### **Paper Link** https://doi.org/10.1177/23982128211053698 *** ## **Abstract** The study assessed a mobile electroencephalography (EEG) system with water-based electrodes for its applicability in cognitive and behavioural neuroscience. It was compared to a standard gel-based wired system. EEG was recorded on two occasions (first with gel-based, then water-based system) as participants completed the flanker task. Technical and practical considerations for the application of the water-based system are reported based on participant and experimenter experiences. Empirical comparisons focused on EEG data noise levels, frequency power across four bands (theta, alpha, low beta and high beta) and event-related components (P300 and ERN). The water-based system registered more noise compared to the gel-based system which resulted in increased loss of data during artefact rejection. Signal to noise ratio was significantly lower for the water-based system in the parietal channels which impacted the observed parietal beta power. It also led to a shift in topography of the maximal P300 activity from parietal to frontal regions. It is also evident, that the water-based system may be prone to slow drift noise which may affect the reliability and consistency of low frequency band analyses. Practical considerations for the use of water-based electrode EEG systems are provided. *** ## **Files** [**Supplementary & Method Files**][1] Includes a supplementary file with methodological details that provide further clarifications to the paper. We also included a more detailed document with Mobita setup and protocol as used in the current study. There is also a video example showing how we checked for electrode signal quality using the live spectral power option in the Acqknowledge software. We provide a screenshot with the evaluation of statistical power in the current study. In the **Flanker task** folder, we provide the Eprime task files that were used to run the study. [**Raw Data Files**][2] Contains folders with raw EEG data recorded with the EasyCap and Mobita and the accompanying Eprime flanker task files. For Mobita, there is an additional folder that contains the files that were used to construct event marker information to be used for EEG analyses. The R script that was used to create the final event marker files is also included. For EasyCap, the Perl script that was used for re-coding response markers is available in the Eprime folder. [**GitHub Repository: EasyCap_Mobita Report**][3] The repository contains the complete log (R Markdown) of all statistical analyses and data visualisation. All data files extracted directly from BrainVision Analyzer are provided. Types of files included in the repository are described in detail in the README file (scroll to the bottom of the repository). [1]: https://osf.io/rtzcg/ [2]: https://osf.io/3wqut/ [3]: https://github.com/m-topor/EasyCap_Mobita_Report