mne.io.read_raw_nicolet

mne.io.read_raw_nicolet(input_fname, ch_type, montage=None, eog=(), ecg=(), emg=(), misc=(), preload=False, verbose=None)[source]

Read Nicolet data as raw object.

Note: This reader takes data files with the extension .data as an input. The header file with the same file name stem and an extension .head is expected to be found in the same directory.

Parameters:
input_fname : str

Path to the data file.

ch_type : str

Channel type to designate to the data channels. Supported data types include ‘eeg’, ‘seeg’.

montage : str | None | instance of montage

Path or instance of montage containing electrode positions. If None, sensor locations are (0,0,0). See the documentation of mne.channels.read_montage() for more information.

eog : list | tuple | ‘auto’

Names of channels or list of indices that should be designated EOG channels. If ‘auto’, the channel names beginning with EOG are used. Defaults to empty tuple.

ecg : list or tuple | ‘auto’

Names of channels or list of indices that should be designated ECG channels. If ‘auto’, the channel names beginning with ECG are used. Defaults to empty tuple.

emg : list or tuple | ‘auto’

Names of channels or list of indices that should be designated EMG channels. If ‘auto’, the channel names beginning with EMG are used. Defaults to empty tuple.

misc : list or tuple

Names of channels or list of indices that should be designated MISC channels. Defaults to empty tuple.

preload : bool 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).

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:
raw : Instance of Raw

A Raw object containing the data.

See also

mne.io.Raw
Documentation of attribute and methods.