mne.concatenate_raws#

mne.concatenate_raws(raws, preload=None, events_list=None, *, on_mismatch='raise', verbose=None)[source]#

Concatenate Raw instances as if they were continuous.

Note

raws[0] is modified in-place to achieve the concatenation. Boundaries of the raw files are annotated bad. If you wish to use the data as continuous recording, you can remove the boundary annotations after concatenation (see mne.Annotations.delete()).

Parameters

rawslist: List of Raw instances to concatenate (in order).
preloadbool, str, or None (default None): 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). If preload is None, preload=True or False is inferred using the preload status of the instances passed in.
events_listNone | list: The events to concatenate. Defaults to None.
on_mismatch‘raise’ | ‘warn’ | ‘ignore’: Can be 'raise' (default) to raise an error, 'warn' to emit a warning, or 'ignore' to ignore when the device-to-head transformation differs between instances.

New in version 0.24.
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: The result of the concatenation (first Raw instance passed in).
eventsndarray of int, shape (n_events, 3): The events. Only returned if event_list is not None.

Examples using `mne.concatenate_raws`#

Working with CTF data: the Brainstorm auditory dataset

Annotate movement artifacts and reestimate dev_head_t

Compute and visualize ERDS maps

Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)

Decoding in time-frequency space using Common Spatial Patterns (CSP)

Representational Similarity Analysis

mne.combine_evoked

mne.equalize_channels

mne.concatenate_raws#

Examples using mne.concatenate_raws#

Examples using `mne.concatenate_raws`#