yasa.SpindlesResults

class yasa.SpindlesResults(events, data, sf, ch_names, hypno, data_filt)

Output class for spindles detection.

Attributes

_eventspandas.DataFrame

Output detection dataframe

_dataarray_like

Original EEG data of shape (n_chan, n_samples).

_data_filtarray_like

Sigma-filtered EEG data of shape (n_chan, n_samples).

_sffloat

Sampling frequency of data.

_ch_nameslist

Channel names.

_hypnoarray_like or None

Sleep staging vector.

__init__(self, events, data, sf, ch_names, hypno, data_filt)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(self, events, data, sf, ch_names, …)	Initialize self.
get_mask(self)	Return a boolean array indicating for each sample in data if this sample is part of a detected event (True) or not (False).
get_sync_events(self[, center, time_before, …])	Return the raw or filtered data of each detected event after centering to a specific timepoint.
plot_average(self[, center, hue, …])	Plot the average spindle.
plot_detection(self)	Plot an overlay of the detected spindles on the EEG signal.
summary(self[, grp_chan, grp_stage, …])	Return a summary of the spindles detection, optionally grouped across channels and/or stage.

get_mask(self)

Return a boolean array indicating for each sample in data if this sample is part of a detected event (True) or not (False).

get_sync_events(self, center='Peak', time_before=1, time_after=1, filt=None, None)

Return the raw or filtered data of each detected event after centering to a specific timepoint.

Parameters

centerstr

Landmark of the event to synchronize the timing on. Default is to use the center peak of the spindles.

time_beforefloat

Time (in seconds) before center.

time_afterfloat

Time (in seconds) after center.

filttuple

Optional filtering to apply to data. For instance, filt=(1, 30) will apply a 1 to 30 Hz bandpass filter, and filt=(None, 40) will apply a 40 Hz lowpass filter. Filtering is done using default parameters in the mne.filter.filter_data() function.

Returns

df_syncpandas.DataFrame

Long-format dataframe:

'Event' : Event number
'Time' : Timing of the events (in seconds)
'Amplitude' : Raw or filtered data for event
'Channel' : Channel
'IdxChannel' : Index of channel in data
'Stage': Sleep stage in which the events occured (if available)

plot_average(self, center='Peak', hue='Channel', time_before=1, time_after=1, filt=None, None, figsize=6, 4.5, **kwargs)

Plot the average spindle.

Parameters

centerstr

Landmark of the event to synchronize the timing on. Default is to use the most prominent peak of the spindle.

huestr

Grouping variable that will produce lines with different colors. Can be either ‘Channel’ or ‘Stage’.

time_beforefloat

Time (in seconds) before center.

time_afterfloat

Time (in seconds) after center.

filttuple

Optional filtering to apply to data. For instance, filt=(1, 30) will apply a 1 to 30 Hz bandpass filter, and filt=(None, 40) will apply a 40 Hz lowpass filter. Filtering is done using default parameters in the mne.filter.filter_data() function.

figsizetuple

Figure size in inches.

**kwargsdict

Optional argument that are passed to seaborn.lineplot().

plot_detection(self)

Plot an overlay of the detected spindles on the EEG signal.

This only works in Jupyter and it requires the ipywidgets (https://ipywidgets.readthedocs.io/en/latest/) package.

To activate the interactive mode, make sure to run:

>>> %matplotlib widget

New in version 0.4.0.

summary(self, grp_chan=False, grp_stage=False, aggfunc='mean', sort=True)

Return a summary of the spindles detection, optionally grouped across channels and/or stage.

Parameters

grp_chanbool

If True, group by channel (for multi-channels detection only).

grp_stagebool

If True, group by sleep stage (provided that an hypnogram was used).

aggfuncstr or function

Averaging function (e.g. 'mean' or 'median').

sortbool

If True, sort group keys when grouping.