EEG Analysis Code Organization with Modular File Structures for Researchers

EEG Analysis Code Organization for Researchers

When working with EEG data, maintaining well-structured code files can significantly improve research efficiency and reproducibility. Organizing analysis scripts into modular files enables better version control and collaborative development.

Preprocessing Modules Separate files for filtering (e.g., implementing FIR/IIR filters), artifact removal (using ICA or threshold-based methods), and normalization (z-score or min-max scaling) allow researchers to easily swap processing pipelines or compare preprocessing methods. This modularity prevents monolithic scripts and simplifies debugging through independent testing of each component.

Feature Extraction Handlers Dedicated files for time-domain (mean, variance, Hjorth parameters), frequency-domain (FFT, power spectral density), and time-frequency analyses (wavelet transforms, STFT) enable clean implementation of different feature calculation methods. Researchers can implement feature selection algorithms and mix-and-match techniques without modifying core analysis logic.

Visualization Utilities Isolating plotting functions (using libraries like Matplotlib or EEGLAB tools) makes it easier to maintain consistent formatting across publications and adapt visualizations for different presentation needs. Custom plotting functions can handle channel layouts, topographic maps, and ERP visualizations with configurable parameters.

Experiment-Specific Configurations Storing paradigm details (task parameters, timing information), event markers (trigger codes, condition labels), and channel configurations (montage settings, bad channel lists) in separate JSON or YAML files allows the same analysis backbone to be reused across multiple studies while maintaining study-specific parameters through configuration files.

This modular structure particularly helps collaborative teams by: Reducing merge conflicts in version control systems like Git Enabling parallel development of different analysis components through clear interfaces Creating natural documentation through logical file organization and function segmentation

For longitudinal studies, this approach also simplifies adapting analyses as new preprocessing techniques or feature extraction methods emerge. The implementation should balance modularity with reasonable file counts, using package structures or namespaces to avoid fragmentation while maintaining clear separation of concerns.