Emotion Specification from Musical Stimuli: An EEG Study with AFA and DFA

TL;DR

This study explores emotional responses to Hindustani ragas using EEG data analyzed with AFA and DFA, revealing DFA's superior effectiveness in biosignal scaling analysis.

Contribution

It introduces a comparative analysis of AFA and DFA for EEG data in emotional music perception, highlighting DFA's robustness.

Findings

DFA provides more rigorous results than AFA in EEG analysis.

EEG data reflects contrasting emotional states induced by different ragas.

Implications for emotion recognition using nonlinear biosignal analysis methods.

Abstract

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 naive 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 biosignal data. The results and implications have been discussed in detail.