## 4 Discussion  
The result of ASR + ICA and the ICA comparison in stroboscopic stimulation, oddball, and resting state procedures did not show significant differences. A possible explanation for the significant effect of factor stimuli in oddball condition is that stimulus X was presented five times more frequently than stimulus O during the experiment. The interaction of stimuli and channel factors in the same analysis did not indicate that the difference between X and O stimuli occurred regularly on the specific channels. These differences may be explained by differences in the number of trials averaging in both conditions.  
The results of all experimental conditions might be interpreted as that both methods, ICA and ASR + ICA, allow for achieving a similar quality of the signal. Assuming that  
Figure 5: Results of the oddball task conditions: (**a**) ERP on the Pz electrode averaged across conditions; (**b**) scalp map of the amplitude averaged for the 280–400 ms time window.  
ICA is a widely known and commonly applied algorithm proven to be efficient in EEG artifact analysis, the comparison of ASR + ICA and ICA approaches shows that the signal might be cleared with an ASR method without data loss. Considering the fact that ASR is automated, significantly faster and therefore more convenient to use than manual artifact removal, it might be successfully used in the process of EEG data analysis. It could be also a special advantage in cases where signal processing is conducted in real time, e.g., to control brain-computer interfaces.  
However, our study does not answer all the questions about the potential advantages or disadvantages of automatic signal correction algorithms. There are a number of methods of EEG analysis with specific requirements for the preprocessing of data, such as connectivity or source-localisation. Additionally, there are markers in the electroencephalogram data typical of special clinical groups or research paradigms, e.g., epileptic seizures. The influence of automatic signal correction on the reliability of these effects requires further research, as well as comparing the results of signal classification by algorithms with the indications of neurophysiological experts.  
From the view of computational cost, continuing the ICA analysis with the use of ASR does not affect the pipeline exhaustedly. Computational cost of ICA method (estimated for FastICA implementation) is O(2 md(d + 1)n), where n is the number of samples, d is the dimension and m is the number of iterations [53]. The computational cost of ASR, which is based on the PCA method, is O(d\({}^{2}\)(d + 1)n) [53] on the assumption that PCA is performed in ASR for data chunks into which the signal is divided. The ICA is a method of non-closed form solution, so its algorithms use iterative procedures with problems of convergence difficulties or high computational load [30]. The PCA-based ASR has a closed-form solution and its operation time is very fast compared with ICA, and the result is achieved in a fraction of the time needed to complete the ICA procedure. Even though the selection of the \(k\) parameter in the ASR procedure [27] might affect the calculation performance slightly, such a potential performance loss is not felt throughout the whole procedure. That is why, from the computational point of view, adding ASR to the procedure does not affect the time of calculations.