@inproceedings{chartier_online_2008,

title = {An online noise filter for eye-tracker data recorded in a virtual environment},

author = {S. Chartier and P. Renaud},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77950329450&doi=10.1145%2f1344471.1344511&partnerID=40&md5=7ba397a215e76c9d511800ccb267b5cd},

doi = {10.1145/1344471.1344511},

isbn = {978-159593982-1 (ISBN)},

year  = {2008},

date = {2008-01-01},

booktitle = {Eye Tracking Research and Applications Symposium (ETRA)},

pages = {153–156},

address = {Savannah, GA},

abstract = {A Recursive Online Weight Average filter (ROWA) is proposed to remove and replace noisy data obtained from eye tracker. Since the filter can be implemented online, it can detect and replace noisy data using solely past records. Simulations results indicate that the filter achieved the same performance compared to other standard offline filters while being simpler. Copyright © 2008 by the Association for Computing Machinery, Inc.},

note = {Journal Abbreviation: Eye Track. Res. Appl. Symp. (ETRA)},

keywords = {Average filter, Eye trackers, Eye-blinks, Eye-tracker, Eye-tracker data, Noise filters, Noise removal, Noisy data, Off-line filters, Online filtering, Virtual environments, virtual reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{chartier_eye-tracker_2006,

title = {Eye-tracker data filtering using pulse coupled neural network},

author = {S. Chartier and P. Renaud},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33751247311&partnerID=40&md5=ee3f159e47fa32d2710c97826dccdf52},

isbn = {0-88986-594-9 978-0-88986-594-5},

year  = {2006},

date = {2006-01-01},

booktitle = {Proceedings of the IASTED International Conference on Modelling and Simulation},

volume = {2006},

pages = {91–96},

address = {Montreal, QC},

abstract = {Data obtained from eye-tracker are contaminated with noise due to eye blink and hardware failure to detect corneal reflection. One solution is to use a nonlinear filter such as the median. However, median filters modify both noisy and noise free data and they are therefore difficult to use in real time applications. To overcome these limits, a simplified pulse coupled neural network (PCNN) is proposed to correctly detect and remove noisy data while leaving uncorrupted data untouched. Results indicated that a filter based on a PCNN achieved a much better performance than the median filter in peak signal-to-noise ratio (PSNR) and in visual inspection.},

note = {ISSN: 10218181},

keywords = {Data reduction, Eye trackers, Eye-tracker, Filter, Median, Neural networks, Noise, Nonlinear filtering, Pulse couple neural network, Pulse coupled neural network (PCNN), Signal to noise ratio, Spurious signal noise, Wave filters},

pubstate = {published},

tppubtype = {inproceedings}

}