Human electroencephalograms seen as fractal time series: Mathematical analysis and visualization

Kulish, Vladimir; Sourin, Alexei; Sourina, Olga

doi:10.1016/j.compbiomed.2004.12.003

Cited by 86 publications

(31 citation statements)

References 4 publications

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“…Additionally, as the brain is a complicated system, the EEG signal is nonlinear and chaotic [15][16]. However, little has been done to investigate chaos of brain for emotion recognition.…”

Section: B Emotion Recognition Algorithmsmentioning

confidence: 99%

“…Early work such as [19] showed that fractal dimension could reflect the change of EEG signal; [20] showed that fractal dimension varied for different mental tasks; a more recent work like [15] revealed that when brain processed tasks which were of emotional difference only, fractal dimension can be used to differ these tasks. Work [21][22] used music as stimuli to elicit emotions, and applied fractal dimension for the analysis of the EEG signal.…”

Section: B Emotion Recognition Algorithmsmentioning

confidence: 99%

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Real-Time EEG-Based Human Emotion Recognition and Visualization

Liu

Sourina

Nguyen

2010

2010 International Conference on Cyberworlds

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232

102

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Abstract-Emotions accompany everyone in the daily life, playing a key role in non-verbal communication, and they are essential to the understanding of human behavior. Emotion recognition could be done from the text, speech, facial expression or gesture. In this paper, we concentrate on recognition of "inner" emotions from electroencephalogram (EEG) signals as humans could control their facial expressions or vocal intonation. The need and importance of the automatic emotion recognition from EEG signals has grown with increasing role of brain computer interface applications and development of new forms of human-centric and humandriven interaction with digital media. We propose fractal dimension based algorithm of quantification of basic emotions and describe its implementation as a feedback in 3D virtual environments. The user emotions are recognized and visualized in real time on his/her avatar adding one more so-called "emotion dimension" to human computer interfaces.

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“…Additionally, as the brain is a complicated system, the EEG signal is nonlinear and chaotic [15][16]. However, little has been done to investigate chaos of brain for emotion recognition.…”

Section: B Emotion Recognition Algorithmsmentioning

confidence: 99%

Section: B Emotion Recognition Algorithmsmentioning

confidence: 99%

Real-Time EEG-Based Human Emotion Recognition and Visualization

Liu

Sourina

Nguyen

2010

2010 International Conference on Cyberworlds

Self Cite

232

102

View full text Add to dashboard Cite

show abstract

“…Significantly, such impairments affect more than 2 million people worldwide [7]. Recently, EEG data of imaginary movement (or mental tasks) have been proposed for use in systems in which the subjects imagine their motor movements (left or right hand) [8,9], and in basic natural human decisions, i.e., "yes/no," has also been proposed [10,11]. Also, several recent studies in the analysis of EEG data are usually employing the fast Fourier transform (FFT).…”

mentioning

confidence: 99%

Fractal-Based EEG Data Analysis of Body Parts Movement Imagery Tasks

Phothisonothai

Nakagawa

2007

J. Physiol. Sci

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Abstract:The objective of this study is to analyze the spontaneous electroencephalographic (EEG) data corresponding to body parts movement imagery tasks in terms of fractal properties. We proposed the six algorithms of fractal dimension (FD) estimators; box-counting algorithm, Higuchi algorithm, variance fractal algorithm, detrended fluctuation analysis, power spectral density analysis, and critical exponent analysis. The different parts of human body movement imagination such as feet, tongue, and index finger are proposed for use as the tasks in this experiment.The EEG data were recorded from three healthy subjects (2 males and 1 female). The experimental results are useful in the measurement of FD changes in EEG data and present different characteristics in terms of variability. The probability density function (PDF) is also applied to show that the FD distribution is along each electrode. This study proposes that the performances of each method can extract information from the EEG data of imagined movement.

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“…Zero entropy denotes that the system is well regulated, while Y. Xin et al / PAF recognition based on multi-scale Rényi entropy of ECG S191 infinity entropy means that the system is completely chaotic. The Rényi entropy [10] is a generalized form of Shannon and other kinds of entropy, but the Rényi entropy often has a better performance than any of them. Based on the physiological characteristics of PAF signal, in order to extract features of HRV signal we proposed a multi-scale Rényi entropy by combining both the wavelet transform and Rényi entropy methods.…”

Section: Methodsmentioning

confidence: 99%

Paroxysmal atrial fibrillation recognition based on multi-scale Rényi entropy of ECG

Yi¹,

Zhao²,

Mu³

et al. 2017

THC

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Abstract. BACKGROUND: Atrial fibrillation (AF) is a common type of arrhythmia disease, which has a high morbidity and can lead to some serious complications. The ability to detect and in turn prevent AF is extremely significant to the patient and clinician. OBJECTIVE: Using ECG to detect AF and develop a robust and effective algorithm is the primary objective of this study. METHODS: Some studies show that after AF occurs, the regulatory mechanism of vagus nerve and sympathetic nerve will change. Each R-R interval will be absolutely unequal. After studying the physiological mechanism of AF, we will calculate the Rényi entropy of the wavelet coefficients of heart rate variability (HRV) in order to measure the complexity of PAF signals, as well as extract the multi-scale features of paroxysmal atrial fibrillation (PAF). RESULTS: The data used in this study is obtained from MIT-BIH PAF Prediction Challenge Database and the correct rate in classifying PAF patients from normal persons is 92.48%. CONCLUSIONS: The results of this experiment proved that AF could be detected by using this method and, in turn, provide opinions for clinical diagnosis.

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Human electroencephalograms seen as fractal time series: Mathematical analysis and visualization

Cited by 86 publications

References 4 publications

Real-Time EEG-Based Human Emotion Recognition and Visualization

Real-Time EEG-Based Human Emotion Recognition and Visualization

Fractal-Based EEG Data Analysis of Body Parts Movement Imagery Tasks

Paroxysmal atrial fibrillation recognition based on multi-scale Rényi entropy of ECG

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