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  • 1 University of Illinois College of Medicine, Peoria, IL, , USA
  • | 2 Department of Neurology, University of Illinois College of Medicine, Peoria, IL, , USA
  • | 3 James Scholar Research Program, University of Illinois College of Medicine, Peoria, IL, , USA
  • | 4 Center for Collaborative Brain Research, Bradley University, Peoria, IL, , USA
  • | 5 Division of Neurological Surgery, Saint Louis University School of Medicine, St. Louis, MO, , USA
  • | 6 Division of Neurological Surgery, University of Missouri School of Medicine, Columbia, MO, , USA
Open access

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, ages 19–28, 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 associated with lower percentages of beta frequencies and higher percentages of alpha frequencies than the music genres. Psytrance was associated with higher percentages of theta and delta frequencies than the other music genres and controls. The lowest percentages of beta frequencies and highest percentages of alpha frequencies occurred in the occipital and parietal regions. The highest percentages of theta and delta frequencies occurred in the frontal and temporal regions. Subjects with prior music training exhibited increased percentages of delta frequencies in the frontal region. Subject gender and music preference did not have a significant influence on frequency band percentages.

Conclusions

Findings from this study support those of previous music therapy studies and provide novel insights regarding music’s influence on human neurophysiology. These findings also support the hypothesis that music may promote changes in cerebral cortex activity that have similarities to non-rapid eye movement (NREM) 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, ages 19–28, 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 associated with lower percentages of beta frequencies and higher percentages of alpha frequencies than the music genres. Psytrance was associated with higher percentages of theta and delta frequencies than the other music genres and controls. The lowest percentages of beta frequencies and highest percentages of alpha frequencies occurred in the occipital and parietal regions. The highest percentages of theta and delta frequencies occurred in the frontal and temporal regions. Subjects with prior music training exhibited increased percentages of delta frequencies in the frontal region. Subject gender and music preference did not have a significant influence on frequency band percentages.

Conclusions

Findings from this study support those of previous music therapy studies and provide novel insights regarding music’s influence on human neurophysiology. These findings also support the hypothesis that music may promote changes in cerebral cortex activity that have similarities to non-rapid eye movement (NREM) 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 historical 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 last 30 years, a large number of scientific studies have generated a robust body of evidence suggesting that music can provide significant benefits as an adjuvant treatment modality in a variety of clinical settings (Aragon et al., 2002; Binek et al., 2003; Bradt and 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 and 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 and Li, 2011; Lee et al., 2005, 2011, 2012; Lepage et al., 2001; Lin et al., 2011; Loomba et al., 2012; Madson and 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 and 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). Some of the many beneficial 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 the symptoms of chronic cognitive illnesses such as Alzheimer’s dementia and Parkinson’s disease, improvements in many physiological variables including heart rate and blood pressure, and more (Aragon et al., 2002; Binek et al., 2003; Bradt and 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 and 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 and Li, 2011; Lee et al., 2005, 2011, 2012; Lepage et al., 2001; Lin et al., 2011; Loomba et al., 2012; Madson and 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 and 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 to various degrees (Baumgartner et al., 2006; Conrad et al., 2007; Jacobs and Friedman, 2004; Kabuto et al., 1993; Koelsch et al., 2003; Lin et al., 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 activity may be part of the central nervous system response (Baumgartner et al., 2006; Jacobs and Friedman, 2004; Kabuto et al., 1993; Lin et al., 2010). Together, these findings suggest that various types of music may promote changes in central nervous system activity and hypothalamic-pituitary-adrenal axis function that have similarities to various stages of sleep, while the listener remains awake (Baumgartner et al., 2006; Britton et al., 2016; Conrad et al., 2007; Jacobs and Friedman, 2004; Kabuto et al., 1993; Lin et al., 2010; Mattei et al., 2013; Salimpoor et al., 2011; White and Richard, 2009).

Additionally, 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 now being composed digitally, including classical symphonies, theatrical soundtracks, cultural music, and more. Digital music composition allows for an expanded range of instruments, melodies, pitches, resonances, 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 in healthy young adults using EEG, by comparing percentages of beta, alpha, theta, and delta frequencies in each major region of the cerebral cortex while subjects listen to a randomized sequence of five unique music genres and two controls. Interregion cortical comparisons will also be investigated, as well as potential influences of subject gender, music preference, and prior music training on resulting EEG data. 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 for the benefit of humankind.

Methods

Subject details

After institutional review board (IRB) approval (IRB #267944-1), 25 healthy adults with normal hearing function, ages 18–29, 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 hrs prior to their study participation. Subjects were also asked to be adequately rested 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 certain stages of sleep, our study aimed to measure 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 and Richard, 2009). Non-rapid eye movement (NREM) sleep is generally categorized into 3 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 prevalence 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 and 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 rapid eye movements, dreaming, and muscle paralysis (Britton et al., 2016; White and Richard, 2009).

Data collection occurred 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 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 subjects could keep their eyes open throughout the duration of the music sequence without any visual input, 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 and 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 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 listened to when providing this rating. After the music sequence was 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 controls (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 critically ill patients (Conrad et al., 2007). Classical music from world-renown composers such as Ludwig van Beethoven, Wolfgang Amadeus Mozart, and Johann Sebastian Bach is widely recognized and is still commonly played in modern times by a variety of both public (e.g., radio, 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 large 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 with sixteenth bass notes (i.e., four bass notes per beat) instead of simple quarter notes. The typical tempo of Psytrance is around 140-160 beats-per-minute (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 x 150 bpm = 600 bpm = 10 Hz). Psytrance is widely reported to be both therapeutic and trance-inducing. Psytrance was 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 sixteenth bass notes), making it an excellent music genre for comparison to Psytrance. Although Goa Trance typically utilizes a standard quarter-note beat structure, it still is typically produced at a tempo very similar to Psytrance (140–160 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 coastal 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 personal 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 featured on popular radio and television stations, including Pop, Hip-Hop, Rock, etc.

Control (white noise) was selected to evaluate subjects’ 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 non-musical sound (e.g., television static, radio static, traffic sounds), including the background noise encountered at OSF Saint Francis Medical Center where data collection took place, but our study team opted to use a natural form of white noise (waterfall recording) to increase the likelihood that subjects would complete the entire music sequence with minimal discomfort and anxiety. Song: Victoria Falls waterfall (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). 19 EEG electrodes were placed according to the standard international 10–20 system by a certified INI 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, then importing to Novatech WinEEG v2.7+ software for quantitative spectral analysis. 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 a ‘monopolar average 1’ montage. No additional processing or modification of the EEG data was performed.

The percentages of beta, alpha, theta, and delta frequencies from each song and control of a subject’s music sequence were determined by 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 percentages of each frequency band that occurred during the individual song or control. This was performed by the principle investigator under the guidance of an attending INI neurologist at OSF Saint Francis Medical Center. The settings used by our study team when applying the WinEEG spectral analysis tool included: epoch length of 4 seconds, overlap of 50%, and Hanning time windows. The EEG data were also grouped into cortical regions by averaging the resulting percentages from the individual leads that correspond 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 our study team did not perform left versus right interhemispheric comparisons. Somatotopic cortical maps were generated from the percentage tables in WinEEG to visually represent the cortical regions where the highest percentages of each frequency band occurred, using a brightness scale of 0–60% (0% = no brightness, 60% = maximum brightness) to provide optimal data visualization since no individual frequency band showed percentages greater than 60% in any cortical region.

Statistical analysis

Processed EEG data tables were exported from WinEEG and imported into a Microsoft Excel spreadsheet for analysis using IBM SPSS software (version 21.0.0). 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 rank-order correlation tests were used to investigate possible influences of subjective variables including subject music preference, gender, and prior music training. A strict statistical significance threshold of P≤0.01 was used to help offset, in part, the relatively small sample size.

Results

Twenty-three out of the twenty-five 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 twenty-three participating study subjects, nine were male and fourteen were female. The subjects were distributed among the following ages: 19 (2), 20 (1), 23 (3), 24 (4), 25 (3), 26 (4), 27 (1), 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 prior 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 percentages of beta, alpha, theta, and delta frequencies within individual cortical regions for all randomized music genres and controls are displayed in Table 1 and Figs. 1A–D.

Table 1.

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

Mean beta, alpha, theta, & delta percentages for randomized music genres & 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
Fig. 1A.
Fig. 1A.

Mean beta frequency percentages for randomized music genres and controls

Citation: Journal of Psychedelic Studies 5, 2; 10.1556/2054.2019.027

Fig. 1B
Fig. 1B

– Mean alpha frequency percentages for randomized music genres and controls

Citation: Journal of Psychedelic Studies 5, 2; 10.1556/2054.2019.027

Fig. 1C
Fig. 1C

– Mean theta frequency percentages for randomized music genres and controls

Citation: Journal of Psychedelic Studies 5, 2; 10.1556/2054.2019.027

Fig. 1D
Fig. 1D

– Mean delta frequency percentages for randomized music genres and controls

Citation: Journal of Psychedelic Studies 5, 2; 10.1556/2054.2019.027

Wilcoxon signed-rank analyses revealed significant differences in percentages of beta frequencies for paired comparisons of music genres and/or controls within individual cortical regions, which are displayed in Table 2A. In the occipital region, Control 1 was associated with lower percentages of beta frequencies compared to both Classical (p=0.002) and Subject Song (p=0.008), while Control 2 was associated with lower percentages of beta frequencies compared to Subject Song (p=0.008) only. In the temporal region, Tribal Downtempo, Psytrance, and Control 2 were associated with lower percentages of beta frequencies compared to Control 1 (p=0.006, p=0.008, p=0.010), while both Psytrance and Goa Trance were associated with lower percentages of beta frequencies compared to Subject Song (p=0.004, p=0.010).

Table 2A.

Wilcoxon signed-rank analyses of paired music genre and control comparisons for the beta frequency band (A = Classical, B = Tribal Downtempo, C = Psytrance, D = Goa Trance, E = Subject Song, C1 = Control 1, C2 = Control 2)

Wilcoxon analyses of paired music genre & control comparisons: beta frequency
FrontalParietalOccipitalTemporal
GenresPGenresPGenresPGenresP
B vs A0.429B vs A0.224B vs A0.191B vs A0.738
C vs A0.248C vs A0.715C vs A0.330C vs A0.136
D vs A0.976D vs A0.543D vs A0.673D vs A0.951
E vs A0.784E vs A0.301E vs A0.484E vs A0.026
Cl vs A0.951Cl vs A0.362Cl vs A0.002*Cl vs A0.039
C2 vs A0.043C2 vs A0.068C2 vs A0.026C2 vs A0.101
C vs B0.879C vs B0.627C vs B0.951C vs B0.191
D vs B0.121D vs B0.153D vs B0.595D vs B0.761
E vs B0.855E vs B0.083E vs B0.144E vs B0.024
CI vs B0.784C1 vs B0.605C1 vs B0.236Cl vs B0.006*
C2 vs B0.831C2 vs B0.236C2 vs B0.171C2 vs B0.301
D vs C0.465D vs B0.484D vs C0.927D vs C0.212
E vs C0.394E vs C0.089E vs C0.019E vs C0.004*
Cl vs C0.627Cl vs C0.605Cl vs C0.248Cl vs C0.008*
C2 vs C0.523C2 vs C0.114C2 vs C0.055C2 vs C0.784
E vs D0.693E vs D0.670E vs D0.171E vs D0.010*
Cl vs D0.627Cl vs D0.784Cl vs D0.110Cl vs D0.033
C2 vs D0.191C2 vs D0.045C2 vs D0.447C2 vs D0.543
Cl vs E0.976Cl vs E0.114Cl vs E0.008*Cl vs E0.584
C2 vs E0.260C2 vs E0.021C2 vs E0.008*C2 vs E0.019
C2 vs Cl0.089C2 vs Cl0.191C2 vs Cl0.903C2 vs Cl0.010*

Wilcoxon signed-rank analyses revealed significant differences in percentages of alpha frequencies for paired comparisons of music genres and/or controls within individual cortical regions, which are displayed in Table 2B. In the frontal region, Control 2 was associated with higher percentages of alpha frequencies compared to Classical (p=0.006), Goa Trance (p=0.003), and Subject Song (p=0.010). In the parietal region, Control 2 was associated with higher percentages of alpha frequencies compared to Psytrance (p=0.007). In the occipital region, Control 1 was associated with higher percentages of alpha frequencies compared to Classical (p=0.010), Tribal Downtempo (p=0.004), Psytrance (p=0.010), and Goa Trance (p=0.005). In the temporal region, Control 2 was associated with higher percentages of alpha frequencies compared to Tribal Downtempo (p=0.001), Psytrance (p=0.004), Goa Trance (p=0.007), and Subject Song (p=0.002).

Table 2B.

Wilcoxon signed-rank analyses of paired music genre and control comparisons for the alpha frequency band (A = Classical, B = Tribal Downtempo, C = Psytrance, D = Goa Trance, E = Subject Song, C1 = Control 1, C2 = Control 2)

Wilcoxon analyses of paired music genre & control comparisons: alpha frequency
FrontalParietalOccipitalTemporal
GenresPGenresPGenresPGenresP
B vs A0.563B vs A0.114B vs A0.068B vs A0.136
C vs A0.715C vs A0.031C vs A0.260C vs A0.316
D vs A0.107D vs A0.114D vs A0.738D vs A0.330
E vs A0.627E vs A0.171E vs A0.563E vs A0.059
C1 vs A0.316C1 vs A0.927C1 vs A0.010*C1 vs A0.761
C2 vs A0.006*C2 vs A0.236C2 vs A0.201C2 vs A0.023
C vs B0.726C vs B0.224C vs B0.465C vs B0.951
D vs B0.301D vs B0.394D vs B0.563D vs B0.429
E vs B0.784E vs B0.429E vs B0.879E vs B0.212
Cl vs B0.021Cl vs B0.503Cl vs B0.004*Cl vs B0.715
C2 vs B0.039C2 vs B0.039C2 vs B0.026C2 vs B0.001*
D vs C0.523D vs C0.412D vs C0.648D vs C0.951
E vs C0.784E vs C0.951E vs C0.761E vs C0.412
Cl vs C0.346Cl vs C0.064Cl vs C0.010*Cl vs C0.523
C2 vs C0.029C2 vs C0.007*C2 vs C0.052C2 vs C0.004*
E vs D0.627E vs D0.903E vs D0.584E vs D0.563
Cl vs D0.068Cl vs D0.260Cl vs D0.005*Cl vs D0.316
C2 vs D0.003*C2 vs D0.068C2 vs D0.394C2 vs D0.007*
Cl vs E0.114Cl vs E0.078Cl vs E0.015Cl vs E0.248
C2 vs E0.010*C2 vs E0.026C2 vs E0.094C2 vs E0.002*
C2 vs Cl0.378C2 vs Cl0.260C2 vs Cl0.503C2 vs Cl0.029

Wilcoxon signed-rank analyses revealed significant differences in percentages of theta frequencies for paired comparisons of music genres and/or controls within individual cortical regions, which are displayed in Table 2C. In the frontal region, Psytrance was associated with higher percentages of theta frequencies compared to Subject Song (p=0.002). In the parietal region, Psytrance was associated with higher percentages of theta frequencies compared to both Subject Song (p=0.002) and Control 1 (p=0.009). In the occipital region, Tribal Downtempo and Psytrance were associated with higher percentages of theta frequencies compared to Control 1 (p=0.003, p=0.001). In the temporal region, Classical, Tribal Downtempo, and Psytrance were associated with higher percentages of theta frequencies compared to Subject Song (p=0.006, p=0.004, p<0.001).

Table 2C.

Wilcoxon signed-rank analyses of paired music genre and control comparisons for the theta frequency band (A = Classical, B = Tribal Downtempo, C = Psytrance, D = Goa Trance, E = Subject Song, C1 = Control 1, C2 = Control 2)

Wilcoxon analyses of paired music genre & control comparisons: theta frequency
FrontalParietalOccipitalTemporal
GenresPGenresPGenresPGenresP
B vs A0.784B vs A0.316B vs A0.019B vs A0.543
C vs A0.073C vs A0.031C vs A0.018C vs A0.048
D vs A0.976D vs A0.693D vs A0.287D vs A0.648
E vs A0.083E vs A0.761E vs A0.648E vs A0.006*
C1 vs A0.693C1 vs A0.976C1 vs A0.301C1 vs A0.394
C2 vs A0.761C2 vs A0.831C2 vs A0.784C2 vs A0.784
C vs B0.191C vs B0.132C vs B0.808C vs B0.315
D vs B0.738D vs B0.412D vs B0.346D vs B0.248
E vs B0.144E vs B0.236E vs B0.089E vs B0.004*
Cl vs B0.831Cl vs B0.128Cl vs B0.003*Cl vs B0.101
C2 vs B0.855C2 vs B0.218C2 vs B0.114C2 vs B0.563
D vs C0.447D vs C0.061D vs C0.412D vs C0.015
E vs C0.002*E vs C0.002*E vs C0.052E vs C<0.001*
Cl vs C0.429Cl vs C0.009*Cl vs C0.001*Cl vs C0.029
C2 vs C0.287C2 vs C0.042C2 vs C0.224C2 vs C0.059
E vs D0.248E vs D0.951E vs D0.176E vs D0.059
Cl vs D0.903Cl vs D0.738Cl vs D0.033Cl vs D0.267
C2 vs D0.808C2 vs D0.808C2 vs D0.693C2 vs D0.715
Cl vs E0.104Cl vs E0.595Cl vs E0.248Cl vs E0.078
C2 vs E0.191C2 vs E0.584C2 vs E0.808C2 vs E0.014
C2 vs Cl0.543C2 vs Cl0.976C2 vs Cl0.224C2 vs Cl0.523

Wilcoxon signed-rank analyses revealed significant differences in percentages of delta frequencies for paired comparisons of music genres and/or controls within individual cortical regions, which are displayed in Table 2D. In the parietal region, Psytrance was associated with higher percentages of delta frequencies compared to Classical (p=0.008), Control 1 (p=0.008), and Control 2 (p=0.010). In the occipital region, both Psytrance and Goa Trance were associated with higher percentages of delta frequencies compared to Control 1 (p=0.009, p=0.010). In the temporal region, Psytrance was associated with higher percentages of delta frequencies compared to Subject Song (p=0.005).

Table 2D.

Wilcoxon signed-rank analyses of paired music genre and control comparisons for the delta frequency band (A = Classical, B = Tribal Downtempo, C = Psytrance, D = Goa Trance, E = Subject Song, C1 = Control 1, C2 = Control 2)

Wilcoxon analyses of paired music genre & control comparisons: delta frequency
FrontalParietalOccipitalTemporal
GenresPGenresPGenresPGenresP
B vs A0.394B vs A0.543B vs A0.068B vs A0.761
C vs A0.212C vs A0.008*C vs A0.101C vs A0.301
D vs A0.447D vs A0.176D vs A0.394D vs A0.465
E vs A0.584E vs A0.783E vs A0.808E vs A0.016
Cl vs A0.761Cl vs A0.412Cl vs A0.162Cl vs A0.465
C2 vs A0.503C2 vs A0.308C2 vs A0.648C2 vs A0.563
C vs B0.855C vs B0.013C vs B0.915C vs B0.114
D vs B0.563D vs B0.316D vs B0.879D vs B0.951
E vs B0.523E vs B0.903E vs B0.354E vs B0.136
Cl vs B0.267Cl vs B0.171Cl vs B0.011Cl vs B0.465
C2 vs B0.191C2 vs B0.236C2 vs B0.070C2 vs B0.648
D vs C0.903D vs C0.301D vs C0.627D vs C0.107
E vs C0.191E vs C0.031E vs C0.114E vs C0.005*
Cl vs C0.543Cl vs C0.008*Cl vs C0.009*Cl vs C0.048
C2 vs C0.089C2 vs C0.010*C2 vs C0.128C2 vs C0.107
E vs D0.976E vs D0.330E vs D0.715E vs D0.114
Cl vs D0.784Cl vs D0.094Cl vs D0.010*Cl vs D0.648
C2 vs D0.403C2 vs D0.128C2 vs D0.287C2 vs D0.543
Cl vs E0.761Cl vs E0.236Cl vs E0.114Cl vs E0.162
C2 vs E0.831C2 vs E0.378C2 vs E0.445C2 vs E0.563
C2 vs Cl0.584C2 vs Cl0.484C2 vs Cl0.503C2 vs Cl0.808

Cortical region comparisons

A somatotopic cortical activity map which summarizes EEG findings for interregion cortical comparisons across all music genres and controls is displayed in Fig. 2. Statistical data analyses leading to this visual somatotopic representation of our interregion cortical comparison findings can be found in Tables 3 9 and the subsequent paragraphs below.

Fig. 2.
Fig. 2.

Somatotopic cortical activity map summarizing 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 5, 2; 10.1556/2054.2019.027

Wilcoxon signed-rank analyses revealed significant differences when comparing the effect of Classical music on percentages of beta, alpha, theta, and delta frequencies between different cortical regions, which are displayed in Table 3. The parietal and occipital regions were both associated with lower percentages of beta frequencies compared to the frontal region (p=0.004, p=0.006). The parietal and occipital regions were both associated with higher percentages of alpha frequencies compared to the frontal (p<0.001, p<0.001) and temporal (p<0.001, p<0.001) regions. The frontal region was associated with higher percentages of theta frequencies compared to the occipital region (p=0.007). The frontal and temporal regions were both associated with higher percentages of delta frequencies compared to the parietal (p<0.001, p<0.001) and occipital (p<0.001, p<0.001) regions.

Table 3.

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

Wilcoxon analyses of paired cortical region comparisons for Classical
BetaAlphaThetaDelta
RegionsPRegionsPRegionsPRegionsP
P vs F0.004*P vs F<0.001*P vs F0.036P vs F<0.001*
O vs F0.006*O vs F<0.001*O vs F0.007*O vs F<0.001*
T vs F0.045T vs F0.107T vs F0.260T vs F0.927
O vs P0.045O vs P0.026O vs P0.014O vs P0.036
T vs P0.171T vs P<0.001*T vs P0.287T vs P<0.001*
T vs O0.036T vs O<0.001*T vs O0.015T vs O<0.001*

Wilcoxon signed-rank analyses revealed significant differences when comparing the effect of Tribal Downtempo on percentages of alpha and delta frequencies between different cortical regions, which are displayed in Table 4. The parietal and occipital regions were both associated with higher percentages of alpha frequencies compared to the frontal (p<0.001, p<0.001) and temporal (p<0.001, p<0.001) regions. The frontal and temporal regions were both associated with higher percentages of delta frequencies compared to the parietal (p<0.001, p<0.001) and occipital (p=0.002, p=0.001) regions.

Table 4.

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

Wilcoxon analyses of paired cortical region comparisons for Tribal Downtempo
BetaAlphaThetaDelta
RegionsPRegionsPRegionsPRegionsP
P vs F0.015P vs F<0.001*P vs F0.330P vs F<0.001*
O vs F0.013O vs F<0.001*O vs F0.144O vs F0.002*
T vs F0.029T vs F0.019T vs F0.595T vs F0.523
O vs P0.029O vs P0.015O vs P0.083O vs P0.236
T vs P0.128T vs P<0.001*T vs P0.761T vs P<0.001*
T vs O0.012T vs O<0.001*T vs O0.181T vs O0.001*

Wilcoxon signed-rank analyses revealed significant differences when comparing the effect of Psytrance on percentages of alpha and delta frequencies between different cortical regions, which are displayed in Table 5. The parietal and occipital regions were both associated with higher percentages of alpha frequencies compared to the frontal (p<0.001, p<0.001) and temporal (p<0.001, p<0.001) regions, while the occipital region was also associated with higher percentages of alpha frequencies compared to the parietal region (p=0.005). The frontal and temporal regions were both associated with higher percentages of delta frequencies compared to the parietal (p=0.001, p<0.001) and occipital (p=0.001, p<0.001) regions.

Table 5.

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

Wilcoxon analyses of paired cortical region comparisons for Psytrance
BetaAlphaThetaDelta
RegionsPRegionsPRegionsPRegionsP
P vs F0.073P vs F<0.001*P vs F0.136P vs F0.001*
O vs F0.018O vs F<0.001*O vs F0.212O vs F0.001*
T vs F0.031T vs F0.128T vs F0.412T vs F0.808
O vs P0.018O vs P0.005*O vs F0.073O vs F0.021
T vs P0.927T vs P<0.001*T vs P0.543T vs P<0.001*
T vs O0.078T vs O<0.001*T vs O0.274T vs O<0.001*

Wilcoxon signed-rank analyses revealed significant differences when comparing the effect of Goa Trance on percentages of beta, alpha, and delta frequencies between different cortical regions, which are displayed in Table 6. The parietal, occipital, and temporal regions were all associated with lower percentages of beta frequencies compared to the frontal region (p=0.005, p=0.001, p=0.007), while the occipital region was also associated with lower percentages of beta frequencies compared to the parietal (p=0.002) and temporal (p=0.007) regions. The parietal and occipital regions were both associated with higher percentages of alpha frequencies compared to the frontal (p<0.001, p<0.001) and temporal (p<0.001, p<0.001) regions, while the occipital region was also associated with higher percentages of alpha frequencies compared to the parietal region (p=0.002). The frontal and temporal regions were both associated with higher percentages of delta frequencies compared to the parietal (p<0.001, p=0.001) and occipital (p<0.001, p<0.001) regions.

Table 6.

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

Wilcoxon analyses of paired cortical region comparisons for Goa Trance
BetaAlphaThetaDelta
RegionsPRegionsPRegionsPRegionsP
P vs F0.005*P vs F<0.001*P vs F0.036P vs F<0.001*
O vs F0.001*O vs F<0.001*O vs F0.012O vs F<0.001*
T vs F0.007*T vs F0.045T vs F0.055T vs F0.523
O vs P0.002*O vs P0.002*O vs P0.078O vs P0.029
T vs P0.503T vs P<0.001*T vs P0.447T vs P0.001*
T vs O0.007*T vs O<0.001*T vs O0.078T vs O<0.001*

Wilcoxon signed-rank analyses revealed significant differences when comparing the effect of Subject Song on percentages of beta, alpha, theta, and delta frequencies between different cortical regions, which are displayed in Table 7. The parietal and occipital regions were both associated with lower percentages of beta frequencies compared to the temporal region (p=0.005, p=0.007). The parietal and occipital regions were both associated with higher percentages of alpha frequencies compared to the frontal (p<0.001, p<0.001) and temporal (p<0.001, p<0.001) regions, while the occipital region was also associated with higher percentages of alpha frequencies compared to the parietal region (p<0.001). The parietal region was associated with higher percentages of theta frequencies compared to the occipital region (p=0.001). The frontal and temporal regions were both associated with higher percentages of delta frequencies compared to the parietal (p=0.001, p=0.002) and occipital (p=0.001, p=0.001) regions.

Table 7.

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

Wilcoxon analyses of paired cortical region comparisons for Subject Song
BetaAlphaThetaDelta
RegionsPRegionsPRegionsPRegionsP
P vs F0.064P vs F<0.001*P vs F0.212P vs F0.001*
O vs F0.033O vs F<0.001*O vs F0.019O vs F0.001*
T vs F0.648T vs F0.236T vs F0.068T vs F0.094
O vs P0.018O vs P<0.001*O vs P0.001*O vs P0.015
T vs P0.005*T vs P<0.001*T vs P0.465T vs P0.002*
T vs O0.007*T vs O<0.001*T vs O0.083T vs O0.001*

Wilcoxon signed-rank analyses revealed significant differences when comparing the effect of Control 1 on percentages of beta, alpha, theta, and delta frequencies between different cortical regions, which are displayed in Table 8. The parietal and occipital regions were both associated with lower percentages of beta frequencies compared to the frontal (p=0.003, p<0.001) and temporal (p=0.004, p<0.001) regions, while the occipital region was also associated with lower percentages of beta frequencies compared to the parietal region (p<0.001). The parietal and occipital regions were both associated with higher percentages of alpha frequencies compared to the frontal (p<0.001, p<0.001) and temporal (p<0.001, p<0.001) regions, while the occipital region was also associated with higher percentages of alpha frequencies compared to the parietal region (p<0.001). The frontal, parietal, and temporal regions were all associated with higher percentages of theta frequencies compared to the occipital region (p=0.001, p=0.002, p=0.001), while the frontal region was also associated with higher percentages of theta frequencies compared to the parietal region (p=0.001). The frontal and temporal regions were both associated with higher percentages of delta frequencies compared to the parietal (p<0.001, p<0.001) and occipital (p<0.001, p<0.001) regions, while the parietal region was also associated with higher percentages of delta frequencies compared to the occipital region (p=0.004).

Table 8.

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

Wilcoxon analyses of paired cortical region comparisons for Control 1
BetaAlphaThetaDelta
RegionsPRegionsPRegionsPRegionsP
P vs F0.003*P vs F<0.001*P vs F0.001*P vs F<0.001*
O vs F<0.001*O vs F<0.001*O vs F0.001*O vs F<0.001*
T vs F0.484T vs F0.523T vs F0.023T vs F0.523
O vs P<0.001*O vs F<0.001*O vs F0.002*Q vs P0.004*
T vs P0.004*T vs P<0.001*T vs P0.121T vs P<0.001*
T vs O<0.001*T vs O<0.001*T vs O0.001*T vs O<0.001*

Wilcoxon signed-rank analyses revealed significant differences when comparing the effect of Control 2 on percentages of beta, alpha, theta, and delta frequencies between different cortical regions, which are displayed in Table 9. The occipital region was associated with lower percentages of beta frequencies compared to both the frontal (p=0.010) and parietal (p=0.004) regions. The parietal and occipital regions were both associated with higher percentages of alpha frequencies compared to the frontal (p<0.001, p<0.001) and temporal (p<0.001, p<0.001) regions, while the occipital region was also associated with higher percentages of alpha frequencies compared to the parietal region (p=0.001). The temporal region was associated with higher percentages of theta frequencies compared to the occipital region (p=0.006). The frontal and temporal regions were both associated with higher percentages of delta frequencies compared to the parietal (p<0.001, p<0.001) and occipital (p=0.001, p<0.001) regions.

Table 9.

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

Wilcoxon analyses of paired cortical region comparisons for Control 2
BetaAlphaThetaDelta
RegionsPRegionsPRegionsPRegionsP
P vs F0.073P vs F<0.001*P vs F0.083P vs F<0.001*
O vs F0.010*O vs F<0.001*O vs F0.014O vs F0.001*
T vs F0.121T vs F0.107T vs F0.248T vs F0.927
O vs P0.004*O vs P0.001*O vs P0.018Q vs P0.018
T vs P0.224T vs P<0.001*T vs P0.162T vs P<0.001*
T vs O0.031T vs O<0.001*T vs O0.006*T vs O<0.001*

Subject music preference

Mean subject ratings and rankings for music genres and controls are displayed in Table 10. A positive correlation (R=0.579) was found between subject ratings for Goa Trance and theta frequency percentages in the temporal region ( p =0.004). There were no other significant correlations observed between subject ratings and frequency band percentages for any other music genre or control in any cortical region. No significant correlations were observed between subject rankings and frequency band percentages for any music genre or control in any cortical region.

Table 10.

Mean subject ratings (1 = worst, 10 = best) and rankings (1 = best, 5 = worst) for music genres and controls

Mean subject ratings & ranking tor randomized music genres & controls
Control 1ClassicalTribalPsytranceGoaSubjectControl 2SD
Ratings3.177.806.435.945.919.63N/A2.16
RankingsN/A2.703.433.743.911.22N/A1.10

Subject gender and music training

Gender was not found to have a significant influence on beta, alpha, theta, or delta frequency percentages in any cortical region. Music training was associated with higher delta frequency percentages in the frontal region (p=0.002), as displayed in Fig. 3.

Fig. 3.
Fig. 3.

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

Citation: Journal of Psychedelic Studies 5, 2; 10.1556/2054.2019.027

Discussion

Several findings from this study support those of previous music therapy studies, while other findings provide novel insights regarding how different music genres and pertinent variables affect neurophysiological activity in the human cerebral cortex. This study also 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 and Friedman, 2004; Kabuto et al., 1993; Lin et al., 2010; White and Richard, 2009).

Music genre comparisons

Lower percentages of beta frequencies were found to be strongly associated with several music genres and controls within individual cortical regions, including Control 1 and 2 in the occipital region, and Tribal Downtempo, Psytrance, Goa Trance, and Control 2 in the temporal region. Given that decreased beta frequency activity is observed during stages of NREM sleep, these findings support those of previous music therapy studies which suggest that various 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; Britton et al., 2016; Jacobs and Friedman, 2004; Kabuto et al., 1993; Lin et al., 2010; White and 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 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 or relaxing (e.g., television static, radio static, traffic noise, etc.).

Higher 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 that observed increased alpha frequency activity in response to pleasant music (Kabuto et al., 1993; Lin et al., 2010). The only difference between Control 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 towards 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 potentially relaxing nature of the overall 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 pleasant control, such as television static, radio static, or an audio recording that is representative of typical non-musical noise encountered during one’s usual daily routine, such as traffic noise.

Higher 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 higher percentages of theta frequencies may be attributed to its unique sixteenth 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 sixteenth 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 higher percentages of theta frequencies compared to the other studied music genres. Given that increased theta frequency activity is observed during stages of NREM sleep, these findings support those of previous music therapy studies which suggest that various 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 and Friedman, 2004; Kabuto et al., 1993; Lin et al., 2010).

Higher 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 higher percentages of delta frequencies may again be attributed to its unique sixteenth 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 the compositional structure of Goa Trance may be 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 sixteenth note beat structure found in Psytrance, it still is typically 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 higher percentages of delta frequencies compared to the other studied music genres. Given that increased delta frequency activity is observed during stages of NREM sleep, these findings support those of previous music therapy studies which suggest that various 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 and Friedman, 2004; Kabuto et al., 1993; Lin et al., 2010).

Overall, we observed several findings consistent with previous music therapy studies, including findings which suggest 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 and Friedman, 2004; Kabuto et al., 1993; Lin et al., 2010). Additionally, 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 higher percentages of theta and delta frequencies.

Cortical region comparisons

Lower percentages of beta frequencies and higher percentages of alpha frequencies were found to be most strongly associated with the parietal and occipital regions in comparison to 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 of lower percentages of beta frequencies in both the parietal and occipital regions appears to be closely correlated to the association we observed of higher percentages of alpha frequencies in both the parietal and occipital regions. Both the frontal and temporal regions showed significantly higher percentages of theta and delta frequencies when compared to other cortical regions. In contrast, the lowest 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 a 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 what was observed in other music-EEG studies, including one which 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 preference

The observed correlation between subject music genre ratings and the association of Goa Trance with higher percentages of theta frequencies in the temporal region suggests that subject music preference 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 other music genre or control in any 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 percentages of beta, alpha, theta, or delta frequencies, these findings suggest overall that subject music preference does not significantly influence the percentages of each frequency band that are generated within a given cortical region when listening to various music genres. Overall, our findings suggest that personal music preferences do not have a significant influence on frequency band percentages in the cerebral cortex.

The lack of significant correlations observed between subject music preferences and frequency band percentages in all cortical regions is supported by other findings discussed in previous sections of this study, such as the finding that the music genres which were most effective at significantly modifying 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 an important role in the subjective sensations (e.g., relaxation, pleasure, happiness, etc.) that are experienced when listening to various music genres.

Subject gender and music training

The lack of significant findings observed when examining the effect of subject gender on 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, our study did not compare individual EEG leads, individual cerebral hemispheres, or other related variables that could still potentially be influenced by gender. Although our study did not find any gender-related differences when comparing individual frequency bands and cortical regions, a previous music-EEG study did observe gender-related differences in cortical 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 elucidate potential gender-related differences in cortical music processing.

The finding that subjects with prior music training showed higher 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 regards to delta activity. This finding may potentially be related to the frontal lobe’s association 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 prior music training did not have a significant influence on percentages of beta, alpha, theta, or delta frequencies in any other area of the cerebral cortex, the potential influence of prior music training on music perception and cortical 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 h), since many medications and chemicals continue having psychoactive effects beyond the 6-h abstinent period required by this study. Subjects also could be screened to rule out recent consumption of long-acting psychoactive chemicals, such as certain amphetamines (e.g., Adderall), benzodiazepines (e.g., Klonopin), antidepressants (e.g., Zoloft), etc. Future studies may be improved by utilizing different subject groups based on certain variables (e.g., age, ethnicity, 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 our 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 utilized in our study, but were unable to thoroughly investigate these data and variables. Although we did investigate the potential influence of subject music preference 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 and familiarity with each music genre.

Future studies may be improved by using a non-musical control that more accurately represents commonly encountered non-musical noise (e.g., television static, radio static, traffic noise, footsteps, etc.). The soft, monotonous waterfall recording used as the only control in this study was suboptimal, as it was quite pleasant and relaxing for many subjects, which may have influenced some of our findings and confounded various comparative analyses. Selecting a more common, less pleasant 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 benefit from gradually fading in the beginning and/or fading out the ending of each song to avoid any unintended abruptness that is created during the process of standardizing song lengths (e.g., cropping), and to avoid atypical introductions or endings of songs that are not truly representative of that music genre (e.g., lyrics at the beginning or end of a song that does not typically have lyrics, such as classical music).

Future studies should consider investigating additional music genres, as there are many common and popular music genres that we did not investigate in this study. In addition to studying other music genres, future studies could also investigate audio recordings from individual musical instruments, such as the piano, flute, trumpet, violin, etc. Future studies should also be performed to investigate the influence of lyrics in 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, melody, 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 percentages of each frequency band, as frequency band power analysis is often utilized by studies that analyze 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 variables with monitoring of EEG data to draw stronger correlations between therapeutic outcomes and specific EEG findings. For example, it would require little additional effort to monitor basic physiological variables such as blood pressure, heart rate, etc., and subjective variables such as mood, pleasure, anxiety, etc., while subjects complete their music sequence using a data collection protocol similar to 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 by 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 the songs (except for Subject Song). This study was also biased by the age and occupation of the subjects, as most subjects were young adults who were graduate or undergraduate students.

Conclusions

Controls were associated with lower percentages of beta frequencies than the music genres, while Psytrance, Goa Trance, and Tribal Downtempo also had significant but weaker associations. Controls were also associated with higher percentages of alpha frequencies than the music genres. Psytrance was associated with higher percentages of theta frequencies than the other music genres and controls, while Tribal Downtempo and Classical also had significant but weaker associations. Psytrance was also associated with higher percentages of delta frequencies than the other music genres and controls, while Goa Trance also had a significant but weaker association.

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

Findings from this study support those of previous music therapy studies and provide novel insights regarding music’s influence on human neurophysiology. These 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. Additionally, findings from this study suggest that certain music genres may have a more robust association with these changes in cerebral cortex activity than other 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 needed 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.

Neurofax EEG system (EEG-1200A)

Pre-music questionnaire

  • What is your Name? What is your Age? What is your Gender? What is your Ethnicity?

  • 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?

  • If so, what music training do you have and how much?

  • What are your three most favorite genres of music (any)?

  • What are your three least favorite genres of music (any)?

  • Have you heard songs from any of this study’s music genres before?

  1. If so, how many times have you heard a song from each genre?

  2. If so, how do you feel about the genres (i.e., like, dislike, neutral)?

  • How much sleep did you get last night?

  • How tired do you feel on a scale of 1–10 (1 = very alert; 10 = extremely tired)?

Post-music questionnaire

  • 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?

  • 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.

  • Did you fall asleep at all during the music therapy session?

  • Do you have any questions or concerns?

Study images

Image 1.
Image 1.

Illinois Neurological Institute (INI) – OSF Saint Francis Medical Center – Peoria, IL, USA

Citation: Journal of Psychedelic Studies 5, 2; 10.1556/2054.2019.027

Image 2.
Image 2.

Nihon Kohden Neurofax EEG hardware

Citation: Journal of Psychedelic Studies 5, 2; 10.1556/2054.2019.027

Image 3.
Image 3.

Data collection during a subject's music sequence (bedside)

Citation: Journal of Psychedelic Studies 5, 2; 10.1556/2054.2019.027

Image 4.
Image 4.

Data collection during a subject's music sequence (monitor room)

Citation: Journal of Psychedelic Studies 5, 2; 10.1556/2054.2019.027

Image 5.
Image 5.

EEG data (as viewed in Nihon Kohden Neurofax software)

Citation: Journal of Psychedelic Studies 5, 2; 10.1556/2054.2019.027

Image 6.
Image 6.

EEG data (as viewed in WinEEG analysis software)

Citation: Journal of Psychedelic Studies 5, 2; 10.1556/2054.2019.027

Image 7.
Image 7.

Quantitative analysis of EEG data (as viewed in WinEEG analysis software)

Citation: Journal of Psychedelic Studies 5, 2; 10.1556/2054.2019.027

Image 8.
Image 8.

Thank you to Illinois Neurological Institute for providing funding for this study

Citation: Journal of Psychedelic Studies 5, 2; 10.1556/2054.2019.027

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    • 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. [Epub 2005].

    • 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.

    • 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.

    • Crossref
    • 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.

    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Britton J. W. , Frey L. C. , Hopp J. L. et al., authors; St. Louis, E. K. , Frey, L. C. editors. (2016). Electroencephalography (EEG): An introductory text and atlas of normal and abnormal findings in adults, children, and infants [internet]. Chicago: 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.

    • 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), 285294.

    • 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.

    • 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.

    • Crossref
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Editor-in-Chief:

Attila Szabo - University of Oslo

E-mail address: attilasci@gmail.com

Associate Editors:

  • Alan K. Davis - The Ohio State University & Johns Hopkins University, USA
  • Zsolt Demetrovics - Eötvös Lóránd University, Budapest, Hungary
  • Ede Frecska, founding Editor-in-Chief - University of Debrecen, Debrecen, Hungary
  • David Luke - University of Greenwich, London, UK
  • Dennis J. McKenna- Heffter Research Institute, St. Paul, USA
  • Jeremy Narby - Swiss NGO Nouvelle Planète, Lausanne, Switzerland
  • Stephen Szára - Retired from National Institute on Drug Abuse, Bethesda, USA
  • Michael Winkelman - Retired from Arizona State University, Tempe, USA 

Book Reviews Editor:

Michael Winkelman - Retired from Arizona State University, Tempe, USA

Editorial Board

  • Gábor Andrássy - University of Debrecen, Debrecen, Hungary
  • Tiago Arruda-Sanchez - Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
  • Paulo Barbosa - State University of Santa Cruz, Bahia, Brazil
  • Michael Bogenschutz - New York University School of Medicine, New York, NY, USA
  • Petra Bokor - University of Pécs, Pécs, Hungary
  • Jose Bouso - Autonomous University of Madrid, Madrid, Spain
  • Zoltán Brys - Multidisciplinary Soc. for the Research of Psychedelics, Budapest, Hungary
  • Susana Bustos - California Institute of Integral Studies San Francisco, USA
  • Robin Carhart-Harris - Imperial College, London, UK
  • Valerie Curran - University College London, London, UK
  • Alicia Danforth - Harbor-UCLA Medical Center, Los Angeles, USA
  • Rick Doblin - Boston, USA
  • Rafael G. dos Santos - University of Sao Paulo, Sao Paulo, Brazil
  • Genis Ona Esteve - Rovira i Virgili University, Spain
  • Silvia Fernandez-Campos
  • Zsófia Földvári - Oslo University Hospital, Oslo, Norway
  • Andrew Gallimore - University of Cambridge, Cambridge, UK
  • Neal Goldsmith - private practice, New York, NY, USA
  • Charles Grob - Harbor-UCLA Medical Center, Los Angeles, CA, USA
  • Stanislav Grof - California Institute of Integral Studies, San Francisco, CA, USA
  • Karen Grue - private practice, Copenhagen, Denmark
  • Jiri Horacek - Charles University, Prague, Czech Republic
  • Lajos Horváth - University of Debrecen, Debrecen, Hungary
  • Robert Jesse - Johns Hopkins University School of Medicine, Baltimore, MD, USA
  • Matthew Johnson - Johns Hopkins University School of Medicine, Baltimore, MD, USA
  • István Kelemen - University of Debrecen, Debrecen, Hungary
  • Eli Kolp - Kolp Institute New, Port Richey, FL, USA
  • Stanley Krippner - Saybrook University, Oakland, CA, USA
  • Evgeny Krupitsky - St. Petersburg State Pavlov Medical University, St. Petersburg, Russia
  • Rafael Lancelotta - Innate Path, Lakewood, CO, USA
  • Anja Loizaga-Velder - National Autonomous University of Mexico, Mexico City, Mexico
  • Luis Luna - Wasiwaska Research Center, Florianópolis, Brazil
  • Katherine MacClean - Johns Hopkins University School of Medicine, Baltimore, MD, USA
  • Deborah Mash - University of Miami School of Medicine, Miami, USA
  • Friedericke Meckel - private practice, Zurich, Switzerland
  • Ralph Metzner - California Institute of Integral Studies, San Francisco, CA, USA
  • Michael Mithoefer - private practice, Charleston, SC, USA
  • Levente Móró - University of Turku, Turku, Finland
  • David Nichols - Purdue University, West Lafayette, IN, USA
  • David Nutt - Imperial College, London, UK
  • Torsten Passie - Hannover Medical School, Hannover, Germany
  • Janis Phelps - California Institute of Integral Studies, San Francisco, CA, USA
  • József Rácz - Semmelweis University, Budapest, Hungary
  • Christian Rätsch - University of California, Los Angeles, Los Angeles, CA, USA
  • Jordi Riba - Sant Pau Institute of Biomedical Research, Barcelona, Spain
  • Sidarta Ribeiro - Federal University of Rio Grande do Norte, Natal, Brazil
  • William Richards - Johns Hopkins School of Medicine, Baltimore, MD, USA
  • Stephen Ross - New York University, New York, NY, USA
  • Brian Rush - University of Toronto, Toronto, Canada
  • Eduardo Schenberg - Federal University of São Paulo, São Paulo, Brazil
  • Ben Sessa - Cardiff University School of Medicine, Cardiff, UK
  • Lowan H. Stewart - Santa Fe Ketamine Clinic, NM, USA (Medical Director)
  • Rebecca Stone - Emory University, Atlanta, GA, USA
  • Rick Strassman - University of New Mexico School of Medicine, Albuquerque, NM, USA
  • Attila Szabó - University of Oslo, Oslo, Norway
  • Csaba Szummer - Károli Gáspár University of the Reformed Church, Budapest, Hungary
  • Manuel Torres - Florida International University, Miami, FL, USA
  • Luís Fernando Tófoli - University of Campinas, Campinas, Brazil State
  • Malin Uthaug - Maastricht University, Maastricht, The Netherlands
  • Julian Vayne - Norwich, UK
  • Nikki Wyrd - Norwich, UK

Attila Szabo
University of Oslo

E-mail address: attilasci@gmail.com

Indexing and Abstracting Services:

  • APA PsycInfo
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2020  
CrossRef Documents 8
WoS Cites 37
WoS H-index 4
Days from submission to acceptance 95
Days from acceptance to publication 75
Acceptance Rate 41%

2019  
WoS
Cites
11
CrossRef
Documents
35
Acceptance
Rate
77%

 

Journal of Psychedelic Studies
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge none
Subscription Information Gold Open Access

Journal of Psychedelic Studies
Language English
Size A4
Year of
Foundation
2016
Volumes
per Year
1
Issues
per Year
2
Founder Akadémiai Kiadó
Debreceni Egyetem
Eötvös Loránd Tudományegyetem
Károli Gáspár Református Egyetem
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
H-4032 Debrecen, Hungary Egyetem tér 1.
H-1053 Budapest, Hungary Egyetem tér 1-3.
H-1091 Budapest, Hungary Kálvin tér 9.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2559-9283 (Online)

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