Archi Banerjee

Detrended Fluctuation and Power Spectral Analysis of alpha and delta EEG brain rhythms to study music elicited emotion

The study reports the effect of two different types of Hindustani music which are supposed to evoke contrasting emotions on brain activity using Electroencephalography (EEG) data. Two different sets of Hindustani music raga clips of contrasting emotion (romantic/sorrow) were used in the study. EEG was performed on five male subjects while they listened to the clips. The linear analysis of their alpha and delta spectral power showed that in most cases arousal based activities were enhanced in both the subjects while they listened to the two music clips. Further, we used a robust nonlinear method called Detrended Fluctuation Analysis (DFA) to analyze the scaling behavior of the observed fluctuations in EEG. The scaling exponent (α) values determined for the different experimental conditions show different levels of neural activity when the two different types of music are played. The implications are discussed in detail.

Chaotic Brain, Musical Mind-A Non-Linear eurocognitive Physics Based Study

Music engages much of the brain, and coordinates a wide range of processing mechanisms. This naturally invites consideration of how music processing in the brain might relate to other complex dynamical abilities. The tremendous ability that music has to affect and manipulate emotions and the brain is undeniable, and yet largely inexplicable. The study of music cognition is drawing an increasing amount of research interest. Like language, music is a human universal involving perceptual discrete elements organized into hierarchically structured sequences. Music can thus provide the study of brain mechanisms, underlying complex sound processing, and also can provide novel insights into the functional and neural architecture of brain functions. The change in the structure and form of music might bring a change in the neural dynamics. So it is important to study and analyze music and see its correlation with the changes it brings about in the neural dynamics. This work is essentially a case report of the various robust scientific nonlinear tools used by us in the assessment of complex neural dynamics induced by a variety of musical clips. Also, the inherent self-similarity in the musical clips can also be studied with the help of these analysis techniques. These methods can be best described taking the example of a mathematical microscope which can wonderfully describe the complex nature of various bio-signals as well as the music signals. The findings and implications are discussed in detail.

Multifractal Detrended Fluctuation Analysis of the music induced EEG signals

The electrical activity of the human brain arising from the effect of listening to music has been recorded with the help of EEG recordings in different electrodes. The non-linear EEG signals arising from various lobes of the brain has been studied with the help of a robust technique called Multifractal Detrended Fluctuation Analysis (MFDFA). The presence of multifractality has been justified by the help of MATLAB software and the multifractal spectrum has been drawn for various signals. The spectral widths and the Hurst exponents are compared with a random set of data to verify the presence of self-similarity in the signals. The results are discussed in detail.

Emotion specification from musical stimuli: An EEG study with AFA and DFA

The present study reports interesting findings in regard to emotional arousal based activities while listening to two Hindustani classical ragas of contrast emotion. EEG data was taken on 5 naïve listeners while they listened to two ragas — Bahar and Mia ki Malhar which are conventionally known to portray contrast emotions. The EEG data were analyzed with the help of two robust non-linear tools viz. Adaptive Fractal Analysis (AFA) and Detrended Fluctuation Analysis (DFA). A comparative study of the Hurst Exponents obtained from the two methods have been shown which shows that DFA provides more rigorous results compared to AFA when it comes to the scaling analysis of bio-signal data. The results and implications have been discussed in detail.

Universality and Domain Specificity of Emotion-A Quantitative Non Linear EEG Based Approach

Music has been present in human culture since time immemorial, some say music came even before speech. The effort to understand the wide variety of emotions evoked by music has started not long back. With the advent and rapid growth of various neurological bio-sensors we can now attempt to quantify various dimensions of emotional experience induced by music especially instrumental music – since it is free from any language barriers. In this study, we took eight (8) cross cultural instrumental clips originating mainly from Indian and Western music. A listening test comprising of 100 participants across the globe was conducted to associate each clip with its corresponding emotional valence. The participants were asked to mark each clip according to their perception of four basic emotions (joy/sorrow and anxiety/serenity) invoked by each instrumental clip. EEG study was then conducted on 10 participants to measure the response evoked by the same instrumental clips in the alpha and theta frequency regions. We took the help of latest non-linear multi-fractal analysis technique – MFDFA to estimate the change in multi-fractal spectral width (corresponding to alpha as well as theta waves) associated with each of the clips in frontal, temporal and occipital lobes. The response in the alpha domain reveals a hint in the direction of universality of music, while in theta domain we have culture specific response. Moreover, we tried to develop alpha as well as theta multi-fractal spectral width as a single parameter with which we can quantify the valence and arousal based effects corresponding to a particular musical clip. The results and implications are discussed in detail.

Musical Perception and Visual Imagery: Do Musicians visualize while Performing?

What happens inside the performer’s brain when he is performing and composing a particular musical piece? Are there some specific regions in brain which are activated when an artist is creating or imaging a musical piece in his brain? Do the regions remain the same when the artist is listening to the same piece sung or played by him? These are the questions that perplexed neuroscientists for a long time. The endeavor to obtain insights to brain processes that take place during listening as well as composing music has been attempted several times by musicologists and psychologists. In this study we strive to answer these questions from a better scientific point of view by using latest state-of-the-art techniques to assess brain response. An EEG experiment was conducted on two eminent performers of Indian classical music, when they mentally created the “alap” of a “raga” (Jay Jayanti) in their mind (without performing) as well as when they listened to their own performance of the same raga. The beauty of Hindustani music lies in the fact that the musician is himself the composer and recreates the imagery of the raga in his mind while performing, hence the scope of creative improvisations are immense. The noise removed EEG time series data were analyzed mainly using robust non linear techniques like MFDFA and MFDXA to quantitatively assess the arousal based activity and the degree of cross-correlation of each EEG frequency rhythm in different combination of electrodes from frontal, occipital and temporal lobes. A strong response was found in the occipital and fronto-occipital region both during mental improvisation and listening of the raga, which is an interesting revelation of this study. Strong retentive features were obtained in regard to both alpha and theta rhythms in musical listening in different parts of the brain.

Emotions from Hindustani Classical Music: An EEG based study including Neural Hysteresis

This chapter deals with the brain responses to the emotional attributes of Hindustani Classical Music that have been long been a source of discussion for musicologists and psychologists. Here, we make use of robust scientific techniques, which are capable of looking into the most intricate dynamics of the complex EEG signal, to decipher how the human brain responds to different ragas of Hindustani Classical Music. Two pair of commonly sung ragas—Chayanat/ Darbari Kanada and Bahar/ Mian ki Malhar, portraying contrast emotions have been taken for this study standardized first by listening test data from a large pool of participants. These ragas were then given as an input to naive listeners while their EEG data were recorded simultaneously. The complex fluctuations in a number of EEG frequency patterns arising from different lobes of the brain were analyzed with the help of Detrended Fluctuation Analysis (DFA) technique, which is capable of measuring long range temporal correlations (LRTC) present in EEG signals. Hence Fractal Dimension (FD) was evaluated for each of the musical clip which we proposed as a marker for brain arousal levels corresponding to each emotion. To look further, a hysteresis like phenomenon was also observed whereby the increased FD values did not return to their basal values even after the removal of stimulus. It was seen that arousal based activities lasted much longer for sad clips as compared to the happy clip. The hysteresis like response was ratified using a number of statistical significance tests. Neural Hysteresis in music signals is first reported in this study.

Quantification and categorization of emotion using cross cultural music: An EEG based fractal study

In this work we have tried to quantify and categorize the emotional appraisal of 8 cross-cultural instrumental clips with the help of neurocognitive physics approach. Human response data is taken for 70 respondents from which the emotional appraisal for a particular musical clip is standardized. EEG is taken for 5 normal patients and response for different frequency bands (alpha, theta, gamma) of the EEG spectrum is analyzed using Detrended Fluctuation Analysis (DFA). The DFA scaling exponent is calculated for all the 3 different frequency bands of EEG signals under the effect of different emotional music clips in the frontal, temporal and occipital lobes. We have tried to develop DFA scaling exponent as a single parameter with which we can characterize and quantify emotional arousal corresponding to different emotional clips and corroborate the findings from human response study Outcomes are discussed in details in the paper.

Musical Improvisation and Brain Correlates: An EEG Based NeurocognitiveStudy Using Hindustani Music

The concept of creativity and perception of a raga is revisited in this work from the brain electrical response of a professional Hindustani musician. EEG was done to record the response when a musician created the imagery of raga Jay Jayanti as well as when he listened to the same raga sung by him. We have quantified the correlation values obtained from different lobes of the brain during these two experimental conditions using a robust non-linear technique called multifractal detrended cross-correlation analysis (MFDXA). With this method, we have seen that both during imagination and perception the degree of cross-correlation is very high in the occipital lobe, purportedly due to the visualization of the raga by the musicians. In other electrodes also, inter/intra-lobe cross correlations have been found to be significantly during different conditions. With this study, we look forward to develop a paradigm with which we can quantify the definition of creativity. The results are discussed in detail.

Emotion and Ambiguity: A Study

This chapter explores the utility of non linear source modeling for categorization and classification of evoked emotion from instrumental clips of Hindustani raga and their possible impacts in human brain. Hindustani Music (HM) has been known to convey a variety of emotional responses to the listeners since time immemorial—but neural processing of these emotional attributes is largely unrevealed. The detection of emotional cues from Hindustani Classical music is a demanding task due to the inherent ambiguity present in the different ragas, which makes it difficult to identify any particular emotion from a certain raga. This necessitates the use of a very high resolution mathematical microscope to procure information about the inherent complexities and time series fluctuations that constitute an acoustic and EEG signal. We chose 3 min alaap (opening) portion of six conventional ragas of Hindustani classical music namely, “Darbari Kanada”, “Yaman”, “Mian ki Malhar”, “Durga”, “Jay Jayanti” and “Hamswadhani” played in three different musical instruments (sitar, sarod and flute) by three maestros of HM. The first three ragas correspond to the negative dimension of the Russel’s emotional sphere, while the last three belong to the positive dimension (conventionally). Most of the musical instruments have resonators that are only approximately harmonic in nature, and their operation and harmonic sound spectrum both rely upon the extreme nonlinearity of their driving mechanisms. Such instruments might be described as ‘essentially nonlinear’. Hence, MFDFA (Multifractal Detrended Fluctuation Analysis) technique was utilized to assess the inherent complexity of the musical clips which proves to be an important parameter for classification of emotional attributes in musical clips. Next, EEG experiment was conducted on a pool of participants who were made to listen to these sets of musical clips of 2 min duration each. The brain response corresponding to each emotional clip analyzed with MFDFA technique is expected to elicit emotion specific arousal activities in different lobes of the brain. The multifractal spectral width obtained from alpha/theta frequency ranges of EEG data can be developed as a parameter for the development of an automated emotion recognition system. The study may prove to have far reaching implications in the development of an automated emotion classifier algorithm in future.