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Electroencephalography (EEG) data recordings can be contaminated by artefacts that reduce the quality and make analysis difficult, and therefore cleaning methods are essential for accurate analysis of EEG data. It is not yet well established how to measure performance based on measured contaminated data since there is no established benchmark for comparison. Here we use "clean" EEG data synthetically contaminated by electrocardiography (ECG), electrooculography (EOG) and electromyography (EMG). This introduces fewer assumptions to the comparison between various cleaning methods, providing a clear datum for comparison. Further contamination is controlled, adding artefacts individually and also as a combination of artefacts. The results show that signal to noise ratio (SNR) of the simulated artefacts was within the same ranges as found with measured artefacts from literature. Popular linear cleaning methods were evaluated on the dataset, showing similar results to those in the literature, further validating the usefulness and accuracy of the semi-synthetic dataset. The semi-synthetic dataset showed comparable characteristics to real measured EEG data and proved useful in the assessment of EEG cleaning methods. The cleaning methods showed varied results when performance was evaluated on individual artefacts. [ABSTRACT FROM AUTHOR]

The simulated data used in eye-tracking-related research has been largely generated using normative eye models with little consideration of how the variations in eye biometry found in the population may influence eye-tracking outcomes. This study investigated the influence that variations in eye model parameters have on the ability of simulated data to predict real-world eye-tracking outcomes. The real-world experiments performed by two pertinent comparative studies were replicated in a simulated environment using a highcomplexity stochastic eye model that includes anatomically accurate distributions of eye biometry parameters. The outcomes showed that variations in anterior corneal asphericity significantly influence simulated eye-tracking outcomes of both interpolation and model-based gaze estimation algorithms. Other, more commonly varied parameters such as the corneal radius of curvature and foveal offset angle had little influence on simulated outcomes., Competing Interests: The author(s) declare(s) that the contents of the article are in agreement with the ethics described in http://biblio.unibe.ch/portale/elibrary/BOP/jemr/ethics.html and that there is no conflict of interest regarding the publication of this paper., (Copyright (©) 2023 Joshua Fischer, David Vandenheever, Johan van der Merwe.)