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Probabilistic dynamic brain response to live Indian classical vocal stimulus
  • +2
  • Satyam Panda ,
  • Dasari Shivakumar ,
  • Yagnyaseni Majumder ,
  • Cota Navin Gupta ,
  • Budhaditya Hazra
Satyam Panda
Indian Institute of Technology

Corresponding Author:[email protected]

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Dasari Shivakumar
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Yagnyaseni Majumder
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Cota Navin Gupta
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Budhaditya Hazra
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Abstract

Numerous studies have been conducted on the connection between music and the brain, and it has been established that listening to music directly affects brain activity and stimulation. The potential benefits of music therapy, which uses music as a tool for healing and fostering well-being, have come to light in a number of circumstances. However, there is a gap in understanding the effects of Indian classical music (ICM) on the brain and its therapeutic applications. In this work, the authors propose a systematic approach for identifying brain regions evoked to live ICM stimuli, considering input and output uncertainties. The brain responses are captured through 24 channel Electroencephalogram (EEG) cap, which is utilized to allocate electrodes to different regions of the brain. The proposed region specific near-automated framework based on eigen perturbation framework provides a measure to capture the time evolution of brain activity for the melodic transition or transition from a raga to relaxation triggered by Indian classical music. This identification is relevant in understanding dynamic changes in brain responses during musical experiences providing a more comprehensive perception and processing of ICM in the human brain. This automated approach can help integrate it into evidence-based music therapy for cognitive, emotional, and psychological conditions. Probabilistic analysis based on extensive experimental studies with live Indian classical vocal stimuli brings forth many interesting results that are worth delving into for future directions in music therapy. The findings of this study provides evidence indicating ragas activate different brain regions based on listener’s musical knowledge.