R Sreenivasan

Florida Atlantic University, Boca Raton, Florida, United States

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Publications (5)3.51 Total impact

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    ABSTRACT: In this paper, a time series complexity analysis of dense array electroencephalogram signals is carried out using the recently introduced Sample Entropy (SampEn) measure. This statistic quantifies the regularity in signals recorded from systems that can vary from the purely deterministic to purely stochastic realm. The present analysis is conducted with an objective of gaining insight into complexity variations related to changing brain dynamics for EEG recorded from the three cases of passive, eyes closed condition, a mental arithmetic task and the same mental task carried out after a physical exertion task. It is observed that the statistic is a robust quantifier of complexity suited for short physiological signals such as the EEG and it points to the specific brain regions that exhibit lowered complexity during the mental task state as compared to a passive, relaxed state. In the case of mental tasks carried out before and after the performance of a physical exercise, the statistic can detect the variations brought in by the intermediate fatigue inducing exercise period. This enhances its utility in detecting subtle changes in the brain state that can find wider scope for applications in EEG based brain studies.
    Journal of Integrative Neuroscience 11/2011; 03(03). · 1.15 Impact Factor
  • R Pravitha, R Sreenivasan, V P N Nampoori
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    ABSTRACT: This article deals with the complexity aspects of the recorded electroencephalogram (EEG) signal from male and female subjects. The analysis follows direct application of time series measures of global linear complexity and characterization of the embedded complexity in the signals using the nonlinear statistic of approximate entropy. The study reveals significant differences in complexity between the two sex groups during passive, no-task conditions, whereas no apparent variation exists during a mental task state. The detection of subtle changes as well as the ease in presenting a global picture of the complexity variation on the human cortical surface makes the nonlinear statistic a better marker of system complexity.
    International Journal of Neuroscience 05/2005; 115(4):445-60. · 1.22 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, a time series complexity analysis of dense array electroencephalogram signals is carried out using the recently introduced Sample Entropy (SampEn) measure. This statistic quantifies the regularity in signals recorded from systems that can vary from the purely deterministic to purely stochastic realm. The present analysis is conducted with an objective of gaining insight into complexity variations related to changing brain dynamics for EEG recorded from the three cases of passive, eyes closed condition, a mental arithmetic task and the same mental task carried out after a physical exertion task. It is observed that the statistic is a robust quantifier of complexity suited for short physiological signals such as the EEG and it points to the specific brain regions that exhibit lowered complexity during the mental task state as compared to a passive, relaxed state. In the case of mental tasks carried out before and after the performance of a physical exercise, the statistic can detect the variations brought in by the intermediate fatigue inducing exercise period. This enhances its utility in detecting subtle changes in the brain state that can find wider scope for applications in EEG based brain studies.
    Journal of Integrative Neuroscience 10/2004; 3(3):343-58. · 1.15 Impact Factor
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    ABSTRACT: In the physical world various systems exhibit highly nonlinear and complex behaviour, the study of which has importance in understanding the dynamical nature of these processes. Human brain is one of the most complex nonlinear systems in nature which is manifested in the time evolution of electrical potential of the brain i.e. electroencephalogram (EEG). In the present paper we investigate the dynamical variations induced by fatigue in human brain dynamics during a mental task by applying the techniques adopted from nonlinear systems theory. The time series are considered for two different conditions: mental arithmetic before and after fatigue. Results indicate strong variations in the characteristic parameters of the underlying neural attractors. Statistical analysis is carried out to determine the significance levels of the evaluated parameters and the effect of fatigue on task-activated complexity is studied.
    01/2003;
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    ABSTRACT: Nonlinear time series analysis is a potential method for gaining information regarding the dynamics of complex physiological systems. The human brain is by far one of the most complex systems in nature and the electrical signal from the brain, the electroencephalogram (EEG) has been a subject of nonlinear techniques to delve the dynamics of the brain. This paper deals with the computation of a measure of complexity for dense array EEG signals and the subsequent analysis of various brain conditions. The complexity measure used in the study is the Sample Entropy (SampEn), which quantifies the regularity and 'randomness' in a time varying signal arising from a system The present work computes the SampEn of the EEG signal recorded for the three cases of passive, eyes closed condition; mental arithmetic task and for epileptic spike data. The analysis helps in gaining an insight into the nature of complexity variations with brain state. It is observed that while during the mental task only specific brain regions exhibit lowered complexity, the dynamical complexity is very much lowered for the pathological spike condition.

Publication Stats

11 Citations
3.51 Total Impact Points

Institutions

  • 2011
    • Florida Atlantic University
      • Center for Complex Systems and Brain Sciences
      Boca Raton, Florida, United States
  • 2004
    • Cochin University of Science and Technology
      • International School of Photonics
      Cochin, Kerala, India