mne.io.read_raw_fif

mne.io.read_raw_fif(fname, allow_maxshield=False, preload=False, on_split_missing='raise', verbose=None)

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' (default) to raise an error, 'warn' to emit a warning, or 'ignore' to ignore when split file is missing.

New in version 0.22.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns

rawinstance of Raw

A Raw object containing FIF data.

Notes

New in version 0.9.0.

When reading a FIF file, note that the first N seconds annotated BAD_ACQ_SKIP are skipped. They are removed from raw.times and raw.n_times parameters but raw.first_samp and raw.first_time are updated accordingly.

Examples using mne.io.read_raw_fif

Overview of MEG/EEG analysis with MNE-Python

Overview of MEG/EEG analysis with MNE-Python

Modifying data in-place

Modifying data in-place

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

Handling bad channels

Handling bad channels

Rejecting bad data spans and breaks

Rejecting bad data spans and breaks

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

Divide continuous data into equally-spaced epochs

Divide continuous data into equally-spaced epochs

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 alignment and coordinate frames

Source alignment and coordinate frames

Computing a covariance matrix

Computing a covariance matrix

Source localization with MNE, dSPM, sLORETA, and eLORETA

Source localization with MNE, dSPM, sLORETA, and eLORETA

Source reconstruction using an LCMV beamformer

Source reconstruction using an LCMV beamformer

EEG source localization given electrode locations on an MRI

EEG source localization given electrode locations on an MRI

Brainstorm Elekta phantom dataset tutorial

Brainstorm Elekta phantom dataset tutorial

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

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

Cortical Signal Suppression (CSS) for removal of cortical signals

Cortical Signal Suppression (CSS) for removal of cortical signals

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

XDAWN Denoising

XDAWN Denoising

How to convert 3D electrode positions to a 2D image

How to convert 3D electrode positions to a 2D image

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 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

Optically pumped magnetometer (OPM) data

Optically pumped magnetometer (OPM) data

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