EEG-cleanse: an automated pipeline for cleaning electroencephalography recordings during full-body movement

Electroencephalography (EEG) data recorded during full-body movement is prone to artifacts that compromise signal quality. We introduce EEG-cleanse, a modular and fully automated preprocessing pipeline for cleaning EEG signals collected in dynamic, real-world contexts. Designed without reliance on specialized hardware, EEG-cleanse was implemented in Python and MATLAB using EEGLAB toolbox. It integrates structured logging and integration of open-source tools: •Enables automated artifact removal in EEG data recorded during full-body movements.•Preserves neural signals by combining motion-adaptive preprocessing methods with a hybrid strategy for labeling.•Demonstrates reproducible performance across immersive exer-learning gaming sessions.Tested on a dataset of movement-contaminated EEG signals, EEG-cleanse retained over 70 % of recording channels and preserved an average of five brain-related independent components per session. Its performance matches that of a state-of-the-art method without requiring reference sensors, supporting high-quality mobile EEG research in movement-intensive settings. This pipeline enables transparent, reproducible preprocessing for mobile and neuroergonomic EEG studies under naturalistic movement.