Eye-movement event matching procedure and computation of the event level Cohen's Kappa as described in:

@Article{zemblys2019gazenet,
  author    = {Zemblys, Raimondas and Niehorster, Diederick C and Holmqvist, Kenneth},
  title     = {gazeNet: End-to-end eye-movement event detection with deep neural networks},
  journal   = {Behavior research methods},
  year      = {2019},
  volume    = {51},
  number    = {2},
  pages     = {840--864},
  publisher = {Springer},
}

This code is a generalised version of the eye-movement event matching procedure described in Hooge et al. 2017. Instead of looking for overlapping test-events occurring earliest in time, for each event in the reference stream, it looks for the event in the test stream that has the largest overlap, and then matches the events in the two streams.

To use ETeval place your datasets with events detected in the root folder, create a job.json file that contains a list of json objects that define the ground truth and test datasets, and run:

python run_eval.py --config job.json

For an example see example_job.json. All jobs files need to be a list of the following json objects:

{
    "root": "etdata", 			# Root folder of the data
    "gt": "lund2013_npy", 		# Ground truth dataset
    "pr": "lund2013_npy_gazeNet", 	# Test dataset
    "dataset": "lund2013-image-test",	# Dataset label
    "alg": "gazeNet"			# Algorithm label
}

For each job, ETeval will output scores for fixations, saccades and PSOs and generate a csv file with the results in your test data folder.

!!! Note that only the evaluation of fixations, saccades and PSO is currently supported and data with status flag False (as defined in the ground truth data) is not evaluated is the current version !!!

The internal data format used by ETeval is a structured numpy array with a following format:

dtype = np.dtype([
	('t', np.float64),	#time in seconds
	('x', np.float32),	#horizontal gaze direction in degrees
	('y', np.float32), 	#vertical gaze direction in degrees
	('status', np.bool),	#status flag. False means trackloss 
	('evt', np.uint8)	#event label:
					#0: Undefined
					#1: Fixation
					#2: Saccade
					#3: Post-saccadic oscillation
					#4: Smooth pursuit
					#5: Blink
])

TODO:

• Describe how to replicate one of the paper results

ETeval was developed and tested using Python 2.7, however it should also work if using Python 3.x. Code requires numpy, scipy, pandas, matplotlib, scikit-learn and tqdm libraries.

In the original paper unmatched negative events in the binary evaluation were treated as true negatives. The following paper describes and compares three approaches to binary event-level agreement scoring.

@Article{zemblys2020binary,
  author    = {Zemblys, Raimondas and Niehorster, Diederick C and Holmqvist, Kenneth},
  title     = {Evaluating three approaches to binary event-level agreement scoring. A reply to Friedman (2020)},
  journal   = {Behavior research methods},
  year      = {2020},
  publisher = {Springer},
}

To use one of the two alternative methods, run script with:

python run_eval.py --config job.json --exp EXP

where EXP is one the the following:

• ignore - removes unmatched negatives
• hard - converts unmatched negatives to errors