phase-data-preprocess

Preprocess raw EEG data by applying modality-specific steps like band-pass filtering, epoching around stimulus markers, and rejecting trials with excessive artifacts. This ensures your dataset is analysis-ready and fully documented.

Cleaning fMRI and ECG data requires modality-specific preprocessing steps to filter noise and format signals. The process adapts cleaning steps based on the data type to ensure the datasets are ready for accurate analysis.

Perform quality control on neuroscientific data by generating essential QC plots and rejection summaries. This validates the preprocessing pipeline by documenting parameter choices and logging any deviations from the original plan.

Yes, this preprocessing approach handles eye-tracking data alongside EEG, fMRI, and ECG. It adapts the cleaning steps based on the specific data type to ensure all datasets are properly prepared for analysis.

Document parameter choices during data preprocessing by logging all settings and deviations from the preprocessing plan. This creates reproducible workflows and generates rejection summaries for quality control.

Epoching raw data around stimulus markers segments continuous recordings into trial-based intervals for specific event analysis. This step is essential for preparing cognitive experiment datasets like EEG for accurate analysis.