smooth-NeuroSurface-method: Smooth Data on a NeuroSurface Object

In bbuchsbaum/neurosurf: Data structures and IO for surface-based neuroimaging data

Smooth Data on a NeuroSurface Object

Description

This method applies smoothing to the data values associated with a NeuroSurface object. Unlike the geometric smoothing applied to SurfaceGeometry, this function smooths the scalar values (e.g., intensity or activation) associated with each vertex on the surface.

Usage

## S4 method for signature 'NeuroSurface'
smooth(x, sigma = 5, ...)

Arguments

x	A NeuroSurface object containing the brain surface and associated data to be smoothed.
sigma	A numeric value specifying the smoothing radius. This defines the neighborhood around each vertex used to compute the smoothed value. Default is 5.
...	Additional arguments passed to the smoothing function.

Details

The smoothing process involves averaging the data values within the neighborhood of each vertex. For each vertex on the surface, the function calculates the mean of its own value and the values of its adjacent vertices within the graph structure of the surface. The result is a smoother representation of the data, which can be useful for reducing noise or visualizing broader trends on the surface.

The smoothing is particularly useful when working with noisy data or when a smoother representation of the underlying signal is desired. It is commonly applied in neuroimaging to enhance visualization or prepare data for further analysis.

Value

A new NeuroSurface object with the smoothed data values. The geometry remains unchanged.

See Also

smooth,SurfaceGeometry-method for smoothing the geometry of a surface.

Examples

## Not run: 
  # Example of smoothing data on a NeuroSurface object
  smoothed_data_surface <- smooth(neuro_surf, sigma=3)

  # The original geometry is preserved, but the data is smoothed
  plot_surface(smoothed_data_surface)

## End(Not run)

bbuchsbaum/neurosurf documentation built on Aug. 29, 2024, 10:53 a.m.