mne.io.read_raw_eeglab#

mne.io.read_raw_eeglab(input_fname, eog=(), preload=False, uint16_codec=None, montage_units='auto', verbose=None) → RawEEGLAB[source]#

Read an EEGLAB .set file.

Parameters:

input_fnamepath-like: Path to the .set file. If the data is stored in a separate .fdt file, it is expected to be in the same folder as the .set file.
eoglist | tuple | 'auto': Names or indices of channels that should be designated EOG channels. If ‘auto’, the channel names containing EOG or EYE are used. Defaults to empty tuple.
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). Note that preload=False will be effective only if the data is stored in a separate binary file.
uint16_codecstr | None: If your set file contains non-ascii characters, sometimes reading it may fail and give rise to error message stating that “buffer is too small”. uint16_codec allows to specify what codec (for example: ‘latin1’ or ‘utf-8’) should be used when reading character arrays and can therefore help you solve this problem.
montage_unitsstr: Units that channel positions are represented in. Defaults to “mm” (millimeters), but can be any prefix + “m” combination (including just “m” for meters).

New in v1.3.

Changed in version 1.6: Support for 'auto' was added and is the new default.
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 RawEEGLAB: A Raw object containing EEGLAB .set data. See mne.io.Raw for documentation of attributes and methods.

Examples using `mne.io.read_raw_eeglab`#

Parsing events from raw data

Plot single trial activity, grouped by ROI and sorted by RT

mne.io.read_raw_eeglab#

Examples using mne.io.read_raw_eeglab#

Examples using `mne.io.read_raw_eeglab`#