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.

[1]  R. Acharya U,et al.  Nonlinear analysis of EEG signals at different mental states , 2004, Biomedical engineering online.

[2]  M. Riley,et al.  Adaptive Fractal Analysis Reveals Limits to Fractal Scaling in Center of Pressure Trajectories , 2013, Annals of Biomedical Engineering.

[3]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[4]  Ranjan Sengupta,et al.  Chaotic Brain, Musical Mind-A Non-Linear eurocognitive Physics Based Study , 2016 .

[5]  Vadim V. Nikulin,et al.  Detrended Fluctuation Analysis: A Scale-Free View on Neuronal Oscillations , 2012, Front. Physio..

[6]  Ranjan Sengupta,et al.  Detrended Fluctuation and Power Spectral Analysis of alpha and delta EEG brain rhythms to study music elicited emotion , 2015, 2015 International Conference on Signal Processing, Computing and Control (ISPCC).

[7]  A. Golinska.  Detrended Fluctuation Analysis ( DFA ) in biomedical signal processing : selected examples , 2013 .

[8]  Ranjan Sengupta,et al.  Quantification and categorization of emotion using cross cultural music: An EEG based fractal study , 2016, 2016 2nd International Conference on Next Generation Computing Technologies (NGCT).

[9]  Nikulin Vadim,et al.  Detrended fluctuation analysis: a scale-free view on neuronal oscillations , 2012 .

[10]  Jianbo Gao,et al.  A tutorial introduction to adaptive fractal analysis , 2012, Front. Physio..

[11]  Michael B. Bakan.  World Music: Traditions and Transformations , 2007 .

[12]  Yudong Wang,et al.  Is WTI crude oil market becoming weakly efficient over time?: New evidence from multiscale analysis based on detrended fluctuation analysis , 2010 .

[13]  L. Trainor,et al.  Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions , 2001 .

[14]  Ranjan Sengupta,et al.  Study on Brain Dynamics by Non Linear Analysis of Music Induced EEG Signals , 2016 .

[15]  H. Stanley,et al.  Analysis of DNA sequences using methods of statistical physics , 1998 .

[16]  Jing Hu,et al.  Culturomics meets random fractal theory: insights into long-range correlations of social and natural phenomena over the past two centuries , 2012, Journal of The Royal Society Interface.

[17]  J. Sloboda,et al.  Music and emotion: Theory and research , 2001 .

[18]  Marcel Ausloos,et al.  Application of the detrended fluctuation analysis (DFA) method for describing cloud breaking , 1999 .

[19]  Gerald Groemer,et al.  Semiosis in Hindustani Music , 1999 .