## 1 Introduction
Recent research on the artifact-correction problem resulted in the development of a new method, artifact subspace reconstruction (ASR) [18]. ASR is a non-stationary method based on a PCA window, dedicated to automatically detecting and removing artifacts. It is especially suited for removing occasional large-amplitude artifacts from EEG recordings. The method presents a different approach to the problem of a artifactual signal. Instead of traditional data cutting or filtering, it reconstructs the noisy part of the signal based on its other fragments. The construction of ASR algorithm demonstrates a similarity to principal component analysis (PCA) methods, where the components with large variances are rejected and the signal is reconstructed based on remaining components [19; 20; 21]. The ASR uses the clean portion of data (called reference data) to define the thresholds for rejecting components. This clean portion of data might be selected automatically. The ASR uses a PCA method and indicates the components with large variances, which are rejected. The signal is reconstructed based on remaining components. Meaningful brain signals detectable across channels, usually clustered in a particular scalp region, are usually of low variation compared with artifacts, so artifacts should be detected against regions with minimised variance sets. A detailed description of the ASR procedure is explained in [22]. In this paper, ASR was applied to a classical event-related potential (ERP) study, where it was proved to be an effective and safe technique which does not distort the ERP results. This method is relatively new and poorly studied. It has been successfully applied to movement-related artifacts correction and other types of high amplitude noise [23]. In [24] ASR was applied to the EEG data of treadmill walking subjects whereas in [25] ASR was successfully adopted in a low-density wearable EEG case study. The authors in [26] described that the optimal selection of ASR parameter allows removing the artifacts without disturbing the signals related to the brain. The experiment was based on calculating ICA components before and after applying ASR, and then the obtained results were compared quantitatively in terms of efficiency and effectiveness. The measures used to compare the obtained results were the percentage of data modification, variance reduced, coefficient correlation, power of source activities and the percentage of retained power. The authors define that the optimal ASR that the optimal ASR cutoff parameter (\(k\)) may be between 10 to 100. The authors pay special attention at inverse proportion of \(k\) and the degree of data modification, for example, for \(k=100\), approximately 3% of data were modified while \(k=10\), above 60% of the data were modified. The ASR with a threshold \(k=100\) allows removing the artifacts with high amplitude while \(k=10\) may cause both artifacts and brain signal to be removed. The authors, in their research, focused on the eye-blink and eye-movement artifacts. The results show that the artifacts related to ocular activity were not removed completely, but ASR significantly reduced the eye activity for the \(k\), which is less than 100. It is recommended to choose the cutoff parameter from the interval between 10 and 100 as a compromise between removing the artifacts and preserving brain waves. In [27] it was shown that the \(k\) parameter determines how aggressive the procedure of the faulty data removing is. The smaller is the \(k\), the higher aggressiveness is obtained, as the rejection criterion (\(\Gamma_{i}\)) is based on \(k\) parameter multiplied by the standard deviation given by:  
\[\Gamma_{i}=\mu_{i}+k\times\sigma_{i} \tag{1}\]  
In [22], authors discussed the effectiveness of ASR and the optimal choice of its parameter. The authors presented an ASR evaluation study performed on twenty EEG recordings gathered during simulated driving trials.