Study on Brain Dynamics by Non Linear Analysis of Music Induced EEG Signals

Banerjee, Archi; Sanyal, Shankha; Patranabis, Anirban; Banerjee, Kaushik; Guhathakurta, T.; Sengupta, Ranjan; Ghosh, Dipak; Ghose, Partha

doi:10.1016/j.physa.2015.10.030

Cited by 70 publications

(37 citation statements)

References 40 publications

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“…By using good and strong stimulation, emotion recognition is more likely to be performed with better results and higher accuracy. There are some types of stimulation as follow: pictures, 12-43 video clips, [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60] music, [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76] memories, 77 self-induction, 78,80,81 environment elicitation like light, humidity and temperature, 79 games, 82 etc. Some ways of eliciting emotions and some induced emotions are listed in Table 1.…”

Section: Emotion Stimulationmentioning

confidence: 99%

A Review on EEG Signals Based Emotion Recognition

Soroush

Maghooli

Setarehdan

et al. 2017

Int Clin Neurosci J

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Section: Emotion Stimulationmentioning

confidence: 99%

A Review on EEG Signals Based Emotion Recognition

Soroush

Maghooli

Setarehdan

et al. 2017

Int Clin Neurosci J

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“…But DFA has its own limitations. Many geophysical signals as well as biosignals do not exhibit mono-fractal scaling behavior, which can be accounted for by a single scaling exponent [44][45][46], therefore different scaling exponents are required for different parts of the series [47]. Consequently a multi-fractal analysis should be applied.…”

Section: Archivos De Medicina Issn 1698-9465 Journal Of Neurology Andmentioning

confidence: 99%

“…The Multi-fractal Detrended Fluctuation Analysis (MFDFA) technique was first conceived by Kantelhardt et al [48] as a generalization of the standard DFA [49,50]. MFDFA has been applied successfully to study multi-fractal scaling behavior of various non-stationary time series [51][52][53] as well as in detection or prognosis of diseases [54,55], scaling of music signals [55].…”

Section: Archivos De Medicina Issn 1698-9465 Journal Of Neurology Andmentioning

confidence: 99%

Universality and Domain Specificity of Emotion-A Quantitative Non Linear EEG Based Approach

Banerjee¹,

Sanyal²,

Sengupta³

et al. 2017

J Neurol Neurosci

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Music has been present in human culture since time immemorial, some say music came even before speech. The effort to understand the wide variety of emotions evoked by music has started not long back. With the advent and rapid growth of various neurological bio-sensors we can now attempt to quantify various dimensions of emotional experience induced by music especially instrumental music -since it is free from any language barriers. In this study, we took eight (8) cross cultural instrumental clips originating mainly from Indian and Western music. A listening test comprising of 100 participants across the globe was conducted to associate each clip with its corresponding emotional valence. The participants were asked to mark each clip according to their perception of four basic emotions (joy/sorrow and anxiety/serenity) invoked by each instrumental clip. EEG study was then conducted on 10 participants to measure the response evoked by the same instrumental clips in the alpha and theta frequency regions. We took the help of latest non-linear multi-fractal analysis technique -MFDFA to estimate the change in multi-fractal spectral width (corresponding to alpha as well as theta waves) associated with each of the clips in frontal, temporal and occipital lobes. The response in the alpha domain reveals a hint in the direction of universality of music, while in theta domain we have culture specific response. Moreover, we tried to develop alpha as well as theta multi-fractal spectral width as a single parameter with which we can quantify the valence and arousal based effects corresponding to a particular musical clip. The results and implications are discussed in detail.

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“…Different types of music has different effects on these bands in different lobes of the brain. Classical music has more pronounce effect on alpha waves, whereas beta waves are more affected by hard rock music [11,12]. Earlier studies report that alpha power decreases in the left frontal lobe due to pleasant music but it decreases in the right frontal lobe due to unpleasant music [13].…”

Section: Introductionmentioning

confidence: 99%

“…We use DFA method for the analysis and elimination of trends from data sets. It has successfully been applied to diverse fields such as DNA sequences [14,15], climate [16], EEG data [17,18], economical time series [19] etc. Another method to determine the Hurst exponent has been used recently [20], called Adaptive Fractal Analysis (AFA), which utilizes an adaptive detrending algorithm to extract globally smooth trend signals from the data and has shown good applications in various real world data [21,22].…”

Section: Introductionmentioning

confidence: 99%

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

Sengupta

Biswas

Nag

et al. 2017

2017 4th International Conference on Signal Processing and Integrated Networks (SPIN)

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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.

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Study on Brain Dynamics by Non Linear Analysis of Music Induced EEG Signals

Cited by 70 publications

References 40 publications

A Review on EEG Signals Based Emotion Recognition

A Review on EEG Signals Based Emotion Recognition

Universality and Domain Specificity of Emotion-A Quantitative Non Linear EEG Based Approach

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

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