**1.4 Artifacts in EEG**

The EEG signal is one of those signals which are most widely used for studying brain functions and for the diagnosis of neurological disorders by physicians, researchers, and scientists. A single misinterpretation can become a cause of

**Figure 7.** *One second recording of clean pure EEG signal.*

misdiagnosis. Henceforth, it is imperative to have a very right and clear image about brain activities being represented by EEG signals shown in **Figure 7**. Skull's low conductivity is the main reason for the poor spatial resolution of scalp EEG.

Furthermore, scalp EEG signals are highly sensitive to the movement of the subject and noises being introduced due to externally likewise human head activation, eye movements, musculature, nearby electrical device interference and because of one's movement conductivity in the electrodes get varies or physicochemical reactions occurred at the electrode sites [6]. Some of the EEG artifacts distributions are displayed in **Figure 8**. All these additional activities are indirectly associated with the subject's current cerebral process and are collectively referred to as background activities. Henceforth, EEG signals are highly enervated and mixed with these non-cerebral

**Figure 8.** *ECG and EOG artifacts.*

impulses known as artifacts or noise. These artifacts or noise fall into two major categories being considered as physiologic and extra-physiologic [5]. Only after removing these artifacts, a true diagnosis can be achieved. Physiologic Artifacts can be produced by any of any sources present in the human body that has an electric dipole or which can generate an electrical or magnetic field that can become a cause of physiologic artifacts.

The following are the types of physiologic artifacts:


The following are the types of extra-physiologic artifacts:


Some of the most EEG corrupting artifacts are discussed as follows:

#### *1.4.1 Electrooculogram (EOG)*

This is mainly used to measure the eye artifacts. Since these measurements are contaminants of EEG signal and so it is not possible to remove this kind of artifacts from the subtraction process only when the exact model of EOG diffusion across the scalp is available [2]. These artifacts are of two types:

I. Eye Blinking.

It is an artifact that is very common in EEG data. This artifact possesses a very high amplitude signal sometimes much greater than the EEG signals of interest. Further, it can corrupt data availed on all electrodes, even those signals too, that are at the back of the head [2].

#### II. Eye Movement.

It is occurring because of the reorientation of the retained corneal dipole [4]. Eye movement's diffusion across the scalp is greater than that being produced by the eye blink artifact.

EOG artifact can be given in the following form:

$$\beta = \frac{\sum (\mathbf{X}\_i - \hat{\mathbf{X}}\_i)(Y\_i - \hat{Y}\_i)}{\sum (\mathbf{X}\_i - \hat{\mathbf{X}}\_i) \uparrow \mathbf{2}} \tag{1}$$

where,

*β* = Estimated EOG present in EEG analysis; *X* = EOG signal; *Y* = EEG signal; *n* = Number of iterations.

#### *1.4.2 Cardiograph (ECG/EKG)*

Cardiograph is generally used to measure pulse or heartbeat, which occurs by an electrode on or near a blood vessel as shown in **Figure 8**. The voltage recording changes due to the expansion and contraction of the vessel [2]. The artifact signal generally has frequency proximity to 1.2 Hz and appears as a sharp spike or smooth wave but it can have a variation that solely depends on the state of the patient. An example has been illustrated below where an EEG signal mixed with ECG/EKG signal and got corrupted due to line interference.

Electrocardiogram signal artifacts can represent by using the following equation:

$$\text{ECG}(t) = \mathbf{R} \cdot \mathbf{s}\_m(t) + \mathbf{R} \cdot \mathbf{s}\_f(t) + \mathbf{N}(t) \tag{2}$$

Where *R* is a random unit vector, *sm*(*t*) and *sf*(*t*) are the three components of the dipole model for the maternal and fetal cardiac vectors, respectively and *N*(*t*) is the noise in each ECG channel at time *t*.

#### *1.4.3 Electromyogram (EMG)*

Electromyogram (EMG) artifacts could be produced because of some movement disorders. Essential tremor and Parkinson's disease could also be responsible for rhythmic 4–6 Hz sinusoidal artifacts which may be mimicked cerebral activity [2].

Following equation shows the EMG signal:

$$\varkappa(n) = \sum\_{r=0}^{N-1} h(r)e(n-r) + w(n) \tag{3}$$

Where.

*x*(*n*) represents EMG signal;

*e*(*n*) point processed, that represent the firing impulse;

*h*(*r*) represents the MUAP (Motor Unit Action Potential);

*w*(*n*) represents zero-mean additive white Gaussian noise;

and *N* represents the number of motor unit firings.

Extra-physiologic Artifacts

These include interference due to electrical equipment, kinesiology artifacts because of the human body or movements of electrodes, and mechanical artifact because of human body movement.
