Documentation of eeglib.helpers module

Inheritance diagram of eeglib.helpers

This module contains helper classes that are useful to iterating over a EEG data stream. Currently there is support only for CSV files.

class eeglib.helpers.CSVHelper(path, *args, **kargs)

Bases: Helper

This class is for applying diferents operations using the EEG class over a csv file.

Methods

getNames([indexes])

Returns the names of the specified indexes of channels.

moveEEGWindow(position)

Moves the window to start at position.

prepareEEG(windowSize)

Prepares and creates the EEG object that the iteration will use with the same parameters that an EEG objects is initialized.

prepareIterator([step, startPoint, endPoint])

Prepares the iterator of the helper.

selectSignals(selectedSignals)

Parameters
__init__(path, *args, **kargs)

The rest of parameters can be seen at Helper.__init__()

Parameters
path: str

The path to the csv file

getNames(indexes=None)

Returns the names of the specified indexes of channels.

Parameters
indexes: Iterable of int, optional.

The indexes of the channels desired. If None it will return all the channels’ names. Default: None.

moveEEGWindow(position)

Moves the window to start at position. Also it returns the inner eeg object.

Parameters
position: int, str or datetime.timedelta

  • int: position of the sample

  • str with format hh:mm:ss.ss: temporal position

  • timedelta: temporal position

Returns
EEG
prepareEEG(windowSize)

Prepares and creates the EEG object that the iteration will use with the same parameters that an EEG objects is initialized. Also it returns the inner eeg object.

Parameters
windowSize: int

The maximun samples the window will store.

Returns
EEG
prepareIterator(step=None, startPoint=0, endPoint=None)

Prepares the iterator of the helper.

Parameters
step: int,optional

Number of samples to be skipped in each iteration.

startPoint: int, optional

The index of first sample from where the iteration will start. By default 0.

endPoint: int, optional

The index of the last sample + 1 until where the iteration will go. By default the size of the data.

selectSignals(selectedSignals)
Parameters
selectedSignalsiterable of str and/or int

The indexes of the desired selected signals. It must be an iterable type containing either str or int.

Returns
None.
class eeglib.helpers.EDFHelper(path, *args, sampleRate=None, **kargs)

Bases: Helper

This class is for applying diferents operations using the EEG class over an edf file.

Methods

getNames([indexes])

Returns the names of the specified indexes of channels.

moveEEGWindow(position)

Moves the window to start at position.

prepareEEG(windowSize)

Prepares and creates the EEG object that the iteration will use with the same parameters that an EEG objects is initialized.

prepareIterator([step, startPoint, endPoint])

Prepares the iterator of the helper.

selectSignals(selectedSignals)

Parameters
__init__(path, *args, sampleRate=None, **kargs)

The rest of parameters can be seen at Helper.__init__()

Parameters
path: str

The path to the edf file

getNames(indexes=None)

Returns the names of the specified indexes of channels.

Parameters
indexes: Iterable of int, optional.

The indexes of the channels desired. If None it will return all the channels’ names. Default: None.

moveEEGWindow(position)

Moves the window to start at position. Also it returns the inner eeg object.

Parameters
position: int, str or datetime.timedelta

  • int: position of the sample

  • str with format hh:mm:ss.ss: temporal position

  • timedelta: temporal position

Returns
EEG
prepareEEG(windowSize)

Prepares and creates the EEG object that the iteration will use with the same parameters that an EEG objects is initialized. Also it returns the inner eeg object.

Parameters
windowSize: int

The maximun samples the window will store.

Returns
EEG
prepareIterator(step=None, startPoint=0, endPoint=None)

Prepares the iterator of the helper.

Parameters
step: int,optional

Number of samples to be skipped in each iteration.

startPoint: int, optional

The index of first sample from where the iteration will start. By default 0.

endPoint: int, optional

The index of the last sample + 1 until where the iteration will go. By default the size of the data.

selectSignals(selectedSignals)
Parameters
selectedSignalsiterable of str and/or int

The indexes of the desired selected signals. It must be an iterable type containing either str or int.

Returns
None.
class eeglib.helpers.Helper(data, sampleRate=None, windowSize=None, names=None, highpass=None, lowpass=None, normalize=False, ICA=False, selectedSignals=None)

Bases: object

This is an abstract class that defines the way every helper works.

Methods

getNames([indexes])

Returns the names of the specified indexes of channels.

moveEEGWindow(position)

Moves the window to start at position.

prepareEEG(windowSize)

Prepares and creates the EEG object that the iteration will use with the same parameters that an EEG objects is initialized.

prepareIterator([step, startPoint, endPoint])

Prepares the iterator of the helper.

selectSignals(selectedSignals)

Parameters
__init__(data, sampleRate=None, windowSize=None, names=None, highpass=None, lowpass=None, normalize=False, ICA=False, selectedSignals=None)
Parameters
data: 2D matrix

The signals data in the shape (nChannels, nSamples).

sampleRate: numeric, optional

The frequency at which the data was recorded. By default its value is the lenght of the data.

windowSize: int, optional

The size of the window in which the calculations will be done. By default its value is the lenght of one second of the data.

names: list of strings

A list containing the names of each channel in the same positions than data channels.

highpass: numeric, optional

The signal will be filtered above this value.

lowpass: numeric, optional

The signal will be filtered bellow this value.

normalize: boolean, optional

If True, the data will be normalizing using z-scores. Default = False.

ICA: boolean, optional

If True, Independent Component Analysis will be applied to the data . It is applied always after normalization if normalize = True. Default: False.

selectedSignals: list of strings or ints

If the data file has names asociated to each columns, those columns can be selected through the name or the index of the column. If the data file hasn’t names in the columns, they can be selected just by the index.

getNames(indexes=None)

Returns the names of the specified indexes of channels.

Parameters
indexes: Iterable of int, optional.

The indexes of the channels desired. If None it will return all the channels’ names. Default: None.

moveEEGWindow(position)

Moves the window to start at position. Also it returns the inner eeg object.

Parameters
position: int, str or datetime.timedelta

  • int: position of the sample

  • str with format hh:mm:ss.ss: temporal position

  • timedelta: temporal position

Returns
EEG
prepareEEG(windowSize)

Prepares and creates the EEG object that the iteration will use with the same parameters that an EEG objects is initialized. Also it returns the inner eeg object.

Parameters
windowSize: int

The maximun samples the window will store.

Returns
EEG
prepareIterator(step=None, startPoint=0, endPoint=None)

Prepares the iterator of the helper.

Parameters
step: int,optional

Number of samples to be skipped in each iteration.

startPoint: int, optional

The index of first sample from where the iteration will start. By default 0.

endPoint: int, optional

The index of the last sample + 1 until where the iteration will go. By default the size of the data.

selectSignals(selectedSignals)
Parameters
selectedSignalsiterable of str and/or int

The indexes of the desired selected signals. It must be an iterable type containing either str or int.

Returns
None.
class eeglib.helpers.Iterator(helper, step, auxPoint, endPoint)

Bases: object

__init__(helper, step, auxPoint, endPoint)