Detrended Fluctuation and Power Spectral Analysis of alpha and delta EEG brain rhythms to study music elicited emotion

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

doi:10.1109/ispcc.2015.7375026

Cited by 17 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the study of the emotional characteristics of EEG by using DFA and Power Spectral Intensity (Sanyal et al, 2015), the correlation was studied between the variation in scaling exponent and the changes in spectral power in the alpha and delta ranges while listening to the contrasting music (eliciting two contrasting emotions, happiness and sorrow) vs. the resting state. The most prominent result was the fact that alpha power decreased during stimuli, while complexity either increases or decreases depending on the music type.…”

Section: Introductionmentioning

confidence: 99%

Detrended Fluctuation, Coherence, and Spectral Power Analysis of Activation Rearrangement in EEG Dynamics During Cognitive Workload

Seleznov

Zyma

Kiyono

et al. 2019

Front. Hum. Neurosci.

View full text Add to dashboard Cite

In the study of human cognitive activity using electroencephalogram (EEG), the brain dynamics parameters and characteristics play a crucial role. They allow to investigate the changes in functionality depending on the environment and task performance process, and also to access the intensity of the brain activity in various locations of the cortex and its dependencies. Usually, the dynamics of activation of different brain areas during the cognitive tasks are being studied by spectral analysis based on power spectral density (PSD) estimation, and coherence analysis, which are de facto standard tools in quantitative characterization of brain activity. PSD and coherence reflect the strength of oscillations and similarity of the emergence of these oscillations in the brain, respectively, while the concept of stability of brain activity over time is not well defined and less formalized. We propose to employ the detrended fluctuation analysis (DFA) as a measure of the EEG persistence over time, and use the DFA scaling exponent as its quantitative characteristics. We applied DFA to the study of the changes in activation in brain dynamics during mental calculations and united it with PSD and coherence estimation. In the experiment, EEGs during resting state and mental serial subtraction from 36 subjects were recorded and analyzed in four frequency ranges: θ1 (4.1–5.8 Hz), θ2 (5.9–7.4 Hz), β1 (13–19.9 Hz), and β2 (20–25 Hz). PSD maps to access the intensity of cortex activation and coherence to quantify the connections between different brain areas were calculated, the distribution of DFA scaling exponent over the head surface was exploited to measure the time characteristics of the dynamics of brain activity. Obtained arrangements of DFA scaling exponent suggest that normal functioning of the brain is characterized by long-term temporal correlations in the cortex. Topographical distribution of the DFA scaling exponent was comparable for θ and β frequency bands, demonstrating the largest values of DFA scaling exponent during cognitive activation. The study shows that the long-term temporal correlations evaluated by DFA can be of great interest for diagnosis of the variety of brain dysfunctions of different etiology in the future.

show abstract

Section: Introductionmentioning

confidence: 99%

Detrended Fluctuation, Coherence, and Spectral Power Analysis of Activation Rearrangement in EEG Dynamics During Cognitive Workload

Seleznov

Zyma

Kiyono

et al. 2019

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Briefly, DFA evaluates the detrended and integrated signal as a function of window size. Commonly used in many fields, including for ECG analysis, DFA has also been found to be beneficial for EEG emotion recognition [ 68 ]. Equations are as given in [ 66 ].…”

Section: Methodsmentioning

confidence: 99%

Feature Selection for Continuous within- and Cross-User EEG-Based Emotion Recognition

Bendrich

Kumar

Scheme

2022

Sensors

View full text Add to dashboard Cite

The monitoring of emotional state is important in the prevention and management of mental health problems and is increasingly being used to support affective computing. As such, researchers are exploring various modalities from which emotion can be inferred, such as through facial images or via electroencephalography (EEG) signals. Current research commonly investigates the performance of machine-learning-based emotion recognition systems by exposing users to stimuli that are assumed to elicit a single unchanging emotional response. Moreover, in order to demonstrate better results, many models are tested in evaluation frameworks that do not reflect realistic real-world implementations. Consequently, in this paper, we explore the design of EEG-based emotion recognition systems using longer, variable stimuli using the publicly available AMIGOS dataset. Feature engineering and selection results are evaluated across four different cross-validation frameworks, including versions of leave-one-movie-out (testing with a known user, but a previously unseen movie), leave-one-person-out (testing with a known movie, but a previously unseen person), and leave-one-person-and-movie-out (testing on both a new user and new movie). Results of feature selection lead to a 13% absolute improvement over comparable previously reported studies, and demonstrate the importance of evaluation framework on the design and performance of EEG-based emotion recognition systems.

show abstract

“…The power spectrum of a time series data describes the distribution of power into frequency components composing that signal. [7]According to Fourier analysis any physical signal can be decomposed into a number of discrete frequencies, or a spectrum of frequencies over a continuous range.…”

Section: Power Spectral Intensitymentioning

confidence: 99%

“…The bins are used as θ (4-7 Hz), α (8-12 Hz) and β (12-30Hz). For these bins we have the band = [4,7,12,30]. The Power Spectral Intensity (PSI) of the kth bin is evaluated as Where k=1, 2, .…”

Section: Power Spectral Intensitymentioning

confidence: 99%

Comparative Analysis of Non-linear Behaviour with Power Spectral Intensity Response Between Normal and Epileptic EEG Signals

Ghosh

Sengupta

et al. 2017

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract-Epilepsy is a neurological condition which affects the nervous system. It is a general term used for a group of disorders in which nerve cells of the brain discharge anomalous electrical impulses from time to time, causing a temporary malfunction of the other nerve cells of the brain.EEG signal provides an important cue for diagnosis and interpretation related to prognosis of epilepsy. In this work we envisage to provide novel tool which can be used to detect the prognosis of epileptic disorder by comparing linear and non-linear modalities of EEG analysis -conventionally used Power spectral analysis and a robust non linear method -Detrended Fluctuation Analysis (DFA). Publicly available dataset is used for this work consisting of 100 normal patients' EEG data as control group and 100 epileptic patients' EEG data for comparison. Response for different frequency bands (alpha, theta, beta) of the EEG spectrum have been analyzed using Detrended Fluctuation Analysis (DFA) and Power Spectral Intensity (PSI). The comparison of the DFA scaling exponent with the spectral power data is calculated for all the 3 different frequency bands of EEG signal provide new and interesting results which have been discussed in detail .

show abstract

Detrended Fluctuation and Power Spectral Analysis of alpha and delta EEG brain rhythms to study music elicited emotion

Cited by 17 publications

References 8 publications

Detrended Fluctuation, Coherence, and Spectral Power Analysis of Activation Rearrangement in EEG Dynamics During Cognitive Workload

Detrended Fluctuation, Coherence, and Spectral Power Analysis of Activation Rearrangement in EEG Dynamics During Cognitive Workload

Feature Selection for Continuous within- and Cross-User EEG-Based Emotion Recognition

Comparative Analysis of Non-linear Behaviour with Power Spectral Intensity Response Between Normal and Epileptic EEG Signals

Contact Info

Product

Resources

About