Evaluation of Ten Open-Source Eye-Movement Classification Algorithms in Simulated Surgical Scenarios

dc.authorid Menekse Dalveren, Gonca Gokce/0000-0002-8649-1909
dc.authorid Cagiltay, Nergiz/0000-0003-0875-9276
dc.authorscopusid 57201658878
dc.authorscopusid 16237826800
dc.authorwosid Menekse Dalveren, Gonca Gokce/HHS-4591-2022
dc.authorwosid Cagiltay, Nergiz/O-3082-2019
dc.contributor.author Dalveren, Gonca Gokce Menekse
dc.contributor.author Cagiltay, Nergiz Ercil
dc.contributor.other Information Systems Engineering
dc.contributor.other Software Engineering
dc.date.accessioned 2024-07-05T15:28:15Z
dc.date.available 2024-07-05T15:28:15Z
dc.date.issued 2019
dc.department Atılım University en_US
dc.department-temp [Dalveren, Gonca Gokce Menekse] Norwegian Univ Sci & Technol, Dept Comp Sci, N-2815 Gjovik, Norway; [Dalveren, Gonca Gokce Menekse] Atilim Univ, Dept Informat Syst Engn, TR-06830 Ankara, Turkey; [Cagiltay, Nergiz Ercil] Atilim Univ, Dept Software Engn, TR-06830 Ankara, Turkey en_US
dc.description Menekse Dalveren, Gonca Gokce/0000-0002-8649-1909; Cagiltay, Nergiz/0000-0003-0875-9276 en_US
dc.description.abstract Despite providing several insights into visual attention and evidence regarding certain brain states and psychological functions, classifying eye movements is a highly demanding process. Currently, there are several algorithms to classify eye movement events which use different approaches. However, to date, only a limited number of studies have assessed these algorithms under specific conditions, such as those required for surgical training programmes. This study presents an investigation of ten open-source eye-movement classification algorithms using the Eye Tribe eye-tracker. The algorithms were tested on the eye-movement records obtained from 23 surgical residents, who performed computer-based surgical simulation tasks under different hand conditions. The aim was to offer data for the improvement of surgical training programmes. According to the results, due to the different classification methods and default threshold values, the ten algorithms produced different results. Considering the fixation duration, the only common event for all of the investigated algorithms, the binocular-individual threshold (BIT) algorithm resulted in a different clustering compared to the other algorithms. Based on the other set of common events, three clusters were determined by eight algorithms (except BIT and event detection (ED)), distinguishing dispersion-based, velocity-based and modified versions of velocity-based algorithms. Accordingly, it was concluded that dispersion-based and velocity-based algorithms provided different results. Additionally, as it individually specifies the threshold values for the eye-movement data, when there is no consensus about the threshold values to be set, the BIT algorithm can be selected. Especially for such cases like simulation-based surgical skill-training, the use of individualised threshold values in the BIT algorithm can be more beneficial in classifying the raw eye data and thus evaluating the individual progress levels of trainees based on their eye movement behaviours. In conclusion, the threshold values had a critical effect on the algorithm results. Since default values may not always be suitable for the unique features of different data sets, guidelines should be developed to indicate how the threshold values are set for each algorithm. en_US
dc.identifier.citationcount 6
dc.identifier.doi 10.1109/ACCESS.2019.2951506
dc.identifier.endpage 161804 en_US
dc.identifier.issn 2169-3536
dc.identifier.scopus 2-s2.0-85077791349
dc.identifier.scopusquality Q1
dc.identifier.startpage 161794 en_US
dc.identifier.uri https://doi.org/10.1109/ACCESS.2019.2951506
dc.identifier.uri https://hdl.handle.net/20.500.14411/2749
dc.identifier.volume 7 en_US
dc.identifier.wos WOS:000497169800071
dc.identifier.wosquality Q2
dc.institutionauthor Dalveren, Gonca Gökçe Menekşe
dc.institutionauthor Çağıltay, Nergiz
dc.language.iso en en_US
dc.publisher Ieee-inst Electrical Electronics Engineers inc en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 9
dc.subject Classification algorithms en_US
dc.subject Surgery en_US
dc.subject Clustering algorithms en_US
dc.subject Heuristic algorithms en_US
dc.subject Training en_US
dc.subject Tracking en_US
dc.subject Open source software en_US
dc.subject Eye-movement classification algorithms en_US
dc.subject eye-movement events en_US
dc.subject eye-tracking en_US
dc.title Evaluation of Ten Open-Source Eye-Movement Classification Algorithms in Simulated Surgical Scenarios en_US
dc.type Article en_US
dc.wos.citedbyCount 7
dspace.entity.type Publication
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