## 1 Introduction
The method was tested on EEG data gathered during such experimental paradigms as steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP) and motor imagery (MI). In [32], the authors proposed a Hardware-Oriented Memory-Limited Online ASR (HMO-ASR) algorithm which uses PCA-based and z-score-based preprocessing to clean the data in each window as well as iterative mean, standard deviation, and covariance updated in a parallel. These modifications enabled obtaining up to a 98.64% reduction in memory size, which makes the HMO-ASR algorithm useful in mobile devices or other hardware with limited memory. In [27] an ASR optimising method was presented. In the paper, the researchers showed that customisation of ASR parameters might have a positive influence on ASR performance in application to the low-density EEG. The aim of our work was to present and compare the effectiveness of two approaches of artifact correction which might be applied automatically. The first approach is a well known, widely applied artifact correction based on the ICA method. The second approach is ASR followed by ICA (ASR + ICA). The ASR is a method less frequently used but which is gaining the trust of researchers, including in EEG-processing pipelines. The main question of the paper is whether there are any differences between pure ICA and ASR with the ICA approach in the context of effects in widely applied, common EEG experimental procedures such as resting-state with eyes open and closed, stroboscopic stimulation with eyes closed and oddball procedure. The results were compared for the signal-to-noise ratio calculated separately for each paradigm. The choice of the procedures was dictated by the purpose of comparing the way ICA and ASR + ICA correct artifacts in various experimental conditions. Each and every procedure tested in the current study allows for the investigation of different mental processes and is frequently used in cognitive neuroscience research and in biomedical applications in general. This type of research is usually characterised by a smaller study group size [33] than in the case of testing machine learning algorithms, for example. They are also conducted by researchers with different backgrounds and competencies in advanced signal analysis. Therefore, they may rely more on the default settings of automatic signal preprocessing algorithms or suggestions from the developers of specific implementations. Hence, our comparisons are based on the default parameters of popular implementations of ICA and ASR and classical experimental procedures in cognitive neuroscience. In addition, we limited the range of the analysed signal to frequencies from 1 Hz to 40 Hz. We decided to do this because of the recommendations of some authors, who suggest using a high-pass filter at 1 or 2 Hz to optimise the results of ICA decomposition [34]. We also wanted to minimise line noise's impact on the algorithms' performance. For each of the selected experimental procedures, the commonly analysed signal changes were also within the chosen frequency range. An experimental procedure commonly used in connectivity analyses and simultaneous recording of EEG-fMRI is the resting-state paradigm. Research results regarding resting-state suggest that the brain should not be considered as being in an idle state and activatedby external stimuli only. The brain is likely to be intrinsically active in an organised way which supports stimulus processing [35]. Resting-state measurement is typically performed with the eyes closed or open. When both conditions are included, the brain's response to visual stimulation can be evaluated, especially in the form of changes in alpha band activity in occipital areas [36]. Stroboscope light stimulation is also used to evaluate visual cortex activity [37]. If the light stimulation frequency is steady, it is possible to observe increased power equal to this frequency in the EEG in occipital areas.