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

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 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.

Persistence and Corresponding Time Scales of Soil Moisture Dynamics During Summer in the Babao River Basin, Northwest China

“…However, AFA presents advantages over rescaled range analysis and detrended fluctuation analysis in managing arbitrary and strong nonlinear trends (Gao et al, 2011;Hu et al, 2009) due to the finer resolution of fractal scaling behavior considered within short time series (Gao et al, 2012) and due to its more accurate Hurst exponent estimations (Gao et al, 2011). Therefore, AFA has been widely used to analyze the persistence and severity of global terrorism trends (Gao et al, 2017), sociocultural phenomena (Gao et al, 2012), electricity power load (Jiang & Gao, 2016), traffic flow (Zhu & Gao, 2014), and bioinformatics patterns (Gao et al, 2011;Gao et al, 2013;Sengupta et al, 2017).…”

EEG Based Emotion Monitoring Using Wavelet and Learning Vector Quantization

“…EEG signals involve a great deal of information about the function of the brain, which may reflect a state of mind, such as level of attention [2], relax condition [3], mental activity [4], human grasping [5] [6], human attention [7], alertness level [8], or emotional conditions [9], [1], [10], and [11], [12]- [18]. Several studies on identification of emotional states through EEG signals are the response of sound stimulation [19], after watching movies [20], watching ads [21], listening to music [11], playing video game [12] , and watching videos [22], [23].…”

“…In general, EEG signal consists of wave components, differentiated by their frequency regions. They are alpha waves (8)(9)(10)(11)(12)(13), very often appears when people are in conscious and relaxed conditions; beta wave (14-30 Hz), often occurs when people are in thinking; theta wave (4-7 Hz), usually happens when people take a nap, feel sleepy, or suffer emotional stress; and delta wave (0.5-3 Hz), very often appears when people are in deep sleep. As a consequence, a lot of researches concerning EEG signal analysis represent the signal into frequency domain.…”

“…It can be used like Power Spectral Density [26] [27] [24], Wavelet [18], [28]- [31] [2], [8], [32] [33]. Besides, EEG model used Autoregressive [5], [34]- [36], and Fractal Dimension [37] [10].…”

Human Emotion Recognition Based on EEG Signal Using Fast Fourier Transform and K-Nearest Neighbor

“…Neurosky Mindwave Mobile and electrode's position Mindwave provided single-channel EEG in the form of an electrode and placed in the forehead or Fp1 position to measure the brain's electrical voltage and ear-clip on the left ear or A1 position as ground. With Neurosky's ThinkGear and eSense technologies, connected devices can receive a real-time visual representation of brain waves generated from the electrodes[19,20].…”