EEG Signal Processing and Machine Learning. Saeid Sanei

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Название EEG Signal Processing and Machine Learning
Автор произведения Saeid Sanei
Жанр Программы
Серия
Издательство Программы
Год выпуска 0
isbn 9781119386933



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a balance of excitatory and inhibitory interactions within and between populations of neurons. Although it is well established that the EEG (or MEG) signals result mainly from extracellular current flow, associated with summed post‐synaptic potentials in synchronously activated and vertically oriented neurons, the exact neurophysiological mechanisms resulting in such synchronization to a given frequency band, remain obscure.

      Networks are already an integral part of human daily social, business, and intellectual life. Networking science is appealing to the field of neuroscience as the brain function stems from the communication and signalling between the neurons. The measured EEG amplitudes probed at each electrode have been the main parameter in evaluating brain function. Synchrony between left and right brain lobes gives further insight into detection of abnormalities such as mental fatigue and dementia and is associated with many other brain states such as emotions, as stated in the next chapter. The synchrony is often measured in the frequency domain where the variations in frequency and phase, corresponding to the time delay between the lobes, can be easily measured. More generally, recent developments in network science, however, have created a new direction in the study of brain normal and abnormal functions.

      Although the fundamental concepts in network science originated from mathematics [28] and are used mostly in communications, a number of well established approaches, such as autoregressive modelling, have been used in characterizing the brain functional connectivity from the multichannel EEG. In addition, graph theory has become popular in designing effective classifiers which can segment the EEGs in time–space into the regions each encompassing a separate functionally connected brain region. In [29], a review of recent advances in neuroscience research in the specific area of brain connectivity as a potential biomarker of Alzheimer's disease with a focus on the application of graph theory can be studied.

      Later in this book, we derive equations for the graphs applied to EEG in a similar way to those of brain connectivity estimators. We also observe that machine learning techniques such as deep neural networks can be directly applied to graphs for recognition of the brain state.

      1 1 Caton, R. (1875). The electric currents of the brain. British Medical Journal 2: 278.

      2 2 Walter, W.G. (1964). Slow potential waves in the human brain associated with expectancy, attention and decision. Archiv für Psychiatrie und Nervenkrankheiten 206: 309–322.

      3 3 Cobb, M. (2002). Exorcizing the animal spirits: Jan Swammerdam on nerve function. Neuroscience 3: 395–400.

      4 4 Danilevsky, V.D. (1877). Investigation into the physiology of the brain. Doctoral thesis. University of Charkov, Quoted after Brazier.

      5 5 Brazier, M.A.B. (1961). A history of the electrical activity of the brain; the first half‐century. New York: Macmillan.

      6 6 Massimo, A. (July 2004). In memoriam Pierre Gloor (1923–2003): an appreciation. Epilepsia 45 (7): 882.

      7 7 Grass, A.M. and Gibbs, F.A. (1938). A Fourier transform of the electroencephalogram. Journal of Neurophysiology 1: 521–526.

      8 8 Haas, L.F. (2003). Hans Berger (1873–1941), Richard Caton (1842–1926), and electroencephalography. Journal of Neurology, Neurosurgery, and Psychiatry 74: 9.

      9 9 Spear, J.H. (2004). Cumulative change in scientific production: research technologies and the structuring of new knowledge. Perspectives on Science 12 (1): 55–85.

      10 10 Kornmüller, A.E. (1935). Der Mechanismus des epileptischen anfalles auf grund bioelektrischer untersuchungen am zentralnervensystem. Fortschritte der Neurologie‐Psychiatrie 7: 391–400; 414–432.

      11 11 Fischer, M.H. (1933). Elektrobiologische auswirkungen von krampfgiften am zentralnervensystem. Medizinische Klinik 29: 15–19.

      12 12 Fischer, M.H. and Lowenbach, H. (1934). Aktionsstrome des zentralnervensystems unter der einwirkung von krampfgiften, 1. Mitteilung Strychnin und Pikrotoxin. Naunyn‐Schmiedebergs Archiv für experimentelle Pathologie und Pharmakologie 174: 357–382.

      13 13 Bremer, F. (1935). Cerveau isole’ et physiologie du sommeil. Compte Rendu de la Sociéte de Biologie (Paris) 118: 1235–1241.

      14 14 Niedermeyer, E. (1999). Chapter 1, Electroencephalography, basic principles, clinical applications, and related fields. In: Historical Aspects, 4e (eds. E. Niedermeyer and F.L. da Silva), 1–14. Lippincott Williams & Wilkins.

      15 15 Berger, H. (1929). Über das Elektrenkephalogramm des Menschen. 7th report. Archiv für Psychiatrie, Nervenkr, 100: 301–320.

      16 16 Avoli, M. (1969). Jasper's Basic Mechanisms of the Epilepsies [Internet]. 4e, Bethesda, MD: NCBI. https://www.ncbi.nlm.nih.gov/books/NBK98150 (accessed 9 September 2020).

      17 17 Motokawa, K. (1949). Electroencephalogram of man in the generalization and differentiation of condition reflexes. The Tohoku Journal of Experimental Medicine 50: 225.

      18 18 Niedermeyer, E. (1973). Common generalized epilepsy. The so‐called idiopathic or centrencephalic epilepsy. European Neurology 9 (3): 133–156.

      19 19 Aserinsky, E. and Kleitman, N. (1953). Regularly occurring periods of eye motility, and concomitant phenomena, during sleep. Science 118: 273–274.

      20 20 Speckmann, E.‐J. and Elger, C.E. (1999). Introduction to the neurophysiological basis of the EEG and DC potentials. In: Electroencephalography, 4e (eds. E. Niedermeyer and F. Da Silva), 15–34. Lippincott Williams and Wilkins.

      21 21 Shepherd, G.M. (1974). The Synaptic Organization of the Brain. London: Oxford University Press.

      22 22 Caspers, H., Speckmann, E.‐J., and Lehmenkühler, A. (1986). DC potentials of the cerebral cortex, seizure activity and changes in gas pressures. Reviews of Physiology, Biochemistry and Pharmacology 106: 127–176.

      23 23 Ka Xiong Charand. (2011). Action potentials. http://hyperphysics.phy‐astr.gsu.edu/hbase/biology/actpot.html (accessed 19 August 2021).

      24 24 Attwood, H.L. and MacKay, W.A. (1989). Essentials of Neurophysiology. Hamilton, Canada: B. C. Decker.

      25 25 Nunez, P.L. (1995). Neocortical Dynamics and Human EEG Rhythms. New York: Oxford University Press.

      26 26 Teplan, M. (2002). Fundamentals of EEG measurements. Measurement Science Review 2 (Sec. 2): 1–11.

      27 27 Bickford, R.D. (1987). Electroencephalography. In: Encyclopedia of Neuroscience (ed. G. Adelman), 371–373. Cambridge (USA): Birkhauser.

      28 28 Sporns, O. (2011). Networks of the Brain. MIT Press.

      29 29 del Etoile, J. and Adeli, H. (2017). Graph theory and brain connectivity in Alzheimer's disease. The Neuroscientist 23 (6): 616–626.

      30 30 Shipton, H.W. (1975). EEG analysis: a history and prospectus. Annual Reviews, University of Iowa, USA: 1–15.

      2.1 Brain Rhythms

      Traditionally, many brain disorders are diagnosed by visual inspection of EEG signals. The