Identifying Correlations Between Hindustani Music and the Brain: A Nonlinear EEG-Based Exploration

Music of any form is a time series variation of different note combinations, where each note has a particular frequency. Neural responses from different brain parts are recorded with the help of an EEG experiment in response to this time-varying combination of notes. These responses are interpreted in the forms of non-linear, non-stationary time series. Since music and brain signals, both are complex time series, it would be interesting to study whether any kind of correlation exists between these two series or not. Using Multifractal Detrended Cross-Correlation Analysis (MFDXA), we have tried to compute the cross-correlation coefficients (γx) between these two complex time signals. Two Indian string instruments, Sitar and Sarod were chosen and certain Alaap sections from live performances of maestros were selected to prepare the clips to be used for the experiment. From the audience response survey, the emotional contents of the prepared clips were marked. Finally, using happy and sad clips of these two string instruments as the input signals, EEG was performed on 2 musicians (M) and 2 non-musicians (NM). The γx values between audio inputs and extracted EEG responses, as well as between EEG responses of different lobe pairs were computed. γx essentially computes the degree of correlation between the source audio signals and the output EEG signals, while the correlation between the lobes essentially provides a cue to the varying neural connections happening while listening to a music. Finally, the comparative natures of cross-correlation trends for different emotions and different audience categories were studied in details. The goal of this pilot study is to develop a novel approach to classify and characterize emotions (happy-sad) and audience categories (M-NM) depending upon the nature of both audio-EEG cross-correlation as well as inter-lobe EEG cross-correlation.