elasticai.preprocessor.referencing.common_referencing#

Module Contents#

Classes#

SettingsReferencing

Class for defining the properties of the common referencing methods Attributes: dim: Integer with applied dimension kernel_size: Kernel size for convolution (must be odd-numbered)

CommonReferencing

Data#

DefaultSettingsReferencing

API#

class elasticai.preprocessor.referencing.common_referencing.SettingsReferencing[source]#

Class for defining the properties of the common referencing methods Attributes: dim: Integer with applied dimension kernel_size: Kernel size for convolution (must be odd-numbered)

kernel_size: int#

None

elasticai.preprocessor.referencing.common_referencing.DefaultSettingsReferencing#

‘SettingsReferencing(…)’

class elasticai.preprocessor.referencing.common_referencing.CommonReferencing(settings: elasticai.preprocessor.referencing.common_referencing.SettingsReferencing)[source]#

Initialization

build_dummy_active_mapping(signal: numpy.ndarray) → numpy.ndarray[source]#

Function for building a dummy active mapper with True values

Parameters:

signal – Numpy array with channel-specific signals

Returns:

Numpy array with boolean (default: true) for channels are used

get_reference_map(signal: numpy.ndarray, active: numpy.ndarray) → numpy.ndarray[source]#

Building the common reference mapper using CAR algorithm (Common Average Referencing) on input signals

Parameters:

  • signal – Input signal of transient analysis with shape [num_channels, num_smaples] or num_channels splitted into electrode design

  • active – Overview of active channels used for referencing

Returns:

Numpy array with convolved signal for doing common average referencing

apply_reference(signal: numpy.ndarray, active: numpy.ndarray) → numpy.ndarray[source]#