At CIMCYC, we develop a range of in-house tools designed to facilitate the work of both our research and technical staff, as well as the wider scientific community. These tools are available through public repositories in our GitHub organization and are accompanied by detailed documentation, practical examples, and step-by-step guides so that users can easily integrate them into their own workflows.
We make these resources openly available to the scientific community because the promotion of Open Science is a strategic commitment of CIMCYC. We consider it essential for increasing the transparency, reproducibility, and impact of our research findings.
Neuroimaging Tools
- DICOM-to-BIDS Converter:
A MATLAB-based tool for converting DICOM data into the standard BIDS format. It uses the dcm2niix conversion engine. For further details, please consult the documentation available in our GitHub repository. - The MVPAlab Toolbox — Multivariate Analysis for M/EEG:
The MVPAlab Toolbox is a software package designed to facilitate multivariate analysis of M/EEG signals. It provides a comprehensive and flexible workflow for decoding analyses, allowing users to prepare data, train and evaluate classification and regression models, and visualize results in a clear and intuitive way. - EEG Preprocessing Pipeline:
A general pipeline for preprocessing EEG data. This tool allows users to select and organize the different preprocessing stages according to their specific needs, as well as configure numerous parameters associated with each stage. For further details, please consult the documentation available in our GitHub repository.
Other Tools
- Latency and Slope Comparison Tool for Time-Series Curves (Jackknife Method):
A tool based on the article “Jackknife-based method for measuring LRP onset latency differences” (2003). This code allows users to measure the temporal difference between two curves (e.g., ERPs, decoding curves, etc.) at different peak thresholds, assessing whether statistically significant differences exist at each threshold. In addition, it evaluates potential differences in the slopes of the curves. The method relies on a jackknife resampling strategy to obtain more robust estimates.