Combined nonlinear metrics to evaluate spontaneous EEG recordings from chronic spinal cord injury in a rat model: a pilot study

Pu, Jiangbo; Xu, Haibo; Wang, Yazhou; Cui, Hongyan; Hu, Yong

doi:10.1007/s11571-016-9394-0

Cited by 6 publications

(4 citation statements)

References 42 publications

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“…The HE values exhibited long range anti-correlation in both epileptic and interictal EEG (Geng et al 2011). Combined nonlinear features of sample entropy with deterended fluctuation analysis (DFA) and kolmogorov complexity are used to evaluate functional plasticity changes in spontaneous EEG recordings of rats before and after spinal cord injury (SCI) (Pu et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Analysis of long range dependence in the EEG signals of Alzheimer patients

2018

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Alzheimer's disease (AD), a cognitive disability is analysed using a long range dependence parameter, hurst exponent (HE), calculated based on the time domain analysis of the measured electrical activity of brain. The electroencephalogram (EEG) signals of controls and mild cognitive impairment (MCI)-AD patients are evaluated under normal resting and mental arithmetic conditions. Simultaneous low pass filtering and total variation denoising algorithm is employed for preprocessing. Larger values of HE observed in the right hemisphere of the brain for AD patients indicated a decrease in irregularity of the EEG signal under cognitive task conditions. Correlations between HE and the neuropsychological indices are analysed using bivariate correlation analysis. The observed reduction in the values of Auto mutual information and cross mutual information in the local antero-frontal and distant regions in the brain hemisphere indicates the loss of information transmission in MCI-AD patients.

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Section: Introductionmentioning

confidence: 99%

Analysis of long range dependence in the EEG signals of Alzheimer patients

2018

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“…In case of music induced emotions, DFA was applied to analyze the scaling pattern of EEG signals in emotional music (Gao et al 2007) and particularly Indian music (Banerjee et al 2016). It has also been applied for patients with neurodegenerative diseases (John et al 2018;Yuvaraj and Murugappan 2016;Bornas et al 2015;Gao et al 2011), to assess their emotional response, to assess the change of neural plasticity in a spinal cord injury rat model (Pu et al 2016) and so on.…”

Section: Fractals and Multifractals In Eeg Studymentioning

confidence: 99%

Music of brain and music on brain: a novel EEG sonification approach

et al. 2018

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Can we hear the sound of our brain? Is there any technique which can enable us to hear the neuro-electrical impulses originating from the different lobes of brain? The answer to all these questions is YES. In this paper we present a novel method with which we can sonify the electroencephalogram (EEG) data recorded in ''control'' state as well as under the influence of a simple acoustical stimuli-a tanpura drone. The tanpura has a very simple construction yet the tanpura drone exhibits very complex acoustic features, which is generally used for creation of an ambience during a musical performance. Hence, for this pilot project we chose to study the nonlinear correlations between musical stimulus (tanpura drone as well as music clips) and sonified EEG data. Till date, there have been no study which deals with the direct correlation between a bio-signal and its acoustic counterpart and also tries to see how that correlation varies under the influence of different types of stimuli. This study tries to bridge this gap and looks for a direct correlation between music signal and EEG data using a robust mathematical microscope called Multifractal Detrended Cross Correlation Analysis (MFDXA). For this, we took EEG data of 10 participants in 2 min ''control condition'' (i.e. with white noise) and in 2 min 'tanpura drone' (musical stimulus) listening condition. The same experimental paradigm was repeated for two emotional music, ''Chayanat'' and ''Darbari Kanada''. These are well known Hindustani classical ragas which conventionally portray contrast emotional attributes, also verified from human response data. Next, the EEG signals from different electrodes were sonified and MFDXA technique was used to assess the degree of correlation (or the cross correlation coefficient c x) between the EEG signals and the music clips. The variation of c x for different lobes of brain during the course of the experiment provides interesting new information regarding the extraordinary ability of music stimuli to engage several areas of the brain significantly unlike any other stimuli (which engages specific domains only). Keywords EEG Á Sonification Á Tanpura drone Á Hindustani Ragas Á MFDXA Á Cross-correlation coefficient ''The Music is a vibration in the brain rather than the ear''

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“…In psychobehavioral research, a major challenge encountered is the biological and behavioral system complications and the fact that complex systems can be modeled in a mathematical manner (Mumtaz et al 2017;Dasdemir et al 2017;Lehrer and Eddie 2013;Nasrolahzadeh and Haddadnia 2014) and the complex changes in bio-systems is evaluated using nonlinear dynamical analysis (Pu et al 2016). In the analysis of the heart rate signals, nonlinear methods have been implemented to calculate heart rate indices because the dynamics of the cardiac system are chaotic (Goshvarpour and Goshvarpour 2013).…”

Section: Introductionmentioning

confidence: 99%

Analysis of heart rate signals during meditation using visibility graph complexity

2018

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In the dynamics analysis of heart rate, the complexity of visibility graphs (VGs) is seen as a sign of short term variability in signals. The present study was conducted to investigate the possible impact of meditation on heart rate signals complexity using VG method. In this study, existing heart rate signals in Physionet database were used. The dynamics of the signals were then studied both before and during meditation by examining the complexity of VGs using graph index complexity (GIC). Generally, the obtained results showed that the heart rate signals were more complex during meditation. The simple process of calculating the GIC of VG and its adaptability to the chaotic nature of the biological signals can help in estimating the heart rate complexity in meditation.

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Combined nonlinear metrics to evaluate spontaneous EEG recordings from chronic spinal cord injury in a rat model: a pilot study

Cited by 6 publications

References 42 publications

Analysis of long range dependence in the EEG signals of Alzheimer patients

Analysis of long range dependence in the EEG signals of Alzheimer patients

Music of brain and music on brain: a novel EEG sonification approach

Analysis of heart rate signals during meditation using visibility graph complexity

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