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Ehinger, B.

Publications and source records attributed to Ehinger, B..

2 recordsLinked to original sources

The Art of Brainwaves: A Survey on Event-Related Potential Visualization Practices

Electroencephalography (EEG) and event-related potentials (ERPs) have been analyzed for more than 70 years. Yet, we know little about how practitioners visualize the results of their analyses. Here, we designed an online survey (n=213) targeting M/EEG practitioners from novice to expert level. Our primary goal is to better understand the visualization tools currently in use, the challenges researchers face, and their experiences and opinions on how best to display their brain data. Finally, we explored whether researchers are aware of more general visualization issues. In this paper we provide an overview of the most popular ERP visualization tools. Then, we found that the community does not have a unique nomenclature to refer to some plot types, and we propose a set of recommendations to name the most popular ERP plot types. Finally, we provide an analysis of practitioner feature preferences for software developers and conclude with further recommendations for ERP practitioners.

neuroscience↗

The effect of estimation time window length on overlap correction in EEG data

Regression Event-Related-Potentials (ERPs) with overlap correction (also referred to, as linear deconvolution, or temporal response functions) are becoming more popular for the analysis of Electroencephalography (EEG) data. A common question for the analyst is, how to specify the length of the estimation windows. Long estimation windows might capture all relevant event-related activity, but might introduce artifacts due to overfit, short estimation windows might not overfit, but also might not capture all (overlapping) activity, and thereby introduce bias. Using a systematic simulation approach, we show that longer rather than shorter time windows should be preferred for typical EEG designs. We further provide an interactive app to visualize various design parameters: https://estimationwindow.ccn2023.s-ccs.de

neuroscience↗