Multifractal Detrended Fluctuation Analysis of alpha and theta EEG rhythms with musical stimuli

Maity, Akash Kumar; Pratihar, Ruchira; Mitra, Anubrato; Dey, Subham; Agrawal, Vishal; Sanyal, Shankha; Banerjee, Archi; Sengupta, Ranjan; Ghosh, Dipak

doi:10.1016/j.chaos.2015.08.016

Cited by 84 publications

(47 citation statements)

References 40 publications

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“…APPROACH EEG signals (Fig.1) are obtained in the temporal domain showing the variation of Extracellular Field Potentials [5] with respect to time. EEG signals has played a crucial role in the domain of music cognition [6] [7] [8]. Digital Signal processing methods allows converting between temporal domains to frequency domain.…”

Section: Methodsmentioning

confidence: 99%

“…On the first day after initialization, a 14 min recording period was started, and the following protocol was followed: The drone signal has been used as a baseline over which the emotional arousal corresponding to other musical clips have been taken. The tanpura drone creates a repetitive buzzing sound which helps to create an atmosphere without evoking any specific emotion [6].…”

Section: Experimental Protocolmentioning

confidence: 99%

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Can musical emotion be quantified with neural jitter or shimmer? A novel EEG based study with Hindustani classical music

Nag

Biswas

Sengupta

et al. 2017

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

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Abstract-The term jitter and shimmer has long been used in the domain of speech and acoustic signal analysis as a parameter for speaker identification and other prosodic features. In this study, we look forward to use the same parameters in neural domain to identify and categorize emotional cues in different musical clips. For this, we chose two ragas of Hindustani music which are conventionally known to portray contrast emotions and EEG study was conducted on 5 participants who were made to listen to 3 min clip of these two ragas with sufficient resting period in between. The neural jitter and shimmer components were evaluated for each experimental condition. The results reveal interesting information regarding domain specific arousal of human brain in response to musical stimuli and also regarding trait characteristics of an individual. This novel study can have far reaching conclusions when it comes to modeling of emotional appraisal. The results and implications are discussed in detail.

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Section: Methodsmentioning

confidence: 99%

Section: Experimental Protocolmentioning

confidence: 99%

Can musical emotion be quantified with neural jitter or shimmer? A novel EEG based study with Hindustani classical music

Nag

Biswas

Sengupta

et al. 2017

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

Self Cite

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“…In case of music induced emotions, a recent study [58,59] used the multi-fractal spectral width as an indicator to assess emotional arousal corresponding to the simplest musical stimuli -a tanpura drone.…”

Section: Journal Of Neurology and Neuroscience Issn 2171-6625mentioning

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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“…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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Multifractal Detrended Fluctuation Analysis of alpha and theta EEG rhythms with musical stimuli

Cited by 84 publications

References 40 publications

Can musical emotion be quantified with neural jitter or shimmer? A novel EEG based study with Hindustani classical music

Can musical emotion be quantified with neural jitter or shimmer? A novel EEG based study with Hindustani classical music

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

A Review on EEG Signals Based Emotion Recognition

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