Order and Chaos in the Brain: Fractal Time Series Analysis of the EEG Activity During a Cognitive Problem Solving Task

Díaz, M Hernán; Córdova, Felisa M.; Cañete, Lucio; Palominos, Fredi Edgardo; Cifuentes, Fernando; Sánchez, Crystian; Herrera, Matías

doi:10.1016/j.procs.2015.07.135

Cited by 39 publications

(17 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The short-term modulation corresponding to a fine tune, moment-to-moment supervision, suggested by the high Hurst Exponent (H) average value around 0.7, indicating long-memory persistent processes in the working substructure. The second, long-term modulation, more relaxed, having short-memory and anti-persistent processes with similar values for H found recently in basal resting conditions for the EEG alpha band [31].…”

Section: Discussionsupporting

confidence: 85%

“…High values of H arise in delta band (0-4 Hz), while lowest values of H has been detected for alpha band (8-13 Hz). Going up from alpha to beta (13-30 Hz) and gamma band (30-64 Hz), it is observed a gradual increase of H values which can be interpreted in terms of the functional constraints toward order that demand brain data processing while working with more complicated information [31]. While delta wave can be considered as a very structured self-dedicated house-keeping process it is not strange that high H values reflects high organization and not-chaotic processes happening in the brain.…”

Section: Chaos and Self-organized Content In The Brainmentioning

confidence: 98%

“…While the former measure the degree of roughness of the trajectory in a 1-D → 2-D fractional dimensional range, the latter estimate the rate of chaos in the signal with values that range from 0 (total unpredictability or chaos), to 1 (total predictability), a proxi way to evaluate the self-organized (order) content in a chaotic medium [31].…”

Section: Fractal Dimension (D)mentioning

confidence: 99%

See 2 more Smart Citations

Individual Differences in the Order/Chaos Balance of the Brain Self-Organization

Díaz

Maureira

Cohen³

et al. 2015

Ann. Data. Sci.

Self Cite

View full text Add to dashboard Cite

We used fractal geometry and fractal dimension introductory argumentation as a framework to start understanding dynamical and complex biological systems to then introduce Hurst exponent estimation of chaos/no-chaos balance trend to explore the phenomenology and the information content of EEG data through time. We searched for measure proxy dynamical variables as potential biomarkers and/or endophenotypes that help us to figure out the multidimensionality and different time-scale of simultaneous and crossed functional phenomena that manifests in the brain during executing any challenging task. We found consistencies in the way intra-and interindividual differences express themselves through the EEG time series data analysis, and some degree of specificity and specialization in the frontal, temporal and occipital locations as well as brain interhemispheric cross-talk interaction modulating the chaos/no-chaos balance in the brain, during a projective process of imaging a dancing choreography. We recorded the brain activity of N = 9 professional dancers while executing the instruction of to imagine (by mean of a typical projective visualization) a future dancing performance as part of the requirement for to approve a specialization modern dance course and workshop (Kosmos In Movement, 2015).

show abstract

Section: Discussionsupporting

confidence: 85%

Section: Chaos and Self-organized Content In The Brainmentioning

confidence: 98%

Section: Fractal Dimension (D)mentioning

confidence: 99%

See 1 more Smart Citation

Individual Differences in the Order/Chaos Balance of the Brain Self-Organization

Díaz

Maureira

Cohen³

et al. 2015

Ann. Data. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Much of the recent work in this field focuses on enhancement techniques and methods of increasing the signal-to-noise ratio [14][15][16][17]. Methods like linear regression, logic regression model, Poisson model and negative binominal model are subject to strong assumption and limitations in applications [18].…”

Section: Literature Overviewmentioning

confidence: 99%

Incorporation of Duffing Oscillator and Wigner-Ville Distribution in Traffic Flow Prediction

Mrgole

Sever

2017

PROMET

View full text Add to dashboard Cite

show abstract

“…A spectrum sensing algorithm based on fractal dimension obtains the detection information of spectrum sensing without the prior information of the primary user by using the frequency domain fractal dimension of the signal. Furthermore, it has strong anti-noise ability and low computational complexity [11,12]. Owing to the above advantages, spectrum sensing algorithms based on fractal dimension has become a research focus.…”

Section: Introductionmentioning

confidence: 99%

Double-Threshold Cooperative Spectrum Sensing Algorithm Based on Sevcik Fractal Dimension

et al. 2017

View full text Add to dashboard Cite

Spectrum sensing is of great importance in the cognitive radio (CR) networks. Compared with individual spectrum sensing, cooperative spectrum sensing (CSS) has been shown to greatly improve the accuracy of the detection. However, the existing CSS algorithms are sensitive to noise uncertainty and are inaccurate in low signal-to-noise ratio (SNR) detection. To address this, we propose a double-threshold CSS algorithm based on Sevcik fractal dimension (SFD) in this paper. The main idea of the presented scheme is to sense the presence of primary users in the local spectrum sensing by analyzing different characteristics of the SFD between signals and noise. Considering the stochastic fluctuation characteristic of the noise SFD in a certain range, we adopt the double-threshold method in the multi-cognitive user CSS so as to improve the detection accuracy, where thresholds are set according to the maximum and minimum values of the noise SFD. After obtaining the detection results, the cognitive user sends local detection results to the fusion center for reliability fusion. Simulation results demonstrate that the proposed method is insensitive to noise uncertainty. Simulations also show that the algorithm presented in this paper can achieve high detection performance at the low SNR region.

show abstract

Order and Chaos in the Brain: Fractal Time Series Analysis of the EEG Activity During a Cognitive Problem Solving Task

Cited by 39 publications

References 19 publications

Individual Differences in the Order/Chaos Balance of the Brain Self-Organization

Individual Differences in the Order/Chaos Balance of the Brain Self-Organization

Incorporation of Duffing Oscillator and Wigner-Ville Distribution in Traffic Flow Prediction

Double-Threshold Cooperative Spectrum Sensing Algorithm Based on Sevcik Fractal Dimension

Contact Info

Product

Resources

About