PyLossless: A non-destructive EEG processing pipeline

Abstract

EEG recordings are typically long and contain large amounts of data, making manual cleaning a time-consuming and error-prone task. Automated pre-processing pipelines can facilitate the efficient and objective extraction of artifacts, enabling standardized and reproducible analyses. However, automated pre-processing pipelines typically remove data considered artifact, and return a subset of irreversibly transformed signals. This approach obfuscates pre-processing decisions, and often makes it impossible to recover the original data or modify the pre-processing steps. Further, it complicates collaboration between research teams working on a common dataset, because different analyses may require specific pre-processing routines. Given the large amount of resources that are devoted to collecting EEG, tools that can help efficiently and transparently pre-process data are greatly needed. PyLossless addresses this need by creating a non-destructive, automated pre-processing pipeline that maintains the continuous EEG structure. It offers a user-friendly API, it is well documented, tested through continuous integration, easily deployable, and integrates with the popular MNE-Python environment. The pipeline further provides a browser-based quality control review (QCR) dashboard that allows researchers to visualize and edit the automated artifact annotations on sensors, time-periods, and independent components. The end product of PyLossless is a lossless annotated data state that can be shared and used with analysis-specific artifact rejection policies, allowing for an optimal balance between flexibility and standardization.