Author: Bradley R. Buchsbaum
Date: 2026-03-28

# HRF Generators

#### Bradley R. Buchsbaum

#### 2026-03-28

## 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                description
    #> 1  bspline generator              5    FALSE   bspline HRF (generator)
    #> 2     tent generator              5    FALSE      tent HRF (generator)
    #> 3  fourier generator              5    FALSE   fourier HRF (generator)
    #> 4 daguerre generator              3    FALSE  daguerre HRF (generator)
    #> 5      fir generator             12    FALSE       fir HRF (generator)
    #> 6      lwu generator       variable    FALSE       lwu HRF (generator)
    #> 7       bs generator              5     TRUE 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)
    #>              1          2            3 4 5 6 7 8
    #> [1,] 0.0000000 0.00000000 0.0000000000 0 0 0 0 0
    #> [2,] 0.3081055 0.02164714 0.0003255208 0 0 0 0 0
    #> [3,] 0.4960938 0.07942708 0.0026041667 0 0 0 0 0
    #> [4,] 0.5844727 0.16259766 0.0087890625 0 0 0 0 0
    #> [5,] 0.5937500 0.26041667 0.0208333333 0 0 0 0 0
    #> [6,] 0.5444336 0.36214193 0.0406901042 0 0 0 0 0

## Visualising FIR Basis Functions

Here is a quick look at an FIR basis generated with 10 bins over a 20 second window:

    # Use the pre-defined FIR basis or create one with gen_hrf
    fir10 <- HRF_FIR  # Pre-defined FIR with 12 basis functions
    resp <- fir10(times)

    fir_df <- data.frame(Time = times, resp)
    fir_long <- tidyr::pivot_longer(fir_df, -Time)

    ggplot(fir_long, aes(Time, value, colour = name)) +
      geom_line(linewidth = 1) +
      labs(title = "Finite Impulse Response Basis",
           x = "Time (s)", y = "Response") +
      theme_minimal() +
      theme(legend.position = "none")

## Summary

Generator functions are simple factories that let you customise flexible HRF bases. They return normal `HRF` objects, which means you can evaluate them, combine them with decorators, or insert them into regressors just like the built-in HRFs. ````