frites.dataset.DatasetEphy#

class frites.dataset.DatasetEphy(x, y=None, z=None, roi=None, agg_ch=True, times=None, multivariate=False, nb_min_suj=False, attrs=None, verbose=None)[source]#

Multi-subjects electrophysiological data container.

This class is a container used to represent the neurophysiological data coming from multiple subjects. In addition to passing the data, this container is also going to need the anatomical information such as the task-related variables (continuous or discret). Then, the created object can be used to compute the mutual information (MI) between the data and the task variable and group-level.

Parameters

xpython:list: List of length (n_subjects,) where each element of the list should be the neural data of single subject. Supported types for each element of the list are the same as in SubjectEphy
y, zpython:list, sting | python:None: List of length (n_subjects,) of continuous or discrete task-related variables per subject. Supported types for each element of the list are the same as in SubjectEphy
roipython:list | python:None: List of length (n_subjects,) where each element represents the anatomical information of each channel. Supported types for each element of the list are the same as in SubjectEphy
agg_chbool | python:True: If multiple channels belong to the same ROI, specify whether if the data should be aggregated across channels (True) or if the information per channel have to be take into account (False - conditional mutual information).
timesnumpy:array_like | python:None: The time vector to use. Supported types are listed in SubjectEphy
multivariatebool | python:False: If 4d input is provided, this parameter specifies whether this axis should be considered as multi-variate (True) or uni-varariate (False)
nb_min_sujpython:int | python:None: The minimum number of subjects per roi. Roi with n_suj < nb_min_suj are going to be skipped. Use None to force selecting all of the subjects
attrspython:dict | {}: Dictionary of additional attributes about the data

Attributes

df_rs: Pandas DataFrame of cross-subjects anatomical repartition.
roi_names: List of brain regions to keep across subjects.
times: Time vector.
x: Multi-subjects electrophysiological data (DataArray).

Methods

`get_connectivity_pairs`([as_blocks, ...])	Get the connectivity pairs for this dataset.
`get_roi_data`(roi[, groupby, mi_type, ...])	Get the data of a single brain region.
`isel`(**kwargs)	Index-based data slicing.
`savgol_filter`(h_freq[, edges, verbose])	Filter the data using Savitzky-Golay polynomial method.
`sel`(**kwargs)	Coordinate-based data slicing.

property df_rs#: Pandas DataFrame of cross-subjects anatomical repartition.

get_connectivity_pairs(as_blocks=False, directed=False, verbose=None)[source]#

Get the connectivity pairs for this dataset.

This method can be used to get the possible connectivity pairs i.e (sources, targets) for directed connectivity (or not). In addition, some pairs are going to be ignored because of a number of subjects to low.

Parameters

directedbool | python:False: Get either directed (True) or non-directed (False) pairs

Returns

df_connpd.DataFrame: The table describing the connectivity informations per pair of brain regions
df_conn_sujpd.DataFrame: The table describing the connectivity informations per subject

get_roi_data(roi, groupby='subjects', mi_type='cc', copnorm=True, gcrn_per_suj=True)[source]#

Get the data of a single brain region.

Parameters

roipython:str: ROI name to get
groupby{‘subjects’}: Specify if the data across subjects have to be concatenated
mi_type{‘cc’, ‘cd’, ‘ccd’}: The type of mutual-information that is then going to be used. This is going to have an influence on how the data are organized and how the copnorm is going to be applied
copnormbool | python:True: Apply the gaussian copula rank normalization
gcrn_per_sujbool | python:True: Specify whether the gaussian copula rank normalization have to be applied per subject (True - RFX) or across subjects (False - FFX)

Returns

daxr.DataArray: The data of the single brain region

isel(**kwargs)[source]#

Index-based data slicing.

Slice the entire dataset based on indexes.

Parameters

kwargs{} | python:None: Additional inputs are sent to to the method xr.DataArray.isel of the data of each subject.

Returns

instinstance of DatasetEphy: The sliced object.

Examples

>>> # define the dataset
>>> ds = DatasetEphy(...)
>>> # temporal slicing of the data between time points (100, 2500)
>>> ds.sel(times=slice(100, 2500))

property roi_names#: List of brain regions to keep across subjects.

savgol_filter(h_freq, edges=None, verbose=None)[source]#

Filter the data using Savitzky-Golay polynomial method.

This method is an adaptation of the mne-python one. Note that this smoothing operation is performed inplace to avoid data copy.

Parameters

h_freqpython:float: Approximate high cut-off frequency in Hz. Note that this is not an exact cutoff, since Savitzky-Golay filtering is done using polynomial fits instead of FIR/IIR filtering. This parameter is thus used to determine the length of the window over which a 5th-order polynomial smoothing is used.
edgespython:int, python:float | python:None: Edge compensation. Use either an integer to drop a specific number of time points (e.g edges=100 remove 100 time points at the begining and at the end) or a float to drop a period (e.g edges=0.2 drop 200ms at the begining and at the end)

Returns

instinstance of DatasetEphy: The object with the filtering applied.

Notes

For Savitzky-Golay low-pass approximation, see:: https://gist.github.com/larsoner/bbac101d50176611136b

Examples using savgol_filter:

Build an electrophysiological dataset

sel(**kwargs)[source]#

Coordinate-based data slicing.

Slice the entire dataset based on the coordinates values.

Parameters

kwargs{} | python:None: Additional inputs are sent to to the method xr.DataArray.sel of the data of each subject.

Returns

instinstance of DatasetEphy: The sliced object.

Examples

>>> # define the dataset
>>> ds = DatasetEphy(...)
>>> # temporal slicing of the data between (-100ms, 1500ms)
>>> ds.sel(times=slice(-0.1, 1.5))

Examples using sel:

Build an electrophysiological dataset

property times#: Time vector.

property x#: Multi-subjects electrophysiological data (DataArray).

Examples using `frites.dataset.DatasetEphy`#

Statistical analysis of a stimulus-specific network

Define an electrophysiological dataset using MNE-Python structures

Build an electrophysiological dataset

Define an electrophysiological dataset using Xarray

MI between two continuous variables conditioned by a discret one

MI between a continuous and a discret variables

MI between two continuous variables

Compute MI across time and frequencies

Investigate relation of order

Compute a conjunction analysis on mutual-information

Mutual-information at the contact level

Estimate comodulations between brain areas

Compare within-subjects statistics when computing mutual information

Compare between-subjects statistics when computing mutual information

Estimate the empirical confidence interval

Generate spatio-temporal ground-truths

frites.dataset.DatasetEphy#

Examples using frites.dataset.DatasetEphy#

Examples using `frites.dataset.DatasetEphy`#