# Extracting Data Epochs and Exporting Pupil Data

`eyeris` was intentionally designed for intuitive, flexible preprocessing of pupillometry data, with support for event-based epoching and BIDS-style organization for reproducible workflows.

In this vignette, we’ll walk through a typical use case:

1. loading raw data,
2. preprocessing it,
3. extracting trial-based epochs,
4. and exporting everything in a clean, analysis-ready format.

We’ll also demonstrate a unique feature we designed to maximize both your productivity as well as data quality: `interactive HTML reports`, which include a record of the steps used to preprocess / epoch any given dataset – and, for epoched data – an interactive “gallery” view to quickly skim through trial-level data from each step of the preprocessing pipeline to make quality control and assurance intuitive and accessible for any dataset (without needing to write any additional code)!

## 1 Load and Preprocess Your Data

    # Load eyeris
    library(eyeris)

    # Load the example memory task file and run default glassbox preproc workflow
    demo_data <- eyelink_asc_demo_dataset()
    eye <- glassbox(demo_data)
    #> ✔ [2026-07-31 09:36:12] [OKAY] Running eyeris::load_asc()
    #> ✔ [2026-07-31 09:36:12] [OKAY] Running eyeris::resample()
    #> ℹ [2026-07-31 09:36:12] [INFO] Processing block: block_1
    #> ✔ [2026-07-31 09:36:12] [OKAY] Running eyeris::deblink() for block_1
    #> ✔ [2026-07-31 09:36:12] [OKAY] Running eyeris::detransient() for block_1
    #> ✔ [2026-07-31 09:36:12] [OKAY] Running eyeris::interpolate() for block_1
    #> ! [2026-07-31 09:36:12] [WARN] Interpolation now leaves gaps longer than 250 ms
    #> as `NA` instead of interpolating across them (following Kret & Sjak-Shie,
    #> 2018). This is a change in default behavior from eyeris <= 3.2.0 and may affect
    #> your results. To restore the previous behavior, set `interpolate =
    #> list(max_gap_ms = Inf)` in `glassbox()` (or `max_gap_ms = Inf` in
    #> `interpolate()`).
    #> ✔ [2026-07-31 09:36:12] [OKAY] Running eyeris::lpfilt() for block_1
    #> ! [2026-07-31 09:36:12] [WARN] Skipping eyeris::downsample() for block_1
    #> ! [2026-07-31 09:36:12] [WARN] Skipping eyeris::bin() for block_1
    #> ! [2026-07-31 09:36:12] [WARN] Skipping eyeris::detrend() for block_1
    #> ✔ [2026-07-31 09:36:12] [OKAY] Running eyeris::zscore() for block_1
    #> ℹ [2026-07-31 09:36:12] [INFO] Block processing summary:
    #> ℹ [2026-07-31 09:36:12] [INFO] block_1: OK (steps: 6, latest:
    #> pupil_raw_deblink_detransient_interpolate_lpfilt_z)
    #> ✔ [2026-07-31 09:36:12] [OKAY] Running eyeris::summarize_confounds()

## 2 Extract Data Epochs

`epoch()` enables flexible extraction of trials using:

- start/stop events,
- string-based patterns,
- and even embedded trial metadata.

### Example A: Fixed Time Epochs Around a Matched Event

> Extract a 2-second window centered around each “PROBE” event.

    eye_1a <- eye |>
      epoch(events = "PROBE*", limits = c(-1, 1))
    #> ℹ [2026-07-31 09:36:12] [INFO] Epoching pupil data...
    #> ℹ [2026-07-31 09:36:12] [INFO] Block 1: found 10 matching events for PROBE
    #> ✔ [2026-07-31 09:36:12] [OKAY] Done!
    #> ✔ [2026-07-31 09:36:12] [OKAY] Block 1: pupil data from 10 unique event
    #> messages extracted
    #> ✔ [2026-07-31 09:36:12] [OKAY] Pupil epoching completed in 0.07 seconds
    #> ℹ [2026-07-31 09:36:12] [INFO] Recalculating epoched confounds for new
    #> epochs...

Now, if you take a look at `eye`, you’ll notice there’s a new list element within this `eyeris` object: `epoch_probe`.

    eye_1a$epoch_probe
    #> $block_1
    #> # A tibble: 20,000 × 21
    #>    block time_orig timebin time_secs time_scaled eye_x eye_y eye      hz type
    #>    <dbl>     <int>   <dbl>     <dbl>       <dbl> <dbl> <dbl> <chr> <dbl> <chr>
    #>  1     1  11335474 0           0.983       0.983  975.  545. R      1000 diamet…
    #>  2     1  11335475 0.00100     0.984       0.984  975.  544. R      1000 diamet…
    #>  3     1  11335476 0.00200     0.985       0.985  975.  544  R      1000 diamet…
    #>  4     1  11335477 0.00300     0.986       0.986  976.  544. R      1000 diamet…
    #>  5     1  11335478 0.00400     0.987       0.987  975.  545  R      1000 diamet…
    #>  6     1  11335479 0.00500     0.988       0.988  975.  545. R      1000 diamet…
    #>  7     1  11335480 0.00600     0.989       0.989  975.  544. R      1000 diamet…
    #>  8     1  11335481 0.00700     0.99        0.99   975.  544. R      1000 diamet…
    #>  9     1  11335482 0.00800     0.991       0.991  975   545. R      1000 diamet…
    #> 10     1  11335483 0.00900     0.992       0.992  975.  546. R      1000 diamet…
    #> # ℹ 19,990 more rows
    #> # ℹ 11 more variables: pupil_raw <dbl>, pupil_raw_deblink <dbl>,
    #> #   pupil_raw_deblink_detransient <dbl>,
    #> #   pupil_raw_deblink_detransient_interpolate <dbl>,
    #> #   pupil_raw_deblink_detransient_interpolate_lpfilt <dbl>,
    #> #   pupil_raw_deblink_detransient_interpolate_lpfilt_z <dbl>,
    #> #   text_unique <chr>, template <chr>, matching_pattern <chr>, …
    #>
    #> $info
    #> $info$block_1
    #> $info$block_1$calc_baseline
    #> [1] FALSE
    #>
    #> $info$block_1$apply_baseline
    #> [1] FALSE
    #>
    #> $info$block_1$epoch_events
    #> [1] "PROBE*"
    #>
    #> $info$block_1$epoch_limits
    #> [1] -1  1
    #>
    #> $info$block_1$n_epochs
    #> [1] 10

By default, the resulting `eyeris` object will contain the epoched data frame within a list element called `epoch_xyz` where `xyz` will be a sanitized version of the original `start` event string you supplied for the pattern matching procedure.

However, you have the ability to customize this label, by passing a value to the `label` argument within `epoch()`.

⚠️ Warning: if no label is specified and there are no event message strings provided for sanitization, then you may obtain a strange-looking epoch list element in your output `eyeris` object (e.g., `epoch_`, or perhaps even `$epoch_nana`, etc.). The extracted data epochs should still be accessible here, however, to avoid ambiguous list objects, we highly recommend you explicitly supply sensible epoch labels here within your `epoch()` calls to be safe.

### Example B: Metadata Parsing with Custom Labels

> Extract the 1-second window after “PROBE_START” and apply a custom label to the resulting epoch set.

    eye_1b <- eye |>
      epoch(
        events = "PROBE_START_{trial}",
        limits = c(0, 1),
        label = "probeAfter"
      )
    #> ℹ [2026-07-31 09:36:13] [INFO] Epoching pupil data...
    #> ℹ [2026-07-31 09:36:13] [INFO] Block 1: found 5 matching events for
    #> PROBESTARTtrial
    #> ✔ [2026-07-31 09:36:13] [OKAY] Done!
    #> ✔ [2026-07-31 09:36:13] [OKAY] Block 1: pupil data from 5 unique event messages
    #> extracted
    #> ✔ [2026-07-31 09:36:13] [OKAY] Pupil epoching completed in 0.02 seconds
    #> ℹ [2026-07-31 09:36:13] [INFO] Recalculating epoched confounds for new
    #> epochs...

    eye_1b |>
      purrr::pluck("epoch_probeAfter") |>
      head()
    #> $block_1
    #> # A tibble: 5,000 × 21
    #>    block time_orig timebin time_secs time_scaled eye_x eye_y eye      hz type
    #>    <dbl>     <int>   <dbl>     <dbl>       <dbl> <dbl> <dbl> <chr> <dbl> <chr>
    #>  1     1  11336474 0            1.98        1.98  972.  550. R      1000 diamet…
    #>  2     1  11336475 0.00100      1.98        1.98  971.  551. R      1000 diamet…
    #>  3     1  11336476 0.00200      1.98        1.98  970.  551. R      1000 diamet…
    #>  4     1  11336477 0.00300      1.99        1.99  970.  550. R      1000 diamet…
    #>  5     1  11336478 0.00400      1.99        1.99  971.  548. R      1000 diamet…
    #>  6     1  11336479 0.00501      1.99        1.99  972.  547. R      1000 diamet…
    #>  7     1  11336480 0.00601      1.99        1.99  972.  548. R      1000 diamet…
    #>  8     1  11336481 0.00701      1.99        1.99  972.  548. R      1000 diamet…
    #>  9     1  11336482 0.00801      1.99        1.99  972.  550. R      1000 diamet…
    #> 10     1  11336483 0.00901      1.99        1.99  972.  550. R      1000 diamet…
    #> # ℹ 4,990 more rows
    #> # ℹ 11 more variables: pupil_raw <dbl>, pupil_raw_deblink <dbl>,
    #> #   pupil_raw_deblink_detransient <dbl>,
    #> #   pupil_raw_deblink_detransient_interpolate <dbl>,
    #> #   pupil_raw_deblink_detransient_interpolate_lpfilt <dbl>,
    #> #   pupil_raw_deblink_detransient_interpolate_lpfilt_z <dbl>,
    #> #   text_unique <chr>, template <chr>, matching_pattern <chr>, …
    #>
    #> $info
    #> $info$block_1
    #> $info$block_1$calc_baseline
    #> [1] FALSE
    #>
    #> $info$block_1$apply_baseline
    #> [1] FALSE
    #>
    #> $info$block_1$epoch_events
    #> [1] "PROBE_START_{trial}"
    #>
    #> $info$block_1$epoch_limits
    #> [1] 0 1
    #>
    #> $info$block_1$n_epochs
    #> [1] 5

💡 Note: You can customize `epoch()` with trial-level metadata!

For instance, here, `{trial}` will not only extract data but also add a `trial` column parsed from the event string, which originally took the form of `PROBE_START_22` (where `22` was the trial number embedded within the event message string we had originally programmed to be sent as event messages at the start of each probe trial on our `PsychoPy` / `EyeLink` experiment.

    #> # A tibble: 5 × 4
    #>   template            matching_pattern    matched_event  trial
    #>   <chr>               <chr>               <chr>          <chr>
    #> 1 PROBE_START_{trial} ^PROBE_START_(.*?)$ PROBE_START_22 22
    #> 2 PROBE_START_{trial} ^PROBE_START_(.*?)$ PROBE_START_22 22
    #> 3 PROBE_START_{trial} ^PROBE_START_(.*?)$ PROBE_START_22 22
    #> 4 PROBE_START_{trial} ^PROBE_START_(.*?)$ PROBE_START_22 22
    #> 5 PROBE_START_{trial} ^PROBE_START_(.*?)$ PROBE_START_22 22

### Example C: Epoch with Subtractive Baselining

> Use the 1-second window before `"DELAY_STOP"` as a baseline and apply it to the epoch data.

    eye_1c <- eye |>
      epoch(
        events = "PROBE_START_{trial}",
        limits = c(0, 1),
        label = "probeEpochs",
        calc_baseline = TRUE,
        apply_baseline = TRUE,
        baseline_type = "sub",
        baseline_events = "DELAY_STOP_*",
        baseline_period = c(-1, 0)
      )

In this example, we’re extracting 1-second epochs following each `"PROBE_START"` event and applying subtractive baseline correction. The baseline is computed from the 1-second window before each corresponding `"DELAY_STOP"` event.

In other words, this means each pupil trace is normalized by subtracting the average pupil size from the pre-probe delay period (i.e., the baseline period).

### Example D: Start/End Event Pair Epoching

> Manually define start and end times for two trials:

    start_events <- data.frame(
      time = c(11334491, 11338691),
      msg = c("TRIALID 22", "TRIALID 23")
    )

    end_events <- data.frame(
      time = c(11337158, 11341292),
      msg = c("RESPONSE_22", "RESPONSE_23")
    )

    eye_1d <- eye |>
      epoch(
        events = list(start_events, end_events, 1), # 1 = block number
        label = "manualTrials"
      )

## 3 Export to a BIDS-like Format

Once epoched, your data is ready to be exported with `bidsify()`, which saves the raw and epoched data in a structured, `BIDS`-inspired format.

    bidsify(
      eyeris = eye_1c,
      bids_dir = "~/Documents/eyeris",
      participant_id = "001",
      session_num = "01",
      task_name = "assocmem",
      run_num = "01",
      save_raw = TRUE, # Also save raw timeseries
      html_report = TRUE # Generate a preprocessing summary
    )

Which will create a directory structure like this:

    eyeris
    └── derivatives
        └── sub-001
            └── ses-01
                ├── eye
                │   ├── sub-001_ses-01_task-assocret_run-01_desc-timeseries.csv
                │   └── sub-001_ses-01_task-assocret_run-01_desc-preproc_pupil_epoch-prePostProbe_bline-sub-prePostProbe.csv
                ├── source
                │   └── figures
                │       └── task-assocret_run-01
                │           ├── epoch_prePostProbe
                │           │   ├── run-01_PROBE_START_22_1.png
                │           │   ├── run-01_PROBE_START_22_2.png
                │           │   ├── run-01_PROBE_START_22_3.png
                │           │   ├── run-01_PROBE_START_22_4.png
                │           │   ├── run-01_PROBE_START_22_5.png
                │           │   ├── run-01_PROBE_START_22_6.png
                │           │   ├── ...
                │           │   ├── run-01_PROBE_STOP_22_1.png
                │           │   ├── run-01_PROBE_STOP_22_2.png
                │           │   ├── run-01_PROBE_STOP_22_3.png
                │           │   ├── run-01_PROBE_STOP_22_4.png
                │           │   ├── run-01_PROBE_STOP_22_5.png
                │           │   ├── run-01_PROBE_STOP_22_6.png
                │           │   ├── ...
                │           ├── task-assocret_run-01_fig-1_desc-histogram.jpg
                │           ├── task-assocret_run-01_fig-1_desc-timeseries.jpg
                ├── sub-001_task-assocret_epoch-prePostProbe_run-01.html
                └── sub-001_task-assocret.html

    9 directories, 80 files

## 💡 Data Previews and QC with Interactive Reports

See the 🔎 QC with Interactive Reports vignette for more details.

## ✨ Summary

This vignette demonstrated how to:

- Load and preprocess raw `.asc` (EyeLink) pupil data files using `eyeris`.
- Extract event-based epochs using both pattern matching and manual timestamps.
- Flexibly apply baseline correction.
- Save out the results in a clean, reproducible, BIDS-like folder structure.

Check out the function documentation for `epoch()` and `bidsify()` to learn more about other customization options that may be useful for your specific workflow.

## 📚 Citing `eyeris`

If you use the `eyeris` package in your research, please cite it!

Run the following in R to get the citation:

    citation("eyeris")
    #> To cite package 'eyeris' in publications use:
    #>
    #>   Schwartz ST, Yang H, Xue AM, He M (2025). "eyeris: A flexible,
    #>   extensible, and reproducible pupillometry preprocessing framework in
    #>   R." _bioRxiv_, 1-37. doi:10.1101/2025.06.01.657312
    #>   <https://doi.org/10.1101/2025.06.01.657312>.
    #>
    #> A BibTeX entry for LaTeX users is
    #>
    #>   @Article{,
    #>     title = {eyeris: A flexible, extensible, and reproducible pupillometry preprocessing framework in R},
    #>     author = {Shawn T Schwartz and Haopei Yang and Alice M Xue and Mingjian He},
    #>     journal = {bioRxiv},
    #>     year = {2025},
    #>     pages = {1--37},
    #>     doi = {10.1101/2025.06.01.657312},
    #>   }