## 2 Related Articles  
A variety of approaches to artifact removal can be found in the literature, differing in complexity, effectiveness, and methodology [3, 4]. Popular techniques include filtering, independent component analysis (ICA), linear regression, hybrid methods, and advanced machine learning algorithms. Each method has its advantages and drawbacks, and the choice of approach depends on the type of artifacts, the specifics of the study, and the quality of the collected EEG data. Linear regression is widely used in the scientific literature as an effective method for eliminating artifacts in EEG signals. A key limitation of its basic form is the requirement for simultaneous recording of a reference channel [5, 6]. Linear regression enables modeling the relationship between artifacts and the true EEG signal, allowing noise to be effectively separated from meaningful information. In [7], researchers compared the effectiveness of the eye movement correction procedure (EMCP) for removing EOG artifacts from EEG signals. EMCP uses EOG channels as references, calculating propagation coefficients via the least squares method to determine how EOG signals affect EEG. In [8], a new regression approach called REBLINCA was introduced to remove blink-related artifacts from EEG. Although it relies on regression, REBLINCA does not require an EOG reference channel. Instead, it uses an EEG channel (e.g., Fpz) to detect blinks that serve as templates for correction. Blind source separation (BSS) methods are also employed to remove artifacts by separating source signals without prior knowledge of their nature. An example is independent component analysis [9], which assumes signal sources are independent--an assumption that can also be a limitation. ICA computes a \"separation matrix\" that disentangles the source signals (including artifacts) from the recorded EEG. Artifact removal then involves zeroing out the components associated with these artifacts. In [10], the authors introduced a modification for estimating the separation matrix by maximizing joint entropy via a gradient ascent algorithm, which yielded more effective ECG artifact removal compared to other ICA methods. Another ICA variant, constrained ICA (cICA) [11], was used to remove pulse-related artifacts (PRA) from EEG recorded during functional magnetic resonance imaging. The publication also discusses ARCI, a method that automatically removes cardiac noise from EEG signals without requiring simultaneous ECG recording. In [12], ICA was used to remove artifacts from EEG recorded during simulated bekeeping tasks, leveraging the ICA tool for automatic classification and the removal of artifact sources. ICLabel is based on neural networks and was trained on over 6000 EEG signals containing more than 200,000 ICA components derived from brain activity, eye movements, cardiac activity, and muscle activity [12, 13]. The cleaned data were subsequently used to train a model designed to detect fatigue. Meanwhile, Reference [14] describes a method for removing muscle artifacts from EEG signals using canonical correlation analysis (CCA) as a blind source separation technique. CCA separates signals based on their different degrees of autocorrelation, isolating muscle artifacts--which typically have low autocorrelation--from brain signals, which exhibit higher autocorrelation. In the literature, alternative approaches for artifact removal have also been proposed. Reference [15] focuses on modifying the artifact subspace reconstruction (ASR) algorithm by incorporating Riemannian geometry to improve artifact removal from EEG signals. In article [16], a novel method for automatic artifact removal from long-term EEG recordings was developed and evaluated. The proposed approach is based on a neurophysiological model and employs an iterative Bayesian estimation algorithm. It operates fully automatically, without requiring user interaction, and was positively assessed by independent experts.