PyTrack

PyTrack performs automated extraction and statistical analysis of eye-tracking data to quantify blinks, fixations, saccades, microsaccades, pupil diameter, and related gaze metrics for behavioral and cognitive research.

Key Features:

• Automated Feature Extraction: Algorithmic extraction of blinks, fixations, saccades, microsaccades, pupil diameter, and drifts from raw eye-tracking recordings.
• High-Resolution Data Handling: Support for processing data sampled up to 2000 Hz to enable analysis of high-temporal-resolution events such as microsaccades and drifts.
• Comprehensive Statistical Analysis: Implements Student's T-Test, Welch T-Test, ANOVA, RMANOVA, n-way ANOVA, and Mixed ANOVA with specification of between-group and within-group factors.
• Visualization Capabilities: Generates gaze plots and other visualizations from raw eye-tracking data to represent eye movement patterns.

Scientific Applications:

• Group and condition comparisons: Comparing eye movement behavior across participant groups or varying stimulus conditions.
• Visual attention studies: Quantitative examination of how experimental factors influence visual attention using extracted gaze metrics.
• Cognitive processing research: Analysis of pupil diameter and temporal eye-movement features to investigate aspects of cognitive processing in behavioral research.

Methodology:

Algorithmic extraction of key parameters from raw eye-tracking data (blinks, fixations, saccades, microsaccades, pupil diameter, drifts) followed by statistical analyses comparing these extracted parameters across experimental conditions or participant groups using the listed statistical tests.