correlation_features¶

Module: `missions.nodes.feature_generation.correlation_features`¶

Extract statistical properties like moments or correlation coefficients

Known issues: No unit tests!

Inheritance diagram for pySPACE.missions.nodes.feature_generation.correlation_features:

Inheritance diagram of pySPACE.missions.nodes.feature_generation.correlation_features

Class Summary¶

`StatisticalFeatureNode`([raw_moment_order, ...])	Compute statistical features, e.g.
`PearsonCorrelationFeatureNode`([segments, ...])	Compute pearson correlation of all pairs of channels
`ClassAverageCorrelationFeatureNode`([segments])	Compute pearson correlation between channel segments and class averages
`CoherenceFeatureNode`([frequency_band, ...])	Compute pairwise coherence of two channels with matplotlib.mlab.cohere

Classes¶

`StatisticalFeatureNode`¶

class pySPACE.missions.nodes.feature_generation.correlation_features.StatisticalFeatureNode(raw_moment_order=1, central_moment_order=1, std=False, std_function=None, quadratic_mean=False, median=False, minimum=False, maximum=False, artifact_threshold=None, **kwargs)[source]¶

Bases: pySPACE.missions.nodes.base_node.BaseNode

Compute statistical features, e.g. central/raw moments, median, min etc.

This node computes statistical features like raw and central moments of k-th order:

raw_moment_of_k_th_order
$\frac{1}{n} \cdot \sum_{i=0}^n (x_i^k)$
- k=1: mean;
- k=2: square root of value is quadratic mean
central_moment_of_k_th_order
$\frac{1}{n} \cdot sum_{i=0}^n (x_i - mean)^k$
- k=1: 0;
- k=2: empirical variance;
- k=3: empirical skewness;
- k=4: kurtosis

In addition there could be computed: median, quadratic mean, standard deviation, minimum/maximum amplitude and the number of amplitudes above a given threshold.

Parameters

raw_moment_order:

The highest order (as an integer number) of raw moments that will be computed. For example:

0:

no raw moment computation,

1:

mean

2:

mean and raw moment 2nd order

3:

etc.

Note

Values greater then 2 might take long computation time and are expected not to have any useful information.

(optional, default: 1)

central_moment_order:

The highest order (as an integer number) of central moments that will be computed. For example:

1:

no central moment computation

2:

empirical variance

3:

empirical variance and empirical skewness

etc.:

(optional, default: 1)

std:
True or False. If True the standard deviation of each channel is computed and retained as feature.

(optional, default: False)

std_function:
A string of a Python eval*able function. Specifies a function, e.g. numpy.std, if the algorithm should not use use the one in *standard_deviation. Specification of this parameter makes only sense if std is “True”.

(optional, default: None)

quadratic_mean:
True or False. If True the square root of the second raw moment (quadratic mean) is computed and retained as feature.

(optional, default: False)

median:
True or False. If True the median of each channel is computed and retained as feature.

(optional, default: False)

minimum:
True or False. If True the minimum value of each channel is stored as a feature.

(optional, default: False)

maximum:
True or False. If True the maximum value of each channel is stored as a feature.

(optional, default: False)

artifact_threshold:

A float value. The number of amplitudes within a window above the given artifact_threshold is calculated and used as feature.

Note

The threshold depends on the actual resolution of the values. Check header file (for EEG data).

(optional, default: None)

Known issues

Exemplary Call

-
    node : Statistical_Features
    parameters :
        central_moment_order : 0
        std : True
        median : True
        artifact_threshold : 500.0

POSSIBLE NODE NAMES:
Author:	Anett Seeland (anett.seeland@dfki.de)
Created:	2009/10/06
Refactoring:	2014/09/11 by Mario Michael Krell
	StatisticalFeatureNode Statistical_Features StatisticalFeature
POSSIBLE INPUT TYPES:
	TimeSeries

Class Components Summary

`_execute`(x)	Calculates statistical features
`amplitudes_above`(one_channel_time_series, ...)	Return the number of values about the given threshold in the given time series.
`input_types`
`raw_moment`(one_channel_time_series, k)	Compute the k’th-order raw moment of a given channel.

__init__(raw_moment_order=1, central_moment_order=1, std=False, std_function=None, quadratic_mean=False, median=False, minimum=False, maximum=False, artifact_threshold=None, **kwargs)[source]¶

raw_moment(one_channel_time_series, k)[source]¶

Compute the k’th-order raw moment of a given channel.

Arguments

one_channel_time_series:

a one dimensional time series

k: order of raw moment

amplitudes_above(one_channel_time_series, threshold)[source]¶

Return the number of values about the given threshold in the given time series.

Arguments

window: a tow dimensional time series

threshold: a number above the values are count.

_execute(x)[source]¶: Calculates statistical features

input_types = ['TimeSeries']¶

`PearsonCorrelationFeatureNode`¶

class pySPACE.missions.nodes.feature_generation.correlation_features.PearsonCorrelationFeatureNode(segments=1, max_segment_shift=0, *args, **kwargs)[source]¶

Bases: pySPACE.missions.nodes.base_node.BaseNode

Compute pearson correlation of all pairs of channels

This node computes for all pairs of channels the Pearson product-moment correlation coefficient of certain time segments and returns each of correlation coefficient as feature.

Parameters

segments:
The number of segments the time series window is split.

(optional, default: 1)

max_segment_shift:

If 0, only the same segments of the two channels are compared. For n, each segment is also compared with the n,n-1,...,0 previous and later segments of the other channel.

(optional, default: 0)

Exemplary Call

-
    node : Pearson_Correlation_Features
    parameters :
        segments : 3
        max_segment_shift : 2

POSSIBLE NODE NAMES:
Author:	Jan Hendrik Metzen (jhm@informatik.uni-bremen.de)
Created:	2009/03/18
	Pearson_Correlation_Features PearsonCorrelationFeature PearsonCorrelationFeatureNode
POSSIBLE INPUT TYPES:
	TimeSeries

Class Components Summary

`_execute`(x)
`input_types`

__init__(segments=1, max_segment_shift=0, *args, **kwargs)[source]¶

_execute(x)[source]¶

input_types = ['TimeSeries']¶

`ClassAverageCorrelationFeatureNode`¶

class pySPACE.missions.nodes.feature_generation.correlation_features.ClassAverageCorrelationFeatureNode(segments=1, *args, **kwargs)[source]¶

Bases: pySPACE.missions.nodes.base_node.BaseNode

Compute pearson correlation between channel segments and class averages

This node computes for all channels the Pearson product-moment correlation coefficient between certain time segments and the class averages. Then each of the correlation coefficients is returned as feature.

Parameters

segments:
The number of segments the time series window is split.

(optional, default: 1)

Exemplary Call

-
    node : Class_Average_Correlation_Features
    parameters :
        segments : 3

POSSIBLE NODE NAMES:
Author:	Jan Hendrik Metzen (jhm@informatik.uni-bremen.de)
Created:	2009/03/18
	ClassAverageCorrelationFeatureNode Class_Average_Correlation_Features ClassAverageCorrelationFeature
POSSIBLE INPUT TYPES:
	TimeSeries

Class Components Summary

`_execute`(x)
`_stop_training`()
`_train`(x, label)
`input_types`
`is_supervised`()	Returns whether this node is trainable
`is_trainable`()	Returns whether this node is trainable

__init__(segments=1, *args, **kwargs)[source]¶

is_trainable()[source]¶: Returns whether this node is trainable

is_supervised()[source]¶: Returns whether this node is trainable

_execute(x)[source]¶

_train(x, label)[source]¶

_stop_training()[source]¶

input_types = ['TimeSeries']¶

`CoherenceFeatureNode`¶

class pySPACE.missions.nodes.feature_generation.correlation_features.CoherenceFeatureNode(frequency_band=None, frequency_resolution=None, *args, **kwargs)[source]¶

Bases: pySPACE.missions.nodes.base_node.BaseNode

Compute pairwise coherence of two channels with matplotlib.mlab.cohere

Parameters

frequency_band:
The frequency band which is used to extract features from the spectrogram. If None, the whole frequency band is used.

(Optional, default: None)

Exemplary Call

-
    node : Coherence_Features
    parameters :
        frequency_band : [0.4,3.5]

POSSIBLE NODE NAMES:
Author:	Jan Hendrik Metzen (jhm@informatik.uni-bremen.de)
Created:	2009/03/18
	CoherenceFeatureNode CoherenceFeature Coherence_Features
POSSIBLE INPUT TYPES:
	TimeSeries

Class Components Summary

`_execute`(x)
`input_types`

__init__(frequency_band=None, frequency_resolution=None, *args, **kwargs)[source]¶

_execute(x)[source]¶

input_types = ['TimeSeries']¶

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correlation_features¶

Module: `missions.nodes.feature_generation.correlation_features`¶

Class Summary¶

Classes¶

`StatisticalFeatureNode`¶

`PearsonCorrelationFeatureNode`¶

`ClassAverageCorrelationFeatureNode`¶

`CoherenceFeatureNode`¶

window:	a tow dimensional time series
threshold:	a number above the values are count.

correlation_features¶

Module: missions.nodes.feature_generation.correlation_features¶

Class Summary¶

Classes¶

StatisticalFeatureNode¶

PearsonCorrelationFeatureNode¶

ClassAverageCorrelationFeatureNode¶

CoherenceFeatureNode¶

Module: `missions.nodes.feature_generation.correlation_features`¶

`StatisticalFeatureNode`¶

`PearsonCorrelationFeatureNode`¶

`ClassAverageCorrelationFeatureNode`¶

`CoherenceFeatureNode`¶