EEG Analysis Code with Multiple Modular Files for Researchers

In research, electroencephalography (EEG) analysis serves as a crucial tool for understanding brain activity. To effectively process and analyze EEG data, researchers require specialized tools and algorithms. This highlights the importance of developing well-structured EEG analysis code that can handle diverse data formats and processing requirements. The codebase is designed to handle various EEG datasets through modular implementation, featuring specialized functions for different processing stages. Key components include preprocessing modules for signal filtering and artifact removal, spectral analysis functions for frequency domain transformations, and statistical packages for hypothesis testing. The architecture employs separate files for distinct functionalities, such as data_loader.py for EEG data import, feature_extractor.m for signal characteristic calculation, and visualization_tools.R for generating multiple plot types including time-series displays, spectrograms, and topographic maps. This modular organization allows researchers to efficiently utilize specific components for their analytical needs while maintaining code reusability. The separation into specialized files enhances maintainability and enables researchers to focus on particular aspects of EEG analysis, such as event-related potential (ERP) computation or connectivity analysis. The implementation typically includes core algorithms like Fast Fourier Transform (FFT) for spectral analysis, Independent Component Analysis (ICA) for artifact removal, and statistical methods such as ANOVA or cluster-based permutation tests. These structured code files prove highly valuable for researchers, providing systematic approaches to understand and interpret EEG data through standardized processing pipelines and visualization techniques.