LT1: Harnessing Existing EEG Datasets for Novel Hypothesis Testing
The field of cognitive neuroscience has accumulated a vast amount of EEG data, yet new studies often prioritize fresh data collection over utilizing these existing resources. Repurposing publicly available EEG datasets presents a cost-effective and efficient way to explore novel hypotheses. However, EEG analysis involves complex preprocessing and interpretation steps, where subjective decisions, such as artifact rejection, filtering techniques, and statistical methods, can significantly impact results. To enhance transparency and reliability, a collaborative framework in which more than one researcher independently analyses the same EEG dataset and identifies robust findings can be a significant step forward. This approach not only mitigates individual biases but also promotes best practices in EEG research within the open science movement. In this talk, we highlight the potential of existing EEG datasets, discuss strategies for fostering community-driven EEG analysis, and call for collective collaboration in this endeavor.