graph LR
Signal_Filtering["Signal Filtering"]
Artifact_Noise_Removal["Artifact & Noise Removal"]
Signal_Filtering -- "uses" --> Data_Management_Core_Models
Artifact_Noise_Removal -- "uses" --> Data_Management_Core_Models
Signal_Filtering -- "uses" --> Processing_and_Algorithm_Modules
Artifact_Noise_Removal -- "uses" --> Processing_and_Algorithm_Modules
Signal_Filtering -- "uses" --> Utility_Modules
Artifact_Noise_Removal -- "uses" --> Utility_Modules
This document provides an overview of the Signal Preprocessing component, detailing its sub-components, their responsibilities, and their relationships within a Neuroscience Data Analysis Library.
This component provides core functionalities for applying various digital filters (e.g., band-pass, low-pass, high-pass, notch) to neurophysiological data. It leverages a FilterMixin to integrate filtering directly into core data structures like Raw, Epochs, and Evoked objects, allowing for efficient in-place data manipulation. This ensures that filtering is a seamlessly integrated and fundamental operation on the primary data models.
Related Classes/Methods:
mne.filter.FilterMixin(2369:2778)mne.filter.filter_data(928:1033)mne.filter.notch_filter(1420:1585)mne.filter.resample(1799:1891)
This component encompasses a comprehensive suite of advanced techniques for detecting, characterizing, and removing various biological and environmental artifacts and noise components from neurophysiological data. This includes methods for ocular (EOG) and cardiac (ECG) artifact removal, muscle artifact suppression, Independent Component Analysis (ICA) for source separation, Maxwell filtering (SSS) for MEG noise reduction, and bad channel interpolation. It aims to clean the data to improve the signal-to-noise ratio for subsequent analyses.
Related Classes/Methods:
mne.preprocessing.ica.ICA(201:2682)mne.preprocessing.create_ecg_epochs(1:1)mne.preprocessing.create_eog_epochs(1:1)mne.preprocessing.maxwell_filter(1:1)mne.preprocessing.interpolate_bad_channels(1:1)mne.preprocessing.compute_proj_ecg(1:1)mne.preprocessing.compute_proj_eog(1:1)