Helix is a complete eye tracking system. From experiment creation through statistical analysis — one integrated Python workflow at half the cost of traditional systems.
Existing eye-tracking systems stall research halfway — data processing is clunky, validation is tedious, and robust statistical analysis tools are missing.
Researchers often cobble together custom scripts across multiple languages just to process and analyze their data.
Trainees spend months writing and debugging code for data processing and analysis, diverting effort from core research.
Leading systems cost $35,000-$40,000, often relying on proprietary formats and outdated software (e.g., MS-DOS).
Different lab members and labs reinvent the wheel, producing pipelines that fail to generalize across projects and labs.
Full pipeline: experiment design through Bayesian statistical analysis—in one integrated system.
Built entirely in Python. Python is free, open-source, and industry-standard for data science.
Research-grade quality at about half the cost of EyeLink or Tobii systems.
Clean CSV files throughout—no proprietary formats. Data organized by type and processing stage.
USB3 machine vision camera, single-computer setup; runs on modern Linux OS.
Backed by eye-tracking researchers with decades of combined experience.
Everything you need in one system: a custom built cantilevered height-adjustable table, desktop eye tracker, chinrest, unified computer tower, keyboards & mice, and chairs.
View System DetailsNo more stitching together code across Python, R, and MATLAB. The Helix software suite integrates experiment creation, data processing, and statistical analysis with function calls not GUIs— all in one Python workflow.
Explore Features# Import Helix software suite functions
import helix
# Import experiment, calibration, and recorded data
E, add_columns = helix.import_experiment_data(base_dir)
C = helix.import_calibration_data(base_dir)
R = helix.import_recorded_data(base_dir, C, marker_key, layout)
# Identify and exclude bad trials
R, E = helix.exclude_bad_trials(R, E, threshold=30)
# Generate Fixation dataframe from recorded dataframe
F = helix.create_fixation_df(R)
Current Status: Beta
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