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Coherence and phase synchrony analyses of EEG signals in Mild Cognitive Impairment (MCI): A study of functional brain connectivity

Z, et al. Quantitative EEG in early Alzheimer’s disease patients-power spectrum and complexity features. Int J Psychophysiol. 2008;68(1):75-80. [5] Adeli H, Ghosh-Dastidar S, Dadmehr N. A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of Alzheimer’s disease. Neurosci Lett. 2008;444(2):190-194. [6] Coben LA, Danziger WL, Storandt M. A longitudinal EEG study of mild senile dementia of Alzheimer type: changes at 1 year and at 2.5 years. Electroencephalogr Clin Neurophysiol. 1985;61(2):101-112. [7] Giaquinto S, Nolfe G. The EEG in

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The Procedure for SPECT and BEAM Images Adjustment Visualisation of EEG Electrodes in SPECT Images

References Chang DJ, Zubal G, Gottschalk C, Necochea A, Stokking R, Studholme C, Corsi M, Slavski J, Spencer SS, Blumenfeld H. Comparison of Statistical Parametric Mapping and SPECT difference imaging in patient with temporal lobe epilepsy, Epilepsia 2002; 43(1): 68-74. Goszczyńska H, Kowalczyk L, Świderski B, Kolebska K, Dec S, Zalewska E, Miszczak J. Ocena ilościowo-porównawcza sekwencji czasowych map EEG w celu ustalenia kierunków propagacji pobudzenia. XV Krajowa Konferencja Biocybernetyki i Inżynierii

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Assessment of myocardial metabolic rate of glucose by means of Bayesian ICA and Markov Chain Monte Carlo methods in small animal PET imaging

;52(2):201-209. [13] Naganawa M, Kimura Y, Ishii K, et al. Temporal and spatial blood information estimation using Bayesian ICA in dynamic cerebral positron emission tomography. Dig Sig Process. 2007; 17(5):979-993. [14] Chen K, Chen X, Renaut R, et al. Characterization of the image-derived carotid artery input function using independent component analysis for the quantitation of [18F] fluorodeoxyglucose positron emission tomography images. Phys Med Biol. 2007;52(23):7055-7071. [15] Su KH, Lee JS, Yang YW, et al. Partial volume correction of the microPET blood input

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Emergence of Convolutional Neural Network in Future Medicine: Why and How. A Review on Brain Tumor Segmentation

. 2004, Springer. p. 115-133. [72] George, D., How the brain might work: A hierarchical and temporal model for learning and recognition. 2008, Citeseer. [73] Bouvrie JV. Hierarchical learning: Theory with applications in speech and vision. 2009, Citeseer. [74] Poggio T. How the brain might work: The role of information and learning in understanding and replicating intelligence. Information: Science and Technology for the New Century, 2007: 45-61. [75] Deng L, Yu D. Deep Learning. Signal Processing. 2014;7:3-4. [76] Hinton GE, Osindero S

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Diversity and distribution of testate amoebae (Amoebozoa, Rhizaria) in reservoirs, Northeastern Bulgaria

time: trends in wader communities on British estuaries. Diversity and Distribution 2012 , 18 , 356-365. [4]. Schwind, L.; Arrieira, R.; Mantovano, T.; Bonecker, C.; Lansac-Tôha, F., Temporal influence on the functional traits of testate amoebae in a floodplain lake, Limnetica 2016 , 35 (2) , 355-364. [5]. Disaster Protection Plan of Razgrad District, 2014 , p. 18. [6]. Deflandre, G., Le genre Arcella Ehrenberg, Arch. Protistenk . 1928 , 64

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