mne.io.read_raw_fif

mne.io.read_raw_fif(fname, allow_maxshield=False, preload=False, 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).

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

Getting averaging info from .fif files

Reading and writing raw files

Reading epochs from a raw FIF file

Read and visualize projections (SSP and other)

Generate simulated evoked data

Generate simulated raw data

Define target events based on time lag, plot evoked response

Transform EEG data using current source density (CSD)

Show EOG artifact timing

Find ECG artifacts

Find EOG artifacts

Find MEG reference channel artifacts

Compare the different ICA algorithms in MNE

Querying epochs with rich metadata

Maxwell filter data with movement compensation

Plot sensor denoising using oversampled temporal projection

Re-referencing the EEG signal

Resampling data

Compute ICA components on epochs

XDAWN Denoising

Visualize channel over epochs as an image

Plotting EEG sensors on the scalp

Whitening evoked data with a noise covariance

Plotting sensor layouts of MEG systems

Show noise levels from empty room data

Compare evoked responses for different conditions

Plot custom topographies for MEG sensors

Compute a cross-spectral density (CSD) matrix

Compute the power spectral density of raw data

Compute Power Spectral Density of inverse solution from single epochs

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) of VectorView and OPM data

Compute induced power in the source space with dSPM

Temporal whitening with AR model

Explore event-related dynamics for specific frequency bands

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

FDR correction on T-test on sensor data

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

Permutation T-test on sensor data

Representational Similarity Analysis

Decoding source space data

Decoding sensor space data with generalization across time and conditions

Analysis of evoked response using ICA and PCA reduction techniques

XDAWN Decoding From EEG data

Compute effect-matched-spatial filtering (EMS)

Linear classifier on sensor data with plot patterns and filters

Compute seed-based time-frequency connectivity in sensor space

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

Compute coherence in source space using a MNE inverse solution

Compute full spectrum source space connectivity between labels

Compute source space connectivity and visualize it using a circular graph

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

Compute all-to-all connectivity in sensor space

Compute MNE-dSPM inverse solution on single epochs

Compute sLORETA inverse solution on raw data

Compute source power using DICS beamformer

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

Compute source power estimate by projecting the covariance with MNE

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

Compute cross-talk functions for LCMV beamformers

Time-frequency beamforming using DICS

Optically pumped magnetometer (OPM) data

Overview of MEG/EEG analysis with MNE-Python

Parsing events from raw data

The Info data structure

Working with sensor locations

Importing data from MEG devices

The Raw data structure: continuous data

Working with events

Annotating continuous data

Built-in plotting methods for Raw objects

Overview of artifact detection

Interpolating bad channels

Rejecting bad data spans

Filtering and resampling data

Repairing artifacts with ICA

Background on projectors and projections

Repairing artifacts with SSP

Setting the EEG reference

Extracting and visualizing subject head movement

Signal-space separation (SSS) and Maxwell filtering

The Epochs data structure: discontinuous data

Visualizing epoched data

Exporting Epochs to Pandas DataFrames

The Evoked data structure: evoked/averaged data

EEG processing and Event Related Potentials (ERPs)

Plotting whitened data

Frequency and time-frequency sensors analysis

Source reconstruction using an LCMV beamformer

Computing a covariance matrix

EEG source localization given electrode locations on an MRI

Source localization with MNE/dSPM/sLORETA/eLORETA

Source alignment and coordinate frames

Non-parametric 1 sample cluster statistic on single trial power

Non-parametric between conditions cluster statistic on single trial power

Spatiotemporal permutation F-test on full sensor data

Permutation t-test 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

Decoding (MVPA)

Corrupt known signal with point spread

Brainstorm Elekta phantom dataset tutorial

Modifying data in-place