(1999). EEG and dementia, Chapter 19. In: Electroencephalography, Basic Principles, Clinical Applications, and Related Fields, 4e (eds. E. Niedermeyer and F.L. Da Silva), 349–359. Lippincott Williams & Wilkins.
46 46 Neufeld, M.Y., Bluman, S., Aitkin, I. et al. (1994). EEG frequency analysis in demented and nondemented parkinsonian patients. Dementia 5: 23–28.
47 47 Niedermeyer, E. (1999). Abnormal EEG patterns: epileptic and paroxysmal, Chapter 13. In: Electroencephalography, Basic Principles, Clinical Applications, and Related Fields, 4e (eds. E. Niedermeyer and F.L. Da Silva), 235–260. Lippincott Williams & Wilkins.
48 48 Hughes, J.R. and Gruener, G.T. (1984). Small sharp spikes revisited: further data on this controversial pattern. Electroencephalography and Clinical Neurophysiology 15: 208–213.
49 49 Hecker, A., Kocher, R., Ladewig, D., and Scollo‐Lavizzari, G. Das Minature‐spike‐wave. Das EEG Labor 1: 51–56.
50 50 Geiger, L.R. and Harner, R.N. (1978). EEG patterns at the time of focal seizure onset. Archives of Neurology 35: 276–286.
51 51 Gastaut, H. and Broughton, R. (1972). Epileptic Seizure. Springfield, IL: Charles C. Thomas.
52 52 Oller‐Daurella, L. and Oller‐Ferrer‐Vidal, L. (1977). Atlas de Crisis Epilepticas. Geigy Division Farmaceut.
53 53 Niedermeyer, E. (1999). Nonepileptic attacks, Chapter 28. In: Electroencephalography, Basic Principles, Clinical Applications, and Related Fields, 4e (eds. E. Niedermeyer and F.L. Da Silva), 586–594. Lippincott Williams & Wilkins.
54 54 Creutzfeldt, H.G. (1968). Uber eine eigenartige herdformige erkrankung des zentralnervensystems. Zeitschrift für die gesamte Neurologie und Psychiatrie 57: 1–18, Quoted after W. R. Kirschbaum, 1920.
55 55 Jakob, A. (1968). Uber eigenartige erkrankung des zentralnervensystems mit bemerkenswerten anatomischen befunden (spastistische pseudosklerose, encephalomyelopathie mit disseminerten degenerationsbeschwerden). Deutsche Zeitschrift für Nervenheilkunde 70: 132, Quoted after W. R. Kirschbaum, 1921.
56 56 Niedermeyer, E. (1999). Epileptic seizure disorders, Chapter 27. In: Electroencephalography, Basic Principles, Clinical Applications, and Related Fields, 4e (eds. E. Niedermeyer and F.L. Da Silva), 476–585. Lippincott Williams & Wilkins.
57 57 Small, J.G. (1999). Psychiatric disorders and EEG, Chapter 30. In: Electroencephalography, Basic Principles, Clinical Applications, and Related Fields, 4e (eds. E. Niedermeyer and F.L. Da Silva), 235–260. Lippincott Williams & Wilkins.
58 58 Marosi, E., Harmony, T., Sanchez, L. et al. (1992). Maturation of the coherence of EEG activity in normal and learning disabled children. Electroencephalography and Clinical Neurophysiology 83: 350–357.
59 59 Linden, M., Habib, T., and Radojevic, V. (1996). A controlled study of the effects of EEG biofeedback on cognition and behavior of children with attention deficit disorder and learning disabilities. Biofeedback and Self‐Regulation 21 (1): 35–49.
60 60 Hermens, D.F., Soei, E.X., Clarke, S.D. et al. (2005). Resting EEG theta activity predicts cognitive performance in attention‐deficit hyperactivity disorder. Pediatric Neurology 32 (4): 248–256.
61 61 Swartwood, J.N., Swartwood, M.O., Lubar, J.F., and Timmermann, D.L. (2003). EEG differences in ADHD‐combined type during baseline and cognitive tasks. Pediatric Neurology 28 (3): 199–204.
62 62 Clarke, A.R., Barry, R.J., McCarthy, R., and Selikowitz, M. (2002). EEG analysis of children with attention‐deficit/hyperactivity disorder and comorbid reading disabilities. Journal of Learning Disabilities 35 (3): 276–285.
63 63 Yordanova, J., Heinrich, H., Kolev, V., and Rothenberger, A. (2006). Increased event‐related theta activity as a psychophysiological marker of comorbidity in children with tics and attention‐deficit/hyperactivity disorders. NeuroImage 32 (2): 940–955.
64 64 Jacobson, S. and Jerrier, H. (2000). EEG in delirium. Seminars in Clinical Neuropsychiatry 5 (2): 86–92.
65 65 Onoe, S. and Nishigaki, T. (2004). EEG spectral analysis in children with febrile delirium. Brain & Development 26 (8): 513–518.
66 66 Brunovsky, M., Matousek, M., Edman, A. et al. (2003). Objective assessment of the degree of dementia by means of EEG. Neuropsychobiology 48 (1): 19–26.
67 67 Koenig, T., Prichep, L., Dierks, T. et al. (2005). Decreased EEG synchronization in Alzheimer's disease and mild cognitive impairment. Neurobiology of Aging 26 (2): 165–171.
68 68 Babiloni, C., Binetti, G., Cassetta, E. et al. (2006). Sources of cortical rhythms change as a function of cognitive impairment in pathological aging: a multicenter study. Clinical Neurophysiology 117 (2): 252–268.
69 69 Bauer, G. and Bauer, R. (1999). EEG, drug effects, and central nervous system poisoning, Chapter 35. In: Electroencephalography, Basic Principles, Clinical Applications, and Related Fields, 4e (eds. E. Niedermeyer and F.L. Da Silva), 671–691. Lippincott Williams & Wilkins.
70 70 Beck, E. and Daniel, P.M. (1969). Degenerative diseases of the central nervous system transmissible to experimental animals. Postgraduate Medical Journal 45 (524): 361–370.
71 71 Naidu, S. and Niedermeyer, E. (1999). Digenerative disorders of the central nervous system, Chapter 20. In: Electroencephalography, Basic Principles, Clinical Applications, and Related Fields, 4e (eds. E. Niedermeyer and F.L. Da Silva), 360–382. Lippincott Williams & Wilkins.
3 EEG Signal Modelling
3.1 Introduction
Generation of electrical potentials or magnetic fields, measurable from the brain, is due to a nonlinear sum/distribution of electrochemical active potentials within all the neurons involved in cognitive or movement‐related processes. An accurate model that can link the chemical processes within corresponding neurons generating the active potentials is hard to achieve due to the involvement of various chemicals and chemical processes. Some models, however, have been introduced since 1950s.
Neurons may be considered as signal converters. The brain is a complicated network of a tremendous number of neurons. Figure 3.1a shows a small network of three neurons. Each axon is extended from a soma which is the main neuron body. Neurons transmit and exchange electric signals called action potentials (APs) or spikes, among each other. Neurons receive the spikes at a synapse. Then, the electric signals or information is transmitted in the direction from a dendrite to an axon. Figure 3.1b illustrates the waveform of APs which have an amplitude of approximately 100 mV in the human brain.
Typical neurons do not generate any spikes without input signals which often come from other neurons. A sufficiently large input pulse causes a neuron to generate an output spike whereas no output spike is generated by a small input. Therefore, a neuron possesses a threshold or all‐or‐none characteristic. There is a special period or timing called the refractory period (the timing of the downstroke of the AP) in which the neuron cannot produce any output spike even though a sufficient amount of inputs is applied to the neuron. Hence, a neuron may be considered as a device which transforms or converts the input spike train to another spike train where each output spike (AP), as we will see later, is the integral of a number of input spikes.
3.2 Physiological Modelling of EEG Generation
The popular physiological models aim to best simulate the coupling between two or more neurons. In [1] three models for generation of brain potentials have been introduced and compared.