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Neuroelectrophysiology Analysis Ontology - Analysis Parameters

Release: 2024-12-06

This version:
http://purl.org/neao/0.1.0/parameters#
Latest version:
http://purl.org/neao/parameters#
Revision:
0.1.0
Issued on:
2024-12-06
Authors:
Cristiano Köhler, Forschungszentrum Jülich
Michael Denker, Forschungszentrum Jülich
License:
https://creativecommons.org/licenses/by/4.0/
Visualization:
Visualize with WebVowl
Provenance of this page
Ontology Specification Draft

Introduction back to ToC

This module in the Neuroelectrophysiology Analysis Ontology (NEAO) contains classes that represent parameters in the analyses.

A parameter is an information entity that controls the behavior of an analysis step, but does not provide data that is used by the step to produce the output.

Namespace declarations

Table 1: Namespaces used in the document
dcterms<http://purl.org/dc/terms/>
neao_base<http://purl.org/neao/base#>
neao_params<http://purl.org/neao/parameters#>
owl<http://www.w3.org/2002/07/owl#>
rdf<http://www.w3.org/1999/02/22-rdf-syntax-ns#>
rdfs<http://www.w3.org/2000/01/rdf-schema#>
skos<http://www.w3.org/2004/02/skos/core#>
vann<http://purl.org/vocab/vann/>
xml<http://www.w3.org/XML/1998/namespace>
xsd<http://www.w3.org/2001/XMLSchema#>

NEAO Analysis Parameters: Overview back to ToC

This ontology has the following classes and properties.

Classes

NEAO Analysis Parameters: Description back to ToC

The classes in this module are subclasses of the AnalysisParameter base class and are associated with the analysis steps (defined in the steps module) through the usesParameter object property. These classes can provide semantic information to analysis parameters

The parameter entities control the behavior of the analysis step. For example, in a low-pass filtering step in the analysis, where a raw wideband signal is transformed to isolate the frequency components smaller than a cutoff frequency, the time series with the raw signal is the data input, and the low-pass frequency cutoff frequency value is a parameter.

A parameter is then represented by an individual associated with the usesParameter object property. This provides flexibility to describe a parameter in detail and make inferences based on semantic information. For example, a low pass filter setting of 250 Hz can be represented by two properties: one for the integer value 250 and another for the unit "Hz". The representation adopted by NEAO enhances machine readability compared to a single string value like "250 Hz". NEAO does not predefine specific properties for the AnalysisParameter class, allowing the use of existing ontologies, such as QUDT, to describe physical quantities. Moreover, the value of usesParameter can be associated with the multiple classes derived from AnalysisParameter that are defined in this module, providing semantic meanings (e.g., using the LowPassFrequencyCutoff class for a low-frequency cutoff parameter).

Cross-reference for NEAO Analysis Parameters classes, object properties and data properties back to ToC

This section provides details for each class and property defined by NEAO Analysis Parameters.

Classes

Bartlett smoothing kernelc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#BartlettSmoothingKernel

The kernel function has a triangular shape with end points at zero.
has super-classes
smoothing kernel c

bin sizec back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#BinSize

When discretizing continuous data into smaller intervals (bins), the parameter value determines the width of the intervals (in the unit of the data being discretized). Bins are adjacent and can be of different sizes.
has super-classes
analysis parameter c
is disjoint with
ASSET analysis parameter c, DBSCAN parameter c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, kernel width c, low-pass frequency cutoff c, number of FFT samples c, peak resolution c, sampling frequency c, shape factor c, smoothing kernel c, temporal resolution c, wavelet center frequency c, window function c, window length in samples c, window overlap factor c, window overlap in samples c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, low-pass frequency cutoff c, peak resolution c, sampling frequency c, shape factor c, window function c, window length in samples c, window overlap factor c, window overlap in samples c

Blackman window functionc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#BlackmanWindowFunction

Window function that uses the first three terms of a summation of cosines, minimizing spectral leakage. It was proposed by Ralph Beebe Blackman. The coefficients are an approximation of the ones used in the exact Blackman window function. Therefore, this window does not remove the third and fourth side lobes but produces smoother edges.
has super-classes
window function c
is disjoint with
discrete prolate spheroidal sequences window function c, exact Blackman window c, Hamming window function c, Hann window function c, Kaiser window function c, discrete prolate spheroidal sequences window function c, Hamming window function c, Hann window function c, Kaiser window function c

Boxcar smoothing kernelc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#BoxcarSmoothingKernel

The kernel function is zero over the entire interval except for a single (smaller) interval where it has a constant value. This non-zero interval is the total width of the kernel.
has super-classes
smoothing kernel c
is disjoint with
exponential smoothing kernel c

DBSCAN minimum number of neighborsc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#DBSCANMinimumNeighbors

Integer value that determines the minimum number of points that must be present in the neighborhood of a sample point such that it is considered a core point. The parameter value includes the sample point being evaluated.
has super-classes
DBSCAN parameter c
is disjoint with
DBSCAN neighborhood radius c

DBSCAN neighborhood radiusc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#DBSCANNeighborhoodRadius

Determines the radius around a sample point used to define its neighborhood. The parameter defines the maximum distance between two sample points such that they are considered to be in the same neighborhood.
has super-classes
DBSCAN parameter c
is disjoint with
DBSCAN minimum number of neighbors c

DBSCAN parameterc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#DBSCANParameter

A parameter used by the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) data clustering method.
has super-classes
analysis parameter c
has sub-classes
DBSCAN minimum number of neighbors c, DBSCAN neighborhood radius c
is disjoint with
ASSET analysis parameter c, bin size c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, kernel width c, low-pass frequency cutoff c, number of FFT samples c, peak resolution c, sampling frequency c, shape factor c, smoothing kernel c, temporal resolution c, wavelet center frequency c, window function c, window length in samples c, window overlap factor c, window overlap in samples c

discrete prolate spheroidal sequences window functionc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#DPSSWindowFunction

A set of orthogonal sequences optimized to simultaneously achieve maximum concentration of energy within a defined frequency band and minimum leakage into neighboring frequency bands. They are frequently used as tapers in multiple applications (e.g., multitaper power spectral density estimation). The first sequence (order 0 or Slepian sequence) has maximal energy concentration in the main lobe.
has super-classes
window function c
is disjoint with
Blackman window function c, exact Blackman window c, Hamming window function c, Hann window function c, Kaiser window function c, Blackman window function c, Hamming window function c, Hann window function c, Kaiser window function c

distance stretch factor in ASSET clusteringc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#ASSETClusteringDistanceStretchFactor

When computing the elliptical distance measure used by the DBSCAN algorithm to cluster the mask matrix entries to find the diagonal structures, the parameter value is used to stretch angular coefficients deviating from 45 degrees. This reflects into the shape of the neighborhood around a point in the DBSCAN procedure. A large stretch factor will produce neighborhoods that are narrower and closer to a line in the 45 degree direction. Smaller stretch factors will produce neighborhoods that are more elliptical, with the major axis in the 45 degree direction, and the minor axis in the 135 degree direction. Therefore, the smaller stretch factor increases the minor axis of this ellipsis. Together with the DBSCAN neighborhood radius parameter, this should be used to tweak the neighborhood ellipsis such that it is contained in the rectangular kernel used to filter the probability matrix entries when computing the joint probability matrix.
has super-classes
ASSET analysis parameter c
is disjoint with
filter shape in ASSET joint probability matrix c, number of largest neighbors in ASSET joint probability matrix c, significance thresholds in ASSET mask matrix c

exact Blackman windowc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#ExactBlackmanWindowFunction

Window function that uses the first three terms of a summation of cosines. It was proposed by Ralph Beebe Blackman. The coefficients are selected to remove the third and fourth side lobes, but the edges are discontinuous.
has super-classes
window function c
is disjoint with
Blackman window function c, discrete prolate spheroidal sequences window function c, Hamming window function c, Hann window function c, Kaiser window function c

exponential smoothing kernelc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#ExponentialSmoothingKernel

The kernel function is zero for points before the kernel center point, maximum at the center point, and decay exponentially for points greater than the center point. This kernel is asymmetric.
has super-classes
smoothing kernel c
is disjoint with
Boxcar smoothing kernel c

filter shape in ASSET joint probability matrixc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#ASSETJointProbabilityMatrixFilterShape

A tuple of integers that determines the width and length of a rectangular kernel used to cover a diagonal structure in the intersection matrix. The kernel is centered in a point in the probability matrix, and defines a neighborhood around that point. Inside the kernel, a chosen number of largest neighbors in the probability matrix is used to compute the joint probability value for the center point. The kernel is rotated in 45 degree angle.
has super-classes
ASSET analysis parameter c
is disjoint with
distance stretch factor in ASSET clustering c, number of largest neighbors in ASSET joint probability matrix c, significance thresholds in ASSET mask matrix c

firing ratec back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#FiringRate

The parameter value determines the average number of spikes fired by a neuron per time unit. It is used to control the behavior of steps that require a firing rate to control the output (e.g., a target mean firing rate when generating an artificial spike train).
has super-classes
analysis parameter c
is disjoint with
ASSET analysis parameter c, bin size c, DBSCAN parameter c, dithering time c, downsample factor c, filter order c, frequency resolution c, high-pass frequency cutoff c, kernel width c, low-pass frequency cutoff c, number of FFT samples c, peak resolution c, sampling frequency c, shape factor c, smoothing kernel c, temporal resolution c, wavelet center frequency c, window function c, window length in samples c, window overlap factor c, window overlap in samples c, bin size c, dithering time c, downsample factor c, filter order c, frequency resolution c, high-pass frequency cutoff c, low-pass frequency cutoff c, peak resolution c, sampling frequency c, shape factor c, window function c, window length in samples c, window overlap factor c, window overlap in samples c

frequency resolutionc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#FrequencyResolution

In analyses producing estimates in the frequency domain, the parameter determines the width of the frequency bins in the output, i.e., the resolution of the output on the frequency axis.
has super-classes
analysis parameter c
is disjoint with
ASSET analysis parameter c, bin size c, DBSCAN parameter c, dithering time c, downsample factor c, filter order c, firing rate c, high-pass frequency cutoff c, kernel width c, low-pass frequency cutoff c, number of FFT samples c, peak resolution c, sampling frequency c, shape factor c, smoothing kernel c, temporal resolution c, wavelet center frequency c, window function c, window length in samples c, window overlap factor c, window overlap in samples c, bin size c, dithering time c, downsample factor c, filter order c, firing rate c, high-pass frequency cutoff c, low-pass frequency cutoff c, peak resolution c, sampling frequency c, shape factor c, window function c, window length in samples c, window overlap factor c, window overlap in samples c

Hamming smoothing kernelc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#HammingSmoothingKernel

The kernel function corresponds to a raised cosine (i.e., a single period of a cosine function "raised") with positive endpoints.
has super-classes
smoothing kernel c

Hamming window functionc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#HammingWindowFunction

A window function that corresponds to a raised cosine (i.e., a single period of a cosine function "raised") but with positive endpoints. Therefore, the function does not eliminate the discontinuities in the signal. It has better cancellation of the nearest side lobe, and a poorer cancellation of the others, with a wide main lobe. It is named after Richard Wesley Hamming.
has super-classes
window function c
is disjoint with
Blackman window function c, discrete prolate spheroidal sequences window function c, exact Blackman window c, Hann window function c, Kaiser window function c, Blackman window function c, discrete prolate spheroidal sequences window function c, Hann window function c, Kaiser window function c

Hann smoothing kernelc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#HannSmoothingKernel

The kernel function corresponds to a raised cosine (i.e., a single period of a cosine function "raised" such that the negative troughs are zero). The endpoints reach zero smoothly at the boundaries.
has super-classes
smoothing kernel c

Hann window functionc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#HannWindowFunction

A window function that corresponds to a raised cosine (i.e., a single period of a cosine function "raised" such that the negative troughs are zero). Therefore, the function reaches zero smoothly at the boundaries and eliminates all discontinuities in the signal. It has a wide main lobe and low side lobes. It is named after Julius von Hann.
has super-classes
window function c
is disjoint with
Blackman window function c, discrete prolate spheroidal sequences window function c, exact Blackman window c, Hamming window function c, Kaiser window function c, Blackman window function c, discrete prolate spheroidal sequences window function c, Hamming window function c, Kaiser window function c

high-pass frequency cutoffc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#HighPassFrequencyCutoff

When applying a filter to a time series, the frequency components below this parameter value will be attenuated. Usually, the parameter value corresponds to the frequency component attenuated by 3 dB in the filter transition band.
has super-classes
analysis parameter c
is disjoint with
ASSET analysis parameter c, bin size c, DBSCAN parameter c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, kernel width c, low-pass frequency cutoff c, number of FFT samples c, peak resolution c, sampling frequency c, shape factor c, smoothing kernel c, temporal resolution c, wavelet center frequency c, window function c, window length in samples c, window overlap factor c, window overlap in samples c, bin size c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, low-pass frequency cutoff c, peak resolution c, sampling frequency c, shape factor c, window function c, window length in samples c, window overlap factor c, window overlap in samples c

Kaiser window functionc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#KaiserWindowFunction

This window function approximates the DPSS window function using Bessel functions. It is easier to compute than the DPSS window function. It was developed by James Kaiser.
has super-classes
window function c
is disjoint with
Blackman window function c, discrete prolate spheroidal sequences window function c, exact Blackman window c, Hamming window function c, Hann window function c, Blackman window function c, discrete prolate spheroidal sequences window function c, Hamming window function c, Hann window function c

low-pass frequency cutoffc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#LowPassFrequencyCutoff

When applying a filter to a time series, the frequency components above this parameter value will be attenuated. Usually, the parameter value corresponds to the frequency component attenuated by 3 dB in the filter transition band.
has super-classes
analysis parameter c
is disjoint with
ASSET analysis parameter c, bin size c, DBSCAN parameter c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, kernel width c, number of FFT samples c, peak resolution c, sampling frequency c, shape factor c, smoothing kernel c, temporal resolution c, wavelet center frequency c, window function c, window length in samples c, window overlap factor c, window overlap in samples c, bin size c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, peak resolution c, sampling frequency c, shape factor c, window function c, window length in samples c, window overlap factor c, window overlap in samples c

number of FFT samplesc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#NumberFFTSamples

Length of the time series considered by application of a fast Fourier transform (FFT). The value determines the frequency bin size based on the sampling frequency of the input data, and the length of the vector representing the result of the discrete Fourier transform.
has super-classes
analysis parameter c
is disjoint with
ASSET analysis parameter c, bin size c, DBSCAN parameter c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, kernel width c, low-pass frequency cutoff c, peak resolution c, sampling frequency c, shape factor c, smoothing kernel c, temporal resolution c, wavelet center frequency c, window function c, window length in samples c, window overlap factor c, window overlap in samples c

number of largest neighbors in ASSET joint probability matrixc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#ASSETJointProbabilityMatrixNumberLargestNeighbors

Defines the number of elements inside the filter kernel that are used to compute the joint probability value for a matrix entry. The probability matrix values inside the kernel for that entry are ordered from the largest to the lowest value, and the first N elements corresponding to the parameter value are taken.
has super-classes
ASSET analysis parameter c
is disjoint with
distance stretch factor in ASSET clustering c, filter shape in ASSET joint probability matrix c, significance thresholds in ASSET mask matrix c

sampling frequencyc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#SamplingFrequency

The parameter determines the number of samples per time unit in sampled data. It is the inverse of the sampling period, i.e., the interval between two consecutive samples.
has super-classes
analysis parameter c
is disjoint with
ASSET analysis parameter c, bin size c, DBSCAN parameter c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, kernel width c, low-pass frequency cutoff c, number of FFT samples c, peak resolution c, shape factor c, smoothing kernel c, temporal resolution c, wavelet center frequency c, window function c, window length in samples c, window overlap factor c, window overlap in samples c, bin size c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, low-pass frequency cutoff c, peak resolution c, shape factor c, window function c, window length in samples c, window overlap factor c, window overlap in samples c

shape factorc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#ShapeFactor

When determining a probability distribution, the parameter affects the shape of the distribution. This is in contrast to other parameters that affect the location (e.g., mean) or scale (e.g., variance) of the distribution.
has super-classes
analysis parameter c
is disjoint with
ASSET analysis parameter c, bin size c, DBSCAN parameter c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, kernel width c, low-pass frequency cutoff c, number of FFT samples c, peak resolution c, sampling frequency c, smoothing kernel c, temporal resolution c, wavelet center frequency c, window function c, window length in samples c, window overlap factor c, window overlap in samples c, bin size c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, low-pass frequency cutoff c, peak resolution c, sampling frequency c, window function c, window length in samples c, window overlap factor c, window overlap in samples c

significance thresholds in ASSET mask matrixc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#ASSETMaskMatrixSignificanceThresholds

Real values (range 0-1) used as thresholds to determine if the entries in the probability matrix and joint probability matrices are significant. Entry significance is defined as a probability value greater than the threshold values specified by the parameter. Significant entries in both probability and joint probability matrices are defined as 1 in the mask matrix.
has super-classes
ASSET analysis parameter c
is disjoint with
distance stretch factor in ASSET clustering c, filter shape in ASSET joint probability matrix c, number of largest neighbors in ASSET joint probability matrix c

smoothing kernelc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#SmoothingKernel

A smoothing kernel can be used in non-parametric smoothing techniques such as kernel smoothing or kernel density estimation. It represents a weighting function that assigns weights to neighboring data points based on their distance from a given point (the kernel center). Closer points are given higher weights. The weighted average of the data points within the kernel is computed to produce a smoothed estimate of the underlying data (e.g., a signal or probability density function). Smoothing kernels are characterized by their shape (e.g., Boxcar, exponential) and bandwidth. The bandwidth determines the extent of influence of neighboring data points on the smoothed estimate.
has super-classes
analysis parameter c
has sub-classes
Bartlett smoothing kernel c, Boxcar smoothing kernel c, Hamming smoothing kernel c, Hann smoothing kernel c, exponential smoothing kernel c
is disjoint with
ASSET analysis parameter c, bin size c, DBSCAN parameter c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, kernel width c, low-pass frequency cutoff c, number of FFT samples c, peak resolution c, sampling frequency c, shape factor c, temporal resolution c, wavelet center frequency c, window function c, window length in samples c, window overlap factor c, window overlap in samples c

temporal resolutionc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#TemporalResolution

The parameter determines the smallest time interval that can be detected or produced by the method. In sampled time series data, this equals to the sampling period, i.e, the time interval between two consecutive samples.
has super-classes
analysis parameter c
is disjoint with
ASSET analysis parameter c, bin size c, DBSCAN parameter c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, kernel width c, low-pass frequency cutoff c, number of FFT samples c, peak resolution c, sampling frequency c, shape factor c, smoothing kernel c, wavelet center frequency c, window function c, window length in samples c, window overlap factor c, window overlap in samples c

wavelet center frequencyc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#WaveletCenterFrequency

The value of the center frequency of a wavelet. This is the frequency at which the wavelet oscillates most strongly, and it influences how effectively the wavelet can capture features of a signal at different frequencies. Wavelets with higher center frequencies are better suited for analyzing high-frequency components of a signal, while those with lower center frequencies are more effective for capturing low-frequency components.
has super-classes
analysis parameter c
is disjoint with
ASSET analysis parameter c, bin size c, DBSCAN parameter c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, kernel width c, low-pass frequency cutoff c, number of FFT samples c, peak resolution c, sampling frequency c, shape factor c, smoothing kernel c, temporal resolution c, window function c, window length in samples c, window overlap factor c, window overlap in samples c

window functionc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#WindowFunction

A mathematical function that is zero-valued outside a defined interval. When multiplied by another function or sampled data, the values outside the window interval will be transformed to zero, and the values inside the window will be weighted by the window function values. Window functions are used to select and modify a finite segment of a signal. This is useful for applying the fast Fourier transform (FFT) to a finite set of data where the length is not an integer number of periods of the signal. In this situation, there will be discontinuities at the boundaries of the signal, and the data in the frequency domain produced by the FFT will have frequency components not present in the original signal (spectral leakage). The window function can be used to reduce this discontinuities and mitigate spectral leakage when performing spectral analysis. Several types of window function exist. They will vary in shape in the time domain and will have distinct frequency characteristics in the frequency domain, with a main lobe and several side lobes. The main lobe is centered at each frequency component in the time domain, and the side lobes approach zero. These characteristics can be used to control the effect of spectral leakage when performing the analysis.
has super-classes
analysis parameter c
has sub-classes
Blackman window function c, Hamming window function c, Hann window function c, Kaiser window function c, discrete prolate spheroidal sequences window function c, exact Blackman window c
is disjoint with
ASSET analysis parameter c, bin size c, DBSCAN parameter c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, kernel width c, low-pass frequency cutoff c, number of FFT samples c, peak resolution c, sampling frequency c, shape factor c, smoothing kernel c, temporal resolution c, wavelet center frequency c, window length in samples c, window overlap factor c, window overlap in samples c, bin size c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, low-pass frequency cutoff c, peak resolution c, sampling frequency c, shape factor c, window length in samples c, window overlap factor c, window overlap in samples c

window length in samplesc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#WindowLengthSamples

Integer value that defines the number of samples encompassed by a window in sampled data, i.e., the window length. The window corresponds to a finite interval in the sampled data (e.g., a time interval in sampled time series data).
has super-classes
analysis parameter c
is disjoint with
ASSET analysis parameter c, bin size c, DBSCAN parameter c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, kernel width c, low-pass frequency cutoff c, number of FFT samples c, peak resolution c, sampling frequency c, shape factor c, smoothing kernel c, temporal resolution c, wavelet center frequency c, window function c, window overlap factor c, window overlap in samples c, bin size c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, low-pass frequency cutoff c, peak resolution c, sampling frequency c, shape factor c, window function c, window overlap factor c, window overlap in samples c

window overlap factorc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#WindowOverlapFactor

Real value (range 0-1) that determines the proportion of overlap between two adjacent windows. The window is a finite interval in the data (e.g., a time interval in time series data).
has super-classes
analysis parameter c
is disjoint with
ASSET analysis parameter c, bin size c, DBSCAN parameter c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, kernel width c, low-pass frequency cutoff c, number of FFT samples c, peak resolution c, sampling frequency c, shape factor c, smoothing kernel c, temporal resolution c, wavelet center frequency c, window function c, window length in samples c, window overlap in samples c, bin size c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, low-pass frequency cutoff c, peak resolution c, sampling frequency c, shape factor c, window function c, window length in samples c, window overlap in samples c

window overlap in samplesc back to ToC or Class ToC

IRI: http://purl.org/neao/parameters#WindowOverlapSamples

Integer value (range 0 to the window length in samples) that determines the number of samples where two adjacent windows overlap. The window corresponds to a finite interval in sampled data (e.g., a time interval in sampled time series data).
has super-classes
analysis parameter c
is disjoint with
ASSET analysis parameter c, bin size c, DBSCAN parameter c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, kernel width c, low-pass frequency cutoff c, number of FFT samples c, peak resolution c, sampling frequency c, shape factor c, smoothing kernel c, temporal resolution c, wavelet center frequency c, window function c, window length in samples c, window overlap factor c, bin size c, dithering time c, downsample factor c, filter order c, firing rate c, frequency resolution c, high-pass frequency cutoff c, low-pass frequency cutoff c, peak resolution c, sampling frequency c, shape factor c, window function c, window length in samples c, window overlap factor c

Legend back to ToC

c: Classes

Acknowledgments back to ToC

This work was performed as part of the Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE) and received funding from the Helmholtz Association of German Research Centres. This project has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreement No. 945539 (Human Brain Project SGA3), the European Union’s Horizon Europe Programme under the Specific Grant Agreement No. 101147319 (EBRAINS 2.0 Project), the Ministry of Culture and Science of the State of North Rhine-Westphalia, Germany (NRW-network "iBehave", grant number: NW21-049), and the Joint Lab "Supercomputing and Modeling for the Human Brain."

The authors would like to thank Silvio Peroni for developing LODE, a Live OWL Documentation Environment, which is used for representing the Cross Referencing Section of this document and Daniel Garijo for developing Widoco, the program used to create the template used in this documentation.