## 1 Introduction
Artifact elimination results achieved with ASR were also verified with independent component analysis (ICA) and an independent component classifier. The study results show that ASR might be a powerful automatic artifact elimination method suitable for offline and online studies. Well-chosen parameters enable significantly reducing eye and muscle component activities while leaving most of the brain components. The mentioned researchers, based on empirical results presented in the paper, suggested that the optimal ASR parameter is between 20 and 30. This value is a good compromise between removing non-brain signals and retaining brain activities. The paper also proves that ASR cleaning positively impacts the quality of a subsequent ICA decomposition. The influence analysis of the ASR cut-off parameter was also conducted in other papers. In [28], the authors performed an influence analysis of the ASR cut-off parameter on EEG recordings in motor tasks. In the study, the EEG signal obtained during a cognitive task, single-leg stance, and fast walking was removed and reconstructed using ASR method. The ratios of EEG obtained using ASR with 10 cut-off parameters were compared with results of visual inspection. What is more, the repeatability and dipolarity of independent components were also analysed with an automatic classification tool to assess the number of brain-related independent components. The paper showed that ASR performed better in motor tasks compared with non-movement tasks and the quality index of independent components reached a maximum for cut-off parameter of 10 and higher, whereas the cut-off parameter did not affect the number of independent components. According to the researchers, cut-off parameters less than 10 are not advisable as there is no benefit to using them. What is more, the literature review shows that majority of researchers using ASR in their studies use the standard, default version of parameters [19; 29]. Furthermore, the process of tuning the ASR parameters such as \(k\) (Equation (1)) and sliding window length on real data is usually not performed as the original pure signal is not available [27]. In literature, there are also presented modifications of the ASR method. In [30], the authors presented Riemann ASR (rASR), a modified version of the ASR algorithm. The applied changes covered using Riemannian geometry instead of traditional Euclidean geometry in the task of computing the covariance matrix make calculations faster and more efficient, as this modification enables performing the calculation of covariance matrix estimation instead of decomposing many covariance matrices for small chunks of data. What is more, the presented method uses the geometry-aware PCA method instead of classical PCA to perform eigen decomposition. According to the results, the proposed version of ASR method is efficient and works faster than the original solution. The tests were performed on the eye-blinks reduction task in visual-evoked potentials (VEP). The analysis was based on EEG recordings gathered from 27 subjects during performing VEP tasks under two conditions: indoors and outdoors. Although the results are promising, more extended research should be performed to check the effectiveness of proposed method on other type of artifacts under different conditions. Another version of ASR was proposed by [31], where [PERSON] et al. developed an adaptive online ASR technique using the Hebbian/anti-Hebbian neural networks integrated into the main algorithm. This improvement consists of applying the Hebbian and anti-Hebbian learning rules in order to segment and self-organise the artifact subspace by updating the synaptic weights. This modification addresses the issue related to the fact that fixed threshold in original ASR may reduce the artifact correction effectiveness, especially in cases of poor quality of the reference data.