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The Effects Of Music On Human Brain

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Abstract

Indian tradition has a considerable amount of empirical musicology research on studying the cognitive impact of swara (musical notes), sruti (pitch) and laya (rhythm) on the human brain. However, there has hardly been any neuro-scientific exploration of these effects of music on human brain using the inherent strengths of Indian classical music. This paper discuses about the importance and need to have such efforts and also describes some of the original works in this direction.

Introduction

Indian tradition has a considerable amount of empirical musicology research on studying the cognitive impact of swara (musical notes), sruti (pitch) and laya (rhythm) on the human brain. However, there has hardly been any neuro-scientific exploration of these effects of music on human brain using the inherent strengths of Indian classical music. Although the term ‘music therapy’ is popular, many think about it as using certain ragas in treating some diseases. The cognitive or neuro-scientific terminologies in which the human emotions are expressed have to be incorporated into music therapy to make it more of a scientific approach.

It has been observed that many people doing high level intellectual work such as scientific research, finds it easier to do their work while listening to music. Actually, the effect of music on them in tackling some of the most complex problems is not in getting into a mood, but to get into a mode of thinking. That is, the music here acts not just as a mood creator, but it actually does more than that. It elevates the brain to a higher plane of thinking. The same experience has been shared by many researchers. At some crucial junctures in their research process, music helped them to think out of the way, leading to fruitful results. Even though the neurological reasons behind this are still more or less unknown, it certainly offers a research area to be explored further.

Intellectual thinking is not the only area where music can benefit you. Even in areas of physical activities such as sports, it can do wonders. For example, Bengaluru based triathlete Anu Vaidyanathan, who has learnt Carnatic vocal and violin, says music taught her to negate performance-inhibiting feelings like fear and fatigue, and create discipline in thinking and frame solutions to problems.

Neuro-scientific exploration

Considering the immense positive effect music can bring about in our life, it is highly surprising that why there is so little neuro-scientific exploration of the effects of music capitalizing on the inherent strengths of Indian classical music. Carnatic musician and neuroscientist Dr Deepti Navaratna, executive director (southern region) of the Indira Gandhi National Council for Arts (IGNCA), and a former Harvard University professor, says there is very little empirical experiment in Indian classical music these days. But in ancient texts dealing with Sankhya philosophy, the Natyashastra and certain lakshanagranthas in music like Swaramelakalanidhi (written by Ramamatya of the Vijayanagara empire in 1550), the psychological impact of musical concepts has been clearly worked out.

Taking rasa (emotion) as the main point, the dominant take on music therapy in India has been to use ragas to heal. There is a large body of literature dealing with Raga Chikitsa (Raga Therapy), which looks at certain intervals and modes being able to produce certain outcomes.

Dr. Navaratna says that by the time Natyashastra was formalized, the psychological impact of certain melodic structures/rhythmic patterns was worked out to the level of being able to prescribe one-jati (raga precursor) to one rasa.

In a recent electroencephalography (EEG) study on the impact of Indian classical music, especially of Hindustani ragas on individuals, Dr Shantala Hegde [1], Assistant Professor, Neuro-psychology Unit, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, says that after listening to Hindustani ragas, 20 musically untrained subjects showed increased overall positive brain wave frequency power, higher even than that in highly relaxed meditative states.

“Listening to certain ragas, for example Desi-Todi, for 30 minutes every day for 20 days, has been shown to produce a significant decrease in systolic and diastolic blood pressure, to reduce stress, anxiety and depression, and to enhance feelings of life satisfaction, experience of hope and optimism,” says Dr. Hegde.

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EEG BASED STUDIES

To advance further in this area, it is important to study and analyze music and see its correlation with the changes it brings about in the neural dynamics. Human brain is the most complex organ found in the Universe, and one of the most important discoveries of the 21st century is that the human brain is organized by Chaos [10]. The working of the brain involves billions of interacting physiological and chemical processes that give rise to experimentally observed neuro-electrical activity, which is called an electroencephalogram (EEG). Music can be regarded as input to the brain system which influences the human mentality along with time. Since music cognition has many emotional aspects, it is expected that EEG recorded during music listening may reflect the electrical activities of brain regions related to those emotional aspects. The results might reflect the level of consciousness and the brain’s activated area during music listening. Such an approach can provide a new perspective on cognitive musicology. The non-linear, non-stationery EEG time series signals recorded from different regions of the scalp is high on temporal resolution and can be best analyzed with the help of various robust nonlinear techniques such as Detrended fluctuation Analysis (DFA) technique proposed by Peng et al. [11].

Due to the advancement of modern technology and high speed computing, this area of research has taken a new dimension. The study of the source characteristics of musical instruments in this perspective is really challenging from the physical point of view. Research on the sound of music involves the estimation of the physical parameters that contribute to the perception of pitch, intensity levels and timbres of all sounds the voice/instrument is capable of producing [3][4]. Of these attributes, timbre poses the greatest challenge to the measurement and specification of the parameters involved in its perception, due to its inherently multidimensional nature. Research has shown that timbre consists of the spectral envelope, an amplitude envelope function, which can be attack, decay or more generally [4], the irregularity of the amplitude of the partials. Timbre is perceived by means of the interaction of a variety of static and dynamic properties of sound grouped into a complex set of auditory attributes. The identification of the contribution of each one of these competitive factors has been the main subject of the acoustics research on timbre perception.

In another study [2], people (both male and female) of different age groups were exposed to different types of Indian classical music (vocal and instrumental), Drone (Tanpura) sound, Folk music (vocal and instrumental), and contemporary music of different genres. EEG signals were recorded while an individual undergoes a specific listening experience as well as in a normal condition without music. The type and character of music changed and the experiment was repeated to obtain replication of the results. The data was preprocessed and ANOVA tests were performed to determine the relation between the mental condition in the presence of music and its absence. Electroencephalography (EEG) data involving the Central Nervous System (CNS) and Peripheral Nervous System (PNS) can provide plentiful information about emotion cognition.

NON-LINEAR TECHNIQUES

The brain is said to be the most complex structure found in the universe and the signals originating from the different lobes of brain are mostly non-linear and non-stationery. Most of the existing studies in this area [5][6][7] do not use non-linear techniques, which is essential to obtain in-depth information behind the complicated waveform of EEG signal.

Non-linear techniques include the following mathematical models:

  • Wavelet analysis
  • Detrended fluctuation analysis (DFA).
  • Multifractal detrended fluctuation analysis (MFDFA)
  • Multifractal cross correlation analysis (MFDXA)

All these techniques make use of Fractal Dimension (FD) or multifractal spectral width (obtained as an output of the MFDFA technique) as an important parameter with which the emotional arousal corresponding to a certain cognitive task, such as listening to music, can be quantified. Moreover, MFDXA is an important tool with which the degree of cross correlation between two non-linear EEG signals originating from different lobes of brain can be accurately measured during higher order cognitive tasks. Hence a quantitative assessment of how the different lobes are cross-correlated during higher order thinking tasks or during the perception of audio or any other stimuli can be made. MFDXA can also be an amazing tool in music signal analysis, where we can estimate the degree of cross-correlation between two non-linear self-similar musical clips. A higher degree of cross-correlation would imply that both the signals are very much similar in certain aspects. This in turn can be used as an important tool to obtain a cue for improvisation in musical performances as well as in the identification of the raga of a musical piece.

EEG signals analyzed in the above mentioned study [2] with appreciable statistics and appropriate protocol provided important new data in the area of neuro-cognitive differentiation of emotion and also indicated prominent change in brain state on application of different music signals related to different emotions. Such techniques can also be used to analyze how rhythm, pitch, loudness etc. interrelate to influence our appreciation of the emotional content of music. The brain electrical response of the person undergoing the study should also be analyzed with global descriptors in order to monitor the course of activation in the time domain in a three-dimensional state space, revealing patterns of global dynamical states of the brain. Another important aspect includes exploration of the resonance characteristics and the amplitude envelope of the musical signals because they play important roles in sound production. The spectral envelope should also be studied in this context to evaluate the attack, decay and steady state timings and the formants/resonant frequencies too. Correlations of the above features and their interdependence should be studied and compared.

Another possibility is to compare the data with available models of emotion, which include the most popular circumplex model, which is a 2-dimensional arousal-valence model and also a 3-dimensional model, based on Hilbert spaces proposed by Ghose [8]. Hilbert spaces are complex linear vector spaces in which length and angle can be defined. They have a rich structure that allows ‘coherence’ resulting, for example, in interference effects as well as ‘entanglement’ which, until recently, was considered a quintessential quantum phenomena. Coherence means that two states, say with different values of some observable (like happiness and unhappiness), can be linearly superposed to obtain a new state which is neither happiness nor unhappiness. Such states are ‘ambiguous states’ which cannot be described by Boolean logic.

The Tanpura is a remarkable drone instrument whose sounding acts as a canvas in Indian Classical Music and provides contrast to the tune and melody without introducing rhythmic content of its own. There are some psycho-acoustically effective ingredients in Tanpura drone that make it almost ubiquitous in accompaniment for Indian music. A study using baseline EEG in the resting condition where the subject has no task to perform was also done in [2]. It hypothesized that drone sounds are sufficiently neutral to the subject in that they are not popping into the fore of cognition, evoking reactions to the stimulus. This assumption was needed in order to define the resting condition where the subject has no task to perform (no-task resting frame). Drone can provide contrast but is not prompting a response. In a laboratory setting, spontaneous brain electrical activity in the form of EEG response were observed during Tanpura drone stimulation and periods of silence. The sound stimulus was given by an electronic substitute Tanpura (EST) that allows controlling of its parameters. The brain electrical response of the subject was analyzed [9] with global descriptors, a way to monitor the course of activation in the time domain in a three-dimensional state space, revealing patterns of global dynamical states of the brain.

CONCLUSIONS

Importance of neuro-scientific exploration of the effects of music on human brain using the inherent strengths of Indian classical music has been discussed in this paper. Also, some of the original works in this direction have been reviewed and various possibilities have been discussed.

References

  1. Hegde S (2010) “Music Emotion And The Brain”, Journal of ITC Sangeet Research Academy, Vol. 24, December, 2010
  2. Sanyal S, Banerjee A, Sengupta R, Ghosh D (2016) “Chaotic Brain, Musical Mind-A Non-Linear Neurocognitive Physics Based Study”, Journal of Neurology and Neuro-science (ISSN: 2171-6625), Vol-7, January, 2016
  3. Scheirer ED (1998) “Tempo and beat analysis of acoustic musical signals”, The Journal of the Acoustical Society of America, 103: 588-601.
  4. Aucouturier JJ, Pachet F, Sandler M (2005) ‘The way it Sounds’: timbre models for analysis and retrieval of music signals. Multimedia 7: 1028-1035.
  5. Sammler D, Grigutsch M, Fritz T, Koelsch S (2007) “Music and emotion: electrophysiological correlates of the processing of pleasant and unpleasant music”, Psychophysiology 44: 293-304
  6. Schmidt LA, Trainor LJ (2001) “Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions”, Cognition & Emotion 15: 487-500.
  7. Lin YP, Wang CH, Jung TP, Wu TL, Jeng SK, et al. (2010) “EEGbased emotion recognition in music listening”, Biomedical Engineering 57: 1798-806.
  8. Ghose P (2015) “A Hilbert space Theory of emotions” Proc of the International Symposium FRSM-2015, November 23-24, 2015, Indian Institute of Technology (IIT), Kharagpur, India.
  9. Braeunig Matthias, Ranjan Sengupta, Anirban Patranabis (2012) ‘On tanpura drone and brain electrical correlates.’ Speech, Sound and Music Processing: Embracing Research in India. Springer Berlin Heidelberg 53-65.
  10. Lehnertz KL, Arnhold J, Grassberger P, Elger CE (2000) “Chaos in brain”. Proceedings of the Workshop. Singapore: World Scientific.
  11. Peng CK, Buldyrev SV, Havlin S, Simons M, Stanley HE, et al. (1994) “Mosaic organization of DNA nucleotides”. Physical Review E 49: 1685.

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