Saeid Sanei - EEG Signal Processing and Machine Learning

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.

Traditionally, many brain disorders are diagnosed by visual inspection of EEG signals. The clinical experts in the field are familiar with manifestation of brain rhythms in the EEGs. In healthy adults, the amplitudes and frequencies of such signals change from one state of a human to another such as wakefulness to sleep and vice versa. The characteristics of the waves also change with age. There are five major brain waves distinguished by their different frequency ranges. These frequency bands from low to high frequencies respectively are called alpha (α), theta (θ), beta (β), delta (δ) and gamma (γ). The alpha and beta waves were introduced by Berger in 1929. Jasper and Andrews (1938) used the term ‘gamma’ to refer to the waves of above 30 Hz. The delta rhythm was introduced by Walter (1936) to designate all frequencies below the alpha range. He also introduced theta waves as those having frequencies within the range 4–7.5 Hz. The notion of a theta wave was introduced by Walter and Dovey in 1944 [1].

Delta waves lie within the range of 0.5–4 Hz. These waves are primarily associated with deep sleep and may be present in the waking state. It is very easy to confuse artefact signals caused by the large muscles of the neck and jaw with the genuine delta response. This is because the muscles are near the surface of the skin and produce large signals, whereas the signal, which is of interest, originates from deep within the brain and is severely attenuated in passing through the skull. Nevertheless, by applying simple signal analysis methods to the EEG, it is very easy to see when the response is caused by excessive movement.

Theta waves lie within the range of 4–7.5 Hz. The term theta might be chosen to allude to its presumed thalamic origin. Theta waves appear as consciousness slips towards drowsiness. Theta waves have been associated with access to unconscious material, creative inspiration and deep meditation. A theta wave is often accompanied by other frequencies and seems to be related to level of arousal. We know that healers and experienced mediators have an alpha wave which gradually lowers in frequency over long periods of time. The theta wave plays an important role in infancy and childhood. Larger contingents of theta wave activity in the waking adult are abnormal and are caused by various pathological problems. The changes in the rhythm of theta waves are examined for maturational and emotional studies [2].

The alpha waves appear in the posterior half of the head and are usually found over the occipital region of the brain, and can be detected in all parts of posterior lobes of the brain. For alpha waves the frequency lies within the range 8–13 Hz, and commonly appears as a round or sinusoidal shape signal. However, in rare cases it may manifest itself as sharp waves. In such cases, the negative component appears to be sharp and the positive component appears to be rounded, similar to the wave morphology of the rolandic mu (μ) rhythm. Alpha waves have been thought to indicate both a relaxed awareness without any attention or concentration. The alpha wave is the most prominent rhythm in the whole realm of brain activity and possibly covers a greater range than has been previously accepted. You can regularly see a peak in the beta wave range in frequencies even up to 20 Hz, which has the characteristics of an alpha wave state rather than one for a beta wave. Again, we very often see a response at 75 Hz which appears in an alpha’ setting. Most subjects produce some alpha waves with their eyes closed and this is why it has been claimed that it is nothing but a waiting or scanning pattern produced by the visual regions of the brain. It is reduced or eliminated by opening the eyes, by hearing unfamiliar sounds, by anxiety or mental concentration or attention. Albert Einstein could solve complex mathematical problems while remaining in the alpha state; although generally, beta and theta waves are also present. An alpha wave has a higher amplitude over the occipital areas and has an amplitude of normally less than 50 μV. The origin and physiological significance of an alpha wave is still unknown and yet more research has to be undertaken to understand how this phenomenon originates from cortical cells [3].

A beta wave is the electrical activity of the brain varying within the range of 14–26 Hz (though in some literature no upper bound is given). A beta wave is the usual waking rhythm of the brain associated with active thinking, active attention, focus on the outside world or solving concrete problems, and is found in normal adults. A high‐level beta wave may be acquired when a human is in a panic state. Rhythmical beta activity is encountered chiefly over the frontal and central regions. Importantly, a central beta rhythm is related to the rolandic mu rhythm and can be blocked by motor activity or tactile stimulation. The amplitude of beta rhythm is normally under 30 μV. Similar to the mu rhythm the beta wave may also be enhanced because of a bone defect [1] and also around tumoural regions.

The frequencies above 30 Hz (mainly up to 45 Hz) correspond to the gamma range (sometimes called as the fast beta wave). Although the amplitudes of these rhythms are very low and their occurrence is rare, detection of these rhythms can be used for confirmation of certain brain diseases. The regions of high EEG frequencies and highest levels of cerebral blood flow (as well as oxygen and glucose uptake) are located in the frontocentral area. The gamma wave band has also been proved to be a good indication of event‐related synchronization (ERS) of the brain and can be used to demonstrate the locus for right and left index finger movement, right toes and the rather broad and bilateral area for tongue movement [4].