HRF Generators

Why Generators?

Most pre-defined HRFs in fmrihrf (like HRF_SPMG1 or HRF_GAUSSIAN) are ready-to-use objects. However, some HRFs are actually generators. A generator is a function that creates a new HRF object when you call it. This allows you to specify the number of basis functions (nbasis) and the time span (span) at creation time.

The library provides generators for flexible basis sets such as B-splines and finite impulse response (FIR) models. They are available through the internal HRF_REGISTRY and are also returned by list_available_hrfs() with type “generator”.

list_available_hrfs(details = TRUE) %>%
  dplyr::filter(type == "generator")
#>       name      type nbasis_default is_alias
#> 1  bspline generator              5    FALSE
#> 2     tent generator              5    FALSE
#> 3  fourier generator              5    FALSE
#> 4 daguerre generator              3    FALSE
#> 5      fir generator             12    FALSE
#> 6      lwu generator       variable    FALSE
#> 7       bs generator              5     TRUE
#>                  description
#> 1   bspline HRF (generator) 
#> 2      tent HRF (generator) 
#> 3   fourier HRF (generator) 
#> 4  daguerre HRF (generator) 
#> 5       fir HRF (generator) 
#> 6       lwu HRF (generator) 
#> 7 bs HRF (generator) (alias)

Creating a Basis with a Generator

To obtain an actual HRF object from a generator, simply call the generator function with your desired parameters. For example, to create a B-spline basis with 8 functions spanning 32 seconds:

# Create a B-spline basis using gen_hrf
bs8 <- gen_hrf(hrf_bspline, N = 8, span = 32)
print(bs8)
#> -- HRF: hrf_bspline --------------------------------------- 
#>    Basis functions: 8 
#>    Span: 32 s

The returned value is a standard HRF object, so you can evaluate it or use it in model formulas like any other HRF.

times <- seq(0, 32, by = 0.5)
mat <- bs8(times)
head(mat)
#>              2          3           4 5 6 7 8 9
#> [1,] 0.0000000 0.00000000 0.000000000 0 0 0 0 0
#> [2,] 0.3472245 0.02905801 0.000516915 0 0 0 0 0
#> [3,] 0.5356084 0.10485991 0.004135320 0 0 0 0 0
#> [4,] 0.5977173 0.21034749 0.013956706 0 0 0 0 0
#> [5,] 0.5661169 0.32846258 0.033082562 0 0 0 0 0
#> [6,] 0.4733728 0.44214696 0.064614378 0 0 0 0 0

Visualising FIR Basis Functions

A finite impulse response (FIR) basis makes no assumption about the shape of the response: each basis function is a boxcar covering one time bin after the event, and the fitted weights trace out the response bin by bin. Here we create a basis with 10 bins over a 20-second window; each bin is a 2-second boxcar, labelled at its top:

fir10 <- hrf_fir_generator(nbasis = 10, span = 20)
print(fir10)
#> -- HRF: fir ----------------------------------------------- 
#>    Basis functions: 10 
#>    Span: 20 s
#>    Parameters: nbasis = 10, span = 20, bin_width = 2

plot_hrfs(fir10, time = seq(0, 22, by = 0.02),
          title = "FIR basis: 10 bins of 2 s")

Ten FIR basis functions, each a 2 second boxcar of height 1 labelled B1 to B10; bin k covers 2(k-1) to 2k seconds after the event, so together they tile 0 to 20 seconds.

HRF Generators

Bradley R. Buchsbaum

2026-10-03

Why Generators?

Creating a Basis with a Generator

Visualising FIR Basis Functions

Summary