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An integrative approach to analyze EEG signals and human brain dynamics in different cognitive states

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Electroencephalograph (EEG) data provide insight into the interconnections and relationships between various cognitive states and their corresponding brain dynamics, by demonstrating dynamic connections between brain regions at different frequency bands. While sensory input tends to stimulate neural activity in different frequency bands, peaceful states of being and self-induced meditation tend to produce activity in the mid-range (Alpha). These studies were conducted with the aim of: (a) testing different equipment in order to assess two (2) different EEG technologies together with their benefits and limitations and (b) having an initial impression of different brain states associated with different experimental modalities and tasks, by analyzing the spatial and temporal power spectrum and applying our movie making methodology to engage in qualitative exploration via the art of encephalography. This study complements our previous study of measuring multichannel EEG brain dynamics using MINDO48 equipment associated with three experimental modalities measured both in the laboratory and the natural environment. Together with Hilbert analysis, we conjecture, the results will provide us with the tools to engage in more complex brain dynamics and mental states, such as Meditation, Mathematical Audio Lectures, Music Induced Meditation, and Mental Arithmetic Exercises. This paper focuses on open eye and closed eye conditions, as well as meditation states in laboratory conditions. We assess similarities and differences between experimental modalities and their associated brain states as well as differences between the different tools for analysis and equipment.
Rocznik
Strony
287--299
Opis fizyczny
Bibliogr. 29 poz., rys.
Twórcy
autor
  • The Embassy of Peace, Whitianga, New Zealand
autor
  • Centre of Artificial Intelligence, Faculty of Engineering and Information Technology University of Technology Sydney, Australia
autor
  • Department of Bioethics, University of Otago, Dunedin, New Zealand
autor
  • Department of Mathematical Sciences, University of Memphis, TN and University of Massachusetts Amherst, MA, USA
Bibliografia
  • [1] W. J. Freeman and R. Quian Quiroga, Imaging Brain Function with EEG, New York: Springer, 2013
  • [2] W. J. Freeman, L. J. Rogers, M. D. Holmes, and D. L. Silbergeld, Spatial spectral analysis of human electrocorticograms including the alpha and gamma bands, J. Neurosci. Methods, vol. 95, pp. 111-21, 2000
  • [3] W. J. Freeman, M. D. Holmes, B. C. Burke, and S. Vanhatalo, Spatial spectra of scalp EEG and EMG from awake humans, Clin. Neurophysiol., vol. 114, no. 6, pp. 1053-1068, 2003
  • [4] W. J. Freeman and J. Zhai, Simulated power spectral density (PSD) of background electro-corticogram(ECoG), Cogn. Neurodyn., vol. 3, no. 1, pp. 97-103,2009
  • [5] W. J. Freeman, C. Ramon, and M. D. Holmes, 1-D spatial autocorrelation function of EEG: a sensitive assay for occult EMG, 16th Conf. Human BrainMapping, #881, 2010
  • [6] G. Buzsaki, Rhythms of The Brain, New York: Oxford University Press, 2006
  • [7] R. Kozma, J. J. Davis, C.-T. Lin, L.-D. Liao and W. J. Freeman, Optimizing EEG/EMG signal to noise ratio at high spatial resolution, SfN Congress, #586.12/NNN11, Nov. 9- 12, 2013, San Diego, CA, USA
  • [8] S. Pockett, G. E. J. Bold, and W. J. Freeman, EEG synchrony during a perceptual-cognitive task: Widespread phase synchrony at all frequencies, Clin. Neurophysiol., vol. 120, pp. 695-708, 2009
  • [9] Y. Ruiz, S. Pockett, W. J. Freeman, E. Gonzalez, and G. Li, A method to study global spatial patterns related to sensory perception in scalp EEG, J. Neurosci. Methods, vol. 191, pp. 110-118, 2010
  • [10] N. Kasabov and E. Capecci, Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes, Information Sciences, vol. 294, pp. 565-575, 2015
  • [11] R. Kozma, J. J. J. Davis, C.-T. Lin, and W. J. Freeman. Spatio-Temporal EEG Pattern Extraction Using High-Density Scalp Arrays, IEEE World Congr. on Comp. Intel., 2016
  • [12] K. Pribram, The Form Within – My Point of View. Westport, CT: Prospecta Press, 2013
  • [13] Mitsar Brain Diagnostics Solutions, EEG Accessories: Available:http://www.mitsar-medical.com/eegaccessories/(last viewed 15 April 2016)
  • [14] J. J. Davis and R. Kozma, Analysis of phase relationshipin ECoG using Hilbert transform and information theoretic measures, 2012 IJCNN, Bris., Australia,10-15 June, 2012
  • [15] J. J. J. Davis, W. J. Freeman, and R. Kozma, Synchronized Minima in ECoG Power at Frequencies Between Beta-Gamma Oscillations Disclose Cortical Singularities in Cognition, Journal of Neuroscience and Neuroengineering, vol. 1, no. 1, pp. 13-23, 2012
  • [16] J. J. Davis and R. Kozma, On the Invariance of Cortical Synchronization Measures Across a Broad Range of Frequencies, 2012 4th iCAST
  • [17] J. J. Davis and R. Kozma, Creation of Knowledge and Meaning Manifested via Cortical Singularitiesin Cognition: Towards a Methodology to Understand Intentionality and Critical Behaviour in Neural Correlates of Awareness, 2013 IEEE Symp. Series on CCMB
  • [18] J. J. J. Davis, W. J. Freeman, and R. Kozma, Neurophysiological evidence of the cognitive cycle and the emergence of awareness, 2013 iCAST –UMEDIA
  • [19] J. J. J. Davis, R. Ilin, R. Kozma, and M. H. Myers, Phase Cone Detection Optimization in EEG Data, IJCNN, 2014
  • [20] J. J. Davis, W. J. Freeman, R. Kozma, and C.-T. Lin, Model-based measurement of eeg data from linear high-density array (Poster Presentation), SfN Ann. Meet., 2014
  • [21] J. J. Davis and R. Kozma, Sensitivity analysis of Hilbert transform with band-pass FIR filters for robust brain computer interface, 2014 IEEE Symposium on CIBCI, Orlando, FL, 2014
  • [22] J. J. J. Davis, G. Gillett, and R. Kozma, Revisiting Brentano on Consciousness: Striking Correlations with Electrocorticogram Findings about the ActionPerception Cycle and the Emergence of Knowledge and Meaning, Mind and Matter, vol. 13, no. 1, pp. 45-69, 2015.
  • [23] J. J. J. Davis, R. Kozma, and W. J. Freeman, The Art of Encephalography to Understand and Discriminate Higher Cognitive Functions Visualizing Big Data on Brain Imaging using Brain DynamicsMovies, INNS Conf. on Big Data, Proc. Comp. Sci., Vol. XXX, pp. 1-8, 2015
  • [24] Mitsar Brain Diagnostics Solutions, Specifications:Available:http://www.mitsar-medical.com/eegmachine/eeg-amplifier-201/specific.html(last viewed 15 April 2016).
  • [25] J. J. J. Davis, The Brain of Melchizedek, M.S. thesis, Cog. Sci. Otago Uni., Dunedin, New Zealand, 2009
  • [26] J. M. Schwartz, H. P. Stapp, and M. Beauregard,Quantum physics in neuroscience and psychology: a neurophysical model of mind–brain interaction, Phil. Trans. R. Soc. B, doi:10.1098/rstb. 1598, 2004
  • [27] F. G. Echenhofer and M. M. Coombs, A Brief review of research and controversies in EEG Biofeedback and meditation, J. of Trans. Psych., vol. 19, no.2, 1987
  • [28] A. Kasamatsu and H. Tomio, An electroencephalographic study on the zen meditation (zazen), Folia Psychiatrica et Neurologica Japonica, vol. 20, no. 4,1966
  • [29] J. H. Austin, Zen and The Brain,Cambridge, MASS: The MIT Press, 1999
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bc590111-62bc-484c-8d13-065982183da3
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