eye-tracking-analysis

You can convert raw eye-tracking gaze data into fixations and AOI metrics by applying I-VT velocity-based fixation segmentation and AOI assignment to calculate first latency, dwell time, and fixation counts from your timestamped gaze coordinates.

The best way to calculate scanpath similarity and generate fixation heatmaps is to use Levenshtein distance for scanpath comparison and Gaussian KDE for visual attention mapping, producing visualization-ready outputs from raw gaze streams.

Blink detection and interpolation for pupillometry baseline correction works by identifying missing data points in pupil diameter streams, interpolating the gaps, and applying event-window baseline correction to quantify event-locked pupil responses.

Yes, you can use Python and pandas for experimental psychology visual attention analysis because this Skill relies on numeric Python dependencies like pandas, numpy, and scipy to preprocess gaze streams and compute human-factors workflow metrics.

For eye-tracking fixation detection and dwell time computation, you need a gaze dataset containing timestamps, x/y gaze coordinates, and pupil diameter to successfully run blink interpolation and I-VT fixation segmentation.