pyETA-toolbox 0.1.0

This toolbox provides a realtime visualization of eye tracking data to be used for designing passive BCI system.

IMPORTANT

In case, this toolbox doesn't works as intended or you find some issues, create an issue in the tracker (https://github.com/VinayIN/pyETA-toolbox/issues) and I will try to resolve it. (Please attach errors & log)

pyETA-toolbox (Eye Tracker Analyzer)

• This provides a real-time eyetracking metric (especially fixation & Saccades).
• This tool also provides for validation of the eye-tracker performance on a user.
• The tool provides 22 channels, following table describes the EEG channel information in order:

No.	Channel Name	Type	Unit
	Left Eye
1	left_gaze_x	gaze	normalized
2	left_gaze_y	gaze	normalized
3	left_pupil_diameter	pupil	mm
4	left_fixated	fixation	boolean
5	left_velocity	velocity	px
6	left_fixation_timestamp	timestamp	s
7	left_fixation_elapsed	duration	s
8	left_filtered_gaze_x	filtered_gaze	normalized
9	left_filtered_gaze_y	filtered_gaze	normalized
	Right Eye
10	right_gaze_x	gaze	normalized
11	right_gaze_y	gaze	normalized
12	right_pupil_diameter	pupil	mm
13	right_fixated	fixation	boolean
14	right_velocity	velocity	px
15	right_fixation_timestamp	timestamp	s
16	right_fixation_elapsed	duration	s
17	right_filtered_gaze_x	filtered_gaze	normalized
18	right_filtered_gaze_y	filtered_gaze	normalized
	Screen Data
19	screen_width	screen	px
20	screen_height	screen	px
21	timestamp	timestamp	s
22	local_clock	timestamp	s

Alternative Installations

Download the pyeta-toolbox-<version>-py3-none-any.whl file from here: https://github.com/VinayIN/pyETA-toolbox/releases and install using pip

# move the .whl file to your workspace
# once moved verify using ls bash command, if found
# replace .whl file with the one just downloaded. it should have this format with a different <version> 
pip install pyeta-toolbox-<version>-py3-none-any.whl

or

CAUTION: This below method installs the development version and might have bugs

pip install git+https://github.com/VinayIN/pyETA-toolbox.git

Usage

There are 2 ways this tool can be used (CLI - track/window/validate & GUI - browser/application), both are shown below:

prerequisite: pyETA-toolbox needs to be installed

1. CLI

Command:

pyETA track

Parameters:

• --push_stream: Pushes the data to an LSL stream.
• --data_rate: Specifies the rate of the data stream.
• --use_mock: Uses a mock service for the eye tracker.
• --fixation: Adds fixation duration to the data stream.
• --velocity: Specifies the velocity threshold for fixation.
• --accept_screen_nans: Avoids correcting for NaNs.
• --save_data: Saves the data to a file.
• --verbose: Displays debug statements.
• --duration: Specifies the duration for which to track the data.
• --screen_index: for multi-screen execution, use the index to capture the data from a specific screen with the resolution

An example with tracker running with fixation and a mock service that runs for a duration of 10sec and stops (if --use_mock is not provided, it searches for the eye tracker)

pyETA track --fixation --use_mock --duration 10

---

Command:

pyETA window

Parameters:

• --use_mock: Use mockup tracker
• --screen_index: Screen index to display the validation window
• --verbose: Enable verbose logging

Example with parameters to run the validation window

    pyETA window --use_mock --screen_index=0 

---

Command:

pyETA validate

Parameters:

• --csv: Name of the output CSV file. (example: myfile.csv)

Example with parameters to fetch the metrics

    pyETA validate --csv=myfile.csv

---

2. GUI

Command:

pyETA application

or execute the pyETA.exe(To be implemented)