mne.io.read_raw_ctf#

mne.io.read_raw_ctf(directory, system_clock='truncate', preload=False, clean_names=False, verbose=None)[source]#

Raw object from CTF directory.

Parameters:
directorypath-like

Path to the CTF data (ending in '.ds').

system_clockstr

How to treat the system clock. Use “truncate” (default) to truncate the data file when the system clock drops to zero, and use “ignore” to ignore the system clock (e.g., if head positions are measured multiple times during a recording).

preloadbool | str

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, it is the name of a freshly created memory-mapped file used to store the data on the hard drive (slower, requires less memory). An existing file is overwritten. The caller owns the file and is responsible for removing it after the Raw object is no longer in use. For supported Raw readers, the exact string "auto" instead reuses decoded data below the directory configured by mne.set_cache_dir(). Entries persist without a size limit and are mapped copy-on-write. Use Path("auto") for a literal filename.

Changed in version 1.13: Support for the "auto" decoded-data cache was added.

clean_namesbool

If True main channel names and compensation channel names will be cleaned from CTF suffixes. The default is False.

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 RawCTF

The raw data.

Notes

New in v0.11.

To read in the Polhemus digitization data (for example, from a .pos file), include the file in the CTF directory. The points will then automatically be read into the mne.io.Raw instance via mne.io.read_raw_ctf.

Examples using mne.io.read_raw_ctf#

Brainstorm raw (median nerve) dataset

Brainstorm raw (median nerve) dataset

From raw data to dSPM on SPM Faces dataset

From raw data to dSPM on SPM Faces dataset

Continuous Target Decoding with SPoC

Continuous Target Decoding with SPoC

Compute spatial filters with Spatio-Spectral Decomposition (SSD)

Compute spatial filters with Spatio-Spectral Decomposition (SSD)

Annotate movement artifacts and reestimate dev_head_t

Annotate movement artifacts and reestimate dev_head_t

Annotate muscle artifacts

Annotate muscle artifacts

Plotting topographic arrowmaps of evoked data

Plotting topographic arrowmaps of evoked data

Plotting sensor layouts of MEG systems

Plotting sensor layouts of MEG systems

Brainstorm CTF phantom dataset tutorial

Brainstorm CTF phantom dataset tutorial

Working with CTF data: the Brainstorm auditory dataset

Working with CTF data: the Brainstorm auditory dataset