ROI analysis shows that reggae music is not only in auditory-related areas, but also in some sports-related areas, mainly when it is compared with classical music.

Abstract - Music neuroscience has attracted much attention in recent years, but the influence of musical styles on the activation of auditory motor areas has not been explored. The purpose of this study was to analyze differences in brain activity when passively listening to non-vocal clips from four different music types (classical, reggaton, electronic, and folk ). Functional magnetic resonance imaging (fMRI) experiments were performed.

This study included 28 participants who had not received music training. They can only passively listen to musical clips from the above genres during FMR acquisition. Imaging analysis was performed at the whole brain level and at the area of ​​interest of auditory motors. In addition, each participant's musical ability was measured and its relationship with the brain activity being studied was analyzed.

The entire brain analysis shows that when listening to reggae music , the activity in the auditory-related areas of the brain is higher than that of other music types. ROI analysis shows that reggae music is not only in auditory-related areas, but also in some sports-related areas, mainly when it is compared with classical music. Positive relationships The relationship between music listening test scores and brain activity during reggae listening was identified by some hearing and movement-related areas. The results showed that subjects who were not experienced in listening to different styles of music induce different brain activity in auditory and motor-related areas. Ray Gaiton is one of the music genres they studied that evokes the highest level of activity in auditory motor networks. These findings are discussed in connection with acoustic analysis of musical stimulation.

Listening to music is an aesthetic human experience. Personal focus on interpreting and evaluating the results of this interpretation. Music processing involves multiple brain regions, resulting in complex patterns of brain activity listening to it. This activation is necessary to process and interpret every element of the sound (ie tone, tone, timbre, and intensity) and every element of the music (ie harmony, melody, and rhythm).

The brain area activated when listening to music includes not only the auditory area, but also the motor area area. The interaction between auditory brain's motor system allows detection and expectations of time-predictable moments in songs. In this sense, both the premotor cortex and the auxiliary motor area are involved in rhythm detection proposed a music-related activity in music-related movements, for example, a match between music and music-related movements. The interaction of hearing and movement is important for understanding the connection between the two musical perception, dance or even speech music genre or style is an essential category.

understands human preferences for music, but it is basically not aware of how abstract the music genre is. All the classifications are expressed in the brain. In this sanity, it can be thought that everyone may have their own music category, and they do not have to overlap with others; others may think that everyone has different preferences, but she/he recognizes that one piece belongs to one category rather than another has a relative category consistency.

different musical styles are made up of their specific acoustic features and instrumental complexity. The superficial musical characteristics are robust attributes of effective music types. Typical identification of human subjects is opposite to the structure, top-down processing of musical characteristics such as those learned through cultural influences), as previously worked on automatic type classification, thus listening to different musical styles leads to different patterns of brain activity.

In this regard, the complex rhythm sequence has been shown to be an important factor in regulating pre-motor zone activity. However, no previous research has focused on the effect of musical style on the activity auditory and motor areas. The hypothetical modular music style may have an effect on auditory motors. The

area may be useful for certain neurological diseases (e.g., Parkinson's disease ), and music is often used to improve athletic performance, especially gait Bruin, etc., or exercise science, and specific inspirational music seems to optimize the economics of running.

Therefore, the purpose of this study was to describe brain activity using functional magnetic resonance imaging in passively four different instrumental (non-vocal) musical styles (classical, reggae, electronic and folk).In addition, the study also aims to identify differences between studies on the musical styles in brain activity when listening passively, focusing on hearing and movement-related areas.

In addition, there are 24 acoustic descriptors that extract the toolbox frame by frame from the excerpt, and then compare different genres (classical, electronic, folk and reggaeton). The extracted descriptor represents commonly used acoustic features. In the psychoacoustic literature, it has both low-level and advanced features. The window length is 25 milliseconds and the underlying features are extracted by 50% overlap, and the window size of 3 s overlaps 33% is used to extract advanced features. The higher-order feature selects a larger 3s time window because it corresponds to a typical estimated sensory memory of auditory length.

low-level functions include sound signals in terms of sound quality, such as brightness, spectral center of mass, and roughness. High-level features include rhythm and tone music (such as pulse and tone clarity). Overall, low-level feature processing appears to rely primarily on bottom-up mechanisms to represent detailed stimulus information.

However, advanced feature processing seems to be regulated by top-down cognitive processes because of cultural influence, familiarity and listening history (this is the example of rhythm and tone musical characteristics equality variance Multi-sample test for each feature to compare the acoustic variance of different genres. The test reveals significant inequality in places for at least one genre for each acoustic feature.

compared with others, which suggests that the differences between different types of acoustics are not the same. In addition, Welch's ANOVA (best approximate variance differences under equal conditions do not satisfy the variance differences in genres.

This is not surprising because genres are largely due to their possession of specific sound footprints. Referring to some differences in the relationship found at a low level Characteristically, electronic music has a significant higher brightness value (at high frequencies) compared to other genres.

On the other hand, classical music has the lowest value of significant spectral flatness, in other words, classical music tends to have more tone-like quality (rather than like noise quality) compared to other types. Similarly, roughness-perceived discordant estimates are estimated to score the highest in electronic and reggae music. html l2

As for the advanced features, classical and folk music displays pulse clarity significantly lower than electronic and reggae music (Supplementary Fig. 2). In other words, electrons and reggae performance are simpler and more obvious compared to classical and folk music . The same type of pairing mode is also observed for fluctuations and fluctuations in the centroids of electrons and ragaetons, classical and folk electronics and reggaetons. Both rhythmic features are average frequency of the period 0 - 10hz range and fluctuating noise spectrum (representing high-level rhythm complexity), respectively.

As for the pattern, it measures the intensity of the major/ minor mode, and the difference between classical music genres is subtle low-value music. Finally, the value of key clarity, which is an estimate of the tone clarity of music, reggaton music is significantly lower than other music.

In summary, despite their acoustic heterogeneity, some similarities in the selected music sample content are observed, i.e. classical and folk seem to be more similar in high-level acoustic content than both electronic music and reggaton.

chunking in the context of a general linear model The meter is used for single topic analysis, and the observation of brain activity in hearing and control conditions. Consideration of the analysis in the analysis is as follows: classical control; Regton control; electronic control and folk control. First-level comparison images were used in random effects using group analysis.

group analysis was performed using random effects method, designed using variance analysis, including age, gender and Edinburgh idiopathic hand scale scores.

each comparison was described with statistics and degrees of freedom, i.e. the total number of valid values ​​was minus 1. The area of ​​interest analysis was performed for area of ​​interest (ROI) analysis of auditory and motor-related areas using the Marseille ROI toolbox. Auditory and motor-related areas were extracted from the map.

In the first hand, the auditory-related areas of the two hemispheres are divided into: primary auditory cortex, secondary auditory cortex auditory cortex, Wernik area and joint auditory area caudal and anterior part of the superior temporal sulcus.

On the other hand, ROI analysis of motors related fields include two hemispheres of PMC, preg and IFG brain. The PMC is divided into five parts: medial, dorsolateral, ventral and ventral. IFG is also divided into five parts: dorsal side of BA44, transverse side of BA44, ventral side of BA44, caudal side of BA45 and snot side of BA45.

Finally, PreCG is divided into five groups of areas: hand; face area; upper limb area; trunk area; tongue and throat area and lower limb area. Repeated measurements of ANOVA test were performed, and statistical significance was taken into account when the corrected p-value was less than 0.05.

When the comparison shows statistical significance, it is found that the brain activity leads to significant musical styles. Brain activity and musical abilities were performed using musical listening tests (MET) for each participant’s musical abilities. MET has been designed to measure both musical abilities and non-musicians.

In short, it was judged by participants in 104 trials whether two music phrases were the same. It is based on two subtests: the melody subtest and the rhythm subtest. Correlation analysis was performed between the overall MET score and the style of brain activity music in the selected roi. Calculate the Pearson correlation coefficient for each pair, and consider the statistical significance below 0.05 when the p-value is. Some limitations of

's current work should be considered. First, we cannot completely rule out the possibility of differences between auditory motors and motors. In the current study, we found that there may be deviations in different processing of this scanner noise. It is believed that the influence of noise does exist and is inevitable, even if sparse sampling techniques are used.

On the other hand, using a higher magnetic field scanner can show finer grained differences in music types in auditory cortex areas are hidden in the performed analysis, and they used a 7T scanner. Future research should also include other variables, such as familiarity scores for musical stimuli, preference for music genres or length of listening to music, which will provide better modulation of the effect of music style on brain activity patterns.