## 1 Introduction  
Electroencephalography (EEG) belongs to non-invasive techniques of neuroimaging, offering high temporal resolution, although its spatial resolution is poor. Despite the fact that the EEG technique is relatively inexpensive and widely applied, artifacts present in the signal are still a subject of research. Artifacts are noisy, non-cerebral origin fragments of the signal. The presence of artifacts limits the usefulness of the signal and might influence the analysis process. Among the most common type of artifacts, one can find ocular movements, eye blinks, or cardiac activity. As different types of artifacts have specific characteristics, in the literature, authors often concentrate on analysing a particular type of artifact [1, 2]. Artifacts can be divided into two groups: physiological and non-physiological origins [3]. Physiologically-originated artifacts are caused by behaviour, movement and physiology of the person examined. This group covers ocular artifacts, including eye movement and blinks, which cause strong, high-frequency interference with large amplitude visible especially in the frontal electrodes [4]. Other common artifacts in this group are muscle artifacts which represent myogenic activity visible when a patient is moving, talking, moving the jaw or walking [3]. The shape and amplitude of the muscular artifactsdiffers depending on type of muscles involved and the degree of their contraction [2]. The most common artifacts originate from the head, face and neck and are detectable by electrodes located over the entire head [5]. Among other popular physiological artifacts, cardiac activity is often considered. Electrical activity of the heart is represented by characteristic, slow periodic waves and might be observed if electrodes are placed over pulsating vessel [6]. To the physiological artifacts group belong also artifacts related to sweat gland and skin potentials including sweating [6]. Non-physiological or technical originated artifacts might be caused by improper skin-to-electrode contact which might be related to badly applied gel or electrode displacement or damage [7]. Other problems might occur in case of inadequately prepared research environments. A well-known factor is the electric field of external electronic devices, which is usually filtered out with notch filter. The problem of artifacts detecting and removing has been well known for years. Performing this task with a minimal loss of data and keeping the signal quality is difficult. The traditional approach covers manual or automatic data cleaning consisting of removing noisy parts of the signal [4,8]. Existing baseline in EEG signal also makes it more difficult for feature extraction. Thus, baseline removal from EEG records is also important [9]. Automatic detection of artifacts was based on rigid measures such as kurtosis, standard deviation or voltage changes [10]. Such procedures are often applied in case of typical artifacts such as eye-, muscle- or electrode-related contaminations. Unfortunately, they might lead to the loss of a significant part of the recorded signal including informative data. Development of brain-computer interfaces, including online signal registration and analysis, forced the development of methods of signal correction instead of cutting out its fragments. In our opinion, it is also possible to use the measures developed this way in traditional offline experiments to avoid the influence of the subjective factor (in the process of manual signal rejection) and high loss of the signal (in case of automatic signal cleaning). Currently, popular methods of EEG artifacts elimination often employ spatial filtering techniques [1]. Among them, well known are independent component analysis (ICA) [11], principal component analysis (PCA) [12] or canonical correlation analysis [13]. Out of these methods, especially ICA has been developed with different implementation versions and extensions [14; 15; 16; 17].