Multiscale Fractal Analysis on EEG Signals for Music-Induced Emotion Recognition

Avramidis, Kleanthis; Zlatintsi, Athanasia; Garoufis, Christos; Maragos, Petros

doi:10.23919/eusipco54536.2021.9616140

Cited by 11 publications

(7 citation statements)

References 31 publications

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“…Completely different is score for the non-fractal parts. All these interesting fractal outcomes refer to the analysed parts of DIAPHONIES from Michael Paouris.All these issues are, possibly, linked to the non-linear behaviour of human brain (e.g., [1,2,14,[58][59][60][61] among some of the related papers). Possibly the composer of DIAPHONIES generated composite sounds that are, potentially, due to inner non-linear brain procedures during composition of DIAPHONIES.…”

Section: Resultsmentioning

confidence: 99%

Fractal and Complex Patterns Existing in Music: Application to the Composition Diaphonies of Michael Paouris

Nikolopoulos,

Petraki

2024

Preprint

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This is a study on fractals existing in music. The application case is the composition DIAPHONIES of Michael Paouris. Sliding-window fractal analysis indicate areas of continuous fractality dipped in non-fractal areas of possibly deterministic and stochastic nature. Diverging cases of pronounced non-fractal areas are addressed and also areas of mixed fractality and non-fractality. Many segments exhibit persistency-P (2

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Section: Resultsmentioning

confidence: 99%

Fractal and Complex Patterns Existing in Music: Application to the Composition Diaphonies of Michael Paouris

Nikolopoulos,

Petraki

2024

Preprint

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“…

features approximate the complexity (or fractality) of the EEG times-series data providing an indication the level of self-similarity of the EEG signal across all time scales. Previously,

features have shown promise for EEG-based emotion recognition [ 13 , 23 , 30 , 31 ]. In this study, we considered several

algorithms commonly used for EEG signal analysis, namely Katz [ 32 ], Petrosian [ 33 ], and Higuchi [ 34 ]; these algorithms are explained below.…”

Section: Methodsmentioning

confidence: 99%

Comprehensive Analysis of Feature Extraction Methods for Emotion Recognition from Multichannel EEG Recordings

Yuvaraj

Thangavel

Thomas

et al. 2023

Sensors

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Advances in signal processing and machine learning have expedited electroencephalogram (EEG)-based emotion recognition research, and numerous EEG signal features have been investigated to detect or characterize human emotions. However, most studies in this area have used relatively small monocentric data and focused on a limited range of EEG features, making it difficult to compare the utility of different sets of EEG features for emotion recognition. This study addressed that by comparing the classification accuracy (performance) of a comprehensive range of EEG feature sets for identifying emotional states, in terms of valence and arousal. The classification accuracy of five EEG feature sets were investigated, including statistical features, fractal dimension (FD), Hjorth parameters, higher order spectra (HOS), and those derived using wavelet analysis. Performance was evaluated using two classifier methods, support vector machine (SVM) and classification and regression tree (CART), across five independent and publicly available datasets linking EEG to emotional states: MAHNOB-HCI, DEAP, SEED, AMIGOS, and DREAMER. The FD-CART feature-classification method attained the best mean classification accuracy for valence (85.06%) and arousal (84.55%) across the five datasets. The stability of these findings across the five different datasets also indicate that FD features derived from EEG data are reliable for emotion recognition. The results may lead to the possible development of an online feature extraction framework, thereby enabling the development of an EEG-based emotion recognition system in real time.

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“…For example, authors [ 30 ] used fractional Gaussian noise and fractional Brownian motion for the decomposition of EEG signals into brain rhythms: delta, theta, alpha, beta, and gamma rhythms. In a study [ 31 ], multifractal EEG analysis was used for the task of emotion recognition.…”

Section: Related Workmentioning

confidence: 99%

Advanced Modeling and Signal Processing Methods in Brain–Computer Interfaces Based on a Vector of Cyclic Rhythmically Connected Random Processes

Lupenko

Butsiy

Shakhovska

2023

Sensors

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In this study, we substantiate the new mathematical model of vector of electroencephalographic signals, registered under the conditions of multiple repetitions of the mental control influences of brain–computer interface operator, in the form of a vector of cyclic rhythmically connected random processes, which, due to taking into account the stochasticity and cyclicity, the variability and commonality of the rhythm of the investigated signals have a number of advantages over the known models. This new model opens the way for the study of multidimensional distribution functions; initial, central, and mixed moment functions of higher order such as for each electroencephalographic signal separately; as well as for their respective compatible probabilistic characteristics, among which the most informative characteristics can be selected. This provides an increase in accuracy in the detection (classification) of mental control influences of the brain–computer interface operators. Based on the developed mathematical model, the statistical processing methods of vector of electroencephalographic signals are substantiated, which consist of statistical evaluation of its probabilistic characteristics and make it possible to conduct an effective joint statistical estimation of the probability characteristics of electroencephalographic signals. This provides the basis for coordinated integration of information from different sensors. The use of moment functions of higher order and their spectral images in the frequency domain, as informative characteristics in brain–computer interface systems, are substantiated. Their significant sensitivity to the mental controlling influence of the brain–computer interface operator is experimentally established. The application of Bessel’s inequality to the problems of reducing the dimensions (from 500 to 20 numbers) of the vectors of informative features makes it possible to significantly reduce the computational complexity of the algorithms for the functioning of brain–computer interface systems. Namely, we experimentally established that only the first 20 values of the Fourier transform of the estimation of moment functions of higher-order electroencephalographic signals are sufficient to form the vector of informative features in brain–computer interface systems, because these spectral components make up at least 95% of the total energy of the corresponding statistical estimate of the moment functions of higher-order electroencephalographic signals.

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Multiscale Fractal Analysis on EEG Signals for Music-Induced Emotion Recognition

Cited by 11 publications

References 31 publications

Fractal and Complex Patterns Existing in Music: Application to the Composition Diaphonies of Michael Paouris

Fractal and Complex Patterns Existing in Music: Application to the Composition Diaphonies of Michael Paouris

Comprehensive Analysis of Feature Extraction Methods for Emotion Recognition from Multichannel EEG Recordings

Advanced Modeling and Signal Processing Methods in Brain–Computer Interfaces Based on a Vector of Cyclic Rhythmically Connected Random Processes

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