eyeris

🧠 Advanced reproducible pupillometry preprocessing in R | Interactive reports, BIDS-compliant, High-throughput database tooling out-of-the-box | Actively developed by cognitive neuroscientists at Stanford University

https://github.com/shawntz/eyeris

Keywords

Repository

🧠 Advanced reproducible pupillometry preprocessing in R | Interactive reports, BIDS-compliant, High-throughput database tooling out-of-the-box | Actively developed by cognitive neuroscientists at Stanford University

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# `eyeris`: Flexible, Extensible, & Reproducible Pupillometry Preprocessing 


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💻 eyeris DevOps Dashboard
  Dive deeper into eyeris' development and operational insights with our new eyeris DevOps Dashboard!



## 💡 Motivation

Despite decades of pupillometry research, many established packages 
and workflows unfortunately lack design principles based on 
(F)indability (A)ccessbility (I)nteroperability (R)eusability (FAIR) 
principles. `eyeris`, on the other hand follows a thoughtful design 
philosophy that results in an intuitive, modular, performant, and extensible 
pupillometry data preprocessing framework. Much of these design principles were 
heavily inspired by `Nipype`. 

`eyeris` also provides a highly opinionated pipeline for tonic and phasic 
pupillometry preprocessing (inspired by `fMRIPrep`). These opinions are the 
product of many hours of discussions from core members and signal processing 
experts from the Stanford Memory Lab (Shawn Schwartz, Mingjian He, Haopei Yang, 
Alice Xue, and Anthony Wagner). 

`eyeris` also introduces a `BIDS`-like 
structure for organizing derivative (preprocessed) pupillometry data, as 
well as an intuitive workflow for inspecting preprocessed pupillometry epochs 
within beautiful, interactive HTML report files (see demonstration below ⬇)!
The package also includes gaze heatmaps that show the distribution of eye 
coordinates across the entire screen area, helping you assess data quality 
and participant attention patterns. These heatmaps are automatically generated 
in the BIDS reports and can also be created manually.

## 🚀 Feature Highlights

- `📦 Modular Design`: Each preprocessing step is a standalone function that can be used independently or combined into custom pipelines.
- `🔍 Interactive Reports`: Beautiful, interactive HTML reports that summarize preprocessing steps and visualize data quality.
- `🔄 Flexible Extensions`: Easily create custom extensions to the preprocessing pipeline by writing your own functions and adding them to the pipeline.
- `📊 Data Quality Assessment`: Automatically generated figures of each preprocessing step and its effect on the pupil signal (at the global and trial levels), as well as gaze heatmaps and binocular correlation plots to assess data quality and participant attention patterns.
- `🗂️ BIDS-like File Structure`: Organizes preprocessed data using a BIDS-like directory structure that supports both monocular and binocular eye-tracking data.
- `📝 Logging Commands`: Automatically capture all console output and errors to timestamped log files.

```{r, echo=FALSE, fig.dpi=300, out.width='100%'}
knitr::include_graphics("https://github.com/shawntz/eyeris/raw/dev/inst/figures/interactive-reports-demo.gif")
```

## 📖 Function Reference

Below is a table of all main `eyeris` functions, organized by feature, with links to their documentation and a brief description.

| **Feature**                | **Function Documentation** | **Description** |
|----------------------------|---------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|
| **Pipeline Orchestration** | [glassbox()](https://shawnschwartz.com/eyeris/reference/glassbox.html)                                              | Run the full recommended preprocessing pipeline with a single function call.                               |
| **BIDSify**                | [bidsify()](https://shawnschwartz.com/eyeris/reference/bidsify.html)                                                | Create a BIDS-like directory structure for preprocessed data as well as interactive HTML reports for data and signal processing provenance.                                              |
| **Data Loading**           | [load_asc()](https://shawnschwartz.com/eyeris/reference/load_asc.html)                                              | Load EyeLink `.asc` files into an `eyeris` object.                                                        |
| **Blink Artifact Removal**          | [deblink()](https://shawnschwartz.com/eyeris/reference/deblink.html)                                                | Remove blink artifacts by extending and masking missing samples.                                           |
| **Transient (Speed-Based) Artifact Removal**      | [detransient()](https://shawnschwartz.com/eyeris/reference/detransient.html)                                        | Remove transient spikes in the pupil signal using a moving MAD filter.                                     |
| **Linear Interpolation**          | [interpolate()](https://shawnschwartz.com/eyeris/reference/interpolate.html)                                        | Interpolate missing (NA) samples in the pupil signal.                                                      |
| **Lowpass Filtering**      | [lpfilt()](https://shawnschwartz.com/eyeris/reference/lpfilt.html)                                                  | Apply a Butterworth lowpass filter to the pupil signal.                                                    |
| **Downsampling**           | [downsample()](https://shawnschwartz.com/eyeris/reference/downsample.html)                                          | Downsample the pupil signal to a lower sampling rate.                                                      |
| **Binning**                | [bin()](https://shawnschwartz.com/eyeris/reference/bin.html)                                                        | Bin pupil data into specified time bins using mean or median.                                              |
| **Detrending**             | [detrend()](https://shawnschwartz.com/eyeris/reference/detrend.html)                                                | Remove slow drifts from the pupil signal by linear detrending.                                             |
| **Z-scoring**              | [zscore()](https://shawnschwartz.com/eyeris/reference/zscore.html)                                                  | Z-score the pupil signal within each block.                                                                |
| **Confound Summary**       | [summarize_confounds()](https://shawnschwartz.com/eyeris/reference/summarize_confounds.html)                        | Summarize and visualize confounding variables for each preprocessing step.                                 |
| **Epoching & Baselining**               | [epoch()](https://shawnschwartz.com/eyeris/reference/epoch.html)                                                    | Extract time-locked epochs from the continuous pupil signal.                                               |
| **Plotting**               | [plot()](https://shawnschwartz.com/eyeris/reference/plot.eyeris.html)                                                 | Plot the pupil signal and preprocessing steps.                                                             |
| **Gaze Heatmaps**          | [plot_gaze_heatmap()](https://shawnschwartz.com/eyeris/reference/plot_gaze_heatmap.html)                                      | Generate heatmaps of gaze position across the screen.                                                      |
| **Binocular Correlation**  | [plot_binocular_correlation()](https://shawnschwartz.com/eyeris/reference/plot_binocular_correlation.html)                    | Compute correlation between left and right eye pupil signals.                                              |
| **Demo (Monocular) Dataset**              | [eyelink_asc_demo_dataset()](https://shawnschwartz.com/eyeris/reference/eyelink_asc_demo_dataset.html)              | Load a demo monocular recording EyeLink dataset for testing and examples.                                                      |
| **Demo (Binocular) Dataset**              | [eyelink_asc_binocular_demo_dataset()](https://shawnschwartz.com/eyeris/reference/eyelink_asc_binocular_demo_dataset.html)              | Load a demo binocular recording EyeLink dataset for testing and examples.                                                      |
| **Logging Commands**       | [eyelogger()](https://shawnschwartz.com/eyeris/reference/eyelogger.html)                                            | Automatically capture all console output and errors to timestamped log files.                             |
| **Database Storage**       | [eyeris_db_collect()](https://shawnschwartz.com/eyeris/reference/eyeris_db_collect.html)            | High-performance database storage and querying alternative to CSV files.                                   |
| **Database Summary**       | [eyeris_db_summary()](https://shawnschwartz.com/eyeris/reference/eyeris_db_summary.html)                | Get comprehensive overview of database contents and metadata.                                              |
| **Database Connection**    | [eyeris_db_connect()](https://shawnschwartz.com/eyeris/reference/eyeris_db_connect.html)                | Connect to eyeris databases for custom queries and operations.                                             |
| **Database Export (Chunked)**    | [eyeris_db_to_chunked_files()](https://shawnschwartz.com/eyeris/reference/eyeris_db_to_chunked_files.html)                | Export large databases in configurable chunks with automatic file size limits.                                             |
| **Database Export (Parquet)**    | [eyeris_db_to_parquet()](https://shawnschwartz.com/eyeris/reference/eyeris_db_to_parquet.html)                | Export database to high-performance Parquet format files.                                             |
| **Read Parquet Files**    | [read_eyeris_parquet()](https://shawnschwartz.com/eyeris/reference/read_eyeris_parquet.html)                | Read and combine eyeris Parquet files with schema-aligned binding.                                             |
| **Database Sharing (Split)**    | [eyeris_db_split_for_sharing()](https://shawnschwartz.com/eyeris/reference/eyeris_db_split_for_sharing.html)                | Split databases into chunks for easier sharing and collaboration.                                             |
| **Database Sharing (Reconstruct)**    | [eyeris_db_reconstruct_from_chunks()](https://shawnschwartz.com/eyeris/reference/eyeris_db_reconstruct_from_chunks.html)                | Reconstruct complete databases from shared chunks.                                             |
| **Custom Extensions**      | _See vignette: [Custom Extensions](https://shawnschwartz.com/eyeris/articles/custom-extensions.html)_ | Learn how to write your own pipeline steps and integrate them with `eyeris`.                               |
| **Internal API Reference** | _See vignette: [Internal API Reference](https://shawnschwartz.com/eyeris/articles/internal-api.html)_ | Comprehensive documentation of all internal functions for advanced users and developers.                   |

> For a full list of all functions, see the [eyeris reference index](https://shawnschwartz.com/eyeris/reference/index.html).

## 📚 Tutorials

### 🌟 Start Here
- [✈ Getting Started: Complete (Opinionated) Pupillometry Pipeline Walkthrough](https://shawnschwartz.com/eyeris/articles/complete-pipeline.html)
- [📁 Extracting Data Epochs and Exporting Pupil Data](https://shawnschwartz.com/eyeris/articles/epoching-bids-reports.html)

### 👀 Pupil Data Quality Control
- [🔎 QC with Interactive Reports](https://shawnschwartz.com/eyeris/articles/reports.html)

### 💯 Advanced Topics
- [🫀 Anatomy of an `eyeris` Object](https://shawnschwartz.com/eyeris/articles/anatomy.html)
- [🛠 Building Your Own Custom Pipeline Extensions](https://shawnschwartz.com/eyeris/articles/custom-extensions.html)
- [🗄 Database Storage Guide: Scalable Alternative to CSV Files](https://shawnschwartz.com/eyeris/articles/database-guide.html)

## 📦 Package Installation

### Stable release from CRAN

You can install the stable release of [`eyeris` from CRAN](https://cran.r-project.org/package=eyeris) with:

```r
install.packages("eyeris")
```

or

``` r
# install.packages("pak")
pak::pak("eyeris")
```

### Development version from GitHub

You can install the development version of [`eyeris` from GitHub](https://github.com/shawntz/eyeris) with:

``` r
# install.packages("devtools")
devtools::install_github("shawntz/eyeris", ref = "dev")
```

## ✏ Example

### The `glassbox()` "prescription" function

This is a basic example of how to use `eyeris` out of the box with our very
*opinionated* set of steps and parameters that one should start out with when
preprocessing pupillometry data. Critically, this is a "glassbox" -- as opposed
to a "blackbox" -- since each step and parameter implemented herein is fully
open and accessible to you. We designed each pipeline step / function to be like
a LEGO brick -- they are intentionally and carefully designed in a way that allows you
to flexibly construct and compare different pipelines.

We hope you enjoy! -Shawn

```{r glassbox-example, echo=TRUE, results='hide', fig.show='hide', fig.dpi=120}
set.seed(32)

library(eyeris)

demo_data <- eyelink_asc_demo_dataset()

eyeris_preproc <- glassbox(
  demo_data,
  lpfilt = list(plot_freqz = FALSE)
)
```

### Step-wise correction of pupillary signal
```{r glassbox-plot, echo=TRUE, eval=FALSE, results='hide', fig.show='hide', warning=TRUE, message=TRUE, fig.width=12, fig.height=7, fig.dpi=300}
plot(eyeris_preproc, add_progressive_summary = TRUE)
```



### Final pre-post correction of pupillary signal (raw ➡ preprocessed)
```{r timeseries-plot, echo=TRUE, fig.width=7, fig.height=4, fig.dpi=120}
start_time <- min(eyeris_preproc$timeseries$block_1$time_secs)
end_time <- max(eyeris_preproc$timeseries$block_1$time_secs)

plot(eyeris_preproc,
  # steps = c(1, 5), # uncomment to specify a subset of preprocessing steps to plot; by default, all steps will plot in the order in which they were executed by eyeris
  preview_window = c(start_time, end_time),
  add_progressive_summary = TRUE
)

plot_gaze_heatmap(
  eyeris = eyeris_preproc,
  block = 1
)
```

## 🗄 Database Storage: Scalable Alternative to CSV Files

`eyeris` includes powerful database functionality powered by `DuckDB` that provides a scalable, efficient alternative to CSV file storage. This is especially valuable for large studies, cloud computing, and collaborative research projects.

### Why Use Databases?

**🚀 Performance at Scale**
- Handle hundreds of subjects efficiently vs. managing thousands of CSV files
- Faster queries: filter and aggregate at the database level instead of loading all data into `R`
- Reduced memory usage: load only the data you need

**💯 Cloud Computing Optimized**
- Reduce I/O costs on AWS, GCP, Azure
- Single database file vs. thousands of CSV files for data transfer
- Bandwidth efficient and cost-effective for large datasets

**🔒 Data Integrity**
- Built-in schema validation prevents data corruption
- Automatic metadata tracking and timestamps

### Quick Start: `eyeris` Project Database Creation

Enable `eyeris` project database storage alongside or instead of CSV files:

```{r database-example, eval=FALSE}
bidsify(
  processed_data,
  bids_dir = "~/my_study",
  participant_id = "001",
  session_num = "01", 
  task_name = "memory_task",
  csv_enabled = TRUE,    # keep traditional BIDS-style CSV output files
  db_enabled = TRUE,     # but also create an eyeris project database
  db_path = "study_database"
)

bidsify(
  processed_data,
  bids_dir = "~/my_study",
  participant_id = "001", 
  session_num = "01",
  task_name = "memory_task", 
  csv_enabled = FALSE,   # skip CSV creation
  db_enabled = TRUE,     # cloud-optimized: Database only (no CSV files)
  db_path = "study_database"
)
```

### Simple Data Extraction

Extract all your data with one function call:

```{r database-extraction, eval=FALSE}
# extract ALL data for ALL subjects
all_data <- eyeris_db_collect("~/my_study", "study_database")

# access specific data types
timeseries_data <- all_data$timeseries
confounds_data <- all_data$run_confounds

# targeted extraction: specific subjects and data types
subset_data <- eyeris_db_collect(
  "~/my_study", 
  "study_database",
  subjects = c("001", "002", "003"),
  data_types = c("timeseries", "epochs", "confounds_summary")
)
```

### Database Overview and Management

```{r database-management, eval=FALSE}
# get a comprehensive database summary
summary <- eyeris_db_summary("~/my_study", "study_database")
summary$subjects      # all subjects in database
summary$data_types    # available data types  
summary$total_tables  # number of tables

# connect to eyeris database for custom operations
con <- eyeris_db_connect("~/my_study", "study_database")
# ... custom SQL queries ...
eyeris_db_disconnect(con)
```

> **💡 Pro Tip**: Use `csv_enabled = FALSE, db_enabled = TRUE` for cloud computing to maximize efficiency and minimize costs.

> **📖 Complete Guide**: See the [Database Storage Guide](https://shawnschwartz.com/eyeris/articles/database-guide.html) for comprehensive tutorials, advanced usage, and real-world examples.

## 📁 BIDS-like file structure

`eyeris` organizes preprocessed data using a BIDS-like directory structure that supports both monocular and binocular eye-tracking data. The `bidsify()` function creates a standardized directory hierarchy with separate organization for different data types.

### Monocular data structure

For single-eye recordings, data are organized in the main eye directory:

```
bids_dir/
└── derivatives/
    └── sub-001/
        └── ses-01/
            ├── sub-001.html
            └── eye/
                ├── sub-001_ses-01_task-test_run-01_desc-timeseries_eye.csv
                ├── sub-001_ses-01_task-test_run-01_desc-confounds.csv
                ├── sub-001_ses-01_task-test_run-01_epoch-stimulus_desc-preproc_pupil.csv
                ├── sub-001_ses-01_task-test_run-01_baseline-stimulus_desc-preproc_pupil.csv
                ├── sub-001_ses-01_task-test_run-01_events.csv
                ├── sub-001_ses-01_task-test_run-01_blinks.csv
                ├── sub-001_ses-01_task-test_run-01_summary.csv
                ├── sub-001_ses-01_task-test_run-01.html
                └── source/
                    ├── figures/
                    │   └── run-01/
                    │       ├── run-01_fig-1_deblink.jpg
                    │       ├── run-01_fig-2_detrend.jpg
                    │       ├── run-01_fig-3_interpolate.jpg
                    │       ├── run-01_fig-4_lpfilt.jpg
                    │       ├── run-01_fig-5_zscore.jpg
                    │       ├── run-01_gaze_heatmap.png
                    │       ├── run-01_detrend.png
                    │       └── run-01_desc-progressive_summary.png
                    └── logs/
                        └── run-01_metadata.json
```

### Binocular data structure

For binocular recordings, data are organized into separate `left` and `right` eye subdirectories:

```
bids_dir/
└── derivatives/
    └── sub-001/
        └── ses-01/
            ├── sub-001-L.html
            ├── sub-001-R.html
            ├── eye-L/
            │   ├── sub-001_ses-01_task-test_run-01_desc-timeseries_eye-L.csv
            │   ├── sub-001_ses-01_task-test_run-01_desc-confounds_eye-L.csv
            │   ├── sub-001_ses-01_task-test_run-01_epoch-stimulus_desc-preproc_pupil_eye-L.csv
            │   ├── sub-001_ses-01_task-test_run-01_baseline-stimulus_desc-preproc_pupil_eye-L.csv
            │   ├── sub-001_ses-01_task-test_run-01_events_eye-L.csv
            │   ├── sub-001_ses-01_task-test_run-01_blinks_eye-L.csv
            │   ├── sub-001_ses-01_task-test_run-01_summary_eye-L.csv
            │   ├── sub-001_ses-01_task-test_run-01_eye-L.html
            │   └── source/
            │       ├── figures/
            │       │   └── run-01/
            │       └── logs/
            │           └── run-01_metadata.json
            └── eye-R/
                ├── sub-001_ses-01_task-test_run-01_desc-timeseries_eye-R.csv
                ├── sub-001_ses-01_task-test_run-01_desc-confounds_eye-R.csv
                ├── sub-001_ses-01_task-test_run-01_epoch-stimulus_desc-preproc_pupil_eye-R.csv
                ├── sub-001_ses-01_task-test_run-01_baseline-stimulus_desc-preproc_pupil_eye-R.csv
                ├── sub-001_ses-01_task-test_run-01_events_eye-R.csv
                ├── sub-001_ses-01_task-test_run-01_blinks_eye-R.csv
                ├── sub-001_ses-01_task-test_run-01_summary_eye-R.csv
                ├── sub-001_ses-01_task-test_run-01_eye-R.html
                └── source/
                    ├── figures/
                    │   └── run-01/
                    └── logs/
                        └── run-01_metadata.json
```

### File naming convention

All files follow a consistent BIDS-like naming pattern:

- **Timeseries data**: `desc-timeseries_eye` (with `_eye-L` or `_eye-R` suffix for binocular data)
- **Confounds**: `desc-confounds` (with eye suffix for binocular data)
- **Epochs**: `epoch-{event}_desc-preproc_pupil` (with eye suffix for binocular data)
- **Baselines**: `baseline-{event}_desc-preproc_pupil` (with eye suffix for binocular data)
- **Events**: `events` (with eye suffix for binocular data)
- **Blinks**: `blinks` (with eye suffix for binocular data)
- **Reports**: HTML files with eye suffix for binocular data

### Events and blinks data

The events and blinks CSV files contain the raw event markers and blink detection data as stored in the eyeris object:

**Events file structure:**

- `block`: Block/run number
- `time`: Timestamp of the event
- `text`: Raw event text from the ASC file
- `text_unique`: Unique event identifier

**Blinks file structure:**

- `block`: Block/run number
- `stime`: Start time of the blink
- `etime`: End time of the blink
- `dur`: Duration of the blink in milliseconds
- `eye`: Eye identifier (L/R for binocular data)

### Key features

- **Organized Structure**: Clear separation between monocular and binocular data
- **Consistent Naming**: Standardized file naming across all data types
- **Complete Documentation**: HTML reports with preprocessing summaries and visualizations
- **Quality Assessment**: Gaze heatmaps and binocular correlation plots for data quality evaluation
- **Reproducibility**: Metadata files documenting preprocessing parameters and call stacks

## 📝 Logging `eyeris` commands with `eyelogger()`

The `eyelogger()` utility lets you run any `eyeris` command (or block of R code) while automatically capturing all console output and errors to timestamped log files. This is especially useful for reproducibility, debugging, or running batch jobs.

**How it works:**

- All standard output (`stdout`) and standard error (`stderr`) are saved to log files in a directory you specify (or a temporary directory by default).
- Each run produces two log files:
  - `.out`: all console output
  - `.err`: all warnings and errors

### Usage

You can wrap any `eyeris` command or block of code in `eyelogger({ ... })`:

```r
library(eyeris)

# log a simple code block with messages, warnings, and prints
eyelogger({
  message("eyeris `glassbox()` completed successfully.")
  warning("eyeris `glassbox()` completed with warnings.")
  print("some eyeris-related information.")
})

# log a real eyeris pipeline run, saving logs to a custom directory
log_dir <- file.path(tempdir(), "eyeris_logs")
eyelogger({
  glassbox(eyelink_asc_demo_dataset(), interactive_preview = FALSE)
}, log_dir = log_dir)
```

### Parameters
- `eyeris_cmd`: The code to run (wrap in `{}` for multiple lines).
- `log_dir`: Directory to save logs (default: a temporary directory).
- `timestamp_format`: Format for log file names (default: `"%Y%m%d_%H%M%S"`).

### What you get
After running, you'll find log files in your specified directory, e.g.:

```
20240614_153012.out   # console output
20240614_153012.err   # warnings and errors
```

This makes it easy to keep a record of your preprocessing runs and debug any issues that arise.

---

## :see_no_evil: `eyeris` dependency graph
```{r, eval=TRUE, echo=FALSE, fig.width=8.5, fig.height=10, fig.dpi=120, warning=FALSE}
depgraph::plot_dependency_graph(
  pkg = ".",
  suggests = FALSE,
  option = "turbo",
  ggrepel_opts = list(max.overlaps = 150)
)
```

---

## 🤝 Contributing to `eyeris`

Thank you for considering contributing to the open-source `eyeris` R package; there are many ways one could contribute to `eyeris`.

We believe the best preprocessing practices emerge from collective expertise and rigorous discussion. Please see the [contribution guidelines](https://shawnschwartz.com/eyeris/CONTRIBUTING.html) for more information on how to get started..

## 📜 Code of Conduct

Please note that the eyeris project is released with a [Contributor Code of Conduct](https://shawnschwartz.com/eyeris/CODE_OF_CONDUCT.html). By contributing
to this project, you agree to abide by its terms.

## 💬 Suggestions, questions, issues?

Please use the issues tab (https://github.com/shawntz/eyeris/issues) to make note of any bugs, comments, suggestions, feedback, etc... all are welcomed and appreciated, thanks!

## 📚 Citing `eyeris`

  If you use the `eyeris` package in your research, please consider citing our preprint!
  
  Run the following in R to get the citation:


```{r}
citation("eyeris")
```

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