mne.io.read_raw_neuralynx#

mne.io.read_raw_neuralynx(fname, *, preload=False, exclude_fname_patterns=None, verbose=None) → RawNeuralynx[source]#

Reader for Neuralynx files.

Parameters:

fnamepath-like: Path to a folder with Neuralynx .ncs files.
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).
exclude_fname_patternslist of str: List of glob-like string patterns to exclude from channel list. Useful when not all channels have the same number of samples so you can read separate instances.
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 RawNeuralynx: A Raw object containing Neuralynx data. See mne.io.Raw for documentation of attributes and methods.

See also

mne.io.Raw: Documentation of attributes and methods of RawNeuralynx.

Notes

Neuralynx files are read from disk using the Neo package. Currently, only reading of the .ncs files is supported.

raw.info["meas_date"] is read from the recording_opened property of the first .ncs file (i.e. channel) in the dataset (a warning is issued if files have different dates of acquisition).

Channel-specific high and lowpass frequencies of online filters are determined based on the DspLowCutFrequency and DspHighCutFrequency header fields, respectively. If no filters were used for a channel, the default lowpass is set to the Nyquist frequency and the default highpass is set to 0. If channels have different high/low cutoffs, raw.info["highpass"] and raw.info["lowpass"] are then set to the maximum highpass and minimumlowpass values across channels, respectively.

Other header variables can be inspected using Neo directly. For example:

from neo.io import NeuralynxIO  # doctest: +SKIP
fname = 'path/to/your/data'  # doctest: +SKIP
nlx_reader = NeuralynxIO(dirname=fname)  # doctest: +SKIP
print(nlx_reader.header)  # doctest: +SKIP
print(nlx_reader.file_headers.items())  # doctest: +SKIP