## 4 Artifacts in the EEG  
As previously noted, EEG signals have potential use in biometric identification [21]. The raw EEG signals can be altered by various neurological and psychological conditions. Numerous factors influence EEG signals, including aging, certain medications or drugs, diseases, sleep deprivation, physical activity, mental states, eye or muscle movements, substance use, metabolic conditions, hydration levels, environmental factors, and even the type and placement of the EEG electrodes.  
EEG is a valuable tool used in diagnosing a range of diseases. For instance, seizures and epilepsy can be identified by analyzing brain activity patterns in EEG recordings [17]. Other neurological diseases, particularly those leading to dementia, can also be detected through EEGs. Structural brain changes, such as tumors, are often reflected in EEG signal alterations. Furthermore, EEG modifications can be helpful in diagnosing and monitoring certain sleep-related disorders, including narcolepsy [13; 15; 74; 75].  
EEG signals are also capable of mirroring an individual's emotions. Distinct brainwave patterns are produced when a person experiences specific emotions such as happiness, sadness, anger, fear, or surprise. Both positive and negative emotions induce changes in EEG activity. For example, sudden emotions like surprise or fear are associated with high-frequency beta activity, indicative of alertness and arousal [48; 88; 89; 90]. Additionally, EEG signals show increased delta and theta activities, which are related to deep relaxation and enhanced mental receptivity. In conclusion, various emotions significantly influence EEG signals [69; 72].  
Muscle movements can significantly influence EEG recordings, often leading to artifacts in the signal. The most common artifact observed in EEG due to muscle activity is the muscle jerk or spike, which is an electrical impulse generated by muscle contractions or relaxations and captured in the EEG recording. Other muscle-related artifacts include movements of the eyes and face, blinking, chewing, and jaw movements. Moreover, muscle movements can modify the characteristics of EEG waveforms, causing them to appear wider, shallower, or to display spikes. These movements can also introduce artifacts into other parts of the EEG recording, such as the baseline and transitions between waveforms [91; 92; 93]. Noise is another element that can impact the EEG signal, leading to interference, distortion, and additional artifacts in the recording. This can complicate the signal analysis process, potentially resulting in inaccurate conclusions. To counteract the effects of noise, various methods are employed, including signal averaging, artifact detection and rejection, signal filtering, multiband or wavelet-based filtering, and the application of robust statistical techniques. These approaches are instrumental in minimizing noise and enhancing the EEG signal's quality for more reliable analysis [94; 95]. The choice of electrodes can also affect the outcome of an EEG recording. Electrodes differ in size, shape, and material, which can influence the EEG signal's quality. Some electrodes, for example, may be better suited for capturing a broader frequency range. The material of the electrode affects its impedance, directly impacting signal quality. The shape of electrodes can be tailored to optimize signal detection from specific scalp locations. Additionally, the conductive gel or paste used to create a connection between the electrode and the skin also influences the signal. Variations in viscosity among different brands or types of conducting gels or pastes can affect the signal quality [96; 97; 41].