Çağıltay, Nergiz

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Citation - WoS: 16
Citation - Scopus: 15
Using Eye-Movement Events To Determine the Mental Workload of Surgical Residents
(int Group Eye Movement Research, 2018) Dalveren, Gonca Gokce Menekse; Cagiltay, Nergiz Ercil
These days, eye-tracking is one of the promising technologies used in different fields such as aviation, arts, sports, psychology and driving for several purposes. Even though it is being used for health purposes, studies involving eye-tracking are rare in the field of endo-neurosurgery. This study aims to use this technology to promote our understanding of the effect related to computer-based instructional materials on mental workload of endo-neurosurgery residents. Four computer-based simulation scenarios are developed based on skill development requirements of endo-neurosurgery residents. Two of them were designed as general models and the other two as simulated surgical models. During these surgery procedures, in real settings, surgical residents need to use their both hands simultaneously to control the endoscope and the operational tool in a coordinated fashion. Therefore, to shed light on the participants' behaviors, these scenarios are performed with dominant-hand, non-dominant hand and, finally with both-hands using haptic interfaces. Twenty-three residents volunteered in this study. Their eye-movements were recorded while performing the scenarios. According to the results of this study, when performing the simulated surgical models, an increase in the participants' mental workload was recorded when compared to the other scenarios. Accordingly, it can be concluded that the eye-movements of surgical residents can provide insights about the anticipated level of difficulty about the skill-based tasks. This information might be very critical to properly design and organize instructional materials for endo-neurosurgery, and also to better guide and evaluate the progress of trainees in computer simulation-based skill training environments.
Citation - WoS: 8
Citation - Scopus: 10
Evaluation of Ten Open-Source Eye-Movement Classification Algorithms in Simulated Surgical Scenarios
(Ieee-inst Electrical Electronics Engineers inc, 2019) Dalveren, Gonca Gokce Menekse; Cagiltay, Nergiz Ercil
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.

Journal	Count
18th IEEE International Symposium on Personal, Indoor and Mobile Radio Communication -- SEP 03-07, 2007 -- Athens, GREECE	7
IEEE Access	5
Computers & Education	3
International Journal of Human–Computer Interaction	3
Surgical Innovation	3

Çağıltay, Nergiz

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