Analyzing Open EEG Data with Machine Learning: A Music Listening Experiment

Abstract

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.