The increasing availability of psychological and neuroscience datasets and the growing integration of machine learning techniques in cognitive neuroscience have expanded opportunities for electroencephalography (EEG) research using existing data. Researchers can now conduct sustained investigations through advanced analysis of openly available data, even when direct EEG measurements are not feasible. This study analyzed open EEG datasets recorded during music listening to address a research question distinct from the original study. We applied machine learning methodologies to the EEG data and explained the fundamental aspects of this approach. To facilitate replication and further research, we have published our analysis program online on OSF (https://doi.org/10.17605/OSF.IO/KYBEF). This study aims to establish a foundational framework for analyzing open data with machine learning, supporting future research endeavors in this field.

Displaying waveforms is essential in event-related potential (ERP) research. Although many studies only present grand average waveforms, this method inadequately represents data distributions and noise levels, potentially distorting data interpretation. Recent trends in psychology and neuroscience research advocate the inclusion of individual data, standard deviations, standard errors, and 95% confidence intervals for comprehensive data visualization. In ERP research, it is recommended to visualize individual waveforms or error ranges in addition to grand average waveforms. However, such advanced data visualization techniques often require a certain level of programming skills, which can be daunting for novices. We have developed an easy-to-use web application for interactive plotting of ERP waveforms and summary statistics (https://kiapp.shinyapps.io/PLOT_ERP/). This paper provides an overview of the application. The R source code for the application is also available on the Open Science Framework project page (https://osf.io/vke3n/).