Читать книгу EEG Signal Processing and Machine Learning - Saeid Sanei - Страница 21

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].

Waves in frequencies much higher than the normal activity range of EEG, mostly in the range of 200–300 Hz have been found in cerebellar structures of animals, but they have not played any role in clinical neurophysiology [5, 6].

Figure 2.1 shows the typical normal brain rhythms with their usual amplitude levels. In general, the EEG signals are the projection of neural activities which are attenuated by leptomeninges, cerebrospinal fluid, dura matter, bone, galea, and the scalp. Cortiographic discharges show amplitudes of 0.5–1.5 mV in range and up to several millivolts for spikes. However, on the scalp the amplitudes commonly lie within 10–100 μV.

Figure 2.1 Five (can be categorized as four) typical dominant brain normal rhythms, from high to low frequencies. The delta wave is observed in infants and sleeping adults, the theta wave in children and sleeping adults, the alpha wave is detected in the occipital brain region when there is no attention, the beta wave appears frontally and parietally with low amplitude during attention and concentration, and gamma for stressed brain under heavy workload.

The above rhythms may last if the state of the subject does not change and therefore they are approximately cyclic in nature. Conversely, there are other brain waveforms, which may:

1 Have a wide frequency range or appear as spiky type signals such as K‐complexes, vertex waves (which happen during sleep), or a breach rhythm, which is an alpha‐type rhythm due to cranial bone defect [7], which does not respond to movement, and is found mainly over the midtemporal region (under electrodes T3 or T4), and some seizure signals.

2 Be a transient such as an event‐related potential (ERP) and contain positive occipital sharp transient (POST) signals (also called rho [ρ]) waves.

3 Originate from the defected regions of the brain such as tumoural brain lesions.

4 Be spatially localized and considered as cyclic in nature, but can be easily blocked by physical movement such as mu rhythm. Mu denotes motor and is strongly related to the motor cortex. Rolandic (central) mu is related to posterior alpha in terms of amplitude and frequency. However, the topography and physiological significance are quite different. From the mu rhythm one can investigate the cortical functioning and the changes in brain (mostly bilateral) activities subject to physical and imaginary movements. The mu rhythm has also been used in feedback training for several purposes such as treatment of epileptic seizure disorder [1].

Also, there are other rhythms introduced by researchers such as:

1 Phi (φ) rhythm (less than 4 Hz) occurring within two seconds of eye closure. The phi rhythm was introduced by Daly [3].

2 The kappa (κ) rhythm, which is an anterior temporal alpha‐like rhythm and it is believed to be the result of discrete lateral oscillations of the eyeballs and is considered to be an artefact signal.

3 The sleep spindles (also called sigma [σ] activity) within the 11–15 Hz frequency range.

4 Tau (τ) rhythm which represents the alpha activity in the temporal region.

5 Eyelid flutter with closed eyes which gives rise to frontal artefacts in the alpha band.

6 Chi rhythm is a mu‐like activity believed to be a specific rolandic pattern of 11–17 Hz. This wave has been observed during the course of Hatha Yoga exercises [8].

7 Lambda (λ) waves are most prominent in waking patients, although they are not very common. They are sharp transients occurring over the occipital region of the head of walking subjects during visual exploration. They are positive and time‐locked to saccadic eye movement with varying amplitude, generally below 90 μV [9].

The chart in Figure 2.2 shows all possible waveforms which may appear in a scalp EEG. The waveforms can be normal or abnormal rhythms during awake or sleep as well as various artefacts.

Often it is difficult to understand and detect the brain rhythms from the scalp EEGs even with trained eyes. Application of advanced signal processing tools, however, should enable separation and analysis of the desired waveforms from within the EEGs. Therefore, definition of foreground and background EEG is very subjective and entirely depends on the abnormalities and applications. We next consider the development in the recording and measurement of EEG signals.

An early model for the generation of brain rhythms is that of Jansen and Rit [10]. This model uses a set of parameters to produce alpha activity through an interaction between inhibitory and excitatory signal generation mechanisms in a single area. The basic idea behind these models is to make excitatory and inhibitory populations interact such that oscillations emerge. This model was later modified and extended to generate and emulate the other main brain rhythms, i.e. delta, theta, beta, and gamma, too [11]. The assumptions and mathematics involved in building the Jansen model and its extension are explained in this chapter. Application of such models in generation of post‐synaptic potentials and using them as the template to detect, separate, or extract ERPs is of great importance. In Chapter 3 of this book, we can see the use of such templates in the extraction of the ERPs.