The use of multimodal data sets is a common practice in the field of mobile neuroimaging. The field, however, suffers from a lack of a unified system for curating data. Discontinued projects and academic positions often leave the data sets or software packages unmaintained, making them difficult to use or scrutinize. This issue critically influences the quality and reproducibility of findings using mobile neuroimaging. We are committed to fostering an environment where Open Science is the norm, in addition to making tangible contributions to the extent possible.
EEGManySteps is a community-driven initiative that arose from the direct needs of researchers. It is an inter-institute collaboration that is forming based on mutual interest in the topic. We strive to implement good scientific practices by informing peers of the concept, pointing them to tools, and implementing guidelines within the group.
In the evolving field of Mobile brain/body imaging (MoBI) imaging using electroencephalography (EEG), understanding how different experimental setups influence results is crucial. EEGManySteps is an international collaborative effort aimed at collecting and analyzing EEG data across multiple laboratories to investigate these influences, with a specific focus on gait-related independent components.
EEGManySteps differs from traditional replication studies by emphasizing data collection across diverse settings rather than replicating specific findings. By leveraging both preexisting datasets and newly acquired data, we seek to understand how variations in experimental setups and (pre)processing pipelines affect the consistency and reliability of EEG results.
To achieve this, we will standardize data acquisition and analysis protocols, allowing us to integrate datasets from different sources. Our goal is to identify which datasets are compatible and determine the most influencing factors for analyzing gait-related EEG activity.