mne.io.read_raw_fif¶

mne.io.read_raw_fif(fname, allow_maxshield=False, preload=False, on_split_missing=None, verbose=None)[source]¶

Reader function for Raw FIF data.

Parameters

fnamestr | file-like: The raw filename to load. For files that have automatically been split, the split part will be automatically loaded. Filenames should end with raw.fif, raw.fif.gz, raw_sss.fif, raw_sss.fif.gz, raw_tsss.fif, raw_tsss.fif.gz, or _meg.fif. If a file-like object is provided, preloading must be used.

Changed in version 0.18: Support for file-like objects.
allow_maxshieldbool | str (default False): If True, allow loading of data that has been recorded with internal active compensation (MaxShield). Data recorded with MaxShield should generally not be loaded directly, but should first be processed using SSS/tSSS to remove the compensation signals that may also affect brain activity. Can also be “yes” to load without eliciting a warning.
preloadbool or str (default False): Preload data into memory for data manipulation and faster indexing. If True, the data will be preloaded into memory (fast, requires large amount of memory). If preload is a string, preload is the file name of a memory-mapped file which is used to store the data on the hard drive (slower, requires less memory).
on_split_missingstr: Can be 'raise' to raise an error, 'warn' (default) to emit a warning, or 'ignore' to ignore when a split file is missing. The default will change from 'warn' to 'raise' in 0.23, set the value explicitly to avoid deprecation warnings.

New in version 0.22.
verbosebool, str, int, or None: If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

Returns

rawinstance of Raw: A Raw object containing FIF data.

Notes

New in version 0.9.0.

Examples using `mne.io.read_raw_fif`¶

Overview of MEG/EEG analysis with MNE-Python — Overview of MEG/EEG analysis with MNE-Python¶

Parsing events from raw data — Parsing events from raw data¶

The Info data structure — The Info data structure¶

Working with sensor locations — Working with sensor locations¶

Importing data from MEG devices — Importing data from MEG devices¶

The Raw data structure: continuous data — The Raw data structure: continuous data¶

Working with events — Working with events¶

Annotating continuous data — Annotating continuous data¶

Built-in plotting methods for Raw objects — Built-in plotting methods for Raw objects¶

Overview of artifact detection — Overview of artifact detection¶

Interpolating bad channels — Interpolating bad channels¶

Rejecting bad data spans — Rejecting bad data spans¶

Filtering and resampling data — Filtering and resampling data¶

Repairing artifacts with ICA — Repairing artifacts with ICA¶

Background on projectors and projections — Background on projectors and projections¶

Repairing artifacts with SSP — Repairing artifacts with SSP¶

Setting the EEG reference — Setting the EEG reference¶

Extracting and visualizing subject head movement — Extracting and visualizing subject head movement¶

Signal-space separation (SSS) and Maxwell filtering — Signal-space separation (SSS) and Maxwell filtering¶

The Epochs data structure: discontinuous data — The Epochs data structure: discontinuous data¶

Visualizing epoched data — Visualizing epoched data¶

Exporting Epochs to Pandas DataFrames — Exporting Epochs to Pandas DataFrames¶

The Evoked data structure: evoked/averaged data — The Evoked data structure: evoked/averaged data¶

EEG processing and Event Related Potentials (ERPs) — EEG processing and Event Related Potentials (ERPs)¶

Plotting whitened data — Plotting whitened data¶

Frequency and time-frequency sensor analysis — Frequency and time-frequency sensor analysis¶

Source reconstruction using an LCMV beamformer — Source reconstruction using an LCMV beamformer¶

Computing a covariance matrix — Computing a covariance matrix¶

EEG source localization given electrode locations on an MRI — EEG source localization given electrode locations on an MRI¶

Source localization with MNE/dSPM/sLORETA/eLORETA — Source localization with MNE/dSPM/sLORETA/eLORETA¶

Source alignment and coordinate frames — Source alignment and coordinate frames¶

Non-parametric 1 sample cluster statistic on single trial power — Non-parametric 1 sample cluster statistic on single trial power¶

Non-parametric between conditions cluster statistic on single trial power — Non-parametric between conditions cluster statistic on single trial power¶

Spatiotemporal permutation F-test on full sensor data — Spatiotemporal permutation F-test on full sensor data¶

Permutation t-test on source data with spatio-temporal clustering — Permutation t-test on source data with spatio-temporal clustering¶

Repeated measures ANOVA on source data with spatio-temporal clustering — Repeated measures ANOVA on source data with spatio-temporal clustering¶

Mass-univariate twoway repeated measures ANOVA on single trial power — Mass-univariate twoway repeated measures ANOVA on single trial power¶

Decoding (MVPA) — Decoding (MVPA)¶

Corrupt known signal with point spread — Corrupt known signal with point spread¶

Brainstorm Elekta phantom dataset tutorial — Brainstorm Elekta phantom dataset tutorial¶

Modifying data in-place — Modifying data in-place¶

Getting averaging info from .fif files — Getting averaging info from .fif files¶

Generate simulated evoked data — Generate simulated evoked data¶

Generate simulated raw data — Generate simulated raw data¶

Define target events based on time lag, plot evoked response — Define target events based on time lag, plot evoked response¶

Transform EEG data using current source density (CSD) — Transform EEG data using current source density (CSD)¶

Show EOG artifact timing — Show EOG artifact timing¶

Find MEG reference channel artifacts — Find MEG reference channel artifacts¶

Compare the different ICA algorithms in MNE — Compare the different ICA algorithms in MNE¶

Maxwell filter data with movement compensation — Maxwell filter data with movement compensation¶

Plot sensor denoising using oversampled temporal projection — Plot sensor denoising using oversampled temporal projection¶

Compute ICA components on epochs — Compute ICA components on epochs¶

XDAWN Denoising — XDAWN Denoising¶

Visualize channel over epochs as an image — Visualize channel over epochs as an image¶

Plotting EEG sensors on the scalp — Plotting EEG sensors on the scalp¶

Whitening evoked data with a noise covariance — Whitening evoked data with a noise covariance¶

Plotting sensor layouts of MEG systems — Plotting sensor layouts of MEG systems¶

Make figures more publication ready — Make figures more publication ready¶

Show noise levels from empty room data — Show noise levels from empty room data¶

Compare evoked responses for different conditions — Compare evoked responses for different conditions¶

Plot custom topographies for MEG sensors — Plot custom topographies for MEG sensors¶

Compute a cross-spectral density (CSD) matrix — Compute a cross-spectral density (CSD) matrix¶

Compute Power Spectral Density of inverse solution from single epochs — Compute Power Spectral Density of inverse solution from single epochs¶

Compute power and phase lock in label of the source space — Compute power and phase lock in label of the source space¶

Compute source power spectral density (PSD) in a label — Compute source power spectral density (PSD) in a label¶

Compute source power spectral density (PSD) of VectorView and OPM data — Compute source power spectral density (PSD) of VectorView and OPM data¶

Compute induced power in the source space with dSPM — Compute induced power in the source space with dSPM¶

Temporal whitening with AR model — Temporal whitening with AR model¶

Explore event-related dynamics for specific frequency bands — Explore event-related dynamics for specific frequency bands¶

Permutation F-test on sensor data with 1D cluster level — Permutation F-test on sensor data with 1D cluster level¶

FDR correction on T-test on sensor data — FDR correction on T-test on sensor data¶

Regression on continuous data (rER[P/F]) — Regression on continuous data (rER[P/F])¶

Permutation T-test on sensor data — Permutation T-test on sensor data¶

Representational Similarity Analysis — Representational Similarity Analysis¶

Decoding source space data — Decoding source space data¶

Decoding sensor space data with generalization across time and conditions — Decoding sensor space data with generalization across time and conditions¶

Analysis of evoked response using ICA and PCA reduction techniques — Analysis of evoked response using ICA and PCA reduction techniques¶

XDAWN Decoding From EEG data — XDAWN Decoding From EEG data¶

Compute effect-matched-spatial filtering (EMS) — Compute effect-matched-spatial filtering (EMS)¶

Linear classifier on sensor data with plot patterns and filters — Linear classifier on sensor data with plot patterns and filters¶

Compute seed-based time-frequency connectivity in sensor space — Compute seed-based time-frequency connectivity in sensor space¶

Compute mixed source space connectivity and visualize it using a circular graph — Compute mixed source space connectivity and visualize it using a circular graph¶

Compute coherence in source space using a MNE inverse solution — Compute coherence in source space using a MNE inverse solution¶

Compute full spectrum source space connectivity between labels — Compute full spectrum source space connectivity between labels¶

Compute source space connectivity and visualize it using a circular graph — Compute source space connectivity and visualize it using a circular graph¶

Compute Phase Slope Index (PSI) in source space for a visual stimulus — Compute Phase Slope Index (PSI) in source space for a visual stimulus¶

Compute all-to-all connectivity in sensor space — Compute all-to-all connectivity in sensor space¶

Compute MNE-dSPM inverse solution on single epochs — Compute MNE-dSPM inverse solution on single epochs¶

Compute sLORETA inverse solution on raw data — Compute sLORETA inverse solution on raw data¶

Compute source power using DICS beamformer — Compute source power using DICS beamformer¶

Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM — Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM¶

Compute source power estimate by projecting the covariance with MNE — Compute source power estimate by projecting the covariance with MNE¶

Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary — Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary¶

Compute cross-talk functions for LCMV beamformers — Compute cross-talk functions for LCMV beamformers¶

Time-frequency beamforming using DICS — Time-frequency beamforming using DICS¶

Optically pumped magnetometer (OPM) data — Optically pumped magnetometer (OPM) data¶