Developing a data processing pipeline for brain mapping with Curry 7 and Python

Abstract

EEG (Electroencephalographic) signal processing and brain mapping face significant challenges due to low signal-to-noise ratio, inter-subject variability, and high data dimensionality. These limitations hinder the identification of neurophysiological patterns and reduce their clinical utility, especially in contexts requiring high diagnostic precision. Addressing these issues requires advanced preprocessing techniques, artifact removal, and spatial localization of brain activity, which adds complexity to the analysis. This study presents an automated pipeline developed in Python to process multichannel EEG signals (64 channels), acquired using the Neuroscan system and Curry Neuroimaging Suite 7 software in resting-state patients with eyes open. The tool enables data loading, visualization, cleaning, analysis, and export, facilitating its use by researchers and clinical professionals. Its main contribution lies in improving EEG data quality through automated procedures that reduce noise without compromising the original signal, thus supporting more accurate and reliable interpretation in clinical and neuroscientific applications.