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The parameters are Activity, Mobility, and Complexity. They are commonly used in the analysis of electroencephalography signals for feature extraction. The parameters are normalised slope descriptors (NSDs) used in EEG.
The method is a tool for investigating periodic structures in frequency spectra. The power cepstrum has applications in the analysis of human speech. The term cepstrum was derived by reversing the first four letters of spectrum. Operations on cepstra are labelled quefrency analysis (or quefrency alanysis [1]), liftering, or cepstral analysis.
In probability theory, an exponentially modified Gaussian distribution (EMG, also known as exGaussian distribution) describes the sum of independent normal and exponential random variables. An exGaussian random variable Z may be expressed as Z = X + Y , where X and Y are independent, X is Gaussian with mean μ and variance σ 2 , and Y is ...
The Hough transform (/ h ĘŚ f /) is a feature extraction technique used in image analysis, computer vision, pattern recognition, and digital image processing. [1] [2] The purpose of the technique is to find imperfect instances of objects within a certain class of shapes by a voting procedure.
EMG measures action potentials, called Motor Unit Action Potentials (MUAPs), created during muscle contraction. A few common uses are determining whether a muscle is active or inactive during movement (onset of activity), assessing the velocity of nerve conduction, and the amount of force generated during movement.
This toolbox is a set of Matlab-based tools for the analysis of functional near-infrared spectroscopy (fNIRS). This toolbox defines the +nirs namespace and includes a series of tools for signal processing, display, and statistics of fNIRS data. This toolbox is built around an object-oriented framework of Matlab classes and namespaces. [18]
In chemometrics non-negative matrix factorization has a long history under the name "self modeling curve resolution". [9] In this framework the vectors in the right matrix are continuous curves rather than discrete vectors.
The spike-triggered averaging (STA) is a tool for characterizing the response properties of a neuron using the spikes emitted in response to a time-varying stimulus. The STA provides an estimate of a neuron's linear receptive field. It is a useful technique for the analysis of electrophysiological data.