitals Music as Medicine. Retrieved January 9, 2011, from http://www.musicasmedicine.com/about/history.cfm

    • Search Google Scholar
    • Export Citation
  • Vaajoki, A., Kankkunen, P., Pietilä, A. M., & Vehviläinen-Julkunen, K. (2011). Music as a nursing intervention: Effects of music listening on blood pressure, heart rate, and respiratory rate in abdominal surgery patients. Nursing & Health Sciences, 13(4), 412418. doi:10.1111/j.1442-2018.2011.00633.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Voss, J. A., Good, M., Yates, B., Baun, M. M., Thompson, A., & Hertzog, M. (2004). Sedative music reduces anxiety and pain during chair rest after open-heart surgery. Pain, 112(1–2), 197203. doi:10.1016/j.pain.2004.08.020

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, S. M., Kulkarni, L., Dolev, J., & Kain, Z. N. (2002). Music and preoperative anxiety: A randomized, controlled study. Anesthesia and Analgesia, 94(6), 14891494. doi:10.1097/00000539-200206000-00021

    • Search Google Scholar
    • Export Citation
  • White, N. E., & Richard, L. M. (2009). Chapter 2 – History of the scientific standards of QEEG normative databases. In T. Budzinsky, H. Budzinski, J. Evans, & A. Abarbanel (Eds.), Introduction to quantitative EEG and neurofeedback (2nd ed., pp. 2960). San Diego, CA: Academic Press.

    • Search Google Scholar
    • Export Citation
  • Yilmaz, E., Ozcan, S., Basar, M., Basar, H., Batislam, E., & Ferhat, M. (2003). Music decreases anxiety and provides sedation in extracorporeal shock wave lithotripsy. Urology, 61(2), 282286. doi:10.1016/S0090-4295(02)02375-0

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zalewsky, S., Vinker, S., Fiada, I., Livon, D., & Kitai, E. (1998). Background music in the family physician’s surgery: Patient reactions. Harefuah, 135(3–4), 9697, 168, 167. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/9885649

    • Search Google Scholar
    • Export Citation
  • Zare, M., Ebrahimi, A. A., & Birashk, B. (2010). The effects of music therapy on reducing agitation in patients with Alzheimer’s disease, a pre-post study. International Journal of Geriatric Psychiatry, 25(12), 13091310. doi:10.1002/gps.2450

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1976 1830 216
PDF Downloads 547 507 184