APICE-Py: An Open-Source MNE-Python Pipeline for Scalable EEG Preprocessing

APICE-Py is an open-source Python package designed for automated, scalable, and reproducible EEG preprocessing, with a special focus on developmental and clinical research. Built on top of MNE-Python, the pipeline integrates flexible preprocessing steps for continuous EEG recordings, including filtering, artifact detection, correction, and segmentation. Key features: Automated artifact handling: Detects and corrects artifacts using adaptive thresholds and multiple algorithms (e.g., PCA-based reconstruction, spherical spline interpolation). Scalable and efficient: Supports parallel computation, enabling large-scale analyses. Flexible and customizable: Users can adapt parameters and extend the pipeline through modular design and Jupyter notebook integration. Comprehensive outputs: Provides cleaned continuous data, epochs, event-related potentials (ERPs), quality-control metrics, and detailed reports for reproducibility. Compatibility: Works with multiple EEG file formats (.fif, .set, .mff) and standardized or custom electrode montages. Originally developed as the Python implementation of the MATLAB-based APICE pipeline (Fló et al., 2022, Developmental Cognitive Neuroscience), APICE-Py makes state-of-the-art preprocessing accessible to a wider community of EEG researchers. 🔗 Repository: https://github.com/neurokidslab/apice-py