eyefeatures 0.1.0

Preprocessing, visualization, statistical analysis, feature engineering, and machine learning of eye movement data.

Package Description

EyeFeatures is an open-source Python package for analyzing eye movement data in any visual task. Its capabilities encompass preprocessing, visualization, statistical analysis, feature engineering and machine learning. Its unique feature is its architecture and versatility. Accepting data in .csv format containing gaze position coordinates, the package allows filtration of raw data to remove noise and detecting fixations and saccades with different algorithms. Having fixations any standard descriptive statistical eye movement features (such as totalFD, meanFD etc.) can be computed, including AOI-wise features. AOIs can be predefined or assigned automatically. More complex features, such as chaos measures, topological features, density maps, scanpath similarities for various distance metrics can be computed as well. The package allows to account for the panel structure of the data, calculating shift features relative to group averages. The visualization module allows output a variety of visualization options, including static and dynamic scanpath plots, customized heatmaps and histograms. The architecture of the package allows seamless embedding of its preprocessing and feature extraction classes in Sklearn pipelines. Moreover, it provides datasets and models for deep learning with Pytorch. Since the work is in progress all functionality will be implemented by the time of the report.

Usage

For now, package is still in development and no pre-release version is uploaded to PyPI. However, you can still use it by cloning the repository on your local machine:

1. Open a command prompt/terminal and move to an empty folder.
2. Write command git clone https://github.com/hse-scila/EyeFeatures (in windows you need to do it in anaconda prompt).
3. Write command cd EyeFeatures.
4. Write command pip install poetry.
5. If you want to use all modules except for the deep module, then write poetry install --only main. If you want to use the deep module, write poetry install --with deep.
6. If you want to explore the library in Jupyter Notebook, execute poetry install --with test.